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2nd international Conference on Business Analytics for Technology and Security, iCBATS 2023

作者： Noaman, Samar Billi ibrahim, Amer Ali, Liaqat iqbal, M.W. Ashraf, Asma Haseeb, Usama Muneer, Salman Almajed, Rasah Hamid, K. Canadian University Dubai Faculty of Management Dubai United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Superior University Department of Software Engineering Lahore54000 Pakistan Superior University Department of Computer Science Lahore54000 Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan American University in the Dubai College of Computer and Information Technology United Arab Emirates

ISBN: (纸本)9798350335644

COViD-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COViD-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COViD-19. The carried out survey is based on the social video games results on players' well-being and additionally on the effects of gamers' health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COViD-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COViD-19 practical life1. © 2023 iEEE.

关键词： Coronavirus

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Prediction of Metabolic Syndrome based on Non-invasive Measurement Features for Chronic Disease Management 22

Prediction of Metabolic Syndrome based on Non-invasive Measu...

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8th international Conference on computer Technology Applications, iCCTA 2022

作者： Shin, Hyunseok Shim, Simon Choo, Charles Hwang, Doosung Nah, Yunmook Oh, Sejong Department of Computer Science Dankook University Korea Republic of Department of Computer Engineering San Jose State University United States Department of Electrical Engineering San Jose State University United States Department of Software Science Dankook University Korea Republic of Department of Applied Computer Engineering Dankook University Korea Republic of

ISBN: (纸本)9781450396226

Metabolic syndrome is a chronic disease in which metabolism-related abnormal factors are complex. Metabolic syndrome is currently diagnosed by determining the number of abnormal factors based on physical measurements and blood tests. Metabolic syndrome causes complications in patients with diabetes or the cardiovascular system;therefore, prevention and management are particularly important. Metabolic syndrome can be prevented effectively by allowing individuals to manage it themselves;however, blood tests are a major obstacle to the public. Therefore, this study is conducted to devise a method that can easily predict metabolic syndrome without requiring a blood test. A dataset containing data of 69,944 adult Korean men and women is used to develop a predictive model. This dataset contains not only physical measurements and blood test results, but also life logs pertaining to diet, food intake, drinking, and smoking. Using these data, we identify features that contribute significantly to the prediction of metabolic syndrome, except for items associated with blood test. Finally, we propose a predictive model that allows the public to easily manage metabolic syndrome using only non-invasive factors. Furthermore, we investigate methods to improve the predictive performance from the perspective of four subgroups, based on waist circumference and blood pressure. © 2022 ACM.

关键词： Forecasting

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A Comparative Analysis of Goal-Oriented Requirements engineering and Model-Based Systems engineering Frameworks for Managing Requirements of Systems of Systems

A Comparative Analysis of Goal-Oriented Requirements Enginee...

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information Technology (ACiT)

作者： Suhair AlHajHassan Samer Murrar Ahmad Samhan Mohammed Odeh Ali Elrashidi Software Engineering Department Applied Science Private University Amman Jordan Computer Science Department Applied Science Private University Amman Jordan Software Engineering Department Zarqa University Zarqa Jordan Global Academy for Digital Health Bristol United Kingdom Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Goal-oriented requirements engineering (GORE) for Systems of Systems (SoS) includes combining individual operational systems local goals to achieve higher-level goals. GORE offers a structured approach to managing complex requirements, ensuring that strategic goals are translated into operational tasks. This paper provides a review of GORE frameworks, including those incorporating Model-Based Systems engineering (MBSE), that could be used in the management of complex Systems of Systems. The paper analyzes recent GORE frameworks such as CGS4Adaptation which combines Goals and SysML for managing adaptive Socio-Cyber-Physical Systems (SCPSs); GORE-based approach to Energy Management Systems (EnMS); Model-Based and Goal-Oriented Approach for the conceptual design of smart grid services; GORE and reference architecture approach for microgrid systems; and Adaptation-Oriented Requirement Modeling approach (ADORE). A comparative analysis is conducted to assess the extent of effectiveness these frameworks provide for improving SoS traceability, adaptability, and system design integrity. The paper concludes with key findings on the strengths and limitations of the considered frameworks, on the basis of which, major conclusions on how GORE combined with MBSE can be used for managing SoS requirements in Smart Grid (SG) and socio-cyber-physical systems could be drawn. This review paper also contributes to the requirements engineering domain by outlining effective strategies for designing and managing complex, adaptive Systems of Systems.

关键词： Adaptation models Analytical models Adaptive systems Reviews Microgrids Smart grids Requirements engineering information technology System analysis and design System of systems

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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iEEE Transactions on industrial informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 iEEE.

关键词： Cloud computing

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Crime Based Evaluation of GPS Network Using Machine Learning Techniques 4

Crime Based Evaluation of GPS Network Using Machine Learning...

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4th international Conference on Smart Systems and inventive Technology, iCSSiT 2022

作者： Rahul Monika Durgapal, Vartika Hari Department Of Software Engineering Delhi Technological University Delhi India Department of Computer Science Shaheed Rajguru College of Applied Sciences for Women University of Delhi Delhi110096 India

ISBN: (纸本)9781665401180

With the increased usage of data transmission, data leakage and privacy protection are becoming increasingly critical. Data comes in a variety of forms, and the amount of protection required for each one differs. With the rapid development and demand of Global Positioning S ystem (GPS) networks in every of big data technology, the vulnerability of the transportation system to unintentional and intentional GPS disruption is also increasing. The GPS signal is degraded and lost as a result of opposing interests' attacks. Potential assaults vary from GPS signal jamming and spoofing to GPS ground stations and satellite disruption. S ecurity issues with respect to data increases during data transmission. Data travelling across an organization's network is frequently sensitive and internet being the storehouse of every kind of data with methods for accessing the precise information serves as an auspicious medium for the hackers [19]. Network security solutions must deal with complicated data in this setting in order to detect and minimize any threats in real time. There are several techniques to the problem of data security such as cryptographic algorithms, classification-based models, and block-chain methods [1]-[6]. Through this research, crime rates are evaluated in a GPS network data with different other parameters using machine learning (ML) algorithms. Moreover, the inferred features effecting the crimes during data transmission are extracted. S ome of the techniques used includes techniques such as 'Decision Tree (DT)', 'K Nearest Neighbors (KNN)', 'Random Forest (RF)', 'Multivariable Regression (MR)' and 'Naïve Bayes (NB)'. it was seen that the best accuracy was given by RF - of approximately 84.25%. © 2022 iEEE

关键词： Crime

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning 20

Exploiting Partial Observability and Optimized Simple State ...

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20th international Conference on Frontiers of information Technology, FiT 2023

作者： Mahmood, Danyaal Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Applications & Nanostructure BCMaterials Basque Center for Materials Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-depth exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent's performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent's decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game. © 2023 iEEE.

关键词： Autonomous agents

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Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

Prediction of Polycystic Ovary Syndrome Using Genetic Algori...

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2023 international Conference on iT and industrial Technologies, iCiT 2023

作者： Faridoon, Fatima Ali, Raja Hashim Ul Abideen, Zain Shahzadi, Nazia ijaz, Ali Zeeshan Arshad, Usama Ali, Nisar imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. it is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. in this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis. © 2023 iEEE.

关键词： Feature Selection

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Efficient Time-Delay System Optimization with Auto-Configured Metaheuristics

Efficient Time-Delay System Optimization with Auto-Configure...

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2023 iEEE international Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Senkerik, Roman Byrski, Aleksander Guzowski, Hubert Janku, Peter Kadavy, Tomas Oplatkova, Zuzana Kominkova Matusu, Radek Pluhacek, Michal Pekar, Libor Smolka, Maciej Viktorin, Adam Tomas Bata University A.I. Lab Faculty of Applied Informatics Department of Informatics and Artificial Intelligence Zlin Czech Republic Tomas Bata University Faculty of Applied Informatics Department of Automation and Control Engineering Zlin Czech Republic Institute of Computer Science Agh University of Science and Technology Krakow Poland

ISBN: (纸本)9798350337020

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DiSH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics. © 2023 iEEE.

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System for Table Run Strategy in Peruvian Billiards with Real-Time Oject Detection using YOLOv8n

System for Table Run Strategy in Peruvian Billiards with Rea...

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international Conference on Optimization and Applications (iCOA)

作者： italo Silva Raquel Chavez José Luis Castillo-Sequera Lenis Wong Department of Software Engineering Peruvian University of Applied Sciences Lima Peru Department of Computer Science University of Alcalá Alcalá de Henares Spain

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

The main challenge for billiard players, regardless of their skill level, is to develop a strategy that allows them to consecutively pocket balls in a single turn, commonly known as “closing the table.” This strategy requires high precision and consistency, especially for less experienced players. This study integrates a YOLOv8n model, a shot suggestion algorithm, and a shot prediction algorithm to build a system that aids players in developing this “table closing” strategy through visual guides indicating the shots to take and where to position the cue ball for the next shot. The proposed system follows these phases: (1) Selection of the video camera, (2) Selection of the object detection technique, (3) Development of the shot suggestion algorithm, (4) Development of the shot prediction algorithm, and (5) System construction. Phases (1) and (2) address the implementation of the YOLOv8n model, while phases (3) and (4) involve the development of algorithms for the “table closing” strategy. To validate the system's effectiveness, an experiment with 24 players of different skill levels was conducted, showing that the system reduced the average number of shots needed to close a table to 22, compared to the 47-shot average of professional players. Beginners accepted 100% of the system's recommendations, while intermediate and professional players showed acceptance rates of 95.1% and 95.6%, respectively. A survey indicated high overall satisfaction, with a median response of 4.5 on a 5-point scale. The system also demonstrates significant competitive advantages over existing solutions by maintaining high detection and prediction accuracy while effectively functioning in real-world scenarios, leveraging the latest advances in computer vision to surpass older techniques in both effectiveness and efficiency.

关键词： Surveys Accuracy Object detection Games Streaming media Prediction algorithms Cameras Real-time systems Object recognition Usability

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A new data label conversion algorithm for YOLO segmentation of medical images

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The European Physical Journal Special Topics 2024年 1-10页

作者： Telceken, Muhammed Okuyar, Mehmet Akgun, Devrim Kacar, Sezgin Vural, Mehmet Sait Institute of Natural Sciences Sakarya University Sakarya Türkiye Computer Engineering Sakarya University of Applied Sciences Sakarya Türkiye Electrical and Electronics Engineering Sakarya University of Applied Sciences Sakarya Türkiye Software Engineering Sakarya University Sakarya Türkiye Computer Engineering Gaziantep Islam Science And Technology University Gaziantep Türkiye

Object detection and segmentation architectures like YOLO have increased accuracy and speed, making them more practical in medical applications, including polyp identification for medical imaging and diagnosis. However, accurately defining segmentation boundaries to train a YOLO model for medical diagnosis requires considerable manual effort. This study developed a new method for automatically generating labels from ground-truth masks to process datasets for training YOLO-based segmentation algorithms. The developed method offers a simple interface for users to readily sample segmentation masks in datasets into labels compatible with the YOLO format. The technique involves performing contour extraction while processing labeled masks to ensure that the resulting contour information represents the object boundaries and makes it possible to map them according to the YOLO format. The efficiency of the method has been shown via training YOLOv5, YOLOv7, and YOLOv8-based models. Comparative results on the polyp segmentation dataset show successful improvements using YOLO-based segmentation over the literature.

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