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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Eke, Christopher Ifeanyi Olamide, Fawaz Ibrahim Phiri, Musa Mwenge, Mulenga Faith Fatokun, B. Shuib, Liyana Federal University of Lafia Faculty of Computing Department of Computer Science Nasarawa State Nigeria Mulungushi University Department of Computer Science and Information Technology Kabwe Zambia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia University of Malaya Faculty of Computer Science & It Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798331542573

Information security remains one of the major challenges faced by organizations and individuals in the current technological era. With the growing popularity of smart devices, the frequency of cyber-attacks targeted at these devices are increasing at a rapid pace. Among these types of attacks on information systems is malicious software also known as malware. A cyber-attack based on malware has the potential to cause loss or manipulation of confidential information. Currently, malware creators who use polymorphic techniques to evade detection can bypass traditional signature-based methods of detecting malware. Thus, various researchers have turned to Machine Learning (ML) techniques to enhance the detection of malware. However, despite the promising results, ML-based classification of malware remains a challenging task that the research community is yet to solve. Therefore, in this study, we propose an ML-based malware detection system that incorporates the AdaBoost, Random Forest, Decision Tree, and K-Nearest Neighbors Algorithm (KNN) algorithms into an ensemble stack with gradient boost as the ensemble boost for the stack meta estimator. We also use Extra Trees Classifier for feature selection. Multiple performance measures are utilized to assess the system's performance while using the Kaggle Malware Dataset. Our findings demonstrate that the proposed method successfully detects malware samples with remarkable accuracy, precision, recall, F1, and area under the curve (AUC) scores of 99.4%, 99.3%, 99.4%, 99.4%, and 99.6%, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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AI in Sustainability:Assessing its Functions and Environmental Impact 10

AI in Sustainability:Assessing its Functions and Environment...

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10th IEEE Smart World Congress, SWC 2024

作者： Alhasan, Khawla Chen, Liming The Arab Open University Faculty of Business Studies Kuwait Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798331520861

Artificial Intelligence (AI) has fundamentally transformed various industries, including healthcare, transportation, agriculture, energy, and media. However, while AI's impact is widely recognized, its sustainability remains a critical concern due to significant computational and environmental costs, as well as ethical issues such as biased systems and misinformation. This paper explores the dual dimensions of AI's impact on sustainability: its potential to promote sustainability across sectors and the sustainability of AI systems themselves. Using a comprehensive review of existing literature, we address both the benefits and challenges associated with AI in achieving sustainable goals and the need for a more integrated approach to understanding its environmental, social, and economic impacts. Our analysis of the 'AI Global Index' dataset, which evaluates AI capabilities across 62 countries, highlights key trends and disparities in AI development. The findings underscore the need for a holistic approach to AI sustainability that encompasses its full range of effects and interactions with various dimensions of sustainability. © 2024 IEEE.

关键词： Sustainable agriculture

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Arduino-Based Gas Leakage Monitoring and Treatment System 1

Arduino-Based Gas Leakage Monitoring and Treatment System

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Muhammad, Faiq Rafi Putra, Alfa Satya Aribowo, Arnold Pelita Harapan University Faculty Of Computer Science Computer Systems Department Tangerang Indonesia Pelita Harapan University Faculty Of Computer Science Information Systems Department Tangerang Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

The use of gas in daily life is very important, especially in household life where it has become a major need. There are risks related to gas usage to the user and the community, such as gas leakage, which can be fatal and can cause fire to break out. Therefore, a system is needed to monitor and safely handle gas leakage. This paper discusses a gas leak monitoring and treatment system that uses gas sensor to detect gas concentration level, solenoid valve to open and close the gas valve, LCD to monitor and display gas concentration level, an exhaust fan to dispose gas from the room, buzzer as warning alarm, push-button switch to manually open gas valve after it has been closed, and Cayenne platform to send email notification to the user when gas leakage is detected. These devices are controlled by an Arduino Mega microcontroller board. From the system design and testing results, it can be concluded that the Arduino-based gas leakage monitoring and treatment system is working as expected in safely handling gas leakages. The success rate of gas sensor, solenoid valve, buzzer, push-button, and exhaust fan testing is 100%, and the success rate of email notification testing is 80%. © 2022 IEEE.

关键词： Solenoid valves

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Analysis and Performance Evaluation of Various Shortest Path Algorithms 3

Analysis and Performance Evaluation of Various Shortest Path...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Aliyan, Mohammad Hasan, Muhammad Zulkifl Qayoom, Hammad Hussain, Muhammad Zunnurain Nosheen, Summaira Mustafa, Muzzamil Chuhan, Saad Hussain Atif Yaqub, Muhammad Awan, Rimsha Bilal, Afshan University of Central Punjab Department of Computer Science Lahore Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan Bahria University Department of Computer Science Lahore Campus Lahore Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350381931

Extensive study has been conducted on the problem of finding the shortest path in a graph, leading to the creation of several approaches. The effectiveness of the following algorithms in finding the shortest path is compared and contrasted in this study: Iterative Deepening A∗ (IDA∗), Uniform Cost Search (UCS), Breadth-First Search (BFS), Depth-First Search (DFS), Dijkstra's, and Bellman-Ford. Due to its consideration of more complex data structures and heuristics, the A∗ algorithm performs better than the Greedy method. Dijkstra's approach may be used to find the shortest route in weighted graphs in an optimum and efficient manner. The Bellman-Ford approach, which handles negative edge weights, is also taken into account in the study. To find the shortest paths, we explore the potential of the graph traversal algorithms BFS and DFS. Whereas IDA∗ efficiently determines the optimal solution by fusing DFS with heuristic approximations, UCS expands Dijkstra's method to incorporate edge cost information. By comparing them, the study aims to provide insight into the capabilities and suitability of these algorithms in different scenarios. The best approach depends on several factors, including the graph's characteristics, the presence of negative weights, and the availability of heuristic information. Every approach has benefits and drawbacks that determine which use cases are best suited for it. In summary, a variety of techniques for locating the shortest path in a graph are offered by the IDA∗, BFS, DFS, Bellman-Ford, Dijkstra, and A∗ algorithms. When selecting a technique, it is important to take into account the unique requirements and constraints of the present problem. © 2024 IEEE.

关键词： Iterative methods

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Secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks

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Alexandria Engineering Journal 2025年 128卷 127-143页

作者： Islam, Shayla Topazal, S.M. Hasan, Mohammad Kamrul Kolandaisamy, Raenu AL Abbas, Huda Saleh Vo, Nguyen Thakkar, Rahul Khan, Muhammad Attique Gupta, Deepak AlHammadi, Dina Abdulaziz Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology the National University of Malaysia Selangor Bangi43600 Malaysia Department of computer science College of Computer Science and Engineering Taibah University Madina Saudi Arabia Victorian Institute of Technology Melbourne Australia Center of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Maharaja Agrasen Institute of Technology Delhi India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia

The emergence of 5 G and 6 G networks promises significant advancements in the Internet of Medical Things (IoMT) by enabling real-time medical data transmission and continuous connectivity for mobile healthcare devices. However, traditional Handover (HO) mechanisms face latency, security, energy consumption, and scalability challenges when supporting fast-moving and dense IoMT environments. This paper presents a secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks, incorporating a predictive algorithm, Blockchain-Based (BC-B) decentralized authentication mechanism, and AI-driven anomaly detection. The predictive algorithm anticipates device movement and pre-allocates resources at the target Base Station (BS), reducing HO latency by 20–30 % in 5 G and up to 40 % in 6 G. Meanwhile, blockchain-based authentication ensures tamper-proof and decentralized security, reducing vulnerability to cyberattacks by 90 % compared to traditional methods. Additionally, the energy consumption during HO events is reduced by 20–35 %, dropping from 15 mJ in traditional schemes to 12 mJ in 5 G and further to 10 mJ in 6 G. The scheme also incorporates AI-driven anomaly detection, achieving an accuracy of 96.7 %, ensuring proactive identification and mitigation of potential security threats. The analyzed scheme maintains consistent throughput with less than 5 % reduction and scales effectively for up to 300 devices per cell. The simulation results demonstrate significant improvements across key performance metrics, including latency, security, energy efficiency, and scalability, making this approach highly suitable for critical IoMT applications such as telesurgery, remote patient monitoring, and innovative healthcare environments. These advancements position the analyzed framework as a cornerstone for next-generation (NG) healthcare networks. © 2025 The Authors

关键词： Remote patient monitoring

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Introduction to the Minitrack on Learning Analytics 2023

Proceedings of the Annual Hawaii International Conference on...

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Proceedings of the Annual Hawaii International Conference on System sciences 2023年 2023-January卷 1269-1270页

作者： Willermark, Sara Islind, Anna Sigridur Deeva, Galina Óskarsdóttir, Mari University West School of Business Economics and IT Sweden Reykjavik University Department of Computer Science Iceland KU Leuven Research Centre for Information Systems Engineering Belgium

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A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

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computers, Materials & Continua 2023年第4期75卷 165-182页

作者： Muhammad Azhar Sehat Ullah Khalil Ullah Habib Shah Abdallah Namoun Khaliq Ur Rahman Department of Computer Science&IT University of MalakandChakdara18800Pakistan Department of Software Engineering University of MalakandChakdara18800Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Statistics Abdul Wali Khan UniversityMardan23200Pakistan

Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time *** (50) volunteers of different ages participated in the experimental *** the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.

关键词： Age estimation gait biometrics classical linear regression model

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Customer Churn Prediction Based on Multiscale Attention and Convolutional Neural Networks 4

Customer Churn Prediction Based on Multiscale Attention and ...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Yang, Chaojie Xia, Guoen Zhang, Xianquan College of Computer Science and Engineering Guangxi Normal University Guilin China Faculty of Business Administration Guangxi University Nanning China

ISBN: (纸本)9798331541729

Most of the feature extraction methods for telecommunication customer churn research tend to lose important information from other scales by stacking the single-scale receptive field information, which leads to limited prediction effect. To address this problem, this paper proposes a customer churn risk prediction model based on a multi-scale convolutional attention module-convolutional neural network. Firstly, the features are reshaped into a form similar to grey scale images, thus allowing the 2D convolutional neural network to extract local spatial features, and using residual blocks to enhance the information flow of the deep network. Based on this, a multi-scale convolutional attention module is designed in this paper to effectively capture multi-scale feature information and enhance feature diversity from different domains, while adaptively learning the weights of each feature channel and feature map pixel point. Ten-fold cross-validation experiments are conducted on two publicly available benchmark datasets, and in order to enhance the recognition ability of the proposed model for minority class instances, SMOTENN is introduced to over- and under-sample the telecom datasets used, and the results show that the proposed model outperforms the comparison model in terms of the scores on Accuracy, Precision, Recall, and F1-score, providing a better decision-making reference for telecom service providers to identify churn. © 2024 IEEE.

关键词： Convolutional neural networks

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Bioacoustics Signal Authentication for E-Medical Records Using Blockchain

Bioacoustics Signal Authentication for E-Medical Records Usi...

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2024 International Conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Sowjanya, S. Reddy, Induru Sunnihith Muralikrishna, Cheruku Prasad, Thota Sai Lalith Reddy, Pundru Chandra Shaker Sharma, Vikas Vardhaman College of Engineering Computer Science and Engineering Telangana Hyderabad India Rmk Engineering College Computer Science and Business Systems Tamilnadu Tiruvallur India Telangana Hyderabad India Teegala Krishna Reddy Engineering College Computer Science and Engineering Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Iimt University School of Computer Science & Applications Uttar Pradesh Meerut India

ISBN: (纸本)9798350359688

Many are wary of storing and processing data in the cloud because of the prevalence of hostile assaults on mobile and wireless communication networks, which raises serious privacy and security concerns. Using Blockchain as an example, this article investigates the feasibility of developing a trustworthy decentralized authentication system. Utilizing a blockchain technique to enhance the heftiness of multiple data checks and an optimized number of secured features from the bioacoustics signal, in place of traditional biometric features, ensure high security for the bioacoustics signal authentication mechanism. Verified authentication and monitoring of terminal activities are both made possible by it. Then, to provide security at every terminal and edge node, lightweight cryptography (LWC) is created. Lastly, the bastion of the catching approach is the belief-propagation(BP) strategy, which retrains the bioacoustics signal's attributes. The hit ratio is improved and the delay time is decreased. With a blockchain paradigm for data openness, the investigational setup utilizes bioacoustics signals for authentication instead of standard biometric features, and the efficiency of numerous checks is improved. Both security and privacy are enhanced when this occurs. The association between MFCC and LPCC was 0.9517. © 2024 IEEE.

关键词： Electronic health record

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Towards Leveraging Explainable Artificial Intelligent (XAI) in Requirements Engineering (RE) to Identify Aspect (Crosscutting Concern): A Systematic Literature Review (SLR) and Bibliometric Analysis 11

Towards Leveraging Explainable Artificial Intelligent (XAI) ...

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11th International Conference on Information Technology, ICIT 2023

作者： Magableh, Aws A. Yarmouk University Faculty of Computer Science and Information Technology Department of Information Systems Irbid Jordan

ISBN: (纸本)9798350320060

The software development life cycle contains many phases, among the most notable ones is requirements engineering (RE), it is considered a very important activity because poorly implemented RE steps can result in poor software quality which might lead to expensive maintenance costs and so many other undesirable issues. Thus, owing to the importance of RE in general and aspect (crosscutting concerns) identification in particular, researchers have proposed many approaches that can help to produce a clear and complete set of requirements document. On the other hand, artificial intelligence methods have a proven record of handling complicated text. Yet there is insufficient understanding of how AI and explainable AI can be employed and incorporated to be utilized in RE. Nevertheless, join forces to produce research for beneficial outcomes in this theme. The present study is a comprehensive SLR that reviewed the current state-of-the-art of AI/XAL in RE and aspect orientation. We reviewed the research from 2007 till 2022, PRISMA methodology is utilized to obtain a final set of (n =23) articles from four major scientific databases (Scopus, and Web of science). Clear and new taxonomies were invented during the review discussion. Additionally, to enhance the contribution of this research, a bibliometric comprehensive analysis was conducted on the basis of annual scientific production, country scientific production, and co-occurrence. As a result, it is deemed a noteworthy topic of extensive exploration especially after we identify critical research gaps with their corresponding solutions and generic thoughts to serve as a guide for future directions. © 2023 IEEE.

关键词： Requirements engineering

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