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Computers, Volume 11, Issue 10 (October 2022) – 11 articles

Cover Story (view full-size image): Most vehicle classification systems now use data from images or videos. However, these visual approaches violate drivers’ privacy and reveal their identities. This study uses seismic waves recorded by geophones to compare time series data between different vehicle types. We applied various artificial intelligence approaches using raw data from three different vehicle sizes (bus/truck, car, and motorcycle) and background noise. By using the geophone data, this study compares the decoding abilities of logistic regression, support vector machine, and naïve Bayes (NB) approaches to determine the class and number of automobiles. Our auto-classification approach provides a low-cost and privacy-preserving traffic monitoring system. View this paper
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17 pages, 4380 KiB  
Article
Machine Learning Models for Classification of Human Emotions Using Multivariate Brain Signals
by Shashi Kumar G. S., Ahalya Arun, Niranjana Sampathila and R. Vinoth
Computers 2022, 11(10), 152; https://doi.org/10.3390/computers11100152 - 13 Oct 2022
Cited by 9 | Viewed by 4206
Abstract
Humans can portray different expressions contrary to their emotional state of mind. Therefore, it is difficult to judge humans’ real emotional state simply by judging their physical appearance. Although researchers are working on facial expressions analysis, voice recognition, and gesture recognition; the accuracy [...] Read more.
Humans can portray different expressions contrary to their emotional state of mind. Therefore, it is difficult to judge humans’ real emotional state simply by judging their physical appearance. Although researchers are working on facial expressions analysis, voice recognition, and gesture recognition; the accuracy levels of such analysis are much less and the results are not reliable. Hence, it becomes vital to have realistic emotion detector. Electroencephalogram (EEG) signals remain neutral to the external appearance and behavior of the human and help in ensuring accurate analysis of the state of mind. The EEG signals from various electrodes in different scalp regions are studied for performance. Hence, EEG has gained attention over time to obtain accurate results for the classification of emotional states in human beings for human–machine interaction as well as to design a program where an individual could perform a self-analysis of his emotional state. In the proposed scheme, we extract power spectral densities of multivariate EEG signals from different sections of the brain. From the extracted power spectral density (PSD), the features which provide a better feature for classification are selected and classified using long short-term memory (LSTM) and bi-directional long short-term memory (Bi-LSTM). The 2-D emotion model considered for the classification of frontal, parietal, temporal, and occipital is studied. The region-based classification is performed by considering positive and negative emotions. The performance accuracy of our previous model’s results of artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (K-NN), and LSTM was compared and 94.95% accuracy was received using Bi-LSTM considering four prefrontal electrodes. Full article
(This article belongs to the Special Issue Multimodal Pattern Recognition of Social Signals in HCI)
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19 pages, 1403 KiB  
Article
Towards Predicting Architectural Design Patterns: A Machine Learning Approach
by Sirojiddin Komolov, Gcinizwe Dlamini, Swati Megha and Manuel Mazzara
Computers 2022, 11(10), 151; https://doi.org/10.3390/computers11100151 - 12 Oct 2022
Cited by 7 | Viewed by 4134
Abstract
Software architecture plays an important role in software development, especially in software quality and maintenance. Understanding the impact of certain architectural patterns on software quality and verification of software requirements has become increasingly difficult with the increasing complexity of codebases in recent years. [...] Read more.
Software architecture plays an important role in software development, especially in software quality and maintenance. Understanding the impact of certain architectural patterns on software quality and verification of software requirements has become increasingly difficult with the increasing complexity of codebases in recent years. Researchers over the years have proposed automated approaches based on machine learning. However, there is a lack of benchmark datasets and more accurate machine learning (ML) approaches. This paper presents an ML-based approach for software architecture detection, namely, MVP (Model–View–Presenter) and MVVM (Model–View–ViewModel). Firstly, we present a labeled dataset that consists of 5973 data points retrieved from GitHub. Nine ML methods are applied for detection of software architecture from source code metrics. Using precision, recall, accuracy, and F1 score, the outstanding ML model performance is 83%, 83%, 83%, and 83%, respectively. The ML model’s performance is validated using k-fold validation (k = 5). Our approach outperforms when compared with the state-of-the-art. Full article
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11 pages, 1262 KiB  
Article
Physical Activity Recommendation System Based on Deep Learning to Prevent Respiratory Diseases
by Usharani Bhimavarapu, M. Sreedevi, Nalini Chintalapudi and Gopi Battineni
Computers 2022, 11(10), 150; https://doi.org/10.3390/computers11100150 - 11 Oct 2022
Cited by 7 | Viewed by 4459
Abstract
The immune system can be compromised when humans inhale excessive cooling. Physical activity helps a person’s immune system, and influenza seasonally affects immunity and respiratory tract illness when there is no physical activity during the day. Whenever people chill excessively, they become more [...] Read more.
The immune system can be compromised when humans inhale excessive cooling. Physical activity helps a person’s immune system, and influenza seasonally affects immunity and respiratory tract illness when there is no physical activity during the day. Whenever people chill excessively, they become more susceptible to pathogens because they require more energy to maintain a healthy body temperature. There is no doubt that exercise improves the immune system and an individual’s fitness. According to an individual’s health history, lifestyle, and preferences, the physical activity framework also includes exercises to improve the immune system. This study developed a framework for predicting physical activity based on information about health status, preferences, calorie intake, race, and gender. Using information about comorbidities, regions, and exercise/eating habits, the proposed recommendation system recommends exercises based on the user’s preferences. Full article
(This article belongs to the Special Issue Advances of Machine and Deep Learning in the Health Domain)
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29 pages, 10938 KiB  
Article
User Analytics in Online Social Networks: Evolving from Social Instances to Social Individuals
by Gerasimos Razis, Stylianos Georgilas, Giannis Haralabopoulos and Ioannis Anagnostopoulos
Computers 2022, 11(10), 149; https://doi.org/10.3390/computers11100149 - 7 Oct 2022
Cited by 2 | Viewed by 2344
Abstract
In our era of big data and information overload, content consumers utilise a variety of sources to meet their data and informational needs for the purpose of acquiring an in-depth perspective on a subject, as each source is focused on specific aspects. The [...] Read more.
In our era of big data and information overload, content consumers utilise a variety of sources to meet their data and informational needs for the purpose of acquiring an in-depth perspective on a subject, as each source is focused on specific aspects. The same principle applies to the online social networks (OSNs), as usually, the end-users maintain accounts in multiple OSNs so as to acquire a complete social networking experience, since each OSN has a different philosophy in terms of its services, content, and interaction. Contrary to the current literature, we examine the users’ behavioural and disseminated content patterns under the assumption that accounts maintained by users in multiple OSNs are not regarded as distinct accounts, but rather as the same individual with multiple social instances. Our social analysis, enriched with information about the users’ social influences, revealed behavioural patterns depending on the examined OSN, its social entities, and the users’ exerted influence. Finally, we ranked the examined OSNs based on three types of social characteristics, revealing correlations between the users’ behavioural and content patterns, social influences, social entities, and the OSNs themselves. Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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11 pages, 1949 KiB  
Article
Vehicle Auto-Classification Using Machine Learning Algorithms Based on Seismic Fingerprinting
by Ahmad Bahaa Ahmad, Hakim Saibi, Abdelkader Nasreddine Belkacem and Takeshi Tsuji
Computers 2022, 11(10), 148; https://doi.org/10.3390/computers11100148 - 30 Sep 2022
Cited by 8 | Viewed by 4170
Abstract
Most vehicle classification systems now use data from images or videos. However, these approaches violate drivers’ privacy and reveal their identities. Due to various disruptions, detecting automobiles using seismic ambient noise signals is challenging. This study uses seismic surface waves to compare time [...] Read more.
Most vehicle classification systems now use data from images or videos. However, these approaches violate drivers’ privacy and reveal their identities. Due to various disruptions, detecting automobiles using seismic ambient noise signals is challenging. This study uses seismic surface waves to compare time series data between different vehicle types. We applied various artificial intelligence approaches using raw data from three different vehicle sizes (Bus/Truck, Car, and Motorcycle) and background noise. By using vertical component seismic data, this study compares the decoding abilities of Logistic Regression, Support Vector Machine, and Naïve Bayes (NB) approaches to determine the class of automobiles. The Multiclass classifiers were trained on 4185 samples and tested on 1395 randomly chosen from actual and synthetic data sets. Additionally, we used the convolutional neural network approach as a baseline to assess the effectiveness of machine learning (ML) methods. The NB method showed relatively high classification accuracy during training for the three multiclass classification situations. Overall, we investigate an ML-based decoding technique that can be used for security and traffic analysis across large geographic areas without endangering driver privacy and is more effective and economical than conventional methods. Full article
(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)
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24 pages, 8147 KiB  
Article
User Authentication and Authorization Framework in IoT Protocols
by Ammar Mohammad, Hasan Al-Refai and Ali Ahmad Alawneh
Computers 2022, 11(10), 147; https://doi.org/10.3390/computers11100147 - 27 Sep 2022
Cited by 7 | Viewed by 4337 | Correction
Abstract
The Internet of Things (IoT) has become one of the most attractive domains nowadays. It works by creating a special network between physical devices such as vehicles, home equipment, and other items. In recent days, the common technologies of communication such as Wi-Fi [...] Read more.
The Internet of Things (IoT) has become one of the most attractive domains nowadays. It works by creating a special network between physical devices such as vehicles, home equipment, and other items. In recent days, the common technologies of communication such as Wi-Fi and 2G/3G/4G cellular networks are insufficient for IoT networks because they are designed to serve appliances with immense processing capabilities such as laptops and PCs. Moreover, most of these technologies are centralized and use an existing infrastructure. Currently, new communication technologies such as Z-Wave, 6LowPAN, and Thread are dedicated to the IoT and have been developed to meet its requirements. These technologies can handle many factors such as range, data requirements, security, power demands, and battery life. Nevertheless, the security issues in IoT systems have major concerns and issues because vulnerabilities in such systems may result in fatal catastrophes. In this paper, an enhanced IoT security framework for authentication and authorization is proposed and implemented to protect the IoT protocols from different types of attacks such as man-in-the-middle attacks, reply attacks, and brute force attacks. The proposed framework combines an enhanced token authentication that has identity verification capabilities and a new sender verification mechanism on the IoT device side based on time stamps, which in turn can mitigate the need for local identity verification methods in IoT devices. The proposed IoT security framework was tested using security analysis with different types of attacks compared with previous related frameworks. The analysis shows the high capability of the proposed framework to protect IoT networks against many types of attacks compared with the currently available security frameworks. Finally, the proposed framework was developed using Windows applications to simulate the framework phases, check its validity through the real network, and calculate the payload time added. Full article
(This article belongs to the Special Issue Innovative Authentication Methods)
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20 pages, 2001 KiB  
Article
ILP-Based and Heuristic Scheduling Techniques for Variable-Cycle Approximate Functional Units in High-Level Synthesis
by Koyu Ohata, Hiroki Nishikawa, Xiangbo Kong and Hiroyuki Tomiyama
Computers 2022, 11(10), 146; https://doi.org/10.3390/computers11100146 - 26 Sep 2022
Cited by 1 | Viewed by 2127
Abstract
Approximate computing is a promising approach to the design of area–power-performance-efficient circuits for computation error-tolerant applications such as image processing and machine learning. Approximate functional units, such as approximate adders and approximate multipliers, have been actively studied for the past decade, and some [...] Read more.
Approximate computing is a promising approach to the design of area–power-performance-efficient circuits for computation error-tolerant applications such as image processing and machine learning. Approximate functional units, such as approximate adders and approximate multipliers, have been actively studied for the past decade, and some of these approximate functional units can dynamically change the degree of computation accuracy. The greater their computational inaccuracy, the faster they are. This study examined the high-level synthesis of approximate circuits that take advantage of such accuracy-controllable functional units. Scheduling methods based on integer linear programming (ILP) and list scheduling were proposed. Under resource and time constraints, the proposed method tries to minimize the computation error of the output value by selectively multi-cycling operations. Operations that have a large impact on the output accuracy are multi-cycled to perform exact computing, whereas operations with a small impact on the accuracy are assigned a single cycle for approximate computing. In the experiments, we explored the trade-off between performance, hardware cost, and accuracy to demonstrate the effectiveness of this work. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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16 pages, 2292 KiB  
Article
Application of Feature Selection Approaches for Prioritizing and Evaluating the Potential Factors for Safety Management in Transportation Systems
by Giuseppe Guido, Sami Shaffiee Haghshenas, Sina Shaffiee Haghshenas, Alessandro Vitale and Vittorio Astarita
Computers 2022, 11(10), 145; https://doi.org/10.3390/computers11100145 - 23 Sep 2022
Cited by 19 | Viewed by 1881
Abstract
Road safety assessment is one of the most important parts of road transport safety management. When road transportation networks are managed safely, they improve the quality of life for citizens and the economy as a whole. On the one hand, there are many [...] Read more.
Road safety assessment is one of the most important parts of road transport safety management. When road transportation networks are managed safely, they improve the quality of life for citizens and the economy as a whole. On the one hand, there are many factors that affect road safety. On the other hand, this issue is a dynamic problem, which means that it is always changing. So, there is a dire need for a thorough evaluation of road safety to deal with complex and uncertain problems. For this purpose, two machine learning methods called “feature selection algorithms” are used. These algorithms include a combination of artificial neural network (ANN) with the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. In this study, two data sets with 202 and 564 accident cases from cities and rural areas in southern Italy are investigated and analyzed based on several factors that affect transportation safety, such as light conditions, weekday, type of accident, location, speed limit, average speed, and annual average daily traffic. When the performance and results of the two models were compared, the results showed that the two models made the same choices. In rural areas, the type of accident and the location were chosen as the highest and lowest priorities, respectively. According to the results, useful suggestions regarding the improvement of road safety on urban and rural roads were provided. The average speed and location were considered the highest and lowest priorities in urban areas, respectively. Finally, there was not a big difference between the results of the two algorithms in terms of how well the algorithm models worked, but the proposed PSO model converged more quickly than the proposed DE model. Full article
(This article belongs to the Special Issue Machine Learning for Traffic Modeling and Prediction)
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11 pages, 657 KiB  
Article
Solution of Extended Multi-Objective Portfolio Selection Problem in Uncertain Environment Using Weighted Tchebycheff Method
by Pavan Kumar
Computers 2022, 11(10), 144; https://doi.org/10.3390/computers11100144 - 22 Sep 2022
Cited by 4 | Viewed by 1621
Abstract
In this paper, a mathematical model for an extended multi-objective portfolio selection (EMOPS) problem is explored with liquidity considered as another objective function besides the risk and return. The model is mathematically formulated in an uncertain environment. The concerned uncertainty is dealt with [...] Read more.
In this paper, a mathematical model for an extended multi-objective portfolio selection (EMOPS) problem is explored with liquidity considered as another objective function besides the risk and return. The model is mathematically formulated in an uncertain environment. The concerned uncertainty is dealt with by employing the fuzzy numbers in the risk matrix and return. While the fuzzy EMOPS model is converted into the corresponding deterministic case based on the αlevel sets of the fuzzy numbers, a weighted Tchebycheff method is implemented by defining relative weights and ideal targets. The merit of the suggested method is the applicability in many real-world situations. At the end, some numerical illustration is exhibited for the utility of the suggested EMOPS problem. Finally, it is concluded that the suggested method is simple to learn and to implement in real-life situations for the decision maker. Full article
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12 pages, 1411 KiB  
Article
Exploiting Augmented Reality Technology in Special Education: A Systematic Review
by Andrianthi Kapetanaki, Akrivi Krouska, Christos Troussas and Cleo Sgouropoulou
Computers 2022, 11(10), 143; https://doi.org/10.3390/computers11100143 - 21 Sep 2022
Cited by 9 | Viewed by 3249
Abstract
In this systematic review, research works pertaining to the use of augmented reality (AR) in special education are investigated. In recent years, the introduction of Information and Communication Technology (ICT) in education has transformed the teaching and learning process. Augmented reality is an [...] Read more.
In this systematic review, research works pertaining to the use of augmented reality (AR) in special education are investigated. In recent years, the introduction of Information and Communication Technology (ICT) in education has transformed the teaching and learning process. Augmented reality is an emerging technology, which has been widely used in education during the past few years. However, only few studies focus on the advantages and limitations of AR use in special education. This review investigates research in AR educational systems addressed to students with special needs. In total, 14 studies between 2014 and 2022 were selected and analyzed. Specifically, this systematic review examines types of students that have been included in learning scenarios supported of AR, distribution of educational AR studies by field of education, types of technology developed to support the use of AR in special education, and the advantages and limitations of AR use in special education. The findings mainly show that AR technology, used on students with special educational needs, has multiple potential advantages. Full article
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24 pages, 5244 KiB  
Article
Efficient, Lightweight Cyber Intrusion Detection System for IoT Ecosystems Using MI2G Algorithm
by Sunil Kaushik, Akashdeep Bhardwaj, Abdullah Alomari, Salil Bharany, Amjad Alsirhani and Mohammed Mujib Alshahrani
Computers 2022, 11(10), 142; https://doi.org/10.3390/computers11100142 - 20 Sep 2022
Cited by 7 | Viewed by 2526
Abstract
The increase in internet connectivity has led to an increased usage of the Internet of Things (IoT) and devices on the internet. These IoT devices are becoming the backbone of Industry 4.0. The dependence on IoT devices has made them vulnerable to cyber-attacks. [...] Read more.
The increase in internet connectivity has led to an increased usage of the Internet of Things (IoT) and devices on the internet. These IoT devices are becoming the backbone of Industry 4.0. The dependence on IoT devices has made them vulnerable to cyber-attacks. IoT devices are often deployed in harsh conditions, challenged with less computational costs, and starved with energy. All these limitations make it tough to deploy accurate intrusion detection systems (IDSs) in IoT devices and make the critical IoT ecosystem more susceptible to cyber-attacks. A new lightweight IDS and a novel feature selection algorithm are introduced in this paper to overcome the challenges of computational cost and accuracy. The proposed algorithm is based on the Information Theory models to select the feature with high statistical dependence and entropy reduction in the dataset. This feature selection algorithm also showed an increase in performance parameters and a reduction in training time of 27–63% with different classifiers. The proposed IDS with the algorithm showed accuracy, Precision, Recall, and F1-Score of more than 99% when tested with the CICIDS2018 dataset. The proposed IDS is competitive in accuracy, Precision, Recall, and training time compared to the latest published research. The proposed IDS showed consistent performance on the UNSWNB15 dataset. Full article
(This article belongs to the Special Issue IoT: Security, Privacy and Best Practices)
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