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IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Raja Hashim Ali Muhammad Zeeshan Shabbir Zain Ul Abideen Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Muhammad Abu Bakar Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models.

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Broadband Corrugated Modified Vivaldi Antenna for Microwave based Imaging Applications 24

Broadband Corrugated Modified Vivaldi Antenna for Microwave ...

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24th International Conference on computer and Information technology, ICCIT 2021

作者： Talukder, Md Siam Samsuzzaman, Md Paul, Liton Chandra Masud, Md Abdul Azim, Rezaul Moniruzzaman, Md Patuakhali Science and Technology University Faculty of Computer Science and Engineering Patuakhali Bangladesh Pabna University of Science and Technology Dept. of ElectricalElectronic and Communication Engineering Pabna Bangladesh University of Chittagong Dept. of Physics Chittagong Bangladesh Universiti Kebangsaan Malaysia Dept. of ElectricalElectronic and Systems Engineering Bangi Malaysia

ISBN: (纸本)9781665494359

This paper explored a Broadband Corrugated Modified Vivaldi Antenna for head imaging applications using microwaves. The antenna is made up of a tapered feeding circular slotted patch and a rectangular modified wing shape ground with an electrical dimension of $0.49\lambda\times 0.41\lambda\times 0.009\lambda$ at lower frequency of 1.67 GHz. Front and back radiator are placed on a substrate of FR-4 material having a permittivity of 4.3 and a thickness of 1.5 mm, which are less expensive and more widely accessible. The designed antenna works between 1.67 and 6.37 GHz, allowing the imaging system to function over a large fractional bandwidth of about 116.92 %. With an appropriate impedance matching and a directionally stable radiation characteristics, the antenna has a decent gain of 9 dBi. A 50$\Omega$ microstrip line feeds the antenna. The simulation tools CST was used to evolve and optimize the design. © 2021 IEEE.

关键词： Microstrip antennas

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Smart Happy Device with IoT : For those who suffer anxiety, stress, and depression

Smart Happy Device with IoT : For those who suffer anxiety, ...

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IEEE Region 10 Humanitarian technology Conference (R10-HTC)

作者： Fitri Yakub Azizul Azizan Inge Dhamanti Ahmad Zahran Md. Khudzari Niranjana Govindan Muljono Muljono Electronic System Engineering Department Malaysia-Japan International Institute of Technology UTM Kuala Lumpur Malaysia Cyberphysical Research Group (Embedded AI & ML Science Lab) Faculty of Artificial Intelligence Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Department of Biosciences & Medical Engineering Faculty of Engineering UTM Johor Bahru Malaysia Department of Computing Technologies Faculty of Engineering & Technology SRMIST Chennai India Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350340204

ISBN: (纸本)9798350340211

Due to the rising cases of Covid-19 in the world especially in Malaysia, the impact had been hit hard for many people. Due to this all people must be isolated in their home. It has affected the well-being and working life of millions of people across the world. Due to this disease, the mental health among people has increased significantly for the past 2 years. According to MoH, out of 983 Malaysian students, about 20.4%, 6.6%, and 2.8% are experienced minimal to moderate, marked to severe, and most extreme levels of anxiety, respectively. The main stressors are the remote online teaching, and uncertainty about the future regarding study and career which can affect mental health. Thus, the main objective of this project is to develop a product that may provide different medication methods to its users by helping people who suffer anxiety, stress, and depression. We used aromatherapy, positive quotes, and music therapy in order to release stress. The user can also check his or her pulse rate by only touching the fingerprint through the sensor. The microcontrollers used in this project are Arduino Uno and Arduino Mega to build the project circuit. The experimental results can improve body health, mind and spirit which enhances both physical and emotional health.

关键词： COVID-19 Uncertainty Pandemics Microcontrollers Anxiety disorders Medical treatment Mental health Fingerprint recognition Biomedical imaging IEEE Regions

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Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation

arXiv

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arXiv 2025年

作者： Naeem, Muhammad Ahtsam Saleem, Muhammad Asim Sharif, Muhammad Imran Akber, Shahzad Saleem, Sajjad Akhtar, Zahid Siddique, Kamran School of Information and Software Engineering University of Electronic Science and Technology of China China Faculty of Computing Riphah International University Faisalabad Campus Pakistan Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Information and Technology Washington University of Science and Technology AlexandriaVA United States Department of Network and Computer Security State University of New York Polytechnic Institute United States Department of Computer Science and Engineering University of Alaska Anchorage Anchorage United States

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image;(2) Feature extraction is performed using a Deep CNN model, then these extracted features are concatenated;(3) Feature selection is performed using wrapper-based feature selection;(4) Classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features. © 2025, CC BY.

关键词： Deep learning

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Model Predictive Control Method With Auto-Tuning Weighting Factor For Bidirectional DC-DC Battery Chargers

Model Predictive Control Method With Auto-Tuning Weighting F...

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Compatibility in Power electronics (CPE)

作者： Naki Guler Sertac Bayhan Hasan Komurcugil Electrical and Electronic Engineering Technology Faculty Gazi University Ankara Turkey Renewable Energy and Smart Grids Application and Research Center Gazi University Ankara Turkey Qatar Environment and Energy Research Institute Doha Qatar Department of Computer Engineering Eastern Mediterranean University Turkey

ISBN: (数字)9798350318265

ISBN: (纸本)9798350318272

This paper proposes a model predictive control (MPC) method with an auto-tuning weighting factor for bidirectional DC-DC battery chargers. The reference current value of DC-DC converter is desired as a DC quantity by neglecting the inductor current ripple. Therefore, MPC changes the switch position to obtain zero error (between reference and predicted current values). Hence, excessive switching frequency occurs. The weighting factor is used to prevent excessive switching frequency in MPC methods devoted to controlling DC-DC converters. The relationship between the weighting factor and inductor current ripple is clarified for bidirectional DC-DC converters. Thus, the switching frequency is kept approximately constant by using the proposed auto-tuning weighting factor. The proposed approach is validated by simulation studies performed on MATLAB/Simulink platform. The results reveal that the proposed auto-tuning strategy significantly reduces the variations in switching frequency that occur under variations in the current reference and input voltage.

关键词： Battery chargers Switching frequency DC-DC power converters Switches Control systems Batteries Inductors

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

SSRN

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Takieldeen, Ali E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

Facial expression recognition plays a pivotal role in comprehending emotions and social interactions, particularly in individuals grappling with autism spectrum disorder (ASD), who often encounter challenges in non-verbal communication. For some with ASD, even subtle shifts in their facial expressions can prove elusive, rendering it arduous to categorize their emotional states accurately. Consequently, the analysis and processing of facial expression data hold the potential to facilitate early autism diagnosis, thereby enabling timely therapeutic interventions and alleviating the economic and psychological burdens experienced by patients. In this study, we introduce an enhanced approach that leverages a fuzzy convolutional neural network (CNN) and an Improved Binary Genetic Algorithm (IBGA) for autism diagnosis based on facial expression data. Our model confronts the intricate task of recognizing facial images by selecting salient features employing the IBGA method. Moreover, it employs fuzzy weights to ascertain the probability of similarity within the classifier layer. Our proposed model has yielded impressive outcomes, boasting an average accuracy of 0.9405 for ASD detection and 0.9365 for the recognition of six distinct facial expressions. These results hold profound implications for the enhancement of emotion recognition systems and the refinement of interventions aimed at bolstering social interactions for individuals grappling with ASD. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification

A Novel Attention-based Explainable Deep Learning Framework ...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Gladys W. Muoka Ding Yi Chiagoziem Chima Ukwuoma Moindi Duke Martin Ahmet Arif Aydin Mugahed A. Al-Antari School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China Department of Computer Science Unicaf University Lilongwe Lilongwe Malawi Department of Computer Engineering Faculty of Engineering İnönü University Turkey Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul Korea

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study’s primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics.

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IoT-Based Contactless Body Temperature Scanning System for a Smart and Safe Campus 4

IoT-Based Contactless Body Temperature Scanning System for a...

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4th International Symposium on Agents, Multi-Agents Systems and Robotics, ISAMSR 2021

作者： Onn, Thoong Chun Alduais, Nayef.A.M. Abdullah, Jiwa Saad, Abdul-Malik H.Y. Ramli, Azizul Azhar Mahdin, Hairulnizam B. Kasim, Shahreen Mustapha, Aida Mostafa, Salama A Jawad, M.Saeed. Ghanem, Waheed Ali H. M. Abdul-Qawy, Antar S.H. Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Johor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu21030 Malaysia Faculty of Science SUMAIT University Science and Information Technology Dept Zanzibar Tanzania United Republic of

ISBN: (纸本)9781728166544

Nowadays, everyone is required to check the body temperature and record personal details whenever he arrives at a different place. However, there are some problems when the actions are performed. Therefore, this paper proposed an Internet of Things (IoT)-based contactless body temperature scanning system. The software and hardware used in this study were Microsoft Visual Studio, ThingSpeak, Arduino IDE, Radio Frequency Identification (RFID), and contactless body temperature sensor. A parallel development model was used as the project management methodology. The proposed system was tested through system testing, user acceptance testing, and accuracy testing. Based on the testing results, the proposed system was successfully implemented and ready to be deployed. The average accuracy of the proposed system was 99.86% compared to a standard device. © 2021 IEEE.

关键词： Physiology

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Energy-Efficient and Privacy-Preserved Incentive Mechanism for Federated Learning in Mobile Edge Computing

Energy-Efficient and Privacy-Preserved Incentive Mechanism f...

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IEEE International Conference on Communications (ICC)

作者： Jingyuan Liu Zheng Chang Geyong Min Yan Zhang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Department of Computer Science University of Exeter Exeter U.K. Department of Informatics University of Oslo Norway

In mobile edge computing (MEC)-assisted federated learning (FL), the MEC users can train data locally and send the results to the MEC server to update the global model. However, the implementation of FL may be prevented by the selfish nature of MEC users, as they need to contribute considerable data and computing resources while scarifying certain data privacy for the FL process. Therefore, it is of great importance to design an efficient incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission and data privacy, we design an energy-efficient and privacy-preserved incentive scheme to facilitate the FL process by investigating interactions between the MEC server and MEC users in a MEC-assisted FL system. Using a Stackelberg game model, we explore the transmit power allocation and privacy budget determination of MEC users and reward strategy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results demonstrate the effectiveness of our proposed scheme.

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Assessing Virtual Reality Sickness in Highly Immersive Virtual Laboratory Environments : Simulator Sickness Questionnaire and Mitigation Strategies

Assessing Virtual Reality Sickness in Highly Immersive Virtu...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Ahmed Jamah Ahmed Alnagrat Abdulaziz E Salem Shagluf Rajaa. F. Mahmoud Sulieman Abubaker Almintisir Abubaker Akeel Rizalafande Che Ismail Syed Zulkarnain Syed Idrus Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Fezzan Libya Department of Information Technologyn Faculty of Technical Engineering Mesallata Mesallata Libya Department of Computer Science Faculty of Science and Arts Gaminis University of Benghazi Benghazi Libya Department of Mathematics and Computer Faculty of Education Wadi Alshatti University Wadi Alshatti Fezzan Libya Faculty of Electronic Engineering Technology (FTKEN) Universiti Malaysia Perlis (UniMAP) Arau Malaysia Department of Communication Faculty of Business & Communication (FPK) Universiti Malaysia Perlis (UniMAP) Arau Malaysia

Virtual Reality (VR) sickness, a common challenge among VR users, has increasingly gained attention in academic research due to its adverse side effects. The Simulator Sickness Questionnaire (SSQ) has been traditionally employed to measure simulator motion sickness; however, the extent to which it measures symptoms specifically attributable to VR sickness remains underexplored. This study investigates the applicability of SSQ in assessing VR sickness in a virtual semiconductor laboratory environment, with a focus on reducing motion sickness in VR devices. A total of 97 participants performed target selection tasks using HP Reverb G2 and Oculus Quest 2 headsets in a virtual laboratory setting. The original SSQ was revised to create a VR Sickness Questionnaire, which served as the measurement index for VR-induced symptoms. The primary objective of this research was to determine the prevalence of VR sickness and explore ways to mitigate motion sickness in VR devices. The Results indicate that the majority of VR-related side effects pertain to nausea dysfunction, with a limited number of symptoms linked to cybersickness arising from various factors. These findings hold significant implications for future studies aiming to measure and design interventions for simulator sickness in VR environments. By shedding light on the effectiveness of the SSQ for evaluating VR sickness, this study contributes to the development of improved VR experiences and technologies.

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