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13th IEEE International Conference on Engineering Education, ICEED 2024

作者： Abidin, Nor Afirdaus Zainal Aminuddin, Raihah Buja, Alya Geogiana College of Computing Informatics and Mathematics University Teknologi Mara Melaka Malaysia

ISBN: (纸本)9798350367416

Telehealth, which uses digital and telecommunication technology to provide healthcare services, is now a crucial component of modern healthcare systems. It covers a broad spectrum of uses, including the use of digital communication channels for consultation and support as well as the monitoring of vital signs and chronic illnesses. Effective remote monitoring and care solutions are in more demand as the world's senior population continues to rise. In response, the educational system has created customized curricula and training materials to address the requirements and difficulties related to senior remote care. This abstract examines how the educational system addresses these crucial issues, emphasizing methods and materials meant to offer caregivers and medical professionals the know-how and abilities required to deliver high-quality remote care. It would assist in offering chances for practical clinical experience with remote care and real patients. Thus, through placements and practical experience, learners acquire clinical competence in remote care, making them ready for real-world situations. Interviews from health workers and IT experts will help in planning strategies such as curriculum integration in the technology training and elder care education, develop online courses focused on elderly care and technology or include training on health policy and advocacy to prepare students to influence policies that support remote monitoring and elderly care. © 2024 IEEE.

关键词： Remote patient monitoring

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Investigating the Implementation of the Artificial Bee Colony Using the ArcGIS Environment: Dubai Drone Box Platform Case Study 12

Investigating the Implementation of the Artificial Bee Colon...

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12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Elsalhy, Mariame Talib, Manar Abu Abbas, Sohail Nasir, Qassim College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350350982

Deploying drones in different real-life world applications can enhance user experience and maximize system benefits. The 'Drone Box' project launched by Dubai aims to reduce the response time to traffic accidents and crimes;therefore, identifying optimal locations for these boxes is crucial. We propose a hybrid AI-GIS-based system to localize the drone boxes across Dubai strategically. We formulate the problem as a P-center Facility Location Problem (FLP) and construct a suitable objective function accordingly. Since the dataset needed was not provided, we designed a random dataset generator for testing purposes. We selected the Genetic Algorithm (GA) for benchmarking and investigated the performance of Artificial Bee Colony (ABC) algorithms due to their relevance to FLP. Geographic Information System (GIS) functions were employed to construct the random dataset generator and implement the ABC algorithm, utilizing the additional functionalities GIS offers for handling geographical data. After testing both algorithms, we deduced that the GA without GIS function integration consistently achieved a higher fitness value in a remarkable time, demonstrating its superiority over the ABC Algorithm. © 2024 IEEE.

关键词： Network security

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Campus Courier: An Autonomous Delivery Robot On-Campus

Campus Courier: An Autonomous Delivery Robot On-Campus

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2024 Advances in Science and Engineering Technology International Conferences, ASET 2024

作者： Rihan, Yazen Zein, Ismail Alaraj, Mustafa Soudan, Bassel College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350344134

The demand for efficient and sustainable logistic solutions on university campuses has been constantly increasing. Conventional delivery schemes might result in delayed deliveries and work inefficiencies. Automating deliveries in working environments tackles these issues. We propose Campus Courier, an autonomous delivery robot that is used within a university campus or any spacious facility. The robot will navigate autonomously across the campus avoiding static and dynamic obstacles. campus courier utilizes a Lidar for object detection. This approach provides high precision mapping and navigation around different complex campus terrains. The system integrates robot operating system (ROS), Navigation2 (Nav2), along with different technologies and algorithms, to deliver items autonomously. Integration testing, black/white box testings, simulations, and real-world scenarios will be used to evaluate our system against many use cases and circumstances. Campus Courier aims to increase work productivity, better resources-utilization, and decreasing the carbon footprint during campus logistics. The system will also contribute to the process of automating services and embedding technological solution to the campuses. In the following sections, this paper will cover the technical background of the robot's components. Also, detailed explanations of Campus Courier's design, encompassing its hardware and software components, will be presented. Finally, the initial findings and performance results of Campus Courier in real-world scenarios will be discussed. © 2024 IEEE.

关键词： Integration testing

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Automatic Prompt Generation and Optimization by Leveraging Large Language Models to Enhance Few-Shot Learning in Biomedical Tasks

Automatic Prompt Generation and Optimization by Leveraging L...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Shi, Yiwen Hu, Xiaohua Drexel University College of Computing and Informatics PhiladelphiaPA United States

ISBN: (纸本)9798350362480

Recent advancements in scaling large language models (LLMs) have enhanced various natural language processing (NLP) tasks. However, open-source moderately sized models, such as BERT, are still needed because of the high computational cost and concerns regarding data privacy from the LLMs, especially in the biomedical area. Prompt-based fine-tuning of BERT has demonstrated good performance in a few-shot setting. However, the prompt selection can result in substantial differences in final accuracy. This study introduces a simple yet effective approach that leverages LLMs, such as GPT-4 Turbo, to automatically generate and optimize task-specific prompts for BERT. Our approach includes two steps: automatic prompt generation and optimization. Initially, we design a framework to generate prompts for LLMs to infer a task-specific candidate prompt set. Subsequently, we employ a dialog with a chatbot to optimize the prompt iteratively. We conduct extensive evaluations and analyses on three different types of biomedical benchmarks. Our method demonstrates superior 5-shot learning performance, outperforming manual prompts by a substantial margin in low-resource settings, achieving up to a 7% absolute accuracy improvement. These results highlight that our method is a task-agnostic approach to utilizing LLMs and automatically enhancing performance on relatively small open-source models with limited resources and human effort. © 2024 IEEE.

关键词： Data privacy

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Identifying Key Mental Health Topic on Youtube Comments using Non-negative Matrix Factorization 22

Identifying Key Mental Health Topic on Youtube Comments usin...

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22nd IEEE Student Conference on Research and Development, SCOReD 2024

作者： Nazaruddin, Atikah Binti Teo, Noor Hasimah Ibrahim Aminordin, Azmi Bin Universiti Teknologi MARA College of Computing Informatics and Mathematics Melaka Malaysia

ISBN: (纸本)9798331510077

This study addresses the challenge of identifying key mental health topics within YouTube comments, a resource-rich yet underutilized data source for mental health discourse analysis. The research employed Non-Negative Matrix Factorization (NMF) for topic modeling, coupled with sentiment analysis to map out the landscape of mental health discussions. The methodology pivoted around the data science lifecycle, ensuring systematic data collection, preparation, and modeling. A notable deliverable was a dynamic dashboard visualizing the findings through various graphical representations, including bar graphs, pie chart, donut chart and word clouds. The analysis unearthed prevalent themes and sentiments, offering granular insights into public discourse on mental health. The implications of this study are twofold: informing mental health providers about emerging trends and sentiments for more tailored intervention strategies and assisting content creators and platform managers in shaping more impactful communication strategies around mental health topics. The study's findings underscore the value of machine learning techniques, particularly NMF and sentiment analysis, in gleaning actionable insights from large-scale, unstructured social media datasets, thereby advancing the conversation around mental health awareness and support. © 2024 IEEE.

关键词： Non-negative matrix factorization

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WESER: Wav2Vec 2.0 Enhanced Speech Emotion Recognizer 5th

WESER: Wav2Vec 2.0 Enhanced Speech Emotion Recognizer

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Matraf, Ahmed Ba Elnagar, Ashraf College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9789819760350

Emotion recognition is essential in human interactions, yet it is challenging due to cultural, linguistic, and individual differences. Advancements in deep learning technologies have effectively addressed these complexities. This paper proposes the WESER model, utilizing the Wav2Vec 2.0 model for Speech Emotion Recognition (SER) on RAVDESS and EMODB datasets. The XLSR variant of Wav2Vec 2.0 is pre-trained on a large collection of linguistic data, making it ideal for tasks with limited datasets. In our methodology, we first standardized audio samples for consistency and then performed fine-tuning. The results were notable, with WESER achieving a speaker-independent (SI) accuracy of 89.24% on RAVDESS and 97.20% on EMODB. However, the speaker-dependent (SD) accuracy was lower, particularly on RAVDESS at 73.33%, highlighting the challenges of adapting to individual speech patterns. The model’s efficiency is validated against existing models, demonstrating significant improvements in emotion recognition from speech. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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Statistical Analysis of CPU Power Consumption for Detecting DGA Botnet Command and Control Communication

Statistical Analysis of CPU Power Consumption for Detecting ...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Ibrahim, Zul-Azri Ismail, Saiful Adli Rahim, Fiza Abdul Abas, Muhammad Hazim bin Khairul Anuar, Muhammad Idris bin Azwan, Aiman Harith Bin Institute of Informatics and Computing in Energy College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia Faculty Artificial Intelligence Universiti Teknologi Malaysia Wilayah Persekutuan Kuala Lumpur Kuala Lumpur Malaysia Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia

ISBN: (纸本)9798331530303

Domain Generation Algorithms (DGA) Botnets represent a significant threat to network security due to their ability to mask Command and Control (C&C) communications with botmaster. Traditional detection methods like domain blacklists have struggle to keep pace with the dynamic nature of these botnets. This research explores an alternative detection approach by investigating the use of CPU power consumption as an indicator for detecting DGA botnet in Internet of Things (IoT) devices. The power consumption data was collected and analyzed from a testbed comprising infected Raspberry Pi devices with DGA-based botnets. Statistical methods that include Analysis of Variance (ANOVA) and post-hoc tests, were used to determine if significant differences in CPU power consumption exist across the botnet phases. The results show that CPU power consumption is significantly higher during both the C&C and attack phases compared to normal operation. Additionally, the attack phase exhibits greater power consumption variance, suggesting unstable resource utilization. These findings highlight the potential of using power consumption data as a key metric for identifying botnet activities in IoT systems. ©2024 IEEE.

关键词： Bot (Internet)

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Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 849-873页

作者： Saad Alahmari Abdulwhab Alkharashi Department of Computer Science Applied CollegeNorthern Border UniversityArar91431Saudi Arabia Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityRiyadh11673Saudi Arabia

The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital *** the development of IoT devices,huge amounts of information,including users’private data,are *** systems face major security and data privacy challenges owing to their integral features such as scalability,resource constraints,and *** challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data,creating an attractive opportunity for *** address these challenges,artificial intelligence(AI)techniques,such as machine learning(ML)and deep learning(DL),are utilized to build an intrusion detection system(IDS)that helps to secure IoT *** learning(FL)is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked *** delivers an effectual tool to defend user confidentiality,mainly in the field of IoT,where IoT devices often obtain privacy-sensitive personal *** study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence(PEFLID-CSAAI)*** main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT ***,the PEFLIDCSAAI technique involves data preprocessing using Z-score normalization to transformthe input data into a beneficial ***,the PEFLID-CSAAI method uses the Osprey Optimization Algorithm(OOA)for the feature selection(FS)*** the classification of intrusion detection attacks,the Self-Attentive Variational Autoencoder(SA-VAE)technique can be ***,the Chameleon Swarm Algorithm(CSA)is applied for the hyperparameter finetuning process that is involved in the SA-VAE model.A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI *** simulated outcomes demonstrated that the PEFLID-CSAAI

关键词： Federated learning internet of things artificial intelligence chameleon swarm algorithm intrusion detection system healthcare IoT devices

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Recyclable Waste Detection using You Look only Once (YOLOv8): A Preliminary Study 22

Recyclable Waste Detection using You Look only Once (YOLOv8)...

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22nd IEEE Student Conference on Research and Development, SCOReD 2024

作者： Hamzah, Salehah Mangshor, Nur Nabilah Abu Kamarudin, Muhammad Syahmie Ibrahim, Shafaf Fadzil, Ahmad Firdaus Ahmad Universiti Teknologi MARA Merlimau College of Computing Informatics and Mathematics Melaka77300 Malaysia Universiti Teknologi MARA College of Computing Informatics and Mathematics Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798331510077

Urban waste management is increasingly challenged by the proliferation of single-use plastics, packaging, and electronic waste, which contribute to a complex waste stream that is difficult to manage. These materials are often non-biodegradable and pose significant environmental risks. To address these issues, waste management authorities need advanced technologies for waste treatment, such as waste sorting and monitoring recycling rates, to recover valuable materials like metals, plastics, and paper for recycling or reuse. This study investigated the use of the YOLOv8 model for detecting recyclable waste materials, focusing on three categories: plastic bottles, aluminum cans, and paper, using a dataset of 2100 images. The methodology involved five stages: data preparation, YOLOv8 modeling, integration with the MobileNet model, evaluation, and deployment on a mobile platform. The results demonstrated that the YOLOv8 model achieved a mean average precision of approximately 90 % across all categories, with an F1 score exceeding 0.9 for each class. © 2024 IEEE.

关键词： Aluminum containers

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Sales Analytics Dashboard with ARIMA and SARIMA Time Series Model 13

Sales Analytics Dashboard with ARIMA and SARIMA Time Series ...

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13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023

作者： Mohamad, Aini Fatina Jasin, Aisyah Mat Asmat, Aszila Canda, Roger Ismail, Juhaida Soom, Afiqah Bazlla Md Universiti Teknologi MARA Computing Sciences Studies College of Computing Informatics and Media Shah Alam Malaysia Universiti Teknologi MARA Computing Sciences Studies College of Computing Informatics and Media Raub Malaysia Universiti Teknologi MARA Mathematical Sciences Studies College of Computing Informatics and Media Raub Malaysia Universiti Teknologi MARA Computing Sciences Studies College of Computing Informatics and Media Jengka Malaysia

ISBN: (纸本)9798350347319

One of the greatest difficulties that modern companies face is keeping up with technology. The limited options available in Power BI as a dashboard for time series in business forecasting models. Hence, this paper presents the use of ARIMA and SARIMA models to forecast sales for DataCo Global Company's dataset. The results are visualized and compared in a web-based dashboard, which displays forecast graphs, density plots, residual plots, and evaluation metric results for each model. The SARIMA model with parameters of (2,1,1) (0,1,1)12 was found to be the best model based on the smallest error measurement values of AIC and BIC. The dashboard provides an effective overview of the data and presents information in a visual format, making it easier for users to understand the results of the analysis. This approach enables data analysts to quickly assess and test the efficacy of forecasting models and assist executives in making informed decisions. © 2023 IEEE.

关键词： Time series

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