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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease

Devising a Set of Compact and Explainable Spoken Language Fe...

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International Symposium on Chinese Spoken Language Processing

作者： Junan Li Yunxiang Li Yuren Wang Xixin Wu Helen Meng Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Dept. of Computer Science & Engineering The Chinese University of Hong Kong

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Alzheimer's disease (AD) has become one of the most significant health challenges in an aging society. The use of spoken language-based AD detection methods has gained prevalence due to their scalability due to their scalability. Based on the Cookie Theft picture description task, we devised an explain-able and effective feature set that leverages the visual capabilities of a large language model (LLM) and the Term Frequency-Inverse Document Frequency (TF-IDF) model. Our experimental results show that the newly proposed features consistently outperform traditional linguistic features across two different classifiers with high dimension efficiency. Our new features can be well explained and interpreted step by step which enhance the interpretability of automatic AD screening.

关键词： Visualization Scalability Large language models Linguistics Aging Feature extraction Alzheimer's disease

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Enhancing Brain Tumor Detection in MRI Images Through Fine-Tuning of VGG 16 Networks: A Precision Diagnostic Approach

Enhancing Brain Tumor Detection in MRI Images Through Fine-T...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This paper explores the impact of fine-tuning on brain tumour diagnosis using MRI data and a pre-trained VGG 16 model on ImageNet. Our objectives in fine-tuning the model were to improve its generalisation ability and diagnostic accuracy. Our results demonstrate a considerable improvement in the model's performance, with decreased validation loss and increased validation accuracy. The findings show that, despite the possibility of overfitting, fine-tuning is a helpful technique for deep learning model optimisation in medical image analysis. By enhancing diagnostic skills in healthcare, particularly in the early detection of brain tumours, this tactic may enhance patient outcomes.

关键词： Training Accuracy Magnetic resonance imaging Brain modeling Data models Telecommunication computing Tuning Optimization Monitoring Overfitting

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Bayesian-Driven Deep Learning and Machine Learning Approaches for Handwritten Word Recognition

Bayesian-Driven Deep Learning and Machine Learning Approache...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Dinesh, Medisetty Sagar, Lingamaneni Moksha, Pallapu Baladhitya, Pappula Srinivas, M. Amrita Vishwa Vidyapeetham Computer Science Engineering Amaravati India Amrita Vishwa Vidyapeetham Dept. of Computer Science Amaravati India

ISBN: (纸本)9798331527518

Identifying handwritten words poses a complicated problem owing to the variances in handwriting styles and the possible noise and distortions present in the data. The performance of used deep learning and traditional machine learning handwriting recognition methods for recognizing handwritten words is studied in this paper and the advantages of using deep learning are discussed. In particular, neural network models such as CNN and 2DLSTM networks had a great work in this area. In contrast, we detect whole handwritten words without the need of segments into standalone characters. The CNNs are used for feature extraction, the Bidirectional LSTMs for sequence prediction and output is decoded via a CTC layer. The model was evaluated over the IAM words dataset from the IAM handwriting database, achieving 87% accuracy and a character error rate of 7.77%. In addition, we experimented with ML techniques, including SVM, RF has got an accuracy of 72% and 80% accuracies, and a Bayesian network of 61% accuracy, to provide a comparative analysis. © 2025 IEEE.

关键词： Support vector machines

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Credit Card Fraud Detection Utilizing Machine Learning, Deep Learning, and Bayesian Networks

Credit Card Fraud Detection Utilizing Machine Learning, Deep...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Arra Keerthan Reddy Harsha Vardhan Rapeti Doduguru Amarnath Reddy M. Srinivas Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Dept. of Computer Science Amrita Vishwa Vidyapeetham Amaravati India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Detecting credit card fraud is a critical challenge in the modern financial landscape, where robust and efficient solutions are essential to mitigate losses and safeguard consumers. This research evaluates and contrasts three primary approaches to this issue: Machine Learning (ML), Deep Learning (DL), and Bayesian Networks (BN). Using publicly available datasets, several ML techniques, including Logistic Regression, Support Vector Machines (SVM), Random Forests, Decision Trees, and XGBoost, were assessed alongside DL models such as Multi-Layer Perceptron (MLP) and Deep Neural Networks (DNNs). Additionally, a Bayesian Network was employed to analyze causal relationships between fraud-related features, offering an interpretable probabilistic framework. While ML and DL approaches achieved high performance regarding accuracy, precision, and recall, the Bayesian Network distinguished itself by providing enhanced interpretability and reliability. This study highlights the complementary strengths of these methods and the potential for their integration to develop adaptive and efficient fraud detection systems

关键词： Deep learning Support vector machines Logistic regression Accuracy Credit cards Probabilistic logic Bayes methods Fraud Ensemble learning Random forests

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Exhaled Breath Analysis Based Diabetes Detection with k-Nearest Neighbors Classifier 16

Exhaled Breath Analysis Based Diabetes Detection with k-Near...

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16th International Symposium on Computational Intelligence and Design, ISCID 2023

作者： Smieja, Piotr J. Lu, Jiang Sun, Qingquan Fu, Xingang Zhang, Ting Dept. of Electrical and Biomedical Engineering University of Nevada Reno Reno United States Department of Computer Engineering University of Houston Clear Lake Houston United States School of Computer Science and Engineering California State University San Bernardino San Bernardino United States Department of Computer Science University of Houston Downtown Houston United States

ISBN: (纸本)9798350317459

The diagnosis of early stages of diseases such as lung cancer or diabetes is challenging as those diseases don’t have many noticeable symptoms. The human breath, however, contains many VOCs (volatile organic compounds) that could be used as a clue for conducting the proper test. In this study, a custom breathalyzer device is developed for noninvasive detection and diagnosis of diabetes with the use of a human breath print. An array of 6 MOS sensors is used in the prototype to collect 8 VOCs of the breath print of the subjects. The k-nearest Neighbor algorithm is implemented to determine the likelihood of a disease. In the results, an accuracy of 93.6% was achieved. ©2023 IEEE.

关键词： Volatile organic compounds

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Power Control of Preamble Transmission for Grant-free Random Access in Cell-Free IoT Networks

Power Control of Preamble Transmission for Grant-free Random...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Jiseung Youn Sunghyun Cho Dept. of Computer Science & Engineering Major in Bio-Artificial Intelligence Hanyang University Seoul Republic of Korea Dept. of Computer Science & Engineering Hanyang University ERICA Ansan Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

As Internet of Things (IoT) technologies are inves-tigated to support key 5G services, significant research efforts are being made to enable machine-type communication (MTC). Cell-free IoT (CF-IoT) systems, characterized by favorable prop-agation and macro diversity, are being studied in conjunction with grant-free random access (GFRA) as a potential enabler to achieve massive connectivity for MTC. However, existing GFRA schemes in CF-IoT transmit preambles at a fixed power level resulting in severe preamble collisions at nearby access points (APs). In this paper, we analyzes the analysis of preamble collisions in massive connectivity scenario and proposes a sim-ple power control scheme to address preamble collision. The proposed scheme mitigates preamble collisions by transmitting preambles only to the nearest AP in high-density access scenes. Simulation results show that the proposed method improves the random access success rate compare to existing GFRA method by 10%.

关键词： 5G mobile communication Simulation Power control Information and communication technology Internet of Things Convergence

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Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) using Natural Language Processing

Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) us...

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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Datta, Suseta Mitra, Poojarini Kundu, Pritam Ministry of Electronics and Information Technology National Institute of Electronics and Information Technology Kolkata India University of Engineering & Management Dept. of Computer Applications Kolkata India University of Engineering & Management Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9781665477192

The COVID pandemic is causing outrageous interference in everyday life and financial activity. Close to two years after the presence of COVID, WHO allotted the variety B.l.l.529 a variety of concern, named 'Omicron'. Online diversion data assessment is created and transformed into a more renowned subject of investigation. In this paper, a sizably voluminous heap of appraisals and assessments are culminated with online redirection information. The evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilising various natural language processing techniques based on positive, negative, and neutral tweets. All potential outcomes are considered for investigating the feelings of Twitter clients. For the most part, tweets are assessed clearly, and this assessment ensures the headway of this investigation study. Different kinds of analyzers are utilised and measured. The 'TextBlob Sentiment Analyzer' has given the highest polarity score based on positivity, negativity, and neutrality rates in terms of inspiration, pessimism, and impartiality. A total dataset is fully determined and classified with all the analyzers, and a comparative result is also measured to find the ideal analyzer. It is intended to apply boosting machine learning methods to increase the accuracy of the proposed architecture before further implementation. © 2022 IEEE.

关键词： Sentiment analysis

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Bayesian-Driven Deep Learning and Machine Learning Approaches for Handwritten Word Recognition

Bayesian-Driven Deep Learning and Machine Learning Approache...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Medisetty Dinesh Lingamaneni Sagar Pallapu Moksha Pappula Baladhitya M. Srinivas Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Dept. of Computer Science Amrita Vishwa Vidyapeetham Amaravati India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

关键词： Deep learning Support vector machines Radio frequency Handwriting recognition Accuracy Error analysis Neural networks Feature extraction Bayes methods Convolutional neural networks

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On Detecting Low-Pass Graph Signals Under Partial Observations

On Detecting Low-Pass Graph Signals Under Partial Observatio...

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Sensor Array and Multichannel Signal Processing Workshop

作者： Hoang-Son Nguyen Hoi-To Wai Dept. of Computer Science & Engineering The Chinese University of Hong Kong Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method.

关键词： Array signal processing Conferences Low-pass filters Signal processing algorithms Detectors Filtering algorithms Complexity theory

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