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Women in Data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Mohammed Gollapalli Maryam Alqusser Amal Althobaiti Lubna Alzaid Roaa Alorefan Sara Alnajim Yasmeen Alsaleem Department of Computer Information Systems College of Computer Science & IT Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Computer Engineering College of Computer Science & IT Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Breast cancer (BC) affects women of all ages after puberty around the world, but the rate spontaneously rises as the women get older. Saudi Arabia is home to a large number of BC patients. Many women are diagnosed with BC and are frequently admitted to the hospital, resulting in a massive amount of clinical data that can be analyzed and used for medical research. However, proper text mining algorithms must be implemented in order to extract valuable insights and knowledge discovery from these medical notes. In this study, 7 years of BC patients' medical notes data (between 2012-2018) were officially obtained from the hospital and experimented to diagnose patient issues using three machine learning (ML) algorithms. The dataset contained 62,845 clinical text records on "X-ray screening results" recorded by the doctors after seeing the mammogram for patients who had visited the hospital through emergency and regular appointments. Natural language processing (NLP) models were employed to exploit the text data and converted into symbols. Furthermore, the text clustering algorithms K-means, K-medoids, and agglomerative clustering were carefully experimented. The results indicated that K-means outperforms the other two algorithms, with seven identified clusters. K-means clustering also yielded 0.198 work saved over sampling (WSS) and 5.5 database performance (DB) values and was identified as the best unsupervised learning algorithm for medical text clustering.

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A Fast, Interactive, Location-Based Food Recommendation Application 21

A Fast, Interactive, Location-Based Food Recommendation Appl...

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8th Multidisciplinary International Social Networks Conference, MISNC 2021

作者： Chiu, Sheng-Min Chen, Yi-Chung Liou, Yow-Shin Lee, Chiang Ying, Jia-Ching Loh, Chee-Hoe Lin, Jou-Wei Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Department of Industrial Engineering and Management National Yunlin University of Science and Technology Taiwan Department of Management Information Systems National Chung Hsing University Taiwan Department of Internal Medicine National Taiwan University Hospital Yunlin Branch Taiwan

ISBN: (纸本)9781450396011

The public is increasingly using social media to facilitate the selection dining options. Most previous studies on this topic focused on the use of personal preferences to generate recommendations. Few studies considered (1) the common preferences of multiple users in a social network or (2) the geographic location of the establishments. In this study, we developed an interactive, location-based food recommendation application, which records the users' favorites, previous search history, and personal information. The proposed system is convenient to use and highly effective in identifying suitable candidate restaurants. © 2021 ACM.

关键词： Location

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

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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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RTBERT: A Transformer Based Approach for Improved Rumor Classification from Tweet

RTBERT: A Transformer Based Approach for Improved Rumor Clas...

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International Conference on computer and Information Technology (ICCIT)

作者： Shakib Mahmud Dipto Mostofa Kamal Sagor Anatte Rozario Sahal Bin Saad Department of Computer Science and Engineering University of Liberal Arts Bangladesh (ULAB) Dhaka Bangladesh Dept. of CSE Computer Vision and Intelligent Systems (CVIS) Brac University Dhaka Bangladesh Department of Computer Science and Engineering Primeasia University Dhaka Bangladesh

In an era where both the general public and established news outlets increasingly rely on social media for real-time information, the abundance of rumors offers a huge difficulty. False information can have far-reaching implications, affecting individuals, communities, and even entire countries. To address this issue, a low-cost, self-regulating, and forward-thinking rumor detection technique is required. This research performs an intensive analysis of the performance of three robust machine learning algorithms, including XGBoost, SVM, and Random Forest, as well as two deep learning-based transformers, namely BERT and DistilBert. In this research, the models are trained and evaluated on a combined dataset comprising data from Twitter15 and Twitter16 datasets. Support Vector Machine (SVM) and Random Forest exhibit the best accuracy among classical machine learning models, reaching 89.05%. In comparison, among the transformer-based deep learning models, BERT achieves the best accuracy of 90.20%. In conclusion, the learning-based transformers beat its competitors in terms of accuracy, recall, precision, and F-measure, proving its efficacy in minimizing the detrimental impact of rumors on people, communities, and society as a whole.

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Early Detection of Childhood Malnutrition Using Survey Data and Machine Learning Approaches

Early Detection of Childhood Malnutrition Using Survey Data ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Thoufiq Zumma Anikur Rahaman Nuzhat Noor Islam Prova Tasriful Haque Julias Caeser Joy Bose Ravina Akter Youki Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science Washington University Science and Technology New York USA Department of Seidenberg School of CSIS Pace University New York USA Master of Information Technology and Systems Victorian Institute of Technology Faculty of Artificial Intelligence and Science University of Wollongong Faculty of Engineering and IT University of Technology Sydney

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Children's physical, mental and emotional development depends heavily on sleep, with age-specific sleep needs fluctuating. Malnutrition may result from eating too little, absorbing nutrients poorly, being unwell, living in poverty or not knowing enough about nutrition. Food allergies and iron insufficiency are two examples of illnesses whose onsets might differ depending on environmental exposures and genetics. By providing proper diet, getting medical attention when necessary and encouraging healthy behaviors, parents play a crucial role in supporting their children's health. For children to grow and develop properly, they need meals high in fruits, vegetables, milk, protein, calcium, iron and fiber. Key advantages of a balanced diet include aiding bone formation, improving heart health and encouraging regular bowel motions and digestion. In general, children's general health and wellness depend on caregivers being aware of their dietary requirements and giving them the proper attention. The findings of the research imply that machine learning algorithms may be useful in understanding and predicting the prevalence of malnutrition in children. Furthermore, these algorithms could provide perceptive data that might help politicians create effective policies. In this research, we make use of five different machine learning algorithms. With a decision tree, the accuracy is 0.92%, followed by svm which provides 0.91%, KNN, which provides 0.84%, GBM classifier, which provides 0.79% and random forest, which provides 0.65%.

关键词： Support vector machines Deep learning Surveys Pediatrics Machine learning algorithms Predictive models Prediction algorithms Iron Decision trees Medical diagnostic imaging

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IoT-Enhanced Gesture Recognition System for Healthcare Applications

IoT-Enhanced Gesture Recognition System for Healthcare Appli...

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Telecommunications Conference (ITC-Egypt), International

作者： Krithika L. B Nallakaruppan MK Mohamed Baza Mahmoud Badr Yuvraj Singh Deora School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Deparment of Computer Science College of charlsron charlsron SC USA Department of Networks and Computer Security College of Engineering State University of New York (SUNY) Polytechnic Institute Utica NY USA

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

In response to the prevalent language barriers in medical settings, this paper proposes an innovative solution leveraging gesture recognition technology. Through the integration of deep learning models and Internet of Things (IoT) tools, the system aims to facilitate seamless communication between patients and healthcare providers. Initially, a deep learning model is developed using a personalized dataset containing various medical gestures, allowing for the recognition of nuanced patient expressions such as “I have Fever” or “Shoulder pain.” Additionally, the system incorporates IoT sensors to monitor vital signs like body temperature and pulse in real-time, ensuring comprehensive patient assessment. With the capability to continuously evolve and adapt to new gestures, this solution not only enhances accessibility but also improves the quality of healthcare delivery, particularly in underserved communities. By bridging the gap between language barriers and medical communication, this innovative approach holds promise for fostering inclusive and effective healthcare practices worldwide. In addressing the challenges posed by language barriers in medical interactions, this paper presents a comprehensive solution that combines gesture recognition technology with deep learning models and IoT devices. By capturing and interpreting patient gestures, such as indicating specific symptoms or discomfort, the system facilitates clear communication between patients and doctors. Furthermore, the integration of IoT sensors enables continuous monitoring of vital signs, allowing for timely interventions and personalized care. With its ability to adapt and expand to accommodate new gestures and medical scenarios, this solution represents a significant advancement in healthcare accessibility and quality. By leveraging cutting-edge technology, this approach has the potential to revolutionize healthcare delivery, particularly in areas with limited access to linguistic resources or medi

关键词： Temperature sensors Deep learning Temperature measurement Shoulder Medical services Gesture recognition Sensors

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Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater’s Condition

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes an O(T max{c,1−c})network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further reduced to O)(log(T)), and the network cumulative constraint violation bound is reduced to O(log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

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Toward Utilizing Bidirectional Multi-head Attention Technique for Automatic Correction of Grammatical Errors

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第11期51卷 1886-1897页

作者： Mahmoud, Zeinab Kryvinska, Natalia Abdalsalm, Mohammed Solyman, Aiman Alfatemi, Ali Musyafa, Ahmad School of Computer Science and Technology Wuhan University of Technology China Department of Information Management and Business Systems Comenius University Slovakia Department of Computer Science University of Milan Italy CIS Department Fordham University BronxNY10458 United States Department of Informatics Engineering Pamulang University South Tangerang15310 Indonesia

Automatic Grammar Error Correction (GEC) models identify and correct a wide range of grammatical errors. Various strategies have been proposed for GEC, with the Neural Machine Translation (NMT) approach being the most effective. However, NMT-based GEC models with encoder-decoder layers rely heavily on the highest layer, leading to potential inaccuracies. Additionally, during inference, exposure bias can cause the model to substitute previously targeted words with incorrect alternatives. Another challenge is data scarcity. This paper introduces a GEC model leveraging the seq-to-seq Transformer framework, specifically designed for low-resource languages like Arabic. We propose a method to generate noise in the text to create synthetic parallel data, addressing the data constraints. Inspired by Capsule Networks (CapsNet), we incorporate CapsNet in GEC to dynamically aggregate information from multiple layers. In order to mitigate exposure bias, we incorporated a bidirectional training approach and a regularization term using Kullback-Leibler divergence to align left-to-right and right-to-left models. Experiments on two benchmarks demonstrate that our model outperforms current Arabic GEC models, achieving the highest scores. The code is available on GitHub (https://***/Zainabobied/ArabicGEC). © (2024), (International Association of Engineers). All rights reserved.

关键词： Neural machine translation

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Di...

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Energy, Materials and Communication engineering (ICEMCE), International Conference on

作者： D. Karthikeyan R. Nivasini Kiruba Thangam Raja D. Madhivadhani B. Sivakumar A.T.R. Krishna Priya Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Guvi Geek Networks IITM Research Park Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electronics and Communication Engineering Kings Engineering College Sriperumbudur Chennai Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari India

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer’s disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper.

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Privacy-Preservation for Distributed Quantized k-means Clustering

Privacy-Preservation for Distributed Quantized k-means Clust...

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Annual Allerton Conference on Communication, control, and Computing

作者： Apostolos I. Rikos Nicolas E. Manitara Gabriele Oliva Christoforos N. Hadjicostis Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston MA USA Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Engineering Unit of Automatic Control Università Campus Bio-Medico di Roma Rome Italy

In this paper, we enhance a distributed version of the well known k-means algorithm with privacy-preservation features. While ensuring that sensitive or confidential information remains undisclosed to unauthorized entities (in our case these entities are curious nodes within the network), we maintain the desirable features of the distributed algorithm: the transmitted values are quantized, which optimizes bandwidth utilization and alleviates communication bottlenecks, and nodes possess the ability to collectively determine when to terminate the algorithm, which enables the conservation of valuable resources. We introduce a novel privacy-preserving protocol that not only preserves the privacy of a node’s state, but it also ensures confidentiality about its cluster affiliation (i.e., it does not reveal to curious nodes whether a node participates in the update calculation for a specific centroid value). Moreover, we precisely characterize topological conditions that guarantee privacy preservation for individual nodes. Our distributed algorithm allows the formation of exclusive clusters within a finite time frame on any static and strongly connected directed graph.

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