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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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Article

30 pages, 1795 KiB  
Article
Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review
by Christos Ziakis and Maro Vlachopoulou
Information 2023, 14(12), 664; https://doi.org/10.3390/info14120664 - 17 Dec 2023
Cited by 38 | Viewed by 50371
Abstract
Artificial intelligence (AI) has rapidly emerged as a transformative force in multiple sectors, with digital marketing being a prominent beneficiary. As AI technologies continue to advance, their potential to reshape the digital marketing landscape becomes ever more apparent, leading to profound implications for [...] Read more.
Artificial intelligence (AI) has rapidly emerged as a transformative force in multiple sectors, with digital marketing being a prominent beneficiary. As AI technologies continue to advance, their potential to reshape the digital marketing landscape becomes ever more apparent, leading to profound implications for businesses and their digital outreach strategies. This research seeks to answer the pivotal question: “How could AI applications be leveraged to optimize digital marketing strategies”? Drawing from a systematic literature review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, this study has identified 211 pertinent articles. Through a comprehensive bibliometric analysis, the findings were categorized into distinct clusters, namely: AI/ML (Machine Learning) Algorithms, Social Media, Consumer Behavior, E-Commerce, Digital Advertising, Budget Optimization, and Competitive Strategies. Each cluster offers insights into how AI applications can be harnessed to augment digital marketing efforts. The conclusion synthesizes key findings and suggests avenues for future exploration in this dynamic intersection of AI and digital marketing. This research contributes to the field by providing a comprehensive bibliometric analysis of AI in digital marketing, identifying key trends, challenges, and future directions. Our systematic approach offers valuable insights for businesses and researchers alike, enhancing the understanding of AI’s evolving role in digital marketing strategies. Full article
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17 pages, 1420 KiB  
Article
Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts
by Francis Marco Maria Cirianni, Antonio Comi and Agata Quattrone
Information 2023, 14(10), 581; https://doi.org/10.3390/info14100581 - 21 Oct 2023
Cited by 18 | Viewed by 6193
Abstract
The application of artificial intelligence (AI) to dynamic mobility management can support the achievement of efficiency and sustainability goals. AI can help to model alternative mobility system scenarios in real time (by processing big data from heterogeneous sources in a very short time) [...] Read more.
The application of artificial intelligence (AI) to dynamic mobility management can support the achievement of efficiency and sustainability goals. AI can help to model alternative mobility system scenarios in real time (by processing big data from heterogeneous sources in a very short time) and to identify network and service configurations by comparing phenomena in similar contexts, as well as support the implementation of measures for managing demand that achieve sustainable goals. In this paper, an in-depth analysis of scenarios, with an IT (Information Technology) framework based on emerging technologies and AI to support sustainable and cooperative digital mobility, is provided. Therefore, the definition of the functional architecture of an AI-based mobility control centre is defined, and the process that has been implemented in a medium-large city is presented. Full article
(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)
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23 pages, 5243 KiB  
Article
Generative Adversarial Networks (GANs) for Audio-Visual Speech Recognition in Artificial Intelligence IoT
by Yibo He, Kah Phooi Seng and Li Minn Ang
Information 2023, 14(10), 575; https://doi.org/10.3390/info14100575 - 19 Oct 2023
Cited by 9 | Viewed by 5095
Abstract
This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal [...] Read more.
This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal modality, which is commonly used in IoT and embedded systems. Examples of suitable IoT applications include in-cabin speech recognition systems for driving systems, AVSR in augmented reality environments, and interactive applications such as virtual aquariums. The application of multimodal sensor data for IoT applications requires efficient information processing, to meet the hardware constraints of IoT devices. The proposed multimodal AVSR GAN architecture is composed of a discriminator and a generator, each of which is a two-stream network, corresponding to the audio stream information and the visual stream information, respectively. To validate this approach, we used augmented data from well-known datasets (LRS2-Lip Reading Sentences 2 and LRS3) in the training process, and testing was performed using the original data. The research and experimental results showed that the proposed multimodal AVSR GAN architecture improved the AVSR classification accuracy. Furthermore, in this study, we discuss the domain of GANs and provide a concise summary of the proposed GANs. Full article
(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)
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26 pages, 1468 KiB  
Article
The Impact of Virtual Reality (VR) Tour Experience on Tourists’ Intention to Visit
by Chourouk Ouerghemmi, Myriam Ertz, Néji Bouslama and Urvashi Tandon
Information 2023, 14(10), 546; https://doi.org/10.3390/info14100546 - 5 Oct 2023
Cited by 15 | Viewed by 14467
Abstract
Drawing on media richness theory, this study investigates the effect of rich media, such as virtual reality (VR), on visit intentions for a specific destination. Specifically, this research employs a mixed-method approach, using abductive theorization to explore and confirm the dimensions of the [...] Read more.
Drawing on media richness theory, this study investigates the effect of rich media, such as virtual reality (VR), on visit intentions for a specific destination. Specifically, this research employs a mixed-method approach, using abductive theorization to explore and confirm the dimensions of the VR visit experience, notably those related to telepresence, a key concept in tourism through VR. Furthermore, the study aims to elucidate how telepresence influences mental imagery, attitudes towards tourist destinations, and actual visit intentions. To do this, qualitative data were gathered between February and June 2022 from 34 semi-structured interviews with respondents who viewed a VR video of the destination. A second study collected quantitative data from 400 participants through face-to-face questionnaires after a VR video view between June and August 2022. The findings reveal that telepresence comprises three dimensions: realism of the virtual environment, immersion, and the sense of presence in the virtual environment. Telepresence, in turn, both directly and indirectly affects actual visit intentions, with mental imagery and attitude toward tourist destinations partially mediating those relationships. This study provides methodological, theoretical, and tourism management implications to enhance our comprehension of telepresence’s facets, its measurement, and the process by which VR influences real visit intentions. Full article
(This article belongs to the Special Issue Digital Economy and Management)
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16 pages, 988 KiB  
Article
Enriching a Traditional Learning Activity in Preschool through Augmented Reality: Children’s and Teachers’ Views
by Sophia Rapti, Theodosios Sapounidis and Sokratis Tselegkaridis
Information 2023, 14(10), 530; https://doi.org/10.3390/info14100530 - 28 Sep 2023
Cited by 15 | Viewed by 3399
Abstract
Nowadays, Augmented Reality flourishes in educational settings. Yet, little is known about teachers’ and children’s views of Augmented Reality applications in Preschool. This paper explores 71 preschoolers’ opinions of Augmented Reality teaching integrated into a traditional learning activity. Additionally, five educators’ views of [...] Read more.
Nowadays, Augmented Reality flourishes in educational settings. Yet, little is known about teachers’ and children’s views of Augmented Reality applications in Preschool. This paper explores 71 preschoolers’ opinions of Augmented Reality teaching integrated into a traditional learning activity. Additionally, five educators’ views of Augmented Reality applications in Preschool are captured. Mixed methods with questionnaires and semi-structured interviews were used. The questionnaires record children’s preferences regarding their favorite learning activity between traditional and the Augmented Reality one. Additionally, they explore the activity preschoolers would like to repeat and found most enjoyable: playful. Regarding quantitative data analysis, independent/paired samples t-tests and chi-square test along with bootstrapping with 1000 samples were used. As for the qualitative data collection, educators’ semi-structured interviews focused on three axes: (a) children’s motivation and engagement in Augmented Reality activities, (b) Augmented Reality’s potential to promote skills, and (c) Augmented Reality as a teaching tool in preschool. The emerging results are: Preschoolers prefer more Augmented Reality activities than traditional ones. There are no statistically significant gender differences in preferences for Augmented Reality activities. Educators regard Augmented Reality technology as an innovative, beneficial teaching approach in preschool. However, they express concern regarding the promotion of collaboration among preschoolers via Augmented Reality. Full article
(This article belongs to the Collection Augmented Reality Technologies, Systems and Applications)
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18 pages, 741 KiB  
Article
Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention
by Lexin Zhang, Ruihan Wang, Zhuoyuan Li, Jiaxun Li, Yichen Ge, Shiyun Wa, Sirui Huang and Chunli Lv
Information 2023, 14(9), 500; https://doi.org/10.3390/info14090500 - 13 Sep 2023
Cited by 18 | Viewed by 21962
Abstract
This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such as non-linearity, high dimensionality, and long-term dependence, [...] Read more.
This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such as non-linearity, high dimensionality, and long-term dependence, the TNN model is designed and implemented. The key innovations of the TNN model lie in the incorporation of the time attention mechanism and kernel filter, allowing the model to allocate different weights to features at each time point, and extract high-level features from the time-series data, thereby improving the model’s predictive accuracy. Additionally, an adaptive weight generator is integrated into the model, enabling the model to automatically adjust weights based on input features. Mainstream time-series forecasting models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTM) are employed as baseline models and comprehensive comparative experiments are conducted. The results indicate that the TNN model significantly outperforms the baseline models in both long-term and short-term prediction tasks. Specifically, the RMSE, MAE, and R2 reach 0.05, 0.23, and 0.95, respectively. Remarkably, even for complex time-series data that contain a large amount of noise, the TNN model still maintains a high prediction accuracy. Full article
(This article belongs to the Special Issue New Deep Learning Approach for Time Series Forecasting)
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35 pages, 9764 KiB  
Article
Using ChatGPT and Persuasive Technology for Personalized Recommendation Messages in Hotel Upselling
by Manolis Remountakis, Konstantinos Kotis, Babis Kourtzis and George E. Tsekouras
Information 2023, 14(9), 504; https://doi.org/10.3390/info14090504 - 13 Sep 2023
Cited by 15 | Viewed by 6165
Abstract
Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This [...] Read more.
Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity, and personalization, recommender systems can effectively influence user decision making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present pilot experiments with a case study involving a hotel recommender system. Our inhouse commercial hotel marketing platform, eXclusivi, was extended with a new software module working with ChatGPT prompts and persuasive ads created for its recommendations. In particular, we developed an intelligent advertisement (ad) copy generation tool for the hotel marketing platform. The proposed approach allows for the hotel team to target all guests in their language, leveraging the integration with the hotel’s reservation system. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between ChatGPT and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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26 pages, 2354 KiB  
Article
Effects of Generative Chatbots in Higher Education
by Galina Ilieva, Tania Yankova, Stanislava Klisarova-Belcheva, Angel Dimitrov, Marin Bratkov and Delian Angelov
Information 2023, 14(9), 492; https://doi.org/10.3390/info14090492 - 7 Sep 2023
Cited by 68 | Viewed by 17085
Abstract
Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to the challenge of providing personalized learning experiences for students, these technologies can increase the workload of instructors due to the maintenance and updates [...] Read more.
Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to the challenge of providing personalized learning experiences for students, these technologies can increase the workload of instructors due to the maintenance and updates required to keep the courses up-to-date. Intelligent chatbots based on generative artificial intelligence (AI) technology can help overcome these disadvantages by transforming pedagogical activities and guiding both students and instructors interactively. In this study, we explore and compare the main characteristics of existing educational chatbots. Then, we propose a new theoretical framework for blended learning with intelligent chatbots integration enabling students to interact online and instructors to create and manage their courses using generative AI tools. The advantages of the proposed framework are as follows: (1) it provides a comprehensive understanding of the transformative potential of AI chatbots in education and facilitates their effective implementation; (2) it offers a holistic methodology to enhance the overall educational experience; and (3) it unifies the applications of intelligent chatbots in teaching–learning activities within universities. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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16 pages, 2143 KiB  
Article
Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm
by Manikandan Natarajan, Thejasree Pasupuleti, Jayant Giri, Neeraj Sunheriya, Lakshmi Narasimhamu Katta, Rajkumar Chadge, Chetan Mahatme, Pallavi Giri, Saurav Mallik and Kanad Ray
Information 2023, 14(8), 439; https://doi.org/10.3390/info14080439 - 3 Aug 2023
Cited by 68 | Viewed by 3638
Abstract
Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. [...] Read more.
Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions. Full article
(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)
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14 pages, 2220 KiB  
Article
Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection
by Zahra Jafari and Ebrahim Karami
Information 2023, 14(7), 410; https://doi.org/10.3390/info14070410 - 16 Jul 2023
Cited by 44 | Viewed by 15230
Abstract
The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for the [...] Read more.
The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for the detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms: neural network (NN), k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM). Our results demonstrate that the NN-based classifier achieves an impressive accuracy of 92% on the RSNA dataset. This dataset is newly introduced and includes two views as well as additional features like age, which contributed to the improved performance. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches. Full article
(This article belongs to the Special Issue Advances in Object-Based Image Segmentation and Retrieval)
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20 pages, 7807 KiB  
Article
INSUS: Indoor Navigation System Using Unity and Smartphone for User Ambulation Assistance
by Evianita Dewi Fajrianti, Nobuo Funabiki, Sritrusta Sukaridhoto, Yohanes Yohanie Fridelin Panduman, Kong Dezheng, Fang Shihao and Anak Agung Surya Pradhana
Information 2023, 14(7), 359; https://doi.org/10.3390/info14070359 - 24 Jun 2023
Cited by 17 | Viewed by 5953
Abstract
Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and [...] Read more.
Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and elevators. In this paper, we present the design and implementation of the Indoor Navigation System using Unity and Smartphone (INSUS). INSUS shows the arrow of the moving direction on the camera view based on a smartphone’s augmented reality (AR) technology. To trace the user location, it utilizes the Simultaneous Localization and Mapping (SLAM) technique with a gyroscope and a camera in a smartphone to track users’ movements inside a building after initializing the current location by the QR code. Unity is introduced to obtain the 3D information of the target indoor environment for Visual SLAM. The data are stored in the IoT application server called SEMAR for visualizations. We implement a prototype system of INSUS inside buildings in two universities. We found that scanning QR codes with the smartphone perpendicular in angle between 60∘ and 100∘ achieves the highest QR code detection accuracy. We also found that the phone’s tilt angles influence the navigation success rate, with 90∘ to 100∘ tilt angles giving better navigation success compared to lower tilt angles. INSUS also proved to be a robust navigation system, evidenced by near identical navigation success rate results in navigation scenarios with or without disturbance. Furthermore, based on the questionnaire responses from the respondents, it was generally found that INSUS received positive feedback and there is support to improve the system. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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17 pages, 3741 KiB  
Article
Trend Analysis of Decentralized Autonomous Organization Using Big Data Analytics
by Hyejin Park, Ivan Ureta and Boyoung Kim
Information 2023, 14(6), 326; https://doi.org/10.3390/info14060326 - 9 Jun 2023
Cited by 9 | Viewed by 3909
Abstract
Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the [...] Read more.
Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the field. This research aims at a data-driven approach for the objective content analysis of big data related to DAOs, using text mining and Latent Dirichlet Allocation (LDA)-based topic modeling. The study analyzed tweets with the hashtag #DAO and all Reddit data with “DAO”. The results were from the identification of the top 100 frequently appearing keywords, as well as the top 20 keywords with high network centrality, and key topics related to finance, gaming, and fundraising, from both Twitter and Reddit. The analysis revealed twelve topics from Twitter and eight topics from Reddit, with the term “community” frequently appearing across many of these topics. The findings provide valuable insights into the current trend and future potential of DAOs, and should be used by researchers to guide further research in the field and by decision makers to explore innovative ways to govern the organizations. Full article
(This article belongs to the Special Issue Advances in Data and Network Sciences Applied to Computational Social Science)
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19 pages, 624 KiB  
Article
Artificially Intelligent Readers: An Adaptive Framework for Original Handwritten Numerical Digits Recognition with OCR Methods
by Parth Hasmukh Jain, Vivek Kumar, Jim Samuel, Sushmita Singh, Abhinay Mannepalli and Richard Anderson
Information 2023, 14(6), 305; https://doi.org/10.3390/info14060305 - 26 May 2023
Cited by 14 | Viewed by 6278
Abstract
Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies. OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy [...] Read more.
Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies. OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy for general text. However, even after decades of intensive research, creating OCR with human-like abilities has remained evasive. One of the challenges has been that OCR models trained on general text do not perform well on localized or personalized handwritten text due to differences in the writing style of alphabets and digits. This study aims to discuss the steps needed to create an adaptive framework for OCR models, with the intent of exploring a reasonable method to customize an OCR solution for a unique dataset of English language numerical digits were developed for this study. We develop a digit recognizer by training our model on the MNIST dataset with a convolutional neural network and contrast it with multiple models trained on combinations of the MNIST and custom digits. Using our methods, we observed results comparable with the baseline and provided recommendations for improving OCR accuracy for localized or personalized handwritten text. This study also provides an alternative perspective to generating data using conventional methods, which can serve as a gold standard for custom data augmentation to help address the challenges of scarce data and data imbalance. Full article
(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Information Systems)
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21 pages, 3627 KiB  
Article
A Study of Machine Learning Regression Techniques for Non-Contact SpO2 Estimation from Infrared Motion-Magnified Facial Video
by Thomas Stogiannopoulos, Grigorios-Aris Cheimariotis and Nikolaos Mitianoudis
Information 2023, 14(6), 301; https://doi.org/10.3390/info14060301 - 23 May 2023
Cited by 12 | Viewed by 5347
Abstract
This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO2), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO2 monitoring utilizing RGB cameras [...] Read more.
This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO2), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO2 monitoring utilizing RGB cameras has already proven successful. This study utilizes the Eulerian Video Magnification (EVM) technique to enhance minor variations in skin pixel intensity in particular facial regions. More specifically, the emphasis in this study is in the utilization of infrared cameras, in order to explore the possibility of contactless SpO2 monitoring under low-light or night-time conditions. Many different methods were employed for regression. A study of machine learning regression methods was performed, including a Generalized Additive Model (GAM) and an Extra Trees Regressor, based on 12 novel features extracted from the extracted amplified photoplethysmography (PPG) signal. Deep learning methods were also explored, including a 3D Convolution Neural Network (CNN) and a Video Vision Transformer (ViViT) architecture on the amplified forehead/cheeks video. The estimated SpO2 values of the best performing method reach a low root mean squared error of 1.331 and an R2 score of 0.465 that fall within the acceptable range for these applications. Full article
(This article belongs to the Section Biomedical Information and Health)
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18 pages, 6442 KiB  
Article
Energy Usage Forecasting Model Based on Long Short-Term Memory (LSTM) and eXplainable Artificial Intelligence (XAI)
by Muhammad Rifqi Maarif, Arif Rahman Saleh, Muhammad Habibi, Norma Latif Fitriyani and Muhammad Syafrudin
Information 2023, 14(5), 265; https://doi.org/10.3390/info14050265 - 29 Apr 2023
Cited by 17 | Viewed by 5010
Abstract
The accurate forecasting of energy consumption is essential for companies, primarily for planning energy procurement. An overestimated or underestimated forecasting value may lead to inefficient energy usage. Inefficient energy usage could also lead to financial consequences for the company, since it will generate [...] Read more.
The accurate forecasting of energy consumption is essential for companies, primarily for planning energy procurement. An overestimated or underestimated forecasting value may lead to inefficient energy usage. Inefficient energy usage could also lead to financial consequences for the company, since it will generate a high cost of energy production. Therefore, in this study, we proposed an energy usage forecasting model and parameter analysis using long short-term memory (LSTM) and explainable artificial intelligence (XAI), respectively. A public energy usage dataset from a steel company was used in this study to evaluate our models and compare them with previous study results. The results showed that our models achieved the lowest root mean squared error (RMSE) scores by up to 0.08, 0.07, and 0.07 for the single-layer LSTM, double-layer LSTM, and bi-directional LSTM, respectively. In addition, the interpretability analysis using XAI revealed that two parameters, namely the leading current reactive power and the number of seconds from midnight, had a strong influence on the model output. Finally, it is expected that our study could be useful for industry practitioners, providing LSTM models for accurate energy forecasting and offering insight for policymakers and industry leaders so that they can make more informed decisions about resource allocation and investment, develop more effective strategies for reducing energy consumption, and support the transition toward sustainable development. Full article
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20 pages, 736 KiB  
Article
A Multi-Key with Partially Homomorphic Encryption Scheme for Low-End Devices Ensuring Data Integrity
by Saci Medileh, Abdelkader Laouid, Mohammad Hammoudeh, Mostefa Kara, Tarek Bejaoui, Amna Eleyan and Mohammed Al-Khalidi
Information 2023, 14(5), 263; https://doi.org/10.3390/info14050263 - 28 Apr 2023
Cited by 26 | Viewed by 3918
Abstract
In today’s hyperconnected world, the Internet of Things and Cloud Computing complement each other in several areas. Cloud Computing provides IoT systems with an efficient and flexible environment that supports application requirements such as real-time control/monitoring, scalability, fault tolerance, and numerous security services. [...] Read more.
In today’s hyperconnected world, the Internet of Things and Cloud Computing complement each other in several areas. Cloud Computing provides IoT systems with an efficient and flexible environment that supports application requirements such as real-time control/monitoring, scalability, fault tolerance, and numerous security services. Hardware and software limitations of IoT devices can be mitigated using the massive on-demand cloud resources. However, IoT cloud-based solutions pose some security and privacy concerns, specifically when an untrusted cloud is used. This calls for strong encryption schemes that allow operations on data in an encrypted format without compromising the encryption. This paper presents an asymmetric multi-key and partially homomorphic encryption scheme. The scheme provides the addition operation by encrypting each decimal digit of the given integer number separately using a special key. In addition, data integrity processes are performed when an untrusted third party performs homomorphic operations on encrypted data. The proposed work considers the most widely known issues like the encrypted data size, slow operations at the hardware level, and high computing costs at the provider level. The size of generated ciphertext is almost equal to the size of the plaintext, and order-preserving is ensured using an asymmetrical encryption version. Full article
(This article belongs to the Special Issue Advances in Cybersecurity and Reliability)
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16 pages, 3234 KiB  
Article
Generation of Nonlinear Substitutions by Simulated Annealing Algorithm
by Alexandr Kuznetsov, Mikolaj Karpinski, Ruslana Ziubina, Sergey Kandiy, Emanuele Frontoni, Oleksandr Peliukh, Olga Veselska and Ruslan Kozak
Information 2023, 14(5), 259; https://doi.org/10.3390/info14050259 - 26 Apr 2023
Cited by 12 | Viewed by 2486
Abstract
The problem of nonlinear substitution generation (S-boxes) is investigated in many related works in symmetric key cryptography. In particular, the strength of symmetric ciphers to linear cryptanalysis is directly related to the nonlinearity of substitution. In addition to being highly nonlinear, S-boxes must [...] Read more.
The problem of nonlinear substitution generation (S-boxes) is investigated in many related works in symmetric key cryptography. In particular, the strength of symmetric ciphers to linear cryptanalysis is directly related to the nonlinearity of substitution. In addition to being highly nonlinear, S-boxes must be random, i.e., must not contain hidden mathematical constructs that facilitate algebraic cryptanalysis. The generation of such substitutions is a complex combinatorial optimization problem. Probabilistic algorithms are used to solve it, for instance the simulated annealing algorithm, which is well-fitted to a discrete search space. We propose a new cost function based on Walsh–Hadamard spectrum computation, and investigate the search efficiency of S-boxes using a simulated annealing algorithm. For this purpose, we conduct numerous experiments with different input parameters: initial temperature, cooling coefficient, number of internal and external loops. As the results of the research show, applying the new cost function allows for the rapid generation of nonlinear substitutions. To find 8-bit bijective S-boxes with nonlinearity 104, we need about 83,000 iterations. At the same time, the probability of finding the target result is 100%. Full article
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22 pages, 1002 KiB  
Article
Digital Audio Tampering Detection Based on Deep Temporal–Spatial Features of Electrical Network Frequency
by Chunyan Zeng, Shuai Kong, Zhifeng Wang, Kun Li and Yuhao Zhao
Information 2023, 14(5), 253; https://doi.org/10.3390/info14050253 - 22 Apr 2023
Cited by 11 | Viewed by 3493
Abstract
In recent years, digital audio tampering detection methods by extracting audio electrical network frequency (ENF) features have been widely applied. However, most digital audio tampering detection methods based on ENF have the problems of focusing on spatial features only, without effective representation of [...] Read more.
In recent years, digital audio tampering detection methods by extracting audio electrical network frequency (ENF) features have been widely applied. However, most digital audio tampering detection methods based on ENF have the problems of focusing on spatial features only, without effective representation of temporal features, and do not fully exploit the effective information in the shallow ENF features, which leads to low accuracy of audio tamper detection. Therefore, this paper proposes a new method for digital audio tampering detection based on the deep temporal–spatial feature of ENF. To extract the temporal and spatial features of the ENF, firstly, a highly accurate ENF phase sequence is extracted using the first-order Discrete Fourier Transform (DFT), and secondly, different frame processing methods are used to extract the ENF shallow temporal and spatial features for the temporal and spatial information contained in the ENF phase. To fully exploit the effective information in the shallow ENF features, we construct a parallel RDTCN-CNN network model to extract the deep temporal and spatial information by using the processing ability of Residual Dense Temporal Convolutional Network (RDTCN) and Convolutional Neural Network (CNN) for temporal and spatial information, and use the branch attention mechanism to adaptively assign weights to the deep temporal and spatial features to obtain the temporal–spatial feature with greater representational capacity, and finally, adjudicate whether the audio is tampered with by the MLP network. The experimental results show that the method in this paper outperforms the four baseline methods in terms of accuracy and F1-score. Full article
(This article belongs to the Special Issue Signal Processing Based on Convolutional Neural Network)
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23 pages, 27370 KiB  
Article
Recognizing the Wadi Fluvial Structure and Stream Network in the Qena Bend of the Nile River, Egypt, on Landsat 8-9 OLI Images
by Polina Lemenkova and Olivier Debeir
Information 2023, 14(4), 249; https://doi.org/10.3390/info14040249 - 20 Apr 2023
Cited by 9 | Viewed by 3578
Abstract
With methods for processing remote sensing data becoming widely available, the ability to quantify changes in spatial data and to evaluate the distribution of diverse landforms across target areas in datasets becomes increasingly important. One way to approach this problem is through satellite [...] Read more.
With methods for processing remote sensing data becoming widely available, the ability to quantify changes in spatial data and to evaluate the distribution of diverse landforms across target areas in datasets becomes increasingly important. One way to approach this problem is through satellite image processing. In this paper, we primarily focus on the methods of the unsupervised classification of the Landsat OLI/TIRS images covering the region of the Qena governorate in Upper Egypt. The Qena Bend of the Nile River presents a remarkable morphological feature in Upper Egypt, including a dense drainage network of wadi aquifer systems and plateaus largely dissected by numerous valleys of dry rivers. To identify the fluvial structure and stream network of the Wadi Qena region, this study addresses the problem of interpreting the relevant space-borne data using R, with an aim to visualize the land surface structures corresponding to various land cover types. To this effect, high-resolution 2D and 3D topographic and geologic maps were used for the analysis of the geomorphological setting of the Qena region. The information was extracted from the space-borne data for the comparative analysis of the distribution of wadi streams in the Qena Bend area over several years: 2013, 2015, 2016, 2019, 2022, and 2023. Six images were processed using computer vision methods made available by R libraries. The results of the k-means clustering of each scene retrieved from the multi-temporal images covering the Qena Bend of the Nile River were thus compared to visualize changes in landforms caused by the cumulative effects of geomorphological disasters and climate–environmental processes. The proposed method, tied together through the use of R scripts, runs effectively and performs favorably in computer vision tasks aimed at geospatial image processing and the analysis of remote sensing data. Full article
(This article belongs to the Special Issue Computer Vision for Security Applications)
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29 pages, 4153 KiB  
Article
Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline
by Konstantinos Filippou, George Aifantis, George A. Papakostas and George E. Tsekouras
Information 2023, 14(4), 232; https://doi.org/10.3390/info14040232 - 9 Apr 2023
Cited by 16 | Viewed by 4789
Abstract
In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the [...] Read more.
In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the Kaggle API. The second utilizes the Keras-Bayesian optimization tuning library to perform hyperparameter optimization. The third focuses on the training process of the machine learning (ML) model using the hyperparameter values estimated in the previous stage, and its evaluation is performed on the testing data by implementing the Neptune AI. The main technologies used to develop a stable and reusable machine learning pipeline are the popular Git version control system, the Google cloud virtual machine, the Jenkins server, the Docker containerization technology, and the Ngrok reverse proxy tool. The latter can securely publish the local Jenkins address as public through the internet. As such, some parts of the proposed pipeline are taken from the thematic area of machine learning operations (MLOps), resulting in a hybrid software scheme. The machine learning model was used to evaluate the pipeline, which is a multilayer perceptron (MLP) that combines typical dense, as well as polynomial, layers. The simulation results show that the proposed pipeline exhibits a reliable and accurate performance while managing to boost the network’s performance in classification tasks. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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16 pages, 8063 KiB  
Article
Using a Machine Learning Approach to Evaluate the NOx Emissions in a Spark-Ignition Optical Engine
by Federico Ricci, Luca Petrucci and Francesco Mariani
Information 2023, 14(4), 224; https://doi.org/10.3390/info14040224 - 6 Apr 2023
Cited by 7 | Viewed by 2605
Abstract
Currently, machine learning (ML) technologies are widely employed in the automotive field for determining physical quantities thanks to their ability to ensure lower computational costs and faster operations than traditional methods. Within this context, the present work shows the outcomes of forecasting activities [...] Read more.
Currently, machine learning (ML) technologies are widely employed in the automotive field for determining physical quantities thanks to their ability to ensure lower computational costs and faster operations than traditional methods. Within this context, the present work shows the outcomes of forecasting activities on the prediction of pollutant emissions from engines using an artificial neural network technique. Tests on an optical access engine were conducted under lean mixture conditions, which is the direction in which automotive research is developing to meet the ever-stricter regulations on pollutant emissions. A NARX architecture was utilized to estimate the engine’s nitrogen oxide emissions starting from in-cylinder pressure data and images of the flame front evolution recorded by a high-speed camera and elaborated through a Mask R-CNN technique. Based on the obtained results, the methodology’s applicability to real situations, such as metal engines, was assessed using a sensitivity analysis presented in the second part of the work, which helped identify and quantify the most important input parameters for the nitrogen oxide forecast. Full article
(This article belongs to the Special Issue Computer Vision, Pattern Recognition and Machine Learning in Italy)
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34 pages, 1619 KiB  
Article
AutoML with Bayesian Optimizations for Big Data Management
by Aristeidis Karras, Christos Karras, Nikolaos Schizas, Markos Avlonitis and Spyros Sioutas
Information 2023, 14(4), 223; https://doi.org/10.3390/info14040223 - 5 Apr 2023
Cited by 16 | Viewed by 5150
Abstract
The field of automated machine learning (AutoML) has gained significant attention in recent years due to its ability to automate the process of building and optimizing machine learning models. However, the increasing amount of big data being generated has presented new challenges for [...] Read more.
The field of automated machine learning (AutoML) has gained significant attention in recent years due to its ability to automate the process of building and optimizing machine learning models. However, the increasing amount of big data being generated has presented new challenges for AutoML systems in terms of big data management. In this paper, we introduce Fabolas and learning curve extrapolation as two methods for accelerating hyperparameter optimization. Four methods for quickening training were presented including Bag of Little Bootstraps, k-means clustering for Support Vector Machines, subsample size selection for gradient descent, and subsampling for logistic regression. Additionally, we also discuss the use of Markov Chain Monte Carlo (MCMC) methods and other stochastic optimization techniques to improve the efficiency of AutoML systems in managing big data. These methods enhance various facets of the training process, making it feasible to combine them in diverse ways to gain further speedups. We review several combinations that have potential and provide a comprehensive understanding of the current state of AutoML and its potential for managing big data in various industries. Furthermore, we also mention the importance of parallel computing and distributed systems to improve the scalability of the AutoML systems while working with big data. Full article
(This article belongs to the Special Issue Multidimensional Data Structures and Big Data Management)
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23 pages, 1086 KiB  
Article
IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
by Shadi Atalla, Saed Tarapiah, Amjad Gawanmeh, Mohammad Daradkeh, Husameldin Mukhtar, Yassine Himeur, Wathiq Mansoor, Kamarul Faizal Bin Hashim and Motaz Daadoo
Information 2023, 14(4), 205; https://doi.org/10.3390/info14040205 - 27 Mar 2023
Cited by 57 | Viewed by 12828
Abstract
The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms [...] Read more.
The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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11 pages, 448 KiB  
Article
Feature Selection Engineering for Credit Risk Assessment in Retail Banking
by Jaber Jemai and Anis Zarrad
Information 2023, 14(3), 200; https://doi.org/10.3390/info14030200 - 22 Mar 2023
Cited by 16 | Viewed by 6060
Abstract
In classification, feature selection engineering helps in choosing the most relevant data attributes to learn from. It determines the set of features to be rejected, supposing their low contribution in discriminating the labels. The effectiveness of a classifier passes mainly through the set [...] Read more.
In classification, feature selection engineering helps in choosing the most relevant data attributes to learn from. It determines the set of features to be rejected, supposing their low contribution in discriminating the labels. The effectiveness of a classifier passes mainly through the set of selected features. In this paper, we identify the best features to learn from in the context of credit risk assessment in the financial industry. Financial institutions concur with the risk of approving the loan request of a customer who may default later, or rejecting the request of a customer who can abide by their debt without default. We propose a feature selection engineering approach to identify the main features to refer to in assessing the risk of a loan request. We use different feature selection methods including univariate feature selection (UFS), recursive feature elimination (RFE), feature importance using decision trees (FIDT), and the information value (IV). We implement two variants of the XGBoost classifier on the open data set provided by the Lending Club platform to evaluate and compare the performance of different feature selection methods. The research shows that the most relevant features are found by the four feature selection techniques. Full article
(This article belongs to the Special Issue Machine Learning: From Tech Trends to Business Impact)
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10 pages, 407 KiB  
Article
Fundamental Research Challenges for Distributed Computing Continuum Systems
by Victor Casamayor Pujol, Andrea Morichetta, Ilir Murturi, Praveen Kumar Donta and Schahram Dustdar
Information 2023, 14(3), 198; https://doi.org/10.3390/info14030198 - 22 Mar 2023
Cited by 25 | Viewed by 4994
Abstract
This article discusses four fundamental topics for future Distributed Computing Continuum Systems: their representation, model, lifelong learning, and business model. Further, it presents techniques and concepts that can be useful to define these four topics specifically for Distributed Computing Continuum Systems. Finally, this [...] Read more.
This article discusses four fundamental topics for future Distributed Computing Continuum Systems: their representation, model, lifelong learning, and business model. Further, it presents techniques and concepts that can be useful to define these four topics specifically for Distributed Computing Continuum Systems. Finally, this article presents a broad view of the synergies among the presented technique that can enable the development of future Distributed Computing Continuum Systems. Full article
(This article belongs to the Special Issue Best IDEAS: International Database Engineered Applications Symposium)
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18 pages, 3214 KiB  
Article
QoS-Aware Resource Management in 5G and 6G Cloud-Based Architectures with Priorities
by Spiros (Spyridon) Louvros, Michael Paraskevas and Theofilos Chrysikos
Information 2023, 14(3), 175; https://doi.org/10.3390/info14030175 - 9 Mar 2023
Cited by 11 | Viewed by 3664
Abstract
Fifth-generation and more importantly the forthcoming sixth-generation networks have been given special care for latency and are designed to support low latency applications including a high flexibility New Radio (NR) interface that can be configured to utilize different subcarrier spacings (SCS), slot durations, [...] Read more.
Fifth-generation and more importantly the forthcoming sixth-generation networks have been given special care for latency and are designed to support low latency applications including a high flexibility New Radio (NR) interface that can be configured to utilize different subcarrier spacings (SCS), slot durations, special scheduling optional features (mini-slot scheduling), cloud- and virtual-based transport network infrastructures including slicing, and finally intelligent radio and transport packet retransmissions mechanisms. QoS analysis with emphasis on the determination of the transmitted packets’ average waiting time is therefore crucial for both network performance and user applications. Most preferred implementations to optimize transmission network rely on the cloud architectures with star network topology. In this paper, as part of our original and innovative contribution, a two-stage queue model is proposed and analytically investigated. Firstly, a two-dimension queue is proposed in order to estimate the expected delay on priority scheduling decisions over the IP/Ethernet MAC layer of IP packet transmissions between gNB and the core network. Furthermore, a one-dimension queue is proposed to estimate the average packet waiting time on the RLC radio buffer before being scheduled mainly due to excessive traffic load and designed transmission bandwidth availability. Full article
(This article belongs to the Special Issue 5G Networks and Wireless Communication Systems)
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20 pages, 12585 KiB  
Article
Non-Invasive Classification of Blood Glucose Level Based on Photoplethysmography Using Time–Frequency Analysis
by Ernia Susana, Kalamullah Ramli, Prima Dewi Purnamasari and Nursama Heru Apriantoro
Information 2023, 14(3), 145; https://doi.org/10.3390/info14030145 - 23 Feb 2023
Cited by 20 | Viewed by 7067
Abstract
Diabetes monitoring systems are crucial for avoiding potentially significant medical expenses. At this time, the only commercially viable monitoring methods that exist are invasive ones. Since patients are uncomfortable while blood samples are being taken, these techniques have significant disadvantages. The drawbacks of [...] Read more.
Diabetes monitoring systems are crucial for avoiding potentially significant medical expenses. At this time, the only commercially viable monitoring methods that exist are invasive ones. Since patients are uncomfortable while blood samples are being taken, these techniques have significant disadvantages. The drawbacks of invasive treatments might be overcome by a painless, inexpensive, non-invasive approach to blood glucose level (BGL) monitoring. Photoplethysmography (PPG) signals obtained from sensor leads placed on specific organ tissues are collected using photodiodes and nearby infrared LEDs. Cardiovascular disease can be detected via photoplethysmography. These characteristics can be used to directly affect BGL monitoring in diabetic patients if PPG signals are used. The Guilin People’s Hospital’s open database was used to produce the data collection. The dataset was gathered from 219 adult respondents spanning an age range from 21 to 86 of which 48 percent were male. There were 2100 sampling points total for each PPG data segment. The methodology of feature extraction from data may assist in increasing the effectiveness of classifier training and testing. PPG data information is modified in the frequency domain by the instantaneous frequency (IF) and spectral entropy (SE) moments using the time–frequency (TF) analysis. Three different forms of raw data were used as inputs, and we investigated the original PPG signal, the PPG signal with instantaneous frequency, and the PPG signal with spectral entropy. According to the results of the model testing, the PPG signal with spectral entropy generated the best outcomes. Compared to decision trees, subspace k-nearest neighbor, and k-nearest neighbor, our suggested approach with the super vector machine obtains a greater level of accuracy. The super vector machine, with 91.3% accuracy and a training duration of 9 s, was the best classifier. Full article
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13 pages, 4182 KiB  
Article
Research on Traffic Congestion Forecast Based on Deep Learning
by Yangyang Qi and Zesheng Cheng
Information 2023, 14(2), 108; https://doi.org/10.3390/info14020108 - 9 Feb 2023
Cited by 12 | Viewed by 5517
Abstract
In recent years, the rapid economic development of China, the increase of the urban population, the continuous growth of private car ownership, the uneven distribution of traffic flow, and the local congestion of the road network have caused traffic congestion. Traffic congestion has [...] Read more.
In recent years, the rapid economic development of China, the increase of the urban population, the continuous growth of private car ownership, the uneven distribution of traffic flow, and the local congestion of the road network have caused traffic congestion. Traffic congestion has become an inevitable problem in the process of urban development, bringing hazards and hidden dangers to citizens’ travel and urban development. The management of traffic congestion first lies in the accurate completion of the identification of road traffic status and the need to predict road congestion in the city, so as to improve the use rate of urban infrastructure road facilities and better alleviate road congestion. In this study, a deep spatial and temporal network model (DSGCN) for predicting traffic congestion status is proposed. First, our study divides the traffic network into grids, where each grid represents a different independent region. In this paper, the centroids of the grid regions are abstracted as nodes, and the dynamic correlations between the nodes are expressed in the form of adjacency matrix. Then, Graph Convolutional Neural Network is used to capture the spatial correlation between regions and a two-layer long and short-term feature model (DSTM) is used to capture the temporal correlation between regions. Finally, the DSGCN outperforms other baseline models and has higher accuracy for traffic congestion prediction as demonstrated by experiments on real PeMS datasets. Full article
(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)
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14 pages, 2181 KiB  
Article
A Comparative Analysis of Supervised and Unsupervised Models for Detecting Attacks on the Intrusion Detection Systems
by Tala Talaei Khoei and Naima Kaabouch
Information 2023, 14(2), 103; https://doi.org/10.3390/info14020103 - 7 Feb 2023
Cited by 34 | Viewed by 7651
Abstract
Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and detect these attacks, including supervised [...] Read more.
Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and detect these attacks, including supervised machine learning. However, these models require large labeled datasets for training and testing. Therefore, this paper compares the performance of supervised and unsupervised learning models in detecting cyber-attacks. The benchmark of CICDDOS 2019 was used to train, test, and validate the models. The supervised models are Gaussian Naïve Bayes, Classification and Regression Decision Tree, Logistic Regression, C-Support Vector Machine, Light Gradient Boosting, and Alex Neural Network. The unsupervised models are Principal Component Analysis, K-means, and Variational Autoencoder. The performance comparison is made in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, processing time, prediction time, training time per sample, and memory size. The results show that the Alex Neural Network model outperforms the other supervised models, while the Variational Autoencoder model has the best results compared to unsupervised models. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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15 pages, 476 KiB  
Article
A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining
by Tarid Wongvorachan, Surina He and Okan Bulut
Information 2023, 14(1), 54; https://doi.org/10.3390/info14010054 - 16 Jan 2023
Cited by 171 | Viewed by 35016
Abstract
Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction of students’ academic achievement) based on predictive models. However, the class imbalance problem in educational datasets could hamper the accuracy of predictive models as [...] Read more.
Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction of students’ academic achievement) based on predictive models. However, the class imbalance problem in educational datasets could hamper the accuracy of predictive models as many of these models are designed on the assumption that the predicted class is balanced. Although previous studies proposed several methods to deal with the imbalanced class problem, most of them focused on the technical details of how to improve each technique, while only a few focused on the application aspect, especially for the application of data with different imbalance ratios. In this study, we compared several sampling techniques to handle the different ratios of the class imbalance problem (i.e., moderately or extremely imbalanced classifications) using the High School Longitudinal Study of 2009 dataset. For our comparison, we used random oversampling (ROS), random undersampling (RUS), and the combination of the synthetic minority oversampling technique for nominal and continuous (SMOTE-NC) and RUS as a hybrid resampling technique. We used the Random Forest as our classification algorithm to evaluate the results of each sampling technique. Our results show that random oversampling for moderately imbalanced data and hybrid resampling for extremely imbalanced data seem to work best. The implications for educational data mining applications and suggestions for future research are discussed. Full article
(This article belongs to the Special Issue Predictive Analytics and Data Science)
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21 pages, 461 KiB  
Article
Generalized Frame for Orthopair Fuzzy Sets: (m,n)-Fuzzy Sets and Their Applications to Multi-Criteria Decision-Making Methods
by Tareq M. Al-shami and Abdelwaheb Mhemdi
Information 2023, 14(1), 56; https://doi.org/10.3390/info14010056 - 16 Jan 2023
Cited by 82 | Viewed by 4023
Abstract
Orthopairs (pairs of disjoint sets) have points in common with many approaches to managing vaguness/uncertainty such as fuzzy sets, rough sets, soft sets, etc. Indeed, they are successfully employed to address partial knowledge, consensus, and borderline cases. One of the generalized versions of [...] Read more.
Orthopairs (pairs of disjoint sets) have points in common with many approaches to managing vaguness/uncertainty such as fuzzy sets, rough sets, soft sets, etc. Indeed, they are successfully employed to address partial knowledge, consensus, and borderline cases. One of the generalized versions of orthopairs is intuitionistic fuzzy sets which is a well-known theory for researchers interested in fuzzy set theory. To extend the area of application of fuzzy set theory and address more empirical situations, the limitation that the grades of membership and non-membership must be calibrated with the same power should be canceled. To this end, we dedicate this manuscript to introducing a generalized frame for orthopair fuzzy sets called “(m,n)-Fuzzy sets”, which will be an efficient tool to deal with issues that require different importances for the degrees of membership and non-membership and cannot be addressed by the fuzzification tools existing in the published literature. We first establish its fundamental set of operations and investigate its abstract properties that can then be transmitted to the various models they are in connection with. Then, to rank (m,n)-Fuzzy sets, we define the functions of score and accuracy, and formulate aggregation operators to be used with (m,n)-Fuzzy sets. Ultimately, we develop the successful technique “aggregation operators” to handle multi-criteria decision-making problems in the environment of (m,n)-Fuzzy sets. The proposed technique has been illustrated and analyzed via a numerical example. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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24 pages, 1544 KiB  
Article
Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning
by Walid Ben Elmir, Allaoua Hemmak and Benaoumeur Senouci
Information 2023, 14(1), 31; https://doi.org/10.3390/info14010031 - 5 Jan 2023
Cited by 19 | Viewed by 13140
Abstract
Despite the efforts of the World Health Organization, blood transfusions and delivery are still the crucial challenges in blood supply chain management, especially when there is a high demand and not enough blood inventory. Consequently, reducing uncertainty in blood demand, waste, and shortages [...] Read more.
Despite the efforts of the World Health Organization, blood transfusions and delivery are still the crucial challenges in blood supply chain management, especially when there is a high demand and not enough blood inventory. Consequently, reducing uncertainty in blood demand, waste, and shortages has become a primary goal. In this paper, we propose a smart platform-oriented approach that will create a robust blood demand and supply chain able to achieve the goals of reducing uncertainty in blood demand by forecasting blood collection/demand, and reducing blood wastage and shortage by balancing blood collection and distribution based on an effective blood inventory management. We use machine learning and time series forecasting models to develop an AI/ML decision support system. It is an effective tool with three main modules that directly and indirectly impact all phases of the blood supply chain: (i) the blood demand forecasting module is designed to forecast blood demand; (ii) blood donor classification helps predict daily unbooked donors thereby enhancing the ability to control the volume of blood collected based on the results of blood demand forecasting; and (iii) scheduling blood donation appointments according to the expected number and type of blood donations, thus improving the quantity of blood by reducing the number of canceled appointments, and indirectly improving the quality and quantity of blood supply by decreasing the number of unqualified donors, thereby reducing the amount of invalid blood after and before preparation. As a result of the system’s improvements, blood shortages and waste can be reduced. The proposed solution provides robust and accurate predictions and identifies important clinical predictors for blood demand forecasting. Compared with the past year’s historical data, our integrated proposed system increased collected blood volume by 11%, decreased inventory wastage by 20%, and had a low incidence of shortages. Full article
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23 pages, 1481 KiB  
Article
Enabling Blockchain with IoMT Devices for Healthcare
by Jameel Almalki, Waleed Al Shehri, Rashid Mehmood, Khalid Alsaif, Saeed M. Alshahrani, Najlaa Jannah and Nayyar Ahmed Khan
Information 2022, 13(10), 448; https://doi.org/10.3390/info13100448 - 25 Sep 2022
Cited by 44 | Viewed by 5517
Abstract
Significant modifications have been seen in healthcare facilities over the past two decades. With the use of IoT-enabled devices, the monitoring and analysis of patient diagnostic parameters is made considerably easy. The new technology shift for medical field is IoMT. However, the problem [...] Read more.
Significant modifications have been seen in healthcare facilities over the past two decades. With the use of IoT-enabled devices, the monitoring and analysis of patient diagnostic parameters is made considerably easy. The new technology shift for medical field is IoMT. However, the problem of privacy for patient data and the security of information still a point to ponder. This research proposed a prototype model to integrate the blockchain and IoMT for providing better analysis of patient health factors. The authors suggested IoMT data to be collected over Edge Computing gateway devices and forward to Cloud Gateway. The three-layered decision making structure ensures the integrity of the data. The further analysis of information collected over sensor-based devices is done in the Cloud IoT Central Hub service. To ensure the secrecy and compliance of the patient data, Smart Contracts are integrated. After the exchange of smart contracts, a block of information is broadcast over the health blockchain. The P2P network makes it viable to retain all health statistics and further processing of information. The paper describes the scenario and experimental setup for a COVID-19 data-set analyzed in the proposed prototype mode. After the collection of information and decision making, the block of data is sent across all peer nodes. Thus, the power of IoMT and blockchain makes it easy for the healthcare worker to diagnose and handle patient data with privacy. The IoMT is integrated with artificial intelligence to enable decision making based on the classification of data. The results are saved as transactions in the blockchain hyperledger. This study demonstrates the prototype model with test data in a testing network with two peer nodes. Full article
(This article belongs to the Special Issue Integrated Technologies-AI, IoT, Blockchain in Smart Cities Applications)
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14 pages, 3119 KiB  
Article
Secure Sensitive Data Sharing Using RSA and ElGamal Cryptographic Algorithms with Hash Functions
by Emmanuel A. Adeniyi, Peace Busola Falola, Mashael S. Maashi, Mohammed Aljebreen and Salil Bharany
Information 2022, 13(10), 442; https://doi.org/10.3390/info13100442 - 20 Sep 2022
Cited by 39 | Viewed by 5908
Abstract
With the explosion of connected devices linked to one another, the amount of transmitted data grows day by day, posing new problems in terms of information security, such as unauthorized access to users’ credentials and sensitive information. Therefore, this study employed RSA and [...] Read more.
With the explosion of connected devices linked to one another, the amount of transmitted data grows day by day, posing new problems in terms of information security, such as unauthorized access to users’ credentials and sensitive information. Therefore, this study employed RSA and ElGamal cryptographic algorithms with the application of SHA-256 for digital signature formulation to enhance security and validate the sharing of sensitive information. Security is increasingly becoming a complex task to achieve. The goal of this study is to be able to authenticate shared data with the application of the SHA-256 function to the cryptographic algorithms. The methodology employed involved the use of C# programming language for the implementation of the RSA and ElGamal cryptographic algorithms using the SHA-256 hash function for digital signature. The experimental result shows that the RSA algorithm performs better than the ElGamal during the encryption and signature verification processes, while ElGamal performs better than RSA during the decryption and signature generation process. Full article
(This article belongs to the Special Issue Secure and Trustworthy Cyber–Physical Systems)
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17 pages, 1739 KiB  
Article
Local Multi-Head Channel Self-Attention for Facial Expression Recognition
by Roberto Pecoraro, Valerio Basile and Viviana Bono
Information 2022, 13(9), 419; https://doi.org/10.3390/info13090419 - 6 Sep 2022
Cited by 57 | Viewed by 4775
Abstract
Since the Transformer architecture was introduced in 2017, there has been many attempts to bring the self-attention paradigm in the field of computer vision. In this paper, we propose LHC: Local multi-Head Channel self-attention, a novel self-attention module that can be [...] Read more.
Since the Transformer architecture was introduced in 2017, there has been many attempts to bring the self-attention paradigm in the field of computer vision. In this paper, we propose LHC: Local multi-Head Channel self-attention, a novel self-attention module that can be easily integrated into virtually every convolutional neural network, and that is specifically designed for computer vision, with a specific focus on facial expression recognition. LHC is based on two main ideas: first, we think that in computer vision, the best way to leverage the self-attention paradigm is the channel-wise application instead of the more well explored spatial attention. Secondly, a local approach has the potential to better overcome the limitations of convolution than global attention, at least in those scenarios where images have a constant general structure, as in facial expression recognition. LHC-Net achieves a new state-of-the-art in the FER2013 dataset, with a significantly lower complexity and impact on the “host” architecture in terms of computational cost when compared with the previous state-of-the-art. Full article
(This article belongs to the Section Artificial Intelligence)
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14 pages, 4173 KiB  
Article
Federated Learning of Explainable AI Models in 6G Systems: Towards Secure and Automated Vehicle Networking
by Alessandro Renda, Pietro Ducange, Francesco Marcelloni, Dario Sabella, Miltiadis C. Filippou, Giovanni Nardini, Giovanni Stea, Antonio Virdis, Davide Micheli, Damiano Rapone and Leonardo Gomes Baltar
Information 2022, 13(8), 395; https://doi.org/10.3390/info13080395 - 20 Aug 2022
Cited by 58 | Viewed by 7912
Abstract
This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has [...] Read more.
This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has been widely investigated exploiting variants of stochastic gradient descent as the optimization method, it has not yet been adequately studied in the context of inherently explainable models. On the one side, XAI permits improving user experience of the offered communication services by helping end users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. These desiderata are often ignored in existing AI-based solutions for wireless network planning, design and operation. In this perspective, the article provides a detailed description of relevant 6G use cases, with a focus on vehicle-to-everything (V2X) environments: we describe a framework to evaluate the proposed approach involving online training based on real data from live networks. FL of XAI models is expected to bring benefits as a methodology for achieving seamless availability of decentralized, lightweight and communication efficient intelligence. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of quality of experience (QoE) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications. Full article
(This article belongs to the Special Issue Advances in Explainable Artificial Intelligence)
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15 pages, 1457 KiB  
Article
Sustainable Mobility as a Service: Demand Analysis and Case Studies
by Giuseppe Musolino
Information 2022, 13(8), 376; https://doi.org/10.3390/info13080376 - 5 Aug 2022
Cited by 23 | Viewed by 4158
Abstract
Urban mobility is evolving today towards the concept of Mobility as a Service (MaaS). MaaS allows passengers to use different transport services as a single option, by using a digital platform. Therefore, according to the MaaS concept, the mobility needs of passengers are [...] Read more.
Urban mobility is evolving today towards the concept of Mobility as a Service (MaaS). MaaS allows passengers to use different transport services as a single option, by using a digital platform. Therefore, according to the MaaS concept, the mobility needs of passengers are the central element of the transport service. The objective of this paper is to build an updated state-of-the-art of the main disaggregated and aggregated variables connected to travel demand in presence of MaaS. According to the above objective, this paper deals with methods and case studies to analyze passengers’ behaviour in the presence of MaaS. The methods described rely on the Transportation System Models (TSMs), in particular with the travel demand modelling component. The travel demand may be estimated by means of disaggregated, or sample, surveys (e.g., individual choices) and of aggregate surveys (e.g., characteristics of the area, traffic flows). The surveys are generally supported by Information Communication System (ICT) tools, such as: smartphones; smartcards; Global Position Systems (GPS); points of interest. The analysis of case studies allows to aggregate the existing scientific literature according to some criteria: the choice dimension of users (e.g., mode, bundle and path, or a combination of them); the characteristics of the survey (e.g., revealed preferences or stated preferences); the presence of behavioural theoretical background and of calibrated choice model(s). Full article
(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)
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25 pages, 1840 KiB  
Article
Sustainable Mobility as a Service: Dynamic Models for Agenda 2030 Policies
by Francesco Russo
Information 2022, 13(8), 355; https://doi.org/10.3390/info13080355 - 25 Jul 2022
Cited by 30 | Viewed by 3802
Abstract
Growth trends in passenger transport demand and gross domestic product have so far been similar. The increase in mobility in one area is connected with the increase in GDP in the same area. This increase is representative of the economic and social development [...] Read more.
Growth trends in passenger transport demand and gross domestic product have so far been similar. The increase in mobility in one area is connected with the increase in GDP in the same area. This increase is representative of the economic and social development of the area. At the same time, the increase in mobility produces one of the most negative environmental impacts, mainly determined by the growth of mobility of private cars. International attention is given to the possibilities of increasing mobility and, therefore, social and economic development without increasing environmental impacts. One of the most promising fields is that of MaaS: Mobility as a Service. MaaS arises from the interaction of new user behavioral models (demand) and new decision-making models on services (supply). Advanced interaction arises from the potentialities allowed by emerging ICT technologies. There is a delay in the advancement of transport system models that consider the updating of utility and choice for the user by means of updated information. The paper introduces sustainability as defined by Agenda 2030 with respect to urban passenger transport, then examines the role of ICT in the development of MaaS formalizing a dynamic model of demand–supply interaction explicating ICT. Finally, the advanced Sustainable MaaS, defined SMaaS, is analyzed, evidencing the contribution to achieving the goals of Agenda 2030. Full article
(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)
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19 pages, 2101 KiB  
Article
Sustainable Mobility as a Service: Supply Analysis and Test Cases
by Corrado Rindone
Information 2022, 13(7), 351; https://doi.org/10.3390/info13070351 - 21 Jul 2022
Cited by 36 | Viewed by 4577
Abstract
Urban mobility is one of the main issues in the pursuit of sustainability. The United Nations 2030 Agenda assigns mobility and transport central roles in sustainable development and its components: economic, social, and environment. In this context, the emerging concept of Mobility as [...] Read more.
Urban mobility is one of the main issues in the pursuit of sustainability. The United Nations 2030 Agenda assigns mobility and transport central roles in sustainable development and its components: economic, social, and environment. In this context, the emerging concept of Mobility as a Service (MaaS) offers an alternative to unsustainable mobility, often based on private car use. From the point of view of sustainable mobility, the MaaS paradigm implies greater insights into the transport system and its components (supply, demand, and reciprocal interactions). This paper proposes an approach to the transport system aimed at overcoming the current barriers to the implementation of the paradigm. The focus is on the implications for the transport supply subsystem. The investigation method is based on the analysis of the main components of such subsystem (governance, immaterial, material, equipment) and its role in the entire transport system. Starting with the first experiences of Finnish cities, the paper investigates some real case studies, which are experimenting with MaaS, to find common and uncommon elements. From the analyses, it emerges that the scientific literature and real experiences mainly focus on the immaterial components alone. To address the challenges related to sustainable mobility, this paper underlines the need to consider all components within a transport system approach. The findings of the paper are useful in several contexts. In the context of research, the paper offers an analysis of the transport supply system from the point of view of the MaaS paradigm. In the real context, the paper offers further useful insights for operators and decision-makers who intend to increase the knowledge and skills necessary to face challenges related to the introduction of MaaS. Full article
(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)
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15 pages, 2061 KiB  
Article
Sustainable Mobility as a Service: Framework and Transport System Models
by Antonino Vitetta
Information 2022, 13(7), 346; https://doi.org/10.3390/info13070346 - 16 Jul 2022
Cited by 36 | Viewed by 5155
Abstract
Passenger mobility plays an important role in today’s society and optimized transport services are a priority. In recent years, MaaS (Mobility as a Service) has been studied and tested as new integrated services for users. In this paper, MaaS is studied considering the [...] Read more.
Passenger mobility plays an important role in today’s society and optimized transport services are a priority. In recent years, MaaS (Mobility as a Service) has been studied and tested as new integrated services for users. In this paper, MaaS is studied considering the sustainability objectives and goals to be achieved with particular reference to the consolidated methodologies adopted in the transport systems engineering for design, management, and monitoring of transport services; it is defined as Sustainable MaaS (S-MaaS). This paper considers the technological and communication platform essential and assumed to be a given considering that it has been proposed in many papers and it has been tested in some areas together with MaaS. Starting from the MaaS platform, the additional components and models necessary for the implementation of an S-MaaS are analyses in relation to: a Decision Support System (DSS) that supports MaaS public administrations and MaaS companies for the design of the service and demand management; a system for the evaluation of intervention policies; and also considers smart planning for a priori and a posteriori evaluation of sustainability objectives and targets. Full article
(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)
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12 pages, 1826 KiB  
Article
Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT
by Omar Abdulkareem Mahmood, Ali R. Abdellah, Ammar Muthanna and Andrey Koucheryavy
Information 2022, 13(7), 328; https://doi.org/10.3390/info13070328 - 7 Jul 2022
Cited by 29 | Viewed by 4394
Abstract
Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their [...] Read more.
Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks. Full article
(This article belongs to the Special Issue Advances in Wireless Communications Systems)
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16 pages, 3021 KiB  
Article
An Intrusion Detection Method for Industrial Control System Based on Machine Learning
by Yixin Cao, Lei Zhang, Xiaosong Zhao, Kai Jin and Ziyi Chen
Information 2022, 13(7), 322; https://doi.org/10.3390/info13070322 - 3 Jul 2022
Cited by 9 | Viewed by 4519
Abstract
The integration of communication networks and the internet of industrial control in Industrial Control System (ICS) increases their vulnerability to cyber attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDS) largely rely on predefined models and are trained mostly on specific cyber attacks, [...] Read more.
The integration of communication networks and the internet of industrial control in Industrial Control System (ICS) increases their vulnerability to cyber attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDS) largely rely on predefined models and are trained mostly on specific cyber attacks, which means the traditional IDS cannot cope with unknown attacks. Additionally, most IDS do not consider the imbalanced nature of ICS datasets, thus suffering from low accuracy and high False Positive Rates when being put to use. In this paper, we propose the NCO–double-layer DIFF_RF–OPFYTHON intrusion detection method for ICS, which consists of NCO modules, double-layer DIFF_RF modules, and OPFYTHON modules. Detected traffic will be divided into three categories by the double-layer DIFF_RF module: known attacks, unknown attacks, and normal traffic. Then, the known attacks will be classified into specific attacks by the OPFYTHON module according to the feature of attack traffic. Finally, we use the NCO module to improve the model input and enhance the accuracy of the model. The results show that the proposed method outperforms traditional intrusion detection methods, such as XGboost and SVM. The detection of unknown attacks is also considerable. The accuracy of the dataset used in this paper reaches 98.13%. The detection rates for unknown attacks and known attacks reach 98.21% and 95.1%, respectively. Moreover, the method we proposed has achieved suitable results on other public datasets. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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15 pages, 2863 KiB  
Article
Digital Twin of a Magnetic Medical Microrobot with Stochastic Model Predictive Controller Boosted by Machine Learning in Cyber-Physical Healthcare Systems
by Hamid Keshmiri Neghab, Mohammad (Behdad) Jamshidi and Hamed Keshmiri Neghab
Information 2022, 13(7), 321; https://doi.org/10.3390/info13070321 - 1 Jul 2022
Cited by 40 | Viewed by 4506
Abstract
Recently, emerging technologies have assisted the healthcare system in the treatment of a wide range of diseases so considerably that the development of such methods has been regarded as a practical solution to cure many diseases. Accordingly, underestimating the importance of such cyber [...] Read more.
Recently, emerging technologies have assisted the healthcare system in the treatment of a wide range of diseases so considerably that the development of such methods has been regarded as a practical solution to cure many diseases. Accordingly, underestimating the importance of such cyber environments in the medical and healthcare system is not logical, as a combination of such systems with the Metaverse can lead to tremendous applications, particularly after this pandemic, in which the significance of such technologies has been proven. This is why the digital twin of a medical microrobot, which is controlled via a stochastic model predictive controller (MPC) empowered by a system identification based on machine learning (ML), has been rendered in this research. This robot benefits from the technology of magnetic levitation, and the identification approach helps the controller to identify the dynamic of this robot. Considering the size, control system, and specifications of such micro-magnetic mechanisms, it can play an important role in monitoring, drug-delivery, or even some sensitive internal surgeries. Thus, accuracy, robustness, and reliability have been taken into consideration for the design and simulation of this magnetic mechanism. Finally, a second-order statistic noise is added to the plant while the controller is updated by a Kalman filter to deal with this environment. The results prove that the proposed controller will work effectively. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Informatics)
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16 pages, 473 KiB  
Article
Multimodal Fake News Detection
by Isabel Segura-Bedmar and Santiago Alonso-Bartolome
Information 2022, 13(6), 284; https://doi.org/10.3390/info13060284 - 2 Jun 2022
Cited by 58 | Viewed by 11766
Abstract
Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools [...] Read more.
Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools for the automatic detection of fake news plays an important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories, such as Manipulated content, Satire, or False connection, strongly benefit from the use of images. Using images also improves the results of the other categories but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model, with an accuracy of 78%. Exploiting both text and image data significantly improves the performance of fake news detection. Full article
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
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12 pages, 2486 KiB  
Article
Efficient Edge-AI Application Deployment for FPGAs
by Stavros Kalapothas, Georgios Flamis and Paris Kitsos
Information 2022, 13(6), 279; https://doi.org/10.3390/info13060279 - 28 May 2022
Cited by 28 | Viewed by 8619
Abstract
Field Programmable Gate Array (FPGA) accelerators have been widely adopted for artificial intelligence (AI) applications on edge devices (Edge-AI) utilizing Deep Neural Networks (DNN) architectures. FPGAs have gained their reputation due to the greater energy efficiency and high parallelism than microcontrollers (MCU) and [...] Read more.
Field Programmable Gate Array (FPGA) accelerators have been widely adopted for artificial intelligence (AI) applications on edge devices (Edge-AI) utilizing Deep Neural Networks (DNN) architectures. FPGAs have gained their reputation due to the greater energy efficiency and high parallelism than microcontrollers (MCU) and graphical processing units (GPU), while they are easier to develop and more reconfigurable than the Application Specific Integrated Circuit (ASIC). The development and building of AI applications on resource constraint devices such as FPGAs remains a challenge, however, due to the co-design approach, which requires a valuable expertise in low-level hardware design and in software development. This paper explores the efficacy and the dynamic deployment of hardware accelerated applications on the Kria KV260 development platform based on the Xilinx Kria K26 system-on-module (SoM), which includes a Zynq multiprocessor system-on-chip (MPSoC). The platform supports the Python-based PYNQ framework and maintains a high level of versatility with the support of custom bitstreams (overlays). The demonstration proved the reconfigurabibilty and the overall ease of implementation with low-footprint machine learning (ML) algorithms. Full article
(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media (SEEDA-CECNSM 2021))
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15 pages, 634 KiB  
Article
Decision-Making Model for Reinforcing Digital Transformation Strategies Based on Artificial Intelligence Technology
by Kyungtae Kim and Boyoung Kim
Information 2022, 13(5), 253; https://doi.org/10.3390/info13050253 - 13 May 2022
Cited by 29 | Viewed by 9929
Abstract
Firms’ digital environment changes and industrial competitions have evolved quickly since the Fourth Industrial Revolution and the COVID-19 pandemic. Many companies are propelling company-wide digital transformation strategies based on artificial intelligence (AI) technology for the digital innovation of organizations and businesses. This study [...] Read more.
Firms’ digital environment changes and industrial competitions have evolved quickly since the Fourth Industrial Revolution and the COVID-19 pandemic. Many companies are propelling company-wide digital transformation strategies based on artificial intelligence (AI) technology for the digital innovation of organizations and businesses. This study aims to define the factors affecting digital transformation strategies and present a decision-making model required for digital transformation strategies based on the definition. It also reviews previous AI technology and digital transformation strategies and draws influence factors. The research model drew four evaluation areas, such as subject, environment, resource, and mechanism, and 16 evaluation factors through the SERM model. After the factors were reviewed through the Delphi methods, a questionnaire survey was conducted targeting experts with over 10 years of work experience in the digital strategy field. The study results were produced by comparing the data’s importance using an Analytic Hierarchy Process (AHP) on each group. According to the analysis, the subject was the most critical factor, and the CEO (top management) was more vital than the core talent or technical development organization. The importance was shown in the order of resource, mechanism and environment, following subject. It was ascertained that there were differences of importance in industrial competition and market digitalization in the demander and provider groups. Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
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16 pages, 905 KiB  
Article
A Study of Inbound Travelers Experience and Satisfaction at Quarantine Hotels in Indonesia during the COVID-19 Pandemic
by Narariya Dita Handani, Aura Lydia Riswanto and Hak-Seon Kim
Information 2022, 13(5), 254; https://doi.org/10.3390/info13050254 - 13 May 2022
Cited by 18 | Viewed by 4199
Abstract
The tourism and hospitality sectors contribute significantly to the Indonesian economy. Meanwhile, COVID-19 affects these sectors. During the pandemic, the Indonesian government applied quarantine regulations at designated hotels to support its tourism industry. Since COVID-19 is becoming endemic and travel bans are being [...] Read more.
The tourism and hospitality sectors contribute significantly to the Indonesian economy. Meanwhile, COVID-19 affects these sectors. During the pandemic, the Indonesian government applied quarantine regulations at designated hotels to support its tourism industry. Since COVID-19 is becoming endemic and travel bans are being relaxed, hotel satisfaction becomes a crucial factor in quarantine hotels. If guests have a positive experience while staying at these hotels, they are likely to return for a staycation or vacation in the near future. The study examined 4856 reviews from Google reviews on 15 quarantine hotels in Indonesia. Following word frequency calculations in a matrix, UCINET 6.0 is used to analyze the network centrality and perform CONCOR analysis. The CONCOR analysis categorizes the review data into five categories. As quantitative analysis was performed, exploratory factor analysis was grouped into six variables: tangible, assurance, frontline, accommodation, quarantine, and location. As a result, tangible, assurance, and frontline negatively impacted guest satisfaction. Furthermore, three other variables: accommodation, quarantine, location, which have a positive influence, will lead to increased trust from inbound travelers. For managerial implication, results allow managers of quarantine hotels in Indonesia to focus more on improving tangible, assurance, and frontline factors. Full article
(This article belongs to the Special Issue Data Analytics and Consumer Behavior)
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28 pages, 3420 KiB  
Article
Enhancing Food Supply Chain Security through the Use of Blockchain and TinyML
by Vasileios Tsoukas, Anargyros Gkogkidis, Aikaterini Kampa, Georgios Spathoulas and Athanasios Kakarountas
Information 2022, 13(5), 213; https://doi.org/10.3390/info13050213 - 20 Apr 2022
Cited by 51 | Viewed by 10999
Abstract
Food safety is a fundamental right in modern societies. One of the most pressing problems nowadays is the provenance of food and food-related products that citizens consume, mainly due to several food scares and the globalization of food markets, which has resulted in [...] Read more.
Food safety is a fundamental right in modern societies. One of the most pressing problems nowadays is the provenance of food and food-related products that citizens consume, mainly due to several food scares and the globalization of food markets, which has resulted in food supply chains that extend beyond nations or even continent boundaries. Food supply networks are characterized by high complexity and a lack of openness. There is a critical requirement for applying novel techniques to verify and authenticate the origin, quality parameters, and transfer/storage details associated with food. This study portrays an end-to-end approach to enhance the security of the food supply chain and thus increase the trustfulness of the food industry. The system aims at increasing the transparency of food supply chain monitoring systems through securing all components that those consist of. A universal information monitoring scheme based on blockchain technology ensures the integrity of collected data, a self-sovereign identity approach for all supply chain actors ensures the minimization of single points of failure, and finally, a security mechanism, that is based on the use of TinyML’s nascent technology, is embedded in monitoring devices to mitigate a significant portion of malicious behavior from actors in the supply chain. Full article
(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media (SEEDA-CECNSM 2021))
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15 pages, 2590 KiB  
Article
Audio Storytelling Innovation in a Digital Age: The Case of Daily News Podcasts in Spain
by Leoz-Aizpuru Asier and Pedrero-Esteban Luis Miguel
Information 2022, 13(4), 204; https://doi.org/10.3390/info13040204 - 18 Apr 2022
Cited by 19 | Viewed by 6070
Abstract
On the 1st of February 2017, The New York Times published the first episode of ‘The Daily’, a news podcast hosted by Michael Barbaro that, five years later, has become the most popular in the world with four million listeners each day and [...] Read more.
On the 1st of February 2017, The New York Times published the first episode of ‘The Daily’, a news podcast hosted by Michael Barbaro that, five years later, has become the most popular in the world with four million listeners each day and more than 3000 million accumulated downloads. The unprecedented success of this audio format, that has emerged in a traditional newspaper, has helped to revamp radio news and has spread in various versions all over the world. This investigation analyses daily podcasts in Spain and, by means of a quantitative and qualitative study, identifies their main themes, narrative structures, and expressive contributions based on four illustrative experiences in this market: ‘Quién dice qué‘, ‘AM’, ‘El Mundo al día’, and ‘Un tema al día’. The results reveal the consolidation of two clearly defined models: a more conventional one based on radio bulletins and news reports; and another, more innovative model that replicates the anglophone formula that opts for depth, dissemination, and a conversational tone to redefine the canons of the audio news narrative and take advantage of the potential of audio as a new distribution channel for newspapers in the digital eco-system. Full article
(This article belongs to the Special Issue Advances in Interactive and Digital Media)
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11 pages, 1388 KiB  
Article
A LiDAR–Camera Fusion 3D Object Detection Algorithm
by Leyuan Liu, Jian He, Keyan Ren, Zhonghua Xiao and Yibin Hou
Information 2022, 13(4), 169; https://doi.org/10.3390/info13040169 - 26 Mar 2022
Cited by 22 | Viewed by 6189
Abstract
3D object detection with LiDAR and camera fusion has always been a challenge for autonomous driving. This work proposes a deep neural network (namely FuDNN) for LiDAR–camera fusion 3D object detection. Firstly, a 2D backbone is designed to extract features from camera images. [...] Read more.
3D object detection with LiDAR and camera fusion has always been a challenge for autonomous driving. This work proposes a deep neural network (namely FuDNN) for LiDAR–camera fusion 3D object detection. Firstly, a 2D backbone is designed to extract features from camera images. Secondly, an attention-based fusion sub-network is designed to fuse the features extracted by the 2D backbone and the features extracted from 3D LiDAR point clouds by PointNet++. Besides, the FuDNN, which uses the RPN and the refinement work of PointRCNN to obtain 3D box predictions, was tested on the public KITTI dataset. Experiments on the KITTI validation set show that the proposed FuDNN achieves AP values of 92.48, 82.90, and 80.51 at easy, moderate, and hard difficulty levels for car detection. The proposed FuDNN improves the performance of LiDAR–camera fusion 3D object detection in the car category of the public KITTI dataset. Full article
(This article belongs to the Topic Big Data and Artificial Intelligence)
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