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26 pages, 3805 KiB

Open AccessArticle

A Methodological Framework for Designing Personalised Training Programs to Support Personnel Upskilling in Industry 5.0

by Francisco Fraile, Foivos Psarommatis, Faustino Alarcón and Jordi Joan

Computers 2023, 12(11), 224; https://doi.org/10.3390/computers12110224 - 2 Nov 2023

Cited by 10 | Viewed by 3927

Abstract

Industry 5.0 emphasises social sustainability and highlights the critical need for personnel upskilling and reskilling to achieve the seamless integration of human expertise and advanced technology. This paper presents a methodological framework for designing personalised training programs that support personnel upskilling, with the [...] Read more.

Industry 5.0 emphasises social sustainability and highlights the critical need for personnel upskilling and reskilling to achieve the seamless integration of human expertise and advanced technology. This paper presents a methodological framework for designing personalised training programs that support personnel upskilling, with the goal of fostering flexibility and resilience amid rapid changes in the industrial landscape. The proposed framework encompasses seven stages: (1) Integration with Existing Systems, (2) Data Collection, (3) Data Preparation, (4) Skills-Models Extraction, (5) Assessment of Skills and Qualifications, (6) Recommendations for Training Program, (7) Evaluation and Continuous Improvement. By leveraging Large Language Models (LLMs) and human-centric principles, our methodology enables the creation of tailored training programs to help organisations promote a culture of proactive learning. This work thus contributes to the sustainable development of the human workforce, facilitating access to high-quality training and fostering personnel well-being and satisfaction. Through a food-processing use case, this paper demonstrates how this methodology can help organisations identify skill gaps and upskilling opportunities and use these insights to drive personnel upskilling in Industry 5.0. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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18 pages, 2324 KiB

Open AccessArticle

The Potential of Machine Learning for Wind Speed and Direction Short-Term Forecasting: A Systematic Review

by Décio Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa and Fernando Morgado-Dias

Computers 2023, 12(10), 206; https://doi.org/10.3390/computers12100206 - 13 Oct 2023

Cited by 8 | Viewed by 3205

Abstract

Wind forecasting, which is essential for numerous services and safety, has significantly improved in accuracy due to machine learning advancements. This study reviews 23 articles from 1983 to 2023 on machine learning for wind speed and direction nowcasting. The wind prediction ranged from [...] Read more.

Wind forecasting, which is essential for numerous services and safety, has significantly improved in accuracy due to machine learning advancements. This study reviews 23 articles from 1983 to 2023 on machine learning for wind speed and direction nowcasting. The wind prediction ranged from 1 min to 1 week, with more articles at lower temporal resolutions. Most works employed neural networks, focusing recently on deep learning models. Among the reported performance metrics, the most prevalent were mean absolute error, mean squared error, and mean absolute percentage error. Considering these metrics, the mean performance of the examined works was 0.56 m/s, 1.10 m/s, and 6.72%, respectively. The results underscore the novel effectiveness of machine learning in predicting wind conditions using high-resolution time data and demonstrated that deep learning models surpassed traditional methods, improving the accuracy of wind speed and direction forecasts. Moreover, it was found that the inclusion of non-wind weather variables does not benefit the model’s overall performance. Further studies are recommended to predict both wind speed and direction using diverse spatial data points, and high-resolution data are recommended along with the usage of deep learning models. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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14 pages, 959 KiB

Open AccessArticle

Video Summarization Based on Feature Fusion and Data Augmentation

by Theodoros Psallidas and Evaggelos Spyrou

Computers 2023, 12(9), 186; https://doi.org/10.3390/computers12090186 - 15 Sep 2023

Cited by 4 | Viewed by 1989

Abstract

During the last few years, several technological advances have led to an increase in the creation and consumption of audiovisual multimedia content. Users are overexposed to videos via several social media or video sharing websites and mobile phone applications. For efficient browsing, searching, [...] Read more.

During the last few years, several technological advances have led to an increase in the creation and consumption of audiovisual multimedia content. Users are overexposed to videos via several social media or video sharing websites and mobile phone applications. For efficient browsing, searching, and navigation across several multimedia collections and repositories, e.g., for finding videos that are relevant to a particular topic or interest, this ever-increasing content should be efficiently described by informative yet concise content representations. A common solution to this problem is the construction of a brief summary of a video, which could be presented to the user, instead of the full video, so that she/he could then decide whether to watch or ignore the whole video. Such summaries are ideally more expressive than other alternatives, such as brief textual descriptions or keywords. In this work, the video summarization problem is approached as a supervised classification task, which relies on feature fusion of audio and visual data. Specifically, the goal of this work is to generate dynamic video summaries, i.e., compositions of parts of the original video, which include its most essential video segments, while preserving the original temporal sequence. This work relies on annotated datasets on a per-frame basis, wherein parts of videos are annotated as being “informative” or “noninformative”, with the latter being excluded from the produced summary. The novelties of the proposed approach are, (a) prior to classification, a transfer learning strategy to use deep features from pretrained models is employed. These models have been used as input to the classifiers, making them more intuitive and robust to objectiveness, and (b) the training dataset was augmented by using other publicly available datasets. The proposed approach is evaluated using three datasets of user-generated videos, and it is demonstrated that deep features and data augmentation are able to improve the accuracy of video summaries based on human annotations. Moreover, it is domain independent, could be used on any video, and could be extended to rely on richer feature representations or include other data modalities. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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24 pages, 5346 KiB

Open AccessArticle

Evaluating User Satisfaction Using Deep-Learning-Based Sentiment Analysis for Social Media Data in Saudi Arabia’s Telecommunication Sector

by Majed A. Alshamari

Computers 2023, 12(9), 170; https://doi.org/10.3390/computers12090170 - 26 Aug 2023

Cited by 5 | Viewed by 3230

Abstract

Social media has become common as a means to convey opinions and express the extent of satisfaction and dissatisfaction with a service or product. In the Kingdom of Saudi Arabia specifically, most social media users share positive and negative opinions about a service [...] Read more.

Social media has become common as a means to convey opinions and express the extent of satisfaction and dissatisfaction with a service or product. In the Kingdom of Saudi Arabia specifically, most social media users share positive and negative opinions about a service or product, especially regarding communication services, which is one of the most important services for citizens who use it to communicate with the world. This research aimed to analyse and measure user satisfaction with the services provided by the Saudi Telecom Company (STC), Mobily, and Zain. This type of sentiment analysis is an important measure and is used to make important business decisions to succeed in increasing customer loyalty and satisfaction. In this study, the authors developed advanced methods based on deep learning (DL) to analyse and reveal the percentage of customer satisfaction using the publicly available dataset AraCust. Several DL models have been utilised in this study, including long short-term memory (LSTM), gated recurrent unit (GRU), and BiLSTM, on the AraCust dataset. The LSTM model achieved the highest performance in text classification, demonstrating a 98.04% training accuracy and a 97.03% test score. The study addressed the biggest challenge that telecommunications companies face: that the company’s services influence customers’ decisions due to their dissatisfaction with the provided services. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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16 pages, 1350 KiB

Open AccessArticle

Face Detection Using a Capsule Network for Driver Monitoring Application

by János Hollósi, Áron Ballagi, Gábor Kovács, Szabolcs Fischer and Viktor Nagy

Computers 2023, 12(8), 161; https://doi.org/10.3390/computers12080161 - 12 Aug 2023

Cited by 3 | Viewed by 2044

Abstract

Bus driver distraction and cognitive load lead to higher accident risk. Driver distraction sources and complex physical and psychological effects must be recognized and analyzed in real-world driving conditions to reduce risk and enhance overall road safety. The implementation of a camera-based system [...] Read more.

Bus driver distraction and cognitive load lead to higher accident risk. Driver distraction sources and complex physical and psychological effects must be recognized and analyzed in real-world driving conditions to reduce risk and enhance overall road safety. The implementation of a camera-based system utilizing computer vision for face recognition emerges as a highly viable and effective driver monitoring approach applicable in public transport. Reliable, accurate, and unnoticeable software solutions need to be developed to reach the appropriate robustness of the system. The reliability of data recording depends mainly on external factors, such as vibration, camera lens contamination, lighting conditions, and other optical performance degradations. The current study introduces Capsule Networks (CapsNets) for image processing and face detection tasks. The authors’ goal is to create a fast and accurate system compared to state-of-the-art Neural Network (NN) algorithms. Based on the seven tests completed, the authors’ solution outperformed the other networks in terms of performance degradation in six out of seven cases. The results show that the applied capsule-based solution performs well, and the degradation in efficiency is noticeably smaller than for the presented convolutional neural networks when adversarial attack methods are used. From an application standpoint, ensuring the security and effectiveness of an image-based driver monitoring system relies heavily on the mitigation of disruptive occurrences, commonly referred to as “image distractions,” which represent attacks on the neural network. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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41 pages, 1074 KiB

Open AccessArticle

Convolutional Neural Networks: A Survey

by Moez Krichen

Computers 2023, 12(8), 151; https://doi.org/10.3390/computers12080151 - 28 Jul 2023

Cited by 175 | Viewed by 20462

Abstract

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing [...] Read more.

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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20 pages, 1187 KiB

Open AccessArticle

Kernel-Based Regularized EEGNet Using Centered Alignment and Gaussian Connectivity for Motor Imagery Discrimination

by Mateo Tobón-Henao, Andrés Marino Álvarez-Meza and Cesar German Castellanos-Dominguez

Computers 2023, 12(7), 145; https://doi.org/10.3390/computers12070145 - 21 Jul 2023

Cited by 2 | Viewed by 1748

Abstract

Brain–computer interfaces (BCIs) from electroencephalography (EEG) provide a practical approach to support human–technology interaction. In particular, motor imagery (MI) is a widely used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, [...] Read more.

Brain–computer interfaces (BCIs) from electroencephalography (EEG) provide a practical approach to support human–technology interaction. In particular, motor imagery (MI) is a widely used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named kernel-based regularized EEGNet (KREEGNet), leveled on centered kernel alignment and Gaussian functional connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intrasubject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multiclass MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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21 pages, 1054 KiB

Open AccessArticle

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

by Saima Khosa, Arif Mehmood and Muhammad Rizwan

Computers 2023, 12(7), 137; https://doi.org/10.3390/computers12070137 - 8 Jul 2023

Cited by 3 | Viewed by 2775

Abstract

The study focuses on news category prediction and investigates the performance of sentence embedding of four transformer models (BERT, RoBERTa, MPNet, and T5) and their variants as feature vectors when combined with Softmax and Random Forest using two accessible news datasets from Kaggle. [...] Read more.

The study focuses on news category prediction and investigates the performance of sentence embedding of four transformer models (BERT, RoBERTa, MPNet, and T5) and their variants as feature vectors when combined with Softmax and Random Forest using two accessible news datasets from Kaggle. The data are stratified into train and test sets to ensure equal representation of each category. Word embeddings are generated using transformer models, with the last hidden layer selected as the embedding. Mean pooling calculates a single vector representation called sentence embedding, capturing the overall meaning of the news article. The performance of Softmax and Random Forest, as well as the soft voting of both, is evaluated using evaluation measures such as accuracy, F1 score, precision, and recall. The study also contributes by evaluating the performance of Softmax and Random Forest individually. The macro-average F1 score is calculated to compare the performance of different transformer embeddings in the same experimental settings. The experiments reveal that MPNet versions v1 and v3 achieve the highest F1 score of 97.7% when combined with Random Forest, while T5 Large embedding achieves the highest F1 score of 98.2% when used with Softmax regression. MPNet v1 performs exceptionally well when used in the voting classifier, obtaining an impressive F1 score of 98.6%. In conclusion, the experiments validate the superiority of certain transformer models, such as MPNet v1, MPNet v3, and DistilRoBERTa, when used to calculate sentence embeddings within the Random Forest framework. The results also highlight the promising performance of T5 Large and RoBERTa Large in voting of Softmax regression and Random Forest. The voting classifier, employing transformer embeddings and ensemble learning techniques, consistently outperforms other baselines and individual algorithms. These findings emphasize the effectiveness of the voting classifier with transformer embeddings in achieving accurate and reliable predictions for news category classification tasks. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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22 pages, 1104 KiB

Open AccessArticle

Exploring Clustering Techniques for Analyzing User Engagement Patterns in Twitter Data

by Andreas Kanavos, Ioannis Karamitsos and Alaa Mohasseb

Computers 2023, 12(6), 124; https://doi.org/10.3390/computers12060124 - 19 Jun 2023

Cited by 7 | Viewed by 3283

Abstract

Social media platforms have revolutionized information exchange and socialization in today’s world. Twitter, as one of the prominent platforms, enables users to connect with others and express their opinions. This study focuses on analyzing user engagement levels on Twitter using graph mining and [...] Read more.

Social media platforms have revolutionized information exchange and socialization in today’s world. Twitter, as one of the prominent platforms, enables users to connect with others and express their opinions. This study focuses on analyzing user engagement levels on Twitter using graph mining and clustering techniques. We measure user engagement based on various tweet attributes, including retweets, replies, and more. Specifically, we explore the strength of user connections in Twitter networks by examining the diversity of edges. Our approach incorporates graph mining models that assign different weights to evaluate the significance of each connection. Additionally, clustering techniques are employed to group users based on their engagement patterns and behaviors. Statistical analysis was conducted to assess the similarity between user profiles, as well as attributes, such as friendship, followings, and interactions within the Twitter social network. The findings highlight the discovery of closely linked user groups and the identification of distinct clusters based on engagement levels. This research emphasizes the importance of understanding both individual and group behaviors in comprehending user engagement dynamics on Twitter. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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17 pages, 428 KiB

Open AccessArticle

A Query Expansion Benchmark on Social Media Information Retrieval: Which Methodology Performs Best and Aligns with Semantics?

by Evangelos A. Stathopoulos, Anastasios I. Karageorgiadis, Alexandros Kokkalas, Sotiris Diplaris, Stefanos Vrochidis and Ioannis Kompatsiaris

Computers 2023, 12(6), 119; https://doi.org/10.3390/computers12060119 - 10 Jun 2023

Cited by 1 | Viewed by 2294

Abstract

This paper presents a benchmarking survey on query expansion techniques for social media information retrieval, with a focus on comparing the performance of methods using semantic web technologies. The study evaluated query expansion techniques such as generative AI models and semantic matching algorithms [...] Read more.

This paper presents a benchmarking survey on query expansion techniques for social media information retrieval, with a focus on comparing the performance of methods using semantic web technologies. The study evaluated query expansion techniques such as generative AI models and semantic matching algorithms and how they are integrated in a semantic framework. The evaluation was based on cosine similarity metrics, including the Discounted Cumulative Gain (DCG), Ideal Discounted Cumulative Gain (IDCG), and normalized Discounted Cumulative Gain (nDCG), as well as the Mean Average Precision (MAP). Additionally, the paper discusses the use of semantic web technologies as a component in a pipeline for building thematic knowledge graphs from retrieved social media data with extended ontologies integrated for the refugee crisis. The paper begins by introducing the importance of query expansion in information retrieval and the potential benefits of incorporating semantic web technologies. The study then presents the methodologies and outlines the specific procedures for each query expansion technique. The results of the evaluation are presented, as well as the rest semantic framework, and the best-performing technique was identified, which was the curie-001 generative AI model. Finally, the paper summarizes the main findings and suggests future research directions. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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17 pages, 3810 KiB

Open AccessArticle

Prototype of a Recommendation Model with Artificial Intelligence for Computational Thinking Improvement of Secondary Education Students

by Raquel Hijón-Neira, Cornelia Connolly, Celeste Pizarro and Diana Pérez-Marín

Computers 2023, 12(6), 113; https://doi.org/10.3390/computers12060113 - 26 May 2023

Cited by 2 | Viewed by 3003

Abstract

There is a growing interest in finding new ways to address the difficult task of introducing programming to secondary students for the first time to improve students’ computational thinking (CT) skills. Therefore, extensive research is required in this field. Worldwide, new ways to [...] Read more.

There is a growing interest in finding new ways to address the difficult task of introducing programming to secondary students for the first time to improve students’ computational thinking (CT) skills. Therefore, extensive research is required in this field. Worldwide, new ways to address this difficult task have been developed: visual execution environments and approaches by text programming or visual programming are among the most popular. This paper addresses the complex task by using a visual execution environment (VEE) to introduce the first programming concepts that should be covered in any introductory programming course. These concepts include variables, input and output, conditionals, loops, arrays, functions, and files. This study explores two approaches to achieve this goal: visual programming (using Scratch) and text programming (using Java) to improve CT. Additionally, it proposes an AI recommendation model into the VEE to further improve the effectiveness of developing CT among secondary education students. This integrated model combines the capabilities of an AI learning system module and a personalized learning module to better address the task at hand. To pursue this task, an experiment has been carried out among 23 preservice secondary teachers’ students in two universities, one in Madrid, Spain, and the other in Galway, Ireland. The overall results showed a significant improvement in the Scratch group. However, when analyzing the results based on specific programming concepts, significance was observed only in the Scratch group, specifically for the Loop concept. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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26 pages, 595 KiB

Open AccessArticle

Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class Balancing

by Maria Nefeli Nikiforos, Konstantina Deliveri, Katia Lida Kermanidis and Adamantia Pateli

Computers 2023, 12(6), 111; https://doi.org/10.3390/computers12060111 - 24 May 2023

Viewed by 2114

Abstract

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational backgrounds, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational [...] Read more.

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational backgrounds, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, natural language processing, and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of machine learning experiments with a domain-specific textual data set while considering two research directions: a. prediction analysis and b. data balancing. Wrong prediction analysis and the features that contributed to misclassification, along with correct prediction analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel four-step methodology was proposed in this paper for the first time, which consists of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtained better results than data undersampling in imbalanced data sets, while hybrid approaches performed reasonably well. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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13 pages, 1736 KiB

Open AccessArticle

Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

by Christos Troussas, Akrivi Krouska, Antonios Koliarakis and Cleo Sgouropoulou

Computers 2023, 12(5), 109; https://doi.org/10.3390/computers12050109 - 22 May 2023

Cited by 7 | Viewed by 3137

Abstract

Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. [...] Read more.

Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. In greater detail, we introduce an enhanced collaborative filtering (CF) approach in order to develop hybrid recommender systems that personalize search results for users. The proposed CF enhancement incorporates user actions beyond explicit ratings to collect data and alleviate the issue of sparse data, resulting in high-quality recommendations. As a testbed for our research, a web-based digital library, incorporating the proposed algorithm, has been developed. Examples of operation of the use of the system are presented using cognitive walkthrough inspection, which demonstrates the effectiveness of the approach in producing personalized recommendations and improving user experience. Thus, the hybrid recommender system, which is incorporated in the digital library, has been evaluated, yielding promising results. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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16 pages, 1370 KiB

Open AccessArticle

Info-Autopoiesis and the Limits of Artificial General Intelligence

by Jaime F. Cárdenas-García

Computers 2023, 12(5), 102; https://doi.org/10.3390/computers12050102 - 7 May 2023

Cited by 5 | Viewed by 3446

Abstract

Recent developments, begun by the ascending spiral of the anticipated endless prospects of ChatGPT, promote artificial intelligence (AI) as an indispensable tool and commodity whose time has come. Yet the sinister specter of a technology that has hidden and unmanageable attributes that might [...] Read more.

Recent developments, begun by the ascending spiral of the anticipated endless prospects of ChatGPT, promote artificial intelligence (AI) as an indispensable tool and commodity whose time has come. Yet the sinister specter of a technology that has hidden and unmanageable attributes that might be harmful to society looms in the background, as well as the likelihood that it will never deliver on the purported promise of artificial general intelligence (AGI). Currently, the prospects for the development of AI and AGI are more a matter of opinion than based on a consistent methodological approach. Thus, there is a need to take a step back to develop a general framework from which to evaluate current AI efforts, which also permits the determination of the limits to its future prospects as AGI. To gain insight into the development of a general framework, a key question needs to be resolved: what is the connection between human intelligence and machine intelligence? This is the question that needs a response because humans are at the center of AI creation and realize that, without an understanding of how we become what we become, we have no chance of finding a solution. This work proposes info-autopoiesis, the self-referential, recursive, and interactive process of self-production of information, as the needed general framework. Info-autopoiesis shows how the key ingredient of information is fundamental to an insightful resolution to this crucial question and allows predictions as to the present and future of AGI. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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16 pages, 2832 KiB

Open AccessArticle

Supporting the Conservation and Restoration OpenLab of the Acropolis of Ancient Tiryns through Data Modelling and Exploitation of Digital Media

by Efthymia Moraitou, Markos Konstantakis, Angeliki Chrysanthi, Yannis Christodoulou, George Pavlidis, George Alexandridis, Konstantinos Kotsopoulos, Nikolaos Papastamatiou, Alkistis Papadimitriou and George Caridakis

Computers 2023, 12(5), 96; https://doi.org/10.3390/computers12050096 - 2 May 2023

Cited by 3 | Viewed by 2165

Abstract

Open laboratories (OpenLabs) in Cultural Heritage institutions are an effective way to provide visibility into the behind-the-scenes processes and promote documentation data collected and produced by domain specialists. However, presenting these processes without proper explanation or communication with specialists may cause issues in [...] Read more.

Open laboratories (OpenLabs) in Cultural Heritage institutions are an effective way to provide visibility into the behind-the-scenes processes and promote documentation data collected and produced by domain specialists. However, presenting these processes without proper explanation or communication with specialists may cause issues in terms of visitors’ understanding. To support OpenLabs and disseminate information, digital media and efficient data management can be utilized. The CAnTi (Conservation of Ancient Tiryns) project seeks to design and implement virtual and mixed reality applications that visualize conservation and restoration data, supporting OpenLab operations at the Acropolis of Ancient Tiryns. Semantic Web technologies will be used to model the digital content, facilitating organization and interoperability with external sources in the future. These applications will be part of the OpenLab activities on the site, enhancing visitors’ experiences and understanding of current and past conservation and restoration practices. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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13 pages, 2463 KiB

Open AccessArticle

You Look like You’ll Buy It! Purchase Intent Prediction Based on Facially Detected Emotions in Social Media Campaigns for Food Products

by Katerina Tzafilkou, Anastasios A. Economides and Foteini-Rafailia Panavou

Computers 2023, 12(4), 88; https://doi.org/10.3390/computers12040088 - 21 Apr 2023

Cited by 5 | Viewed by 2354

Abstract

Understanding the online behavior and purchase intent of online consumers in social media can bring significant benefits to the ecommerce business and consumer research community. Despite the tight links between consumer emotions and purchase decisions, previous studies focused primarily on predicting purchase intent [...] Read more.

Understanding the online behavior and purchase intent of online consumers in social media can bring significant benefits to the ecommerce business and consumer research community. Despite the tight links between consumer emotions and purchase decisions, previous studies focused primarily on predicting purchase intent through web analytics and sales historical data. Here, the use of facially expressed emotions is suggested to infer the purchase intent of online consumers while watching social media video campaigns for food products (yogurt and nut butters). A FaceReader Online^TM multi-stage experiment was set, collecting data from 154 valid sessions of 74 participants. A set of different classification models was deployed, and the performance evaluation metrics were compared. The models included Neural Networks (NNs), Logistic Regression (LR), Decision Trees (DTs), Random Forest (RF,) and Support Vector Machine (SVM). The NNs proved highly accurate (90–91%) in predicting the consumers’ intention to buy or try the product, while RF showed promising results (75%). The expressions of sadness and surprise indicated the highest levels of relative importance in RF and DTs correspondingly. Despite the low activation scores in arousal, micro expressions of emotions proved to be sufficient input in predicting purchase intent based on instances of facially decoded emotions. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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22 pages, 6757 KiB

Open AccessArticle

Data-Driven Solution to Identify Sentiments from Online Drug Reviews

by Rezaul Haque, Saddam Hossain Laskar, Katura Gania Khushbu, Md Junayed Hasan and Jia Uddin

Computers 2023, 12(4), 87; https://doi.org/10.3390/computers12040087 - 21 Apr 2023

Cited by 6 | Viewed by 2941

Abstract

With the proliferation of the internet, social networking sites have become a primary source of user-generated content, including vast amounts of information about medications, diagnoses, treatments, and disorders. Comments on previously used medicines, contained within these data, can be leveraged to identify crucial [...] Read more.

With the proliferation of the internet, social networking sites have become a primary source of user-generated content, including vast amounts of information about medications, diagnoses, treatments, and disorders. Comments on previously used medicines, contained within these data, can be leveraged to identify crucial adverse drug reactions, and machine learning (ML) approaches such as sentiment analysis (SA) can be employed to derive valuable insights. However, given the sheer volume of comments, it is often impractical for consumers to manually review all of them before determining a purchase decision. Therefore, drug assessments can serve as a valuable source of medical information for both healthcare professionals and the general public, aiding in decision making and improving public monitoring systems by revealing collective experiences. Nonetheless, the unstructured and linguistic nature of the comments poses a significant challenge for effective categorization, with previous studies having utilized machine and deep learning (DL) algorithms to address this challenge. Despite both approaches showing promising results, DL classifiers outperformed ML classifiers in previous studies. Therefore, the objective of our study was to improve upon earlier research by applying SA to medication reviews and training five ML algorithms on two distinct feature extractions and four DL classifiers on two different word-embedding approaches to obtain higher categorization scores. Our findings indicated that the random forest trained on the count vectorizer outperformed all other ML algorithms, achieving an accuracy and F1 score of 96.65% and 96.42%, respectively. Furthermore, the bidirectional LSTM (Bi-LSTM) model trained on GloVe embedding resulted in an even better accuracy and F1 score, reaching 97.40% and 97.42%, respectively. Hence, by utilizing appropriate natural language processing and ML algorithms, we were able to achieve superior results compared to earlier studies. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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32 pages, 3612 KiB

Open AccessArticle

Long-Term Effects of Perceived Friendship with Intelligent Voice Assistants on Usage Behavior, User Experience, and Social Perceptions

by Carolin Wienrich, Astrid Carolus, André Markus, Yannik Augustin, Jan Pfister and Andreas Hotho

Computers 2023, 12(4), 77; https://doi.org/10.3390/computers12040077 - 13 Apr 2023

Cited by 5 | Viewed by 5799

Abstract

Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time [...] Read more.

Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time to establish, we equipped 106 participants with Alexa or Google assistants and some smart home devices and observed their interactions for nine months. We analyzed diverse subjective (questionnaire) and objective data (interaction data). By combining social science and data science analyses, we identified two distinct clusters—users who assigned a friendship role to IVAs over time and users who did not. Interestingly, these clusters exhibited significant differences in their usage behavior, user experience, and social perceptions of the devices. For example, participants who assigned a role to IVAs attributed more friendship to them used them more frequently, reported more enjoyment during interactions, and perceived more empathy for IVAs. In addition, these users had distinct personal requirements, for example, they reported more loneliness. This study provides valuable insights into the role-specific effects and consequences of voice assistants. Recent developments in conversational language models such as ChatGPT suggest that the findings of this study could make an important contribution to the design of dialogic human–AI interactions. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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17 pages, 4969 KiB

Open AccessArticle

SmartWatcher©: A Solution to Automatically Assess the Smartness of Buildings

by Yu Ye, Alfonso P. Ramallo-González, Valentina Tomat, Juan Sanchez Valverde and Antonio Skarmeta-Gómez

Computers 2023, 12(4), 76; https://doi.org/10.3390/computers12040076 - 12 Apr 2023

Viewed by 2122

Abstract

Buildings have now adopted a new dimension: the dimension of smartness. The rapid arrival of connected devices, together with the smart features that they provide, has allowed for the transition of existing buildings towards smart buildings. The assessment of the smartness of the [...] Read more.

Buildings have now adopted a new dimension: the dimension of smartness. The rapid arrival of connected devices, together with the smart features that they provide, has allowed for the transition of existing buildings towards smart buildings. The assessment of the smartness of the large number of existing buildings could exhaust resources, but some organisations are requesting this regardless (such as the smart readiness indicator of the European Union). To tackle this issue, this work describes a tool that was created to find connected devices to automatically evaluate smartness. The tool, which was given the name SmartWatcher, uses a design-for-purpose natural language processing algorithm that converts verbal information into numerical information. The method was tested on real buildings in four different geographical locations. SmartWatcher is shown to be powerful, as it was capable of obtaining numerical values from verbal descriptions of devices. Additionally, a preliminary comparison of values obtained using the automatic engine and clipboard assessments showed that although the results were still far from being perfect, some visual correlation could be seen. This anticipates that, with the addition of appropriate techniques that refine this algorithm, or with the addition of new ones (with other more advanced natural language processing methods), the accuracy of this tool could be greatly increased. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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22 pages, 2823 KiB

Open AccessArticle

How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers

by Nane Kratzke

Computers 2023, 12(3), 57; https://doi.org/10.3390/computers12030057 - 3 Mar 2023

Cited by 2 | Viewed by 5147

Abstract

Background: This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. Methods: This study recorded the German-language Twitter stream over two [...] Read more.

Background: This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. Methods: This study recorded the German-language Twitter stream over two months, recording about 6.7M accounts and their 75.5M interactions (33M retweets). This study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. Results: The purely structural detection approach identified an echo chamber (red community, 66K accounts) focused on a few topics with a triad of anti-Covid, right-wing populism and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community (113K accounts) was much more heterogeneous and showed “normal” communication interaction patterns. Conclusions: The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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20 pages, 1217 KiB

Open AccessArticle

Shared Language: Linguistic Similarity in an Algebra Discussion Forum

by Michelle P. Banawan, Jinnie Shin, Tracy Arner, Renu Balyan, Walter L. Leite and Danielle S. McNamara

Computers 2023, 12(3), 53; https://doi.org/10.3390/computers12030053 - 27 Feb 2023

Cited by 2 | Viewed by 2739

Abstract

Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the [...] Read more.

Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse reveals “shared language” among its participants that can promote inclusion or affinity. Shared language is characterized in terms of linguistic features and lexical, syntactical, and semantic similarities. We leverage a multi-method approach, including (1) feature engineering using state-of-the-art natural language processing techniques to select the most appropriate features, (2) the bag-of-words classification model to predict linguistic similarity, (3) explainable AI using the local interpretable model-agnostic explanations to explain the model, and (4) a two-step cluster analysis to extract innate groupings between linguistic similarity and emotion. We found that linguistic similarity within and between the threaded discussions was significantly varied, revealing the dynamic and unconstrained nature of the discourse. Further, word choice moderately predicted linguistic similarity between posts within threaded discussions (accuracy = 0.73; F1-score = 0.67), revealing that discourse participants’ lexical choices effectively discriminate between posts in terms of similarity. Lastly, cluster analysis reveals profiles that are distinctly characterized in terms of linguistic similarity, trust, and affect. Our findings demonstrate the potential role of linguistic similarity in supporting social cohesion and affinity within online discourse communities. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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16 pages, 2091 KiB

Open AccessArticle

An ML-Powered Risk Assessment System for Predicting Prospective Mass Shooting

by Ahmed Abdelmoamen Ahmed and Nneoma Okoroafor

Computers 2023, 12(2), 42; https://doi.org/10.3390/computers12020042 - 17 Feb 2023

Cited by 2 | Viewed by 4744

Abstract

The United States has had more mass shooting incidents than any other country. It is reported that more than 1800 incidents occurred in the US during the past three years. Mass shooters often display warning signs before committing crimes, such as childhood traumas, [...] Read more.

The United States has had more mass shooting incidents than any other country. It is reported that more than 1800 incidents occurred in the US during the past three years. Mass shooters often display warning signs before committing crimes, such as childhood traumas, domestic violence, firearms access, and aggressive social media posts. With the advancement of machine learning (ML), it is more possible than ever to predict mass shootings before they occur by studying the behavior of prospective mass shooters. This paper presents an ML-based system that uses various unsupervised ML models to warn about a balanced progressive tendency of a person to commit a mass shooting. Our system used two models, namely local outlier factor and K-means clustering, to learn both the psychological factors and social media activities of previous shooters to provide a probabilistic similarity of a new observation to an existing shooter. The developed system can show the similarity between a new record for a prospective shooter and one or more records from our dataset via a GUI-friendly interface. It enables users to select some social and criminal observations about the prospective shooter. Then, the webpage creates a new record, classifies it, and displays the similarity results. Furthermore, we developed a feed-in module, which allows new observations to be added to our dataset and retrains the ML models. Finally, we evaluated our system using various performance metrics. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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17 pages, 294 KiB

Open AccessArticle

A Testset-Based Method to Analyse the Negation-Detection Performance of Lexicon-Based Sentiment Analysis Tools

by Maurizio Naldi and Sandra Petroni

Computers 2023, 12(1), 18; https://doi.org/10.3390/computers12010018 - 13 Jan 2023

Cited by 5 | Viewed by 2586

Abstract

The correct detection of negations is essential to the performance of sentiment analysis tools. The evaluation of such tools is currently conducted through the use of corpora as an opportunistic approach. In this paper, we advocate using a different evaluation approach based on [...] Read more.

The correct detection of negations is essential to the performance of sentiment analysis tools. The evaluation of such tools is currently conducted through the use of corpora as an opportunistic approach. In this paper, we advocate using a different evaluation approach based on a set of intentionally built sentences that include negations, which aim to highlight those tools’ vulnerabilities. To demonstrate the effectiveness of this approach, we propose a basic testset of such sentences. We employ that testset to evaluate six popular sentiment analysis tools (with eight lexicons) available as packages in the R language distribution. By adopting a supervised classification approach, we show that the performance of most of these tools is largely unsatisfactory. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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16 pages, 2104 KiB

Open AccessArticle

Topic Classification of Online News Articles Using Optimized Machine Learning Models

by Shahzada Daud, Muti Ullah, Amjad Rehman, Tanzila Saba, Robertas Damaševičius and Abdul Sattar

Computers 2023, 12(1), 16; https://doi.org/10.3390/computers12010016 - 9 Jan 2023

Cited by 17 | Viewed by 9363

Abstract

Much news is available online, and not all is categorized. A few researchers have carried out work on news classification in the past, and most of the work focused on fake news identification. Most of the work performed on news categorization is carried [...] Read more.

Much news is available online, and not all is categorized. A few researchers have carried out work on news classification in the past, and most of the work focused on fake news identification. Most of the work performed on news categorization is carried out on a benchmark dataset. The problem with the benchmark dataset is that model trained with it is not applicable in the real world as the data are pre-organized. This study used machine learning (ML) techniques to categorize online news articles as these techniques are cheaper in terms of computational needs and are less complex. This study proposed the hyperparameter-optimized support vector machines (SVM) to categorize news articles according to their respective category. Additionally, five other ML techniques, Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Naïve Bayes (NB), were optimized for comparison for the news categorization task. The results showed that the optimized SVM model performed better than other models, while without optimization, its performance was worse than other ML models. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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25 pages, 1829 KiB

Open AccessArticle

Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis

by Lukáš Korel, Uladzislau Yorsh, Alexander S. Behr, Norbert Kockmann and Martin Holeňa

Computers 2023, 12(1), 14; https://doi.org/10.3390/computers12010014 - 6 Jan 2023

Cited by 6 | Viewed by 6836

Abstract

The paper presents a machine-learning based approach to text-to-ontology mapping. We explore a possibility of matching texts to the relevant ontologies using a combination of artificial neural networks and classifiers. Ontologies are formal specifications of the shared conceptualizations of application domains. While describing [...] Read more.

The paper presents a machine-learning based approach to text-to-ontology mapping. We explore a possibility of matching texts to the relevant ontologies using a combination of artificial neural networks and classifiers. Ontologies are formal specifications of the shared conceptualizations of application domains. While describing the same domain, different ontologies might be created by different domain experts. To enhance the reasoning and data handling of concepts in scientific papers, finding the best fitting ontology regarding description of the concepts contained in a text corpus. The approach presented in this work attempts to solve this by selection of a representative text paragraph from a set of scientific papers, which are used as data set. Then, using a pre-trained and fine-tuned Transformer, the paragraph is embedded into a vector space. Finally, the embedded vector becomes classified with respect to its relevance regarding a selected target ontology. To construct representative embeddings, we experiment with different training pipelines for natural language processing models. Those embeddings in turn are later used in the task of matching text to ontology. Finally, the result is assessed by compressing and visualizing the latent space and exploring the mappings between text fragments from a database and the set of chosen ontologies. To confirm the differences in behavior of the proposed ontology mapper models, we test five statistical hypotheses about their relative performance on ontology classification. To categorize the output from the Transformer, different classifiers are considered. These classifiers are, in detail, the Support Vector Machine (SVM), k-Nearest Neighbor, Gaussian Process, Random Forest, and Multilayer Perceptron. Application of these classifiers in a domain of scientific texts concerning catalysis research and respective ontologies, the suitability of the classifiers is evaluated, where the best result was achieved by the SVM classifier. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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12 pages, 1489 KiB

Open AccessArticle

CLCD-I: Cross-Language Clone Detection by Using Deep Learning with InferCode

by Mohammad A. Yahya and Dae-Kyoo Kim

Computers 2023, 12(1), 12; https://doi.org/10.3390/computers12010012 - 4 Jan 2023

Cited by 21 | Viewed by 2825

Abstract

Source code clones are common in software development as part of reuse practice. However, they are also often a source of errors compromising software maintainability. The existing work on code clone detection mainly focuses on clones in a single programming language. However, nowadays [...] Read more.

Source code clones are common in software development as part of reuse practice. However, they are also often a source of errors compromising software maintainability. The existing work on code clone detection mainly focuses on clones in a single programming language. However, nowadays software is increasingly developed on a multilanguage platform on which code is reused across different programming languages. Detecting code clones in such a platform is challenging and has not been studied much. In this paper, we present CLCD-I, a deep neural network-based approach for detecting cross-language code clones by using InferCode which is an embedding technique for source code. The design of our model is twofold: (a) taking as input InferCode embeddings of source code in two different programming languages and (b) forwarding them to a Siamese architecture for comparative processing. We compare the performance of CLCD-I with LSTM autoencoders and the existing approaches on cross-language code clone detection. The evaluation shows the CLCD-I outperforms LSTM autoencoders by 30% on average and the existing approaches by 15% on average. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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19 pages, 6571 KiB

Open AccessArticle

Improved Optimization Algorithm in LSTM to Predict Crop Yield

by Usharani Bhimavarapu, Gopi Battineni and Nalini Chintalapudi

Computers 2023, 12(1), 10; https://doi.org/10.3390/computers12010010 - 3 Jan 2023

Cited by 33 | Viewed by 5924

Abstract

Agriculture is the main occupation across the world with a dependency on rainfall. Weather changes play a crucial role in crop yield and were used to predict the yield rate by considering precipitation, wind, temperature, and solar radiation. Accurate early crop yield prediction [...] Read more.

Agriculture is the main occupation across the world with a dependency on rainfall. Weather changes play a crucial role in crop yield and were used to predict the yield rate by considering precipitation, wind, temperature, and solar radiation. Accurate early crop yield prediction helps market pricing, planning labor, transport, and harvest organization. The main aim of this study is to predict crop yield accurately. The incorporation of deep learning models along with crop statistics can predict yield rates accurately. We proposed an improved optimizer function (IOF) to get an accurate prediction and implemented the proposed IOF with the long short-term memory (LSTM) model. Manual data was collected between 1901 and 2000 from local agricultural departments for training, and from 2001 to 2020 from government websites of Andhra Pradesh (India) for testing purposes. The proposed model is compared with eight standard methods of learning, and outcomes revealed that the training error is small with the proposed IOF as it handles the underfitting and overfitting issues. The performance metrics used to compare the loss after implementing the proposed IOF were r, RMSE, and MAE, and the achieved results are r of 0.48, RMSE of 2.19, and MAE of 25.4. The evaluation was performed between the predicted crop yield and the actual yield and was measured in RMSE (kg/ha). The results show that the proposed IOF in LSTM has the advantage of crop yield prediction with accurate prediction. The reduction of RMSE for the proposed model indicates that the proposed IOFLSTM can outperform the CNN, RNN, and LSTM in crop yield prediction. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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21 pages, 2066 KiB

Open AccessArticle

A Ranking Learning Model by K-Means Clustering Technique for Web Scraped Movie Data

by Kamal Uddin Sarker, Mohammed Saqib, Raza Hasan, Salman Mahmood, Saqib Hussain, Ali Abbas and Aziz Deraman

Computers 2022, 11(11), 158; https://doi.org/10.3390/computers11110158 - 8 Nov 2022

Cited by 8 | Viewed by 5239

Abstract

Business organizations experience cut-throat competition in the e-commerce era, where a smart organization needs to come up with faster innovative ideas to enjoy competitive advantages. A smart user decides from the review information of an online product. Data-driven smart machine learning applications use [...] Read more.

Business organizations experience cut-throat competition in the e-commerce era, where a smart organization needs to come up with faster innovative ideas to enjoy competitive advantages. A smart user decides from the review information of an online product. Data-driven smart machine learning applications use real data to support immediate decision making. Web scraping technologies support supplying sufficient relevant and up-to-date well-structured data from unstructured data sources like websites. Machine learning applications generate models for in-depth data analysis and decision making. The Internet Movie Database (IMDB) is one of the largest movie databases on the internet. IMDB movie information is applied for statistical analysis, sentiment classification, genre-based clustering, and rating-based clustering with respect to movie release year, budget, etc., for repository dataset. This paper presents a novel clustering model with respect to two different rating systems of IMDB movie data. This work contributes to the three areas: (i) the “grey area” of web scraping to extract data for research purposes; (ii) statistical analysis to correlate required data fields and understanding purposes of implementation machine learning, (iii) k-means clustering is applied for movie critics rank (Metascore) and users’ star rank (Rating). Different python libraries are used for web data scraping, data analysis, data visualization, and k-means clustering application. Only 42.4% of records were accepted from the extracted dataset for research purposes after cleaning. Statistical analysis showed that votes, ratings, Metascore have a linear relationship, while random characteristics are observed for income of the movie. On the other hand, experts’ feedback (Metascore) and customers’ feedback (Rating) are negatively correlated (−0.0384) due to the biasness of additional features like genre, actors, budget, etc. Both rankings have a nonlinear relationship with the income of the movies. Six optimal clusters were selected by elbow technique and the calculated silhouette score is 0.4926 for the proposed k-means clustering model and we found that only one cluster is in the logical relationship of two rankings systems. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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16 pages, 1716 KiB

Open AccessArticle

Feature Encoding and Selection for Iris Recognition Based on Variable Length Black Hole Optimization

by Tara Othman Qadir Saraf, N. Fuad and N. S. A. M. Taujuddin

Computers 2022, 11(9), 140; https://doi.org/10.3390/computers11090140 - 16 Sep 2022

Cited by 10 | Viewed by 2518

Abstract

Iris recognition as a biometric identification method is one of the most reliable biometric human identification methods. It exploits the distinctive pattern of the iris area. Typically, several steps are performed for iris recognition, namely, pre-processing, segmentation, normalization, extraction, coding and classification. In [...] Read more.

Iris recognition as a biometric identification method is one of the most reliable biometric human identification methods. It exploits the distinctive pattern of the iris area. Typically, several steps are performed for iris recognition, namely, pre-processing, segmentation, normalization, extraction, coding and classification. In this article, we present a novel algorithm for iris recognition that includes in addition to iris features extraction and coding the step of feature selection. Furthermore, it enables selecting a variable length of features for iris recognition by adapting our recent algorithm variable length black hole optimization (VLBHO). It is the first variable length feature selection for iris recognition. Our proposed algorithm enables segments-based decomposition of features according to their relevance which makes the optimization more efficient in terms of both memory and computation and more promising in terms of convergence. For classification, the article uses the famous support vector machine (SVM) and the Logistic model. The proposed algorithm has been evaluated based on two iris datasets, namely, IITD and CASIA. The finding is that optimizing feature encoding and selection based on VLBHO is superior to the benchmarks with an improvement percentage of 0.21%. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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17 pages, 4611 KiB

Open AccessArticle

The Application of Artificial Intelligence to Automate Sensory Assessments Combining Pretrained Transformers with Word Embedding Based on the Online Sensory Marketing Index

by Kevin Hamacher and Rüdiger Buchkremer

Computers 2022, 11(9), 129; https://doi.org/10.3390/computers11090129 - 26 Aug 2022

Cited by 1 | Viewed by 3913

Abstract

We present how artificial intelligence (AI)-based technologies create new opportunities to capture and assess sensory marketing elements. Based on the Online Sensory Marketing Index (OSMI), a sensory assessment framework designed to evaluate e-commerce websites manually, the goal is to offer an alternative procedure [...] Read more.

We present how artificial intelligence (AI)-based technologies create new opportunities to capture and assess sensory marketing elements. Based on the Online Sensory Marketing Index (OSMI), a sensory assessment framework designed to evaluate e-commerce websites manually, the goal is to offer an alternative procedure to assess sensory elements such as text and images automatically. This approach aims to provide marketing managers with valuable insights and potential for sensory marketing improvements. To accomplish the task, we initially reviewed 469 related peer-reviewed scientific publications. In this process, manual reading is complemented by a validated AI methodology. We identify relevant topics and check if they exhibit a comprehensible distribution over the last years. We recognize and discuss similar approaches from machine learning and the big data environment. We apply state-of-the-art methods from the natural language processing domain for the principal analysis, such as word embedding techniques GloVe and Word2Vec, and leverage transformers such as BERT. To validate the performance of our newly developed AI approach, we compare results with manually collected parameters from previous studies and observe similar findings in both procedures. Our results reveal a functional and scalable AI approach for determining the OSMI for industries, companies, or even individual (sub-) websites. In addition, the new AI selection and assessment procedures are extremely fast, with only a small loss in performance compared to a manual evaluation. It resembles an efficient way to evaluate sensory marketing efforts. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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29 pages, 344 KiB

Open AccessArticle

Transforming Points of Single Contact Data into Linked Data

by Pavlina Fragkou and Leandros Maglaras

Computers 2022, 11(8), 122; https://doi.org/10.3390/computers11080122 - 11 Aug 2022

Cited by 1 | Viewed by 2163

Abstract

Open data portals contain valuable information for citizens and business. However, searching for information can prove to be tiresome even in portals tackling domains similar information. A typical case is the information residing in the European Commission’s portals supported by Member States aiming [...] Read more.

Open data portals contain valuable information for citizens and business. However, searching for information can prove to be tiresome even in portals tackling domains similar information. A typical case is the information residing in the European Commission’s portals supported by Member States aiming to facilitate service provision activities for EU citizens and businesses. The current work followed the FAIR principles (Findability, Accessibility, Interoperability, and Reuse of digital assets) as well as the GO-FAIR principles and tried to transform raw data into fair data. The innovative part of this work is the mapping of information residing in various governmental portals (Points of Single Contacts) by transforming information appearing in them in RDF format (i.e., as Linked data), in order to make them easily accessible, exchangeable, interoperable and publishable as linked open data. Mapping was performed using the semantic model of a single portal, i.e., the enriched Greek e-GIF ontology and by retrieving and analyzing raw, i.e., non-FAIR data, by defining the semantic model and by making data linkable. The Data mapping process proved to require a significant manual effort and revealed that data value remains unexplored due to poor data representation. It also highlighted the need for appropriately designing and implementing horizontal actions addressing an important number of recipients in an interoperable way. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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Review

Jump to: Research

18 pages, 4038 KiB

Open AccessReview

Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation

by Hamed Taherdoost and Mitra Madanchian

Computers 2023, 12(4), 72; https://doi.org/10.3390/computers12040072 - 31 Mar 2023

Cited by 33 | Viewed by 24486

Abstract

The process of generating, disseminating, using, and managing an organization’s information and knowledge is known as knowledge management (KM). Conventional KM has undergone modifications throughout the years, but documentation has always been its foundation. However, the significant move to remote and hybrid working [...] Read more.

The process of generating, disseminating, using, and managing an organization’s information and knowledge is known as knowledge management (KM). Conventional KM has undergone modifications throughout the years, but documentation has always been its foundation. However, the significant move to remote and hybrid working has highlighted the shortcomings in current procedures. These gaps will be filled by artificial intelligence (AI), which will also alter how KM is transformed and knowledge is handled. This article analyzes studies from 2012 to 2022 that examined AI and KM, with a particular emphasis on how AI may support businesses in their attempts to successfully manage knowledge and information. This critical review examines the current approaches in light of the literature that is currently accessible on AI and KM, focusing on articles that address practical applications and the research background. Furthermore, this review provides insight into potential future study directions and improvements by presenting a critical evaluation. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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15 pages, 3255 KiB

Open AccessReview

Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research

by Hamed Taherdoost and Mitra Madanchian

Computers 2023, 12(2), 37; https://doi.org/10.3390/computers12020037 - 7 Feb 2023

Cited by 61 | Viewed by 35098

Abstract

As part of a business strategy, effective competitive research helps businesses outperform their competitors and attract loyal consumers. To perform competitive research, sentiment analysis may be used to assess interest in certain themes, uncover market conditions, and study competitors. Artificial intelligence (AI) has [...] Read more.

As part of a business strategy, effective competitive research helps businesses outperform their competitors and attract loyal consumers. To perform competitive research, sentiment analysis may be used to assess interest in certain themes, uncover market conditions, and study competitors. Artificial intelligence (AI) has improved the performance of multiple areas, particularly sentiment analysis. Using AI, sentiment analysis is the process of recognizing emotions expressed in text. AI comprehends the tone of a statement, as opposed to merely recognizing whether particular words within a group of text have a negative or positive connotation. This article reviews papers (2012–2022) that discuss how competitive market research identifies and compares major market measurements that help distinguish the services and goods of the competitors. AI-powered sentiment analysis can be used to learn what the competitors’ customers think of them across all aspects of the businesses. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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22 pages, 762 KiB

Open AccessReview

Agile Development Methodologies and Natural Language Processing: A Mapping Review

by Manuel A. Quintana, Ramón R. Palacio, Gilberto Borrego Soto and Samuel González-López

Computers 2022, 11(12), 179; https://doi.org/10.3390/computers11120179 - 7 Dec 2022

Viewed by 3609

Abstract

Agile software development is one of the most important development paradigms these days. However, there are still some challenges to consider to reduce problems during the documentation process. Some assistive methods have been created to support developers in their documentation activities. In this [...] Read more.

Agile software development is one of the most important development paradigms these days. However, there are still some challenges to consider to reduce problems during the documentation process. Some assistive methods have been created to support developers in their documentation activities. In this regard, Natural Language Processing (NLP) can be used to create various related tools (such as assistants) to help with the documentation process. This paper presents the current state-of-the-art NLP techniques used in the agile development documentation process. A mapping review was done to complete the objective, the search strategy is used to obtain relevant studies from ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Willey. The search results after inclusion and exclusion criteria application left 47 relevant papers identified. These papers were analyzed to obtain the most used NLP techniques and NLP toolkits. The toolkits were also classified by the kind of techniques that are available in each of them. In addition, the behavior of the research area over time was analyzed using the relevant paper found by year. We found that performance measuring methods are not standardized, and, in consequence, the works are not easily comparable. In general, the number of related works and its distribution per year shows a growing trend of the works related to this topic in recent years; this indicates that the adoption of NLP techniques to improve agile methodologies is increasing. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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