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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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Incentivizing Recycling Behavior: Repro-A Reward Based CNN-Enabled Framework for Smart Waste Collection

Incentivizing Recycling Behavior: Repro-A Reward Based CNN-E...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Irfan, Haider Arshad, Usama Anwer, Hammad Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Topi Pakistan

ISBN: (纸本)9798350349719

Efficient waste management together with certain measures to promote the recycling process are essential for over-coming global environmental issues. In this study, we describe Repro an app, which is targeted at transforming how we collect waste and recycle by integrating CNN technology and a reward-based system. Users, with the Repro app, take pictures of any trash they discard and rely on the underlying CNN model to identify the type of trash correctly. If a user's trash accumulation reaches beyond a given limit the system sends a notification to the trash collectors via the riders app to direct for a timely waste collection. User tokens are provided to users after trash collection, which can be redeemed within the Repro app for various product items and coupons. Moreover, this motivating approach encourages responsible waste management which goes along with the development of environmental custodianship among users. We describe the technical implementation of the CNN-based framework, assess the performance of the level of waste classification accuracy, and predict the behavioral implications of the reward system on recycling behavior. The study illustrates that Repro is a scalable solution for smarter waste collection and recycling endeavors, consequently creating a more eco-minded and sustainable environment. © 2024 IEEE.

关键词： Waste management

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A Systematic Review of Federated Learning Mechanism for The Internet of Medical Things 1

A Systematic Review of Federated Learning Mechanism for The ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Aravind, P. Jerlin, M Asha Devarajan, Ganesh Gopal Nagarajan, Senthil Murugan School of Computer Science and Engineering Vellore Institute of Technology Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India Medicine University of Luxembourg Faculty of Science Technology Department of Mathematics Luxembourg

ISBN: (纸本)9798350353648

The rapid evolution of healthcare technology, no-tably the integration of the Internet of Medical Things (loMT), has revolutionized patient care, diagnosis, and treatment method-ologies. However, this progress introduces substantial challenges, primarily concerning the security, privacy, and interoperability of sensitive medical data. Federated Learning, a decentralized ma-chine learning approach, emerges as a possible solution to address these difficulties since it allows for collaborative model training without requiring raw patient data exchange. This systematic review explores and evaluates various FL mechanisms tailored for 10MT applications. The review examines existing literature, encompassing the amalgamation of loT and healthcare, the challenges faced in traditional health care systems, and the role of FL in mitigating these challenges. It highlights applications, privacy-preserving techniques, security measures, advantages, limitations, and potential enhancements in utilizing FL within 10MT environments. The review also presents a structured methodology for evaluating FL paradigms, encompassing performance metrics, privacy/security analysis, advantages/limitations assessment, and comparative analysis. Moreover, the paper pro-poses an innovative framework for federated learning in 10MT, detailing its architecture, modules, algorithms, functionalities, ethical considerations, and expected impact. Through a comprehensive analysis and synthesis of existing literature and proposed strategies, this review aims to provide insights for health care stakeholders in selecting and implementing optimal FL strategies for collaborative medical advancements while upholding stringent data privacy and security standards in 10MT. © 2024 IEEE.

关键词： Federated learning

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Review of CMOS Gilbert Cell Mixers for 5G 1

Review of CMOS Gilbert Cell Mixers for 5G

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1st International Conference on Computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Abdelmounim, Sellidj Said, Gaoua Abderrahim, Belalia Rachida, Touhami Yagoub, Mustapha Cherif Eddine USTHB Faculty of Electronics and Computer Science LINS laboratory Algeria School of eletrical engineering and computer science University of Ottawa Ottawa Canada

ISBN: (纸本)9798350351736

The ever-increasing demand for higher and higher 5G wireless communication system performance has a direct impact on the design of highly efficient RF blocks, in which mixers play a key role. In fact, with the exponentially grow of data traffic and the need for high data-rate and low latency, mixers should maintain a high performance while working in challenging conditions. The main requirements for mixers to work properly in 5G wireless networks are related to their conversion gain, noise figure, and linearity. These are indeed the key design elements to consider but the technology to be used as well as the mixer compactness and integration into mobile devices must be carefully evaluated. In this paper, recent CMOS Gilbert-Cell mixer designs for 5G applications are discussed, while highlighting the techniques used to improve their electrical parameters. Some limitations are mentioned as well. © 2024 IEEE.

关键词： CMOS integrated circuits

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Development of Dementia Care Training System Using AR and Large Language Model

Development of Dementia Care Training System Using AR and La...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miyawaki, Tomoya Nishiura, Yuuki Fukuda, Ryouta Nakashima, Kazuto Kurazume, Ryo Graduate School of Information Science and Electrical Engineering Kyushu University Japan Kyushu University Faculty of Engineering Department of Electrical Engineering and Computer Science Japan Kyushu University Faculty of Information Science and Electrical Engineering Japan

ISBN: (纸本)9781665410205

We have developed HEARTS, a dementia care training system using augmented reality based on Humanitude. Humanitude is a multimodal comprehensive care technique for dementia, and has attracted attention as a method to reduce the burden on both caregivers and patients. However, the HEARTS developed so far could not evaluate 'speaking' skills based on the content of conversations among 'seeing,' 'touching,' and 'speaking,' all of which are fundamental skills in Humanitude. Therefore, we attempted a new quantitative evaluation of trainees' 'speaking' skills by estimating the emotional value of conversational content using GPT-4 named HEARTS 5. A survey of caregivers was conducted using the developed system and was well received by the participants. We also developed a HEARTS 5 conversational version based on HEARTS 5, with the addition of GPT-4 conversation generation and Azure Text-to-Speech. © 2024 IEEE.

关键词： Augmented reality

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Early Detection of Cyber-Physical Attacks on Electric Vehicles Fast Charging Stations Using Wavelets and Deep Learning

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 220-231页

作者： Abu-Nassar, Ahmad M. Morsi, Walid G. Electrical Computer and Software Engineering Department Faculty of Engineering and Applied Science OshawaONL1G 0C5 Canada

Transportation electrification plays an important role in the operation of the smart grid through the integration of the electric vehicle fast charging stations (EVFCSs), which allows the electric vehicles to provide regulation services to the grid through the vehicle-to-grid (V2G) concept. However, such an integration makes smart grid assets prone to cyber vulnerability threats. In this paper, a cyber-physical attack detection approach is developed to early detect such attacks. The proposed approach combines the continuous wavelet transform (CWT) and the convolution neural network (CNN) to provide an effective detection technique. The proposed detection approach has undergone rigorous testing that considered 420 realistic operational scenarios. Unlike in previous work, the proposed detection approach was found to be effective in automatically learning the salient features from the data as well as identifying the frequency bands that hold such features and using them in the classification process. Furthermore, this work investigated the cyber-attack detection accuracy using different time resolutions of smart meters. The results have shown that the proposed approach effectively detects cyber-physical attacks with an accuracy of 99.76% and a low computational time of 1.8 seconds. © 2024 IEEE.

关键词： Feature extraction

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A Novel Genetic Algorithm Based Timetable Generator for Optimized University Timetable Solution

A Novel Genetic Algorithm Based Timetable Generator for Opti...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Khan, Ali Hasan Imtiaz, Talha Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Topi Pakistan

ISBN: (纸本)9798350349719

In educational institutes, efficient timetable generation is critical for optimizing resource utilization and fostering a conducive learning environment. Timetable challenges, such as clashes among faculty schedules and lack of real-Time updates, underscore the need for innovative solutions in the field. The problem lies in addressing clashes, transparency issues, and inflexibility in manual scheduling systems, leading to disruptions and impacting both students and faculty. We employed a genetic algorithm, combining one-point crossover and mutation operations, to iteratively evolve and adapt timetables to enhances genetic diversity, addresses scheduling conflicts, and promotes the creation of optimal timetables. The results exhibit dynamic and evolving timetables that prioritize efficiency, clash prevention, and overall optimization. The significance lies in fostering an environment conducive to effective teaching and learning, emphasizing fairness, adaptability, and overall well-being. Our work introduces a genetic algorithm-based methodology that not only addresses existing challenges in educational timetable generation but also significantly contributes to fostering a more efficient, transparent, and adaptable scheduling system. © 2024 IEEE.

关键词： Genetic algorithms

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Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality 40

Reasons for the Superiority of Stochastic Estimators over De...

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40th International Conference on Machine Learning, ICML 2023

作者： Ohayon, Guy Adrai, Theo Elad, Michael Michaeli, Tomer Faculty of Computer Science Technion Haifa Israel Faculty of Electrical and Computer Engineering Technion Haifa Israel

Stochastic restoration algorithms allow to explore the space of solutions that correspond to the degraded input. In this paper we reveal additional fundamental advantages of stochastic methods over deterministic ones, which further motivate their use. First, we prove that any restoration algorithm that attains perfect perceptual quality and whose outputs are consistent with the input must be a posterior sampler, and is thus required to be stochastic. Second, we illustrate that while deterministic restoration algorithms may attain high perceptual quality, this can be achieved only by filling up the space of all possible source images using an extremely sensitive mapping, which makes them highly vulnerable to adversarial attacks. Indeed, we show that enforcing deterministic models to be robust to such attacks profoundly hinders their perceptual quality, while robustifying stochastic models hardly influences their perceptual quality, and improves their output variability. These findings provide a motivation to foster progress in stochastic restoration methods, paving the way to better recovery algorithms. © 2023 Proceedings of Machine Learning Research. All rights reserved.

关键词： Stochastic models

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Towards Seamless Glucose Monitoring: A Smart Non-Invasive System for Diabetes Patients 2

Towards Seamless Glucose Monitoring: A Smart Non-Invasive Sy...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Pawar, Adesh Kelzarkar, Sanket G. Choudhary, Vishal Kumar Daburkar, Ashwini Kumar, Praveen Hore, Rutuja Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Blood Glucose Monitoring levels are essential to the treatment of diabetes and must be done continuously. Patients often don't comply with conventional glucose monitoring techniques since they cause discomfort and are invasive. An Arduino microcontroller and infrared (I.R.) sensor are used in this work to provide a robust, non-invasive glucose monitoring system. I.R. light is transmitted through the skin to measure the quantity of light reflected, and this relationship exists between the amount of light and the amount of glucose in the interstitial fluid. In real-time, the Arduino analyses blood glucose levels by analyzing IR Spectroscopy and using machine learning techniques. The device features wireless connectivity, enabling it to send glucose readings to a mobile application for continuous monitoring, historical data analysis, and customized alerts. Initial trials indicate that this non-invasive system provides accurate glucose measurements comparable to traditional methods, significantly improving user comfort and compliance. This innovative approach promises a pain-free, user-friendly, and cost-effective solution for diabetes management, paving the way for more widespread adoption of continuous glucose monitoring. © 2024 IEEE.

关键词： Glucose sensors

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Developing a Low-Cost Prosthetic Hand with Real-Time Machine Learning Capabilities 23

Developing a Low-Cost Prosthetic Hand with Real-Time Machine...

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8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Adani, M. Syakhisk Naufal Widasari, Edita Rosana Setiawan, Eko Dept. Computer Engineering Faculty of Computer Science Brawijaya University Indonesia

ISBN: (纸本)9798400708503

The global prevalence of arm amputations, particularly transradial or below-elbow amputations, presents a pressing challenge due to the high costs of commercially available prosthetic hands. This issue is particularly acute in developing countries, where financial constraints hinder access to functional prosthetic solutions. Addressing this gap, this research introduces an innovative approach that merges electromyography (EMG) signal recognition and machine learning to develop an affordable and versatile bionic below-elbow prosthetic hand. The integration of EMG signals for control, coupled with machine learning algorithms, enables accurate classification of hand movements, resulting in enhanced movement precision and a broader range of gestures. This solution overcomes limitations in existing prosthetic hands, offering advanced EMG-based control and comprehensive movement capabilities, empowering users to perform intricate tasks and regain functional autonomy. The study undertakes a comprehensive analysis of the developed system's performance, encompassing hardware, software, and machine learning components. Results show promising accuracy levels for movement classification using the K-Nearest Neighbors (KNN) machine learning method. Execution time variations are observed among subjects and gestures, influenced by factors such as subject sensitivity and gesture complexity. Energy consumption analysis reveals disparities in power usage among different movements, emphasizing the impact of movement complexity on power requirements. A cost analysis demonstrates the affordability of the prototype, positioning it as a cost-effective alternative to existing prosthetic hand options. Comparing this solution to other research and commercial prosthetic hands highlights its uniqueness, particularly its emphasis on affordability. © 2023 Owner/Author.

关键词： Nearest neighbor search

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