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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Rahayu, Anita Zamahsari, Gamal Kusuma Adi, Puput Dani Prasetyo Arifuddin, Rahman Dirgantara, Wahyu Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Digital Language Learning Center Communication Science Department Jakarta11480 Indonesia Research Center for Telecommunication Bandung Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350378573

Augmented Reality (AR) is rapidly transforming educational landscapes, offering an immersive and interactive learning experience. This literature mapping review explores the current landscape of AR in higher education research. By systematically analyzing peer-reviewed publications in the last five years from 2019 to 2023, the review identifies key trends, application areas, author collaboration patterns, and research foci, were extracted, analyzed, and visualized using R and VOSViewer. The analysis reveals a growing body of research, with a surge in publications after 2020. science, Technology, engineering, and Mathematics (STEM) subjects dominate AR applications, highlighting its potential to visualize abstract concepts. The review further investigates the impact of AR on student outcomes, including engagement and learning processes. It also examines the geographical distribution of research and identifies knowledge gaps for future exploration. Overall, this review provides a comprehensive map of AR in higher education research, laying the groundwork for further investigation into its pedagogical effectiveness and broader implementation across disciplines. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid Approach to Optimal Power Allocation in MIMO-NOMA: Integrating DGNN and Tyrannosaurus Optimization 4

A Hybrid Approach to Optimal Power Allocation in MIMO-NOMA: ...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Agrawal, Anurag Vijay Anuradha, A. Patil, Kranti D. Patil, Vilaskumar Karthikumar, K. Tallapragada, S. V. V. Satyanarayana Bhagwant Institute of Technology Department of Electronics and Communication Engineering Uttar Pradesh Muzaffarnagar251315 India Sri Aditya Degree College Department of Computer Science Andhra Pradesh Bhimavaram534202 India Trinity Academy of Engineering Ghrcem Department of E & Tc Maharashtra Pune411048 India Sharnbasva University Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Kalaburagi585103 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronic Engineering Avadi Tamil Nadu Chennai600062 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798331540364

One of the primary technologies necessary for the upcoming sixth generation of communication networks (6G) is massive multi-input multi-output, or MIMO. However, enormous MIMO presents a challenge in reducing energy use and the sum rate demand due to the power use of the several radio frequencies (RF) links in 6G technology. Therefore, this research presented a novel Deformable Graph Neural Network (DGNN) with Tyrannosaurus optimization for the optimal power distribution that maximized the sum rate and energy efficiency of the MIMO NOMA system. The adaptive mechanism of DGNN enables more accurate and efficient power allocation decisions. The integrated Tyrannosaurus optimization ensures the model converging on optimal power allocation strategies that maximize both sum rate and energy efficiency. The efficiency of the recommended model is compared in regarding sum rate and Energy Efficiency (EE) with the some of the recent approaches. The proposed method gained the greater sum rate and EE outperforming all other compared models. © 2024 IEEE.

关键词： Radio links

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A Comparative Analysis of SMS Spam Detection employing Machine Learning Methods 6

A Comparative Analysis of SMS Spam Detection employing Machi...

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6th International Conference on Computing Methodologies and communication, ICCMC 2022

作者： Aliza, Humaira Yasmin Nagary, Kazi Aahala Ahmed, Eshtiak Puspita, Kazi Mumtahina Rimi, Khadiza Akter Khater, Ankit Faisal, Fahad Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Tampere University Faculty of Information Technology and Communication Sciences Tampere Finland Jadavpur University Department of Computer Science and Engineering India

ISBN: (纸本)9781665410281

In recent times, the increment of mobile phone usage has resulted in a huge number of spam messages. Spammers continuously apply more and more new tricks that cause managing or preventing spam messages a challenging task. The aim of this study is to detect spam message to prevent different cybercrimes as spam messages have become a security threat nowadays. In this paper, studies on SMS spam problems to perform a better accuracy using several different techniques such as Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Random Forest, Logistic Regression and some more are performed. The result indicated that Support Vector Machine achieved the highest accuracy of 99%, indicating it might be useful as an effective machine learning system for future research. © 2022 IEEE.

关键词： Support vector machines

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Indoor Electromagnetic Radiation Intensity Relationship to Total Energy of Household Appliances

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computers, Materials & Continua 2022年第3期70卷 5421-5435页

作者： Murad A.A.Almekhlafi Lamia Osman Widaa Fahd N.Al-Wesabi Mohammad Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The rapid technological developments in the modern era have led to increased electrical equipment in our daily lives,work,and *** this standpoint,the main objective of this study is to evaluate the potential relationship between the intensity of electromagnetic radiation and the total energy of household appliances in the living environment within the building by measuring and analyzing the strength of the electric field and the entire electromagnetic radiation flux density of electrical devices operating at frequencies(5 Hz to 1 kHz).The living room was chosen as a center for measurement at 15 homes in three different environmental regions(urban,suburbs,and open areas).The three measurement methods are(Mode 1:people in a sitting position with electrical appliances *** 2:People in a standing position with electrical appliances *** 3:People are in the upright positionwhile turning off the electrical devices)in the living *** measurement methods and their results reinforce the importance of this *** results showed that the average electric field strengthmeasured inMode 2 ismuch greater than the two methods,and we also found less electromagnetic radiation in Mode 3 than in the two *** results remain within the recommended overall exposure developed by the International Committee for the Prevention of Non-Ionizing Radiation and the International Electrotechnical Commission.

关键词： Exposure assessment electromagnetic field household appliances indoor various environments epidemiology

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Automated User Experience (UX) Testing for Mobile Application: Strengths and Limitations

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第4期16卷 30-45页

作者： Abuaddous, Hayfa. Y. Saleh, Ashraf Mousa Enaizan, Odai Ghabban, Fahad Al-Badareen, Anas Bassam Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Department of Management Information System University of Tabuk Tabuk Saudi Arabia College of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Software Engineering Aqaba University of Technology Aqaba Jordan

User Experience (UX) evaluation has a significant importance for any interactive application. Mobile device applications have additional limitations to convey good user experiences (UX) due to the usage and features of mobile devices in ubiquitous computing contexts. There are a lot of automated tools available for evaluating, such applications particularly developed for Android and iOS. Automated UX evaluation provides a number of strengths which are able to overcome the limitations of non-automated (moderated) counterparts. Automated UX evaluation is regarded as unexplored field, especially when it is adopted for the UX evaluation of complex mobile application. Despite these, automated UX evaluation tools are unexplored and should only be used in coupling with traditional UX evaluation techniques. Hence, this study aims to discuss the literature in automated UX evaluation and to highlight the UX measurements that merit further investigation. This paper presents the most fre-quently used automated UX evaluation tools for mobile applications. Therefore, the study analyzes existing automated UX tools and frameworks and identifies which UX attributes and measurements of automated UX evaluation are likely to be of use in future research. Moreover, we discuss the strengths and limitations of automated UX evaluation methods for mobile applications. Finally, the paper provides an overview of automated UX evaluation techniques and tools. It also presents the strengths and limitations for comparing various evaluation methods. © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Automation

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MotionNeXt: A Deep Learning Neural Network for Recognizing Human Motions Related Activities Using Inertial Sensors 23

MotionNeXt: A Deep Learning Neural Network for Recognizing H...

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23rd International Symposium on communications and Information Technologies, ISCIT 2024

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350353952

Recognition of human activities using wearable sensors is essential for applications related to health and well-being. However, the complex data captured by inertial sensors poses challenges in accurately identifying actions. This research introduces MotionNeXt, a residual deep neural network incorporating aggregated transformation. MotionNeXt utilizes residual and multi-kernel blocks to extract spatial and temporal characteristics from raw IMU data. Subsequently, it employs global average pooling, a fully connected layer, and softmax for classification purposes. An attention layer enables the model to focus on specific segments of the input sequence while categorizing each time step. MotionNeXt is evaluated using a publicly available dataset of activities collected from wearable inertial measurement devices. It achieves state-of-the-art accuracy, boasting an F1-score of 99.37%, surpassing previous deep learning methods by 2- 5%. By integrating accelerometer, gyroscope, and magnetometer modalities, MotionNeXt mitigates the limitations of individual inputs, significantly improving accuracy across various models. © 2024 IEEE.

关键词： Deep neural networks

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Exploring the Benefits of Deep Learning for Forecasting Time Series Data 3

Exploring the Benefits of Deep Learning for Forecasting Time...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Daivagna, Umesh.M. Vinay Kumar, S.B. Agarwal, Ankita Jha, Gaurav Sharmila, P. Dodia, Shubham J. Atlas SkillTech University Department of UGDX Maharastra Mumbai India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

Deep present day is swiftly emerging as an effective device for forecasting time series records. Current advances in deep mastering fashions permit computerized characteristic extraction and trendy contemporary complex nonlinear dynamic relationships among inputs and outputs. The ability to symbolize high-level temporal and temporal-spatial systems of modern-day multivariate time collection facts gives extended accuracy and reliability in forecasting. Moreover, recent strategies skip entering facts via temporal convolutional neural networks to forecast destiny values in present-day time-collection statistics. This article delves into the use of deep learning for forecasting time series data. Deep learning methods, specifically recurrent neural networks, are found to outperform traditional forecasting techniques in accuracy and efficiency. The ability of deep learning to automatically extract patterns and relationships from large amounts of data makes it a valuable tool for forecasting in various industries such as finance, healthcare, and energy. Additionally, the integration of deep learning with other techniques, such as feature selection and data preprocessing, can further improve its forecasting capabilities. Overall, the benefits of using deep learning for time series forecasting make it a promising approach for decision-making and strategic planning in a rapidly changing world. It has enabled the improvement of brand-new state-of-the-art deep ultra-modern models that could seize the dynamics of contemporary statistics sequences without counting on any prior expertise, resulting in correct forecasts. This paper evaluation the modern deep, brand-new techniques for time collection forecasting and discusses the recent traits in the field. Capability-demanding situations and opportunities within the application of modern deep modern day for time series forecasting are addressed in detail in the paper. © 2023 IEEE.

关键词： convolutional developments forecasting learning techniques

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Enhancing IT Risk Management in XYZ Vocational School: Leveraging COBIT 5 Framework with the APO12 Domain

Enhancing IT Risk Management in XYZ Vocational School: Lever...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Winanti Fernando, Erick Gate, Jullend Universitas Insan Pembangunan Department of Information Systems Indonesia Universitas Multimedia Nusantara Information System Faculty of Engineering and Informatics Tangerang Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350372229

This article explores the importance of IT risk management, especially at XYZ Vocational School. The agency is equipped to manage business risks, emphasizing the need to improve decision-making and risk mitigation by identifying potential hazards and evaluating internal and external threats, including hacking risks. The research investigates IT risk management at XYZ Vocational School using a qualitative and quantitative method with a focus on the APO12 COBIT 5 domain: Align, plan, and organize. It assesses subdomains such as data collection, risk analysis, profile maintenance, risk articulation, portfolio management, and response strategies. Conclusively, the research reveals institutional IT capabilities at level 1 (Performed Process) in the APO12 subdomain. Recommendations include developing COBIT-compliant SOPs for processes at this level, highlighting the need for further improvements in IT governance. This study provides valuable insights into IT risk management practices, offering recommendations for improving IT governance in educational settings. © 2024 IEEE.

关键词： APO12 Domain COBIT 5 Information technology Risk Management Vocational School

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A Novel and Accurate Method of Diagnosis via AI and ML for Smart HC system 4

A Novel and Accurate Method of Diagnosis via AI and ML for S...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Elizabeth, J. Ruby Emmanuel, Mathews Aswini, E. Pathak, Shriram Bhuvaneswari, M. Choubey, Abhishek Nehru Institute of Engineering and Technology Department of Computer Science and Business System Tamil Nadu T.M.Palayam India Sathyabhama University of Science and Technology chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering India Ies Insititute of Technology and Management Ies University Department of Mechanical Engineering M.P. Bhopal India Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore India Sreenidhi Institute of Science and Technology ghatkesar Hyderabad Yamnampet India

ISBN: (纸本)9798350360165

Recent breakthroughs in the internet of Issues (IoT), computing through the cloud, and the use of AI have changed the standard healthcare system, making a case for smart health care. By combining key technologies including IoT and AI, there is a major improvement in medical services. The merging of IoT and AI offers multiple possibilities within the healthcare industry. This study piece presents a new artificial intelligence- and internet of things convergence-based framework for disease detection in smart health care systems, emphasizing especially on cardiovascular diseases and diabetes. The suggested model includes separate steps, including data collection, preparation, classification, and parameter modification. Utilizing Internet of Things devices like smartwatches along with sensors enables smooth collection of information, while AI methods harness this data for the detection of illnesses. The technique presents a new approach, applying to the Crowd Search Optimizer algorithm-based Flowed Long and Short-Term Memory (CSO-CLSTM) approach to disease detection. CSO is applied to improve both 'weights' while 'bias' factors of the CLSTM framework, while the isolation Fg (iForest) method is implemented to remove outliers. Through testing with health care information, the CSO-LSTM model got excellent accuracy ratings of 96.16% when 97.26% in identifying heart disease while diabetes, respectively. Consequently, the suggested CSO-LSTM model appears as a solid and reliable method of disease evaluation in digital healthcare systems, showing its potential for broad application. © 2024 IEEE.

关键词： Electronic health record

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A Framework for Evaluating the Effectiveness of Explainability Methods in Deep Learning

A Framework for Evaluating the Effectiveness of Explainabili...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bani Ahmad, Ahmad Y. A. Sarkar, Prithu Goswami, Brijesh Patil, Priyanka Rajesh Al-Said, Khaleel Al Said, Nidal Middle East University Faculty of Business Department of Accounting and Finance Science Amman11831 Jordan Amity University Amity School of Communication Kolkata India Gla University Institute of Business Management Mathura India Prof Ram Meghe College of Engineering & Management Department of Computer Science and Engineering Badnera India Middle East University Faculty of Arts and Educational Sciences Department of Educational Technology Amman Jordan Ajman University College of Mass Communication Ajman United Arab Emirates

ISBN: (纸本)9798331508685

The Internet of Things (IoT) is a new framework that is transforming societies across the globe into smart towns. Nevertheless, because of IoT systems' ongoing connection and data exchange, their rise has been followed by several cybersecurity problems. In response to this issue, several intrusion detection structures (IDSs) have been developed as safety precautions. These methods have shown effectiveness in thwarting several IoT-based assaults, particularly when deep learning (DL) techniques are employed. While there is a lot to discuss on the topic of eXplainable Artificial Intelligence (XAI) nowadays, more research is still required to completely comprehend how effective XAI is at identifying assault areas and routes when implemented in cyber safety uses. This research provides an innovative, explainable IDS for IoT systems. This study designed an IDS utilizing a Short-Term Long Memory (LSTM) framework to detect intrusions and analyze the system's choices. For training and assessing the LSTM algorithm, an innovative collection of source characteristics is gathered using a new SPIP (S- Shapley Addition ExPlanations, P- Permuting Characteristic Significance, I- Individual Conditioned Expectations, P- Partial Dependent Graph) architecture. When contrasted with existing competitor approaches, the SPIP architecture outperformed them in terms of identification accuracy, processing duration, and comprehension of data characteristics and models. The suggested method could help managers and decision-makers comprehend complicated assault activity. © 2025 IEEE.

关键词： Cybersecurity

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