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Effect of E-Learning on Public Health and Environment During COVID-19 Lockdown

Big Data Mining and Analytics 2021年第2期4卷 104-115页

作者： Avani Agarwal Sahil Sharma Vijay Kumar Manjit Kaur Department of Computer Science Thapar Institute of Engineering and TechnologyPatiala 147001India Department of Computer Science and Engineering National Institute of TechnologyHamirpurHimachal Pradesh 177005India Department of Computer Science Engineering School of Engineering and Applied SciencesBennett UniversityGreater Noida 201310India

E-learning is the most promising venture in the entire world. During the COVID-19 lockdown, e-learning is successfully providing potential information to the students and researchers. In developing nations like India, with limited resources, e-learning tools and platforms provide a chance to make education available to middle and low income households. This paper gives insights about three different online services, namely Google Classroom,Zoom, and Microsoft Teams being used by three different educational institutions. We aim to analyze the efficiency and acceptability of e-learning tools among Indian students during the COVID-19 lockdown. The paper also aims to evaluate the impact of e-learning on the environment and public health during COVID-19 lockdown. It is found that e-learning has potential to reduce carbon emissions, which has beneficial impact on the environment. However, the mental health is impacted as e-learning may lead to self-isolation and reduction in academic achievements that may lead to anxiety and mental depression. Due to usage of electronic devices for learning, the eyes and neck muscles may be put in strain, having deleterious effects on physical health.

关键词： e-learning environment health COVID-19

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Optimization Algorithms for Sidelobes SSL Reduction: A Comparative Study

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Journal of computer and Communications 2024年第7期12卷 120-132页

作者： Mohsen Denden Aymen Alhamdan Department of Computer Science Higher Institute of Applied Sciences of Sousse Sousse University Sousse Tunisia Department of Electrical Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

The development of new technologies in smart cities is often hailed as it becomes a necessity to solve many problems like energy consumption and transportation. Wireless networks are part of these technologies but implementation of several antennas, using different frequency bandwidths for many applications might introduce a negative effect on human health security. In wireless networks, most antennas generate sidelobes SSL. SSL causes interference and can be an additional resource for RF power that can affect human being health. This paper aims to study algorithms that can reduce SSL. The study concerns typical uniform linear antenna arrays. Different optimum side lobe level reduction algorithms are presented. Genetic algorithm GA, Chebyshev, and Particle Swarm Optimization algorithm are used in the optimization process. A comparative study between the indicated algorithms in terms of stability, precision, and running time is shown. Results show that using these algorithms in optimizing antenna parameters can reduce SSL. A comparison of these algorithms is carried out and results show the difference between them in terms of running time and SSL reduction Level.

关键词： SSL Radio Wave Interference SAR Linear Antenna Arrays Genetic Algorithm Chebyshev Particle Swarm Optimization

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Pricing Policy with the Effect of Fairness Concern, Imprecise Greenness, and Prices in Imprecise Market for a Dual Channel

Pricing Policy with the Effect of Fairness Concern, Imprecis...

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International Conference on Nonlinear applied Analysis and Optimization, ICNAAO-2021

作者： Ganguly, Sanchari Das, Pritha Maiti, Manoranjan Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur WB Howrah711103 India Department of Applied Mathematics Oceanology and Computer Programming Vidyasagar University WB Midnapore721102 India

ISBN: (纸本)9789819905966

Fairness concern behavior, a well-known cognitive bias, refers to a person’s attitude of dissatisfaction for unequal pay-offs in someone’s favor. Against environmental pollution, many firms are focused on green manufacturing to maintain sustainable development. The adoption of online shopping by customers around the globe has altered the dynamics of competitiveness in the retail supply chain (SC). Considering the above facts, the effect of fairness concern on optimal prices and channel members’ profits in a two-level dual channel SC is investigated. Here, we consider an SC comprising a green manufacturer and a fair-minded retailer where the manufacturer produces and sells the green product through both offline and online channels to consumers. Due to uncertainty in the real world, the fuzziness is associated with market demand, price elasticity, and the coefficient of greenness. The fuzzy objectives and constraints are reduced to crisp ones using expectation and possibility measures, respectively. Both centralized and decentralized models (with and without cognitive bias) are formulated and solved by the Stackelberg game for the optimal prices and product greenness level. The models’ optimal solutions are analyzed and compared with the deterministic models numerically. The effects of the fairness concern coefficient on the optimal prices, product greenness level, and channel members’ profits are investigated. The sensitivity analyses are presented to study the effects of the fuzzy degree of customers’ sensitivity toward greenness on channel members’ profits. Retailer’s fairness is harmful to the manufacturer but beneficial for her own profit. Finally, some conclusions and managerial insights are presented. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Game theory

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Sensor-Based Hand Gesture Recognition Using One-Dimensional Deep Convolutional and Residual Bidirectional Gated Recurrent Unit Neural Network

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Lobachevskii Journal of mathematics 2025年第1期46卷 464-480页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao 56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand

Abstract: Hand gesture recognition (HGR) is a crucial domain of study within human-computer interaction (HCI), encompassing applications such as drone operation, virtual and augmented reality, and sign language interpretation. This research introduces an innovative method for HGR utilizing wearable sensors integrated with a sophisticated neural network architecture. We propose an integrated deep residual model called 1D-CNN-ResBiGRU, which amalgamates a one-dimensional convolutional neural network (CNN) with residual bidirectional gated recurrent units (ResBiGRU) to analyze data from wearable sensors to enhance the accuracy and robustness of hand gesture recognition. Our experimental findings indicate that this method attains exceptional accuracy rates of 93.03 and 98.49 on the GesHome and UWave datasets, respectively, in recognizing diverse hand motions. The achievement substantially surpasses current methodologies, exhibiting enhancements of up to 7.16 percentage points compared to state-of-the-art models. The suggested system demonstrates notable proficiency in managing intricate motions and ensuring uniformity across user demographics. It is appropriate for utilization in human-computer interaction, smart home management, and assistive technologies. Ablation experiments further confirm the efficacy of each component in our design, with the 1D-CNN blocks and ResBiGRU component greatly enhancing the model’s capability to accomplish. © Pleiades Publishing, Ltd. 2025.

关键词： bidirectional gated recurrent unit neural network convolutional neural network deep residual network hand gesture recognition wearable sensors

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Explainability of Highly Associated Fuzzy Churn Patterns in Binary Classification

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Expert Systems 2025年第7期42卷

作者： Wang, D.Y.C. Jordanger, Lars Arne Lin, Jerry Chun-Wei Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

Customer churn, particularly in the telecommunications sector, influences both costs and profits. As the explainability of models becomes increasingly important, this study emphasises not only the explainability of customer churn through machine learning models, but also the importance of identifying multivariate patterns and setting soft bounds for intuitive interpretation. The main objective is to use a machine learning model and fuzzy-set theory with top-k HUIM to identify highly associated patterns of customer churn with intuitive identification, referred to as Highly Associated Fuzzy Churn Patterns (HAFCP). Moreover, this method aids in uncovering association rules among multiple features across low, medium, and high distributions. Such discoveries are instrumental in enhancing the explainability of findings. Experiments show that when the top-5 HAFCPs are included in five datasets, a mixture of performance results is observed, with some showing notable improvements. It becomes clear that high importance features enhance explanatory power through their distribution and patterns associated with other features. As a result, the study introduces an innovative approach that improves the explainability and effectiveness of customer churn prediction models. © 2025 John Wiley & Sons Ltd.

关键词： Fuzzy rules

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Comparative Analysis of Sequential and Parallel Computing for Object Detection Using Deep Learning Model 24

Comparative Analysis of Sequential and Parallel Computing fo...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Amsaad, Fathi Al-Eidi, Shorouq Darwish, Omar Computer Science and Engineering Wright State University Ohio United States Tafila Technical University Computer Science Department Tafila Jordan Information Security and Applied Computing Eastern Michigan University Michigan United States

ISBN: (纸本)9798350384307

Recently, parallel and distributed computing has become indispensable in various application domains, such as cloud computing and extensive data analysis, to meet the growing demands for efficiency and reliability. In this study, we compared sequential and parallel computing techniques applied to a deep learning model for object detection. By leveraging multiple evaluation metrics, such as F1 score, execution time, and accuracy, we meticulously evaluated the model's performance. Our analysis revealed that parallel execution considerably decreased the training time of model, resulting in an average time savings of 40 % while maintaining the training process's accuracy and quality. These finding have significant implications for real world applications, as they demonstrate tremendously the potential for expedited deep learning model training without compromising accuracy. This can tremendously benefit medical imaging and realtime video analysis, where accuracy and efficiency are paramount. © 2023 IEEE.

关键词： Deep neural networks Parallel computing Sequential computing

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Enhanced Support Vectors based Oversampling Method 25

Enhanced Support Vectors based Oversampling Method

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Sabbah, Thabit Ashraf, Mahmoud Abrar, Mohammad Dhofar University Collage of Arts and Applied Sciences P.O.Box:2509 Salalah211 Oman University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah Saudi Arabia Arab Open University Faculty of Computer Studies Muscat122 Oman

ISBN: (纸本)9798331540012

Class distribution imbalance in the dataset is a critical factor in machine learning based classification performance. Many oversampling techniques were used to overcome the effect of this problem and retrieve the balance state for effective modelling. This paper presents an enhanced technique based on the Support Vectors for oversampling the minority class by generating synthetic data for better performance. The proposed method is tested on machine-generated datasets for binary classification problems. Experiments show an average improvement of approximately 15% in the classification recall and AUC performance measures on both datasets using the proposed method over the leading of the studied oversampling techniques. The proposed method can benefit the real-life machine learning classification problems where the positive class forms the minority of the training dataset like fraud-detection, medical diagnosis, and other problems, by expanding the positive class samples with more discriminative and evenly distributed related synthetic samples. © 2024 IEEE.

关键词： Adversarial machine learning

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

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computer Systems science & engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed Hamza Hala J.Alshahrani Jaber S.Alzahrani Heba Mohsen Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent *** refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the Arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： Arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

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Advancements in Wireless Networks: Transition from 5G to Beyond 2

Advancements in Wireless Networks: Transition from 5G to Bey...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Malarvizhi, C. Banu, M. Ameena Anthonisamy, Arun Hemalatha, M. Manikandan, Raja University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chembarambakkam India NPR College of Engineering and Technology Department of Electronics and Communication Engineering Natham India Panimalar Engineering College Department of Computer Science and Business System Tamil Nadu Chennai600123 India Nandha Arts and Science College Department of Information Technology Tamil Nadu Erode638052 India K. Ramakrishnan College of Technology Department of ECE Tamil Nadu Trichy India

ISBN: (纸本)9798331534950

Earlier generations of mobile technology experienced exponential growth in mobile customers, disruptive ecosystems like Internet of Things applications, and massive channel capacity needed to connect trillions of devices. 5G networks are a vital solution. 5G aims to provide 10 Gbps to a single user, as well as quality of service, increased spectrum efficiency, ultra-reliable communication, extended device battery life, massive machine-type communication (mMTC), and a 1000-fold capacity gain. Due to the heavy traffic volume and high resource consumption of 5G applications like augmented reality (AR), virtual reality (VR), and wireless body area networks, there are major barriers to channel capacity and potential service disruptions for other users. New strategies are needed to increase spectral efficiency and channel capacity. This paper explores many major new wireless technologies that aim to increase spectral efficiency and channel capacity to meet growing user demands and improve network performance. This paper also examines these technologies' benefits and research challenges. Massive Multi-Input Multi-Output (mMIMO) technology's effectiveness and potential distinguish it for 5G and Beyond 5G (B5G) networks, offering a variety of benefits and characteristics. This article can also assist researchers who are conducting in-depth studies, as well as wireless network operators who aim to streamline the development of 5G and B5G networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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Reinforcement Learning Approach to Solve: PBL Markov Model 5th

Reinforcement Learning Approach to Solve: PBL Markov Model

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Mishra, Vishnu Kumar Mishra, Megha Dewangan, Bhupesh Kumar Parijatha, Kimidi Choudhury, Tanupriya Kotecha, Ketan Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science and Engineering OP Jindal University Raigarh India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana Secunderabad India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Maharashtra Pune412115 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819997060

PBL is an educational approach that centers on learners actively engaging with open-ended problems rather than just absorbing predetermined content. This method encourages in-depth understanding and the development of problem-solving skills. Notably, the Markov PBL approach doesn’t focus on providing answers but instead emphasizes the cultivation of valuable skills and attributes. These include attention to detail, enhanced coordination, and the strengthening of group dynamics and communication. The field of pedagogy has experienced a significant impact worldwide due to the pandemic. This global crisis led to the temporary closure of schools, colleges, and universities. In response, many educational institutions swiftly adopted online learning as the primary mode of instruction. Online learning encompasses various courses delivered entirely via digital platforms, with the exception of Massive Open Online Courses (MOOCs). Compared to traditional brick-and-mortar classroom settings, online learning via virtual classes conducted over the internet offers a different set of advantages. It provides learners with opportunities to enhance their skills through practical application and critical assessment. Additionally, it facilitates the retrieval of consolidated knowledge and encourages continuous learning within a collaborative team environment. This study aims to explore both the advantages and disadvantages of applying the Markov PBL method within the context of online learning. While the benefits are apparent, there are also challenges and limitations associated with this approach, which we will delve into further. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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