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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Bose, Sayantan Biswas, Satarupa Hazra, Joydev Heritage Institute of Technology Department of Computer Science and Business Systems Kolkata India Heritage Institute of Technology Department of Information Technology Kolkata India

ISBN: (纸本)9798350351378

In the realm of modern communication, the transmission of substantial data, predominantly in the form of images, necessitates a delicate balance between data capacity optimization and ensuring secure transmission. This paper addresses this challenge through the introduction of a novel Compression-then-Encryption (CtE) scheme. The scheme incorporates a tailored luminance quantization matrix, optimizing luminance preservation, and employs sparse matrices to efficiently compress chrominance components. To fortify the security of image transmission, the CtE scheme integrates the standard AES-256 encryption algorithm. Through a synergistic approach, this scheme aims to enhance compression efficiency in the context of contemporary communication channels. © 2024 IEEE.

关键词： Cryptography

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Diabetes Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Diabetes Prediction Using Machine Learning Techniques: A Cas...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Batyha, Radwan Samara, Ghassan Al Henawi, Esraa Al-Safarini, Maram Y. Samhan, Ahmad A. Elrashidi, Ali Alluwaici, Mo'Ath Al-Omari, Deana It Zarqa University Department of Data Science Ai Faculty Zarqa Jordan Private University Department of Computer Science Faculty of It Applied Science Amman Jordan Zarqa University Department of Computer Science Faculty of It Zarqa Jordan Zarqa University Department of Software Engineering Faculty of It Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Diabetes is one of the most common diseases in Jordan. It is the main reason of death among Jordanian adult citizens. Worldwide, 48% of all deaths are due to Diabetes occurred before the age of 70 years. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that BayesNet and NaiveBayes are the best classification models to handle the task of the early prediction of Diabetes in Jordan © 2024 IEEE.

关键词： Adversarial machine learning

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A Data science Approach for Predicting Soccer Passes Using Positional Data 26th

A Data Science Approach for Predicting Soccer Passes Using ...

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26th International Conference on Information Integration and Web Intelligence, iiWAS 2024

作者： Eigenrauch, Sebastian Bischofberger, Jonas Baca, Arnold Schikuta, Erich Research Group Workflow Systems and Technology Faculty of Computer Science University of Vienna Vienna Austria Centre for Sport Science and University Sports University of Vienna Vienna Austria

ISBN: (纸本)9783031780899

Data-driven approaches for evaluating tactical team behavior in soccer are nowadays a widespread method in sport analytics. The large amount of data collections enables experts to generate a deep tactical understanding and extract valuable measurements out of team-performances. However, these approaches are often limited in their comprehensibility and applicability for domain experts. Additionally, defensive behaviour in soccer is notoriously difficult to measure and has been receiving less attention in research and practice compared to measuring offensive performance. The motivation of this research is the design, implementation and validation of data science algorithms, that predict tactical motion of defending players after an occurring event of a pass, one of the most common events in soccer matches. The focus is the establishment and validation of different sets of rules, which simulate the movement behavior of the defending team, based on domain knowledge. The approach provides a high level of applicability for domain experts, in order to use and combine variable predefined rules for prediction, simulation and evaluation of different tactical approaches of defensive behavior. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data science

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Assessing the Role of Environmental Awareness in Air Quality Enhancement 3

Assessing the Role of Environmental Awareness in Air Quality...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Hidayat, Wahyu Haryani, Calandra Alencia Tesla Arshaka Faturahman, Virendra Lindzani, Nur Aulia Wahyudin Anugrah, Rio University of Raharja Dept. of Computer Systems Tangerang Indonesia University of Pelita Harapan Dept. of Computer Science Tangerang Indonesia Alfabet Inkubator Indonesia Dept. of Digital Business Tangerang Indonesia Pandawan Sejahtera Indonesia Dept. of Digital Business Tangerang Indonesia Pandawan Sejahtera Indonesia Dept. of Computer Systems Tangerang Indonesia

ISBN: (纸本)9798350367492

Air pollution is an increasingly concerning environmental issue in Indonesia due to the rise in industrial activities and rapid urban growth. This study aims to assess the role of environmental awareness in enhancing air quality, focusing on industrial activities and public involvement as mediating variables. The research gap addressed is the lack of understanding of how environmental awareness impacts air quality through environmentally friendly industrial practices and public participation. Utilizing Structural Equation Modeling (SEM) via SmartPLS, this study analyzes data collected from a diverse sample of respondents across various societal levels in Indonesia. The findings reveal that environmental awareness has a significant positive effect on air quality. Moreover, industrial activities and public involvement play crucial mediating roles in this relationship. The results suggest that higher levels of environmental awareness lead to better air quality by promoting sustainable industrial practices and encouraging public participation in environmental initiatives. The study's implications are significant for policymakers, indicating that enhancing public environmental awareness and fostering green industrial practices are essential strategies for mitigating air pollution. Additionally, active public engagement in environmental monitoring and advocacy can further strengthen efforts to improve air quality. The novelty of this research lies in its comprehensive approach, integrating industrial activities and public involvement as mediators, providing new insights for effective and sustainable environmental policies in Indonesia. © 2024 IEEE.

关键词： Environmental monitoring

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on Information Communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sensor based Classification of Table-Tennis Strokes 1

On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sen...

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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Ezzeldin, Mustafa Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Helwan University Faculty of Computers AI Software Engineering Cairo Egypt Helwan University Faculty of Computers and AI Computer Science Cairo Egypt Helwan University Faculty of Computers and AI Information Systems Cairo Egypt Helwan Univeristy HCI-LAB Faculty of Computers and AI Computer Science Cairo Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350335569

Table Tennis is a renowned competitive and recreational sport. An Olympic sport since 1988, table tennis in-cludes several movements, shots (i.e., strokes), and positions. Consequently, many factors can affect the strokes' accuracy and strength. This paper aims twofold: to classify the type of shot and to enhance the performance of the classification by improving the recognition accuracy and reducing the computational time. To track the movement of the body parts and extract table tennis stroke information, sensors (both Accelerometers and Gyroscopes) were utilized. The extracted data are processed and passed to a P-Dollar classifier to recognize six different table tennis strokes. This work utilizes two classification approaches to evaluate the performance, both linear and hierarchical. The presented results demonstrate that the hierarchical classification yields a better accuracy of 88%, achieved in 4 seconds, while traditional linear classification achieved 83% within 6 seconds. © 2023 IEEE.

关键词： Gyroscopes

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Cancer Text Article Categorization and Prediction Model Based on Machine Learning Approach 3

Cancer Text Article Categorization and Prediction Model Base...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Mondal, Subhash Barman, Anup Kumar Basumatary, Subungshri Barman, Manas Rai, Chirag Nag, Amitava Bengaluru India Central Institute of Technology Kokrajhar Computer Science and Engineering Assam Kokrajhar India Meghnad Saha Institute of Technology Computer Science and Business Systems Kolkata India Central Institute of Technology Kokrajhar Computer Science and Engineering Kokrajhar India

ISBN: (纸本)9798350340358

Many research studies have been carried out to identify different variations of cancers in the human body throughout the globe. Due to numerous volumes, the primary concern for healthcare researchers is to segregate the article into a well-thought-out manner. Biomedical text classification is a globally volitional realm due to its numerous uses in different fields. This study motivates us to use a dataset related to cancer articles text containing 7570 research papers with categories into three classes. Like the Word2Vec and BERT models, the word embedding technique converts the text into a numeric vector. The text classification-based prediction model considers nine Machine Learning (ML) classifiers. The models are tested with k-fold cross-validation and evaluate the prediction performances by considering the different standard metrics. The model trade-off between bias and variance is controlled by calculating the standard deviation and k-fold mean accuracy to overcome the overfitting problem of the deployed model. The outcome is observed on the Random Forest (RF) classifier in the case of Word2Vec embedding with a mean accuracy of 99.98%. The BERT models also performed well, with about 97.70% mean accuracy and other parameters compared to the Word2Vec model. The other parameters' evaluation score was near 1.00, promising in multiclass text classification deployed model performances. © 2023 IEEE.

关键词： Support vector machines

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DNN-based Skeleton Independent Motion Retargeting 27

DNN-based Skeleton Independent Motion Retargeting

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Akber, Syed Muhammad Abrar Kazmi, Sadia Nishat Szczesna, Agnieszka Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

In the modern digital world, the use of animated applications has increased significantly and such applications have quietly become an integral part of life. Capturing human motions is a fundamental aspect of these applications which is performed by motion-capturing (MoCap) systems. Due to the heterogeneity of MoCap systems and marker configurations, the captured skeletons may vary in their structure, number of joints, and bone lengths. The need for motion retargeting arises to transfer motion captured from one articulated actor to another. Although motion retargeting is a long-standing issue, current methods cannot automatically execute retargeting between skeletons. This paper presents the retraining of a popular motion retargeting network with a new large-scale motion dataset. The retraining aims to demonstrate the capabilities of the motion retargeting network to handle the problem of motion retargeting between skeletons that may have different structures. The paper first, presents the motion retargeting network that employs an encoder-decoder architecture. Afterward, we re-train the motion retargeting network with a new MoCap dataset. To validate the effectiveness of the network, we visually compare the generated results with the existing retargeting model. The results show that the motion retargeting network effectively retargets the different motions. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Decoder Deep neural network Encoder Motion retargeting

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