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arXiv 2024年

作者： Shoaib, Mohamed R. Emara, Heba M. Zhao, Jun El-Shafai, Walid Soliman, Naglaa F. Mubarak, Ahmed S. Omer, Osama A. Abd El-Samie, Fathi E. Esmaiel, Hamada School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Electrical Engineering Department Aswan Faculty of Engineering Aswan University Aswan81542 Egypt Department of 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 Riyadh11671 Saudi Arabia Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and the prevention of eventual blindness. Traditional diagnostic methods, relying on human interpretation of these medical images, face challenges in terms of accuracy and efficiency. In the present research, we introduce a novel method that offers superior precision in DR diagnosis, compared to these traditional methods, by employing advanced deep learning techniques. Central to this approach is the concept of transfer learning. This entails using pre-existing, well-established models, specifically InceptionResNetv2 and Inceptionv3, to extract features and fine-tune select layers to cater to the unique requirements of this specific diagnostic task. Concurrently, we also present a newly devised model, DiaCNN, which is tailored for the classification of eye diseases. To validate the efficacy of the proposed methodology, we leveraged the Ocular Disease Intelligent Recognition (ODIR) dataset, which comprises eight different eye disease categories. The results were promising. The InceptionResNetv2 model, incorporating transfer learning, registered an impressive 97.5% accuracy in both the training and testing phases. Its counterpart, the Inceptionv3 model, achieved an even more commendable 99.7% accuracy during training, and 97.5% during testing. Remarkably, the DiaCNN model showcased unparalleled precision, achieving 100% accuracy in training and 98.3% in testing. These figures represent a significant leap in classification accuracy when juxtaposed with existing state-of-the-art diagnostic methods. Such advancements hold immense promise for the future, emphasizing the potential of our propos

关键词： Deterioration

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Proposing System to Recognize Emotions in Public Network Using Phobert Deep Learning Model 9

Proposing System to Recognize Emotions in Public Network Usi...

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9th NAFOSTED Conference on Information and computer Science, NICS 2022

作者： Huu, Phat Nguyen Anh, Tuan Nguyen Phi, Long Hoang Dang, Dinh Dang Le Bao, Chau Nguyen Minh, Quang Tran School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Hanoi-Amsterdam Highschool for the Gifted Hanoi Viet Nam Ho Chi Minh City University of Technology Faculty of Computer Science and Engineering Department of Information Systems Hochiminh Viet Nam Vietnam National University Hochiminh HoChiMinh City Viet Nam

ISBN: (纸本)9781665454223

People can receive information faster especially in the 4.0 revolution with the continuous development of revolutions. The information can affect our emotional, psychological and spiritual well-being, especially in the recent high school graduation exam across the country. Therefore, we propose to build a user emotion analysis system in a public network with the PhoBert training model in the paper. Besides, we built our dataset aggregated from social networks, articles, blogs, etc. We next use the PhoBert model to solve the processing data problems. The simulation results have shown an accuracy of 86.5% on the training and 81.32% on the validation dataset with a training time of 3 hours (about 180 minutes). The results also show that we can build a warning system to avoid health and psychological effects with great emotion. © 2022 IEEE.

关键词： Deep learning

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Adaptive Direction of Arrival Estimation Algorithms for 5G Network and beyond

Adaptive Direction of Arrival Estimation Algorithms for 5G N...

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2020 International Conference on computer, Control, Electrical, and electronics engineering, ICCCEEE 2020

作者： Abdo, Adam Mohamed Ahmed Mohamed, Aamer Mohamed Huroon Nerma, Mohamed H. M. Mohamed, Sara Yahia Altahir Mohammed, Adam Osman Ahmed Fculty of Engineering Science University of Nyala Department of Electrical and Electronic Engineering Nyala Sudan Institute of Electronic Engineering National Chiao Tung University Taiwan Tabuk University Fculty of Computer and Information Technology Dept of Computer Engineering Tabuk Saudi Arabia Dept. O F Electronic Engineering College O F Engineering Sudan University of Sceince and Technology Khartoum Sudan Sudan International University Faculty of Engineering Department of Electrical Engineering Khartoum Sudan Nyala Technical Collage Department of Electronic Engineering Nyala Sudan

ISBN: (纸本)9781728191119

Multiuser-multiple input multiple output (MU-MIMO) systems can provide a substantial performance for cellular networks by applying direction of arrival (DOA) technique, in which the direction of sidelobe signals can be estimated. Therefore, this paper presents a different adaptive DOA algorithms where the performance of communication networks can be enhanced, in the meantime the minimizing interferences. Meanwhile, The DOA algorithm such as multi signal classification (MUSIC), minimum norm (Min-Norm), minimum variance distortionless response (MVDR) and Bartlett algorithms are studied and compared extensively regarding the estimation angles and peak power spectrum. In this regard, uniform linear array (ULA) antenna is employed to construct the weight vector for incoming sensor signals. Furthermore, this paper compares the DOA algorithms concerning the number of antenna elements as well as the incoming signal angles. The simulation results approve that the algorithms are likely have the same performance in accuracy of scanning angles. However, it is realized that all algorithms have different peak power values in order to beamwidth. Moreover, the results indicate that the DOA algorithms with the different number of antenna elements have a significant impact on a directivity and gain. Hereafter, this work can be extended to higher frequency of mmWave communication with massive number of antenna, which can meet the requirement of fifth generation (5G) network. © 2021 IEEE.

关键词： Direction of arrival

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Adaptive Rate Reaching Law Based Sliding Mode Control for a Three-Phase F-type Converter Operated as Bidirectional Battery Charger and SAPF

Adaptive Rate Reaching Law Based Sliding Mode Control for a ...

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Annual Conference of Industrial electronics Society

作者： Hasan Komurcugil Naki Guler Sertac Bayhan Seyfullah Dedeoglu Department of Computer Engineering Eastern Mediterranean University Famagusta Türkiye Department of Electrical-Electronic Engineering Technology Faculty Gazi University Ankara Türkiye Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Department of Electrical and Electronics Engineering Faculty of Engineering Hitit University Çorum Türkiye

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This paper presents an adaptive rate reaching law based sliding mode control method for a three-phase F-type converter. Unlike the existing reaching laws in which the gain is always constant, the proposed reaching law employs an adaptive gain that is continuously updated with the grid current errors. This means that the gain can reach to its smallest value when the grid current errors are zero. It is shown that the minimum gain reduces the reaching time. As a consequence of using adaptive gain, the dynamic response of system is improved considerably (i.e: the reaching time is reduced). Moreover, it is shown that the three-phase F-type converter can be operated as bidirectional battery charger as well as shunt active power filter (SAPF) for compensating the nonlinear loads connected at the point of common coupling. The proposed control and correct operation of system are validated by MATLAB/Simulink simulations under various operating conditions which include battery charging mode, battery discharging mode, and simultaneous operation as SAPF and battery charging/discharging.

关键词： Battery chargers Industrial electronics Dynamic response Couplings Simulation Active filters Batteries MATLAB Sliding mode control Load modeling

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Coded Distributed Computing For Vehicular Edge Computing With Dual-Function Radar Communication

Coded Distributed Computing For Vehicular Edge Computing Wit...

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IEEE Conference on Vehicular technology (VTC)

作者： Hoai Linh Nguyen Thi Hung Le Hoang Nguyen Cong Luong Tien Hoa Nguyen Sa Xiao Junjie Tan Dusit Niyato School of Electrical and Electronic Engineering Hanoi University of Science and Technology Vietnam Faculty of Computer Science PHENIKAA University Vietnam National Key Lab. of Wireless Communications University of Electronic Science and Technology of China China Kashi Institute of Electronics and Information Industry Kashi China Department of Electrical and Computer Engineering Western University London ON Canada School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we propose a coded distributed computing (CDC)-based vehicular edge computing (VEC) framework. The framework allows a task vehicle (TV) equipped with the dual-function radar communication (DFRC) to offload its computing tasks to the nearby service vehicles (SVs) using the (m, k) maximum distance separable (MDS). The framework is thus able to address the straggler effect that is typically caused by the high mobility of the vehicles. We then formulate an optimization problem for the TV that aims to i) minimize the overall computing latency, ii) minimize the offloading cost, and iii) maximize the radar range subject to the connection duration. For this, we optimize the MDS parameters, i.e., the number of selected SVs (m) and the number of subtasks for coding (k), and the fractions of power allocated to the radar and communication functions. Under the high dynamic vehicular environment, the uncertainty of the SVs’ computing resource and networking resources, we propose a deep reinforcement learning (DRL) algorithm based on Double Deep Q-Network (DDQN) to solve the TV’s problem. To further improve the performance, we propose to incorporate a parameter norm penalty in the loss function. Simulation results show that the proposed DDQN algorithm outperforms both the DQN algorithm and the non-learning algorithm in terms of computation latency, radar range, and offloading cost.

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Performance Comparison of dSPACE and MCU MPC Implementation in PV Power Optimizer

Performance Comparison of dSPACE and MCU MPC Implementation ...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Morcos Metry Naki Guler Wesam Rohouma Robert S. Balog Department of Electrical and Computer Engineering Texas A&M University Doha Qatar Department of Electrical-Electronic Engineering Technology Faculty Gazi University Ankara Turkey Department of Electrical Engineering University of Doha for Science and Technology Doha Qatar Department of Electrical and Computer Engineering Texas A&M University College Station Texas USA

Finite control set model predictive control (MPC) can be computationally intensive especially when scaled up for more complicated converters since each state variable in the system is evaluated for all possible switching configurations. Multi-objective optimization of the cost function adds to the complexity of MPC. Much of the MPC literature in power electronics relies on expensive rapid prototyping (RP) platforms (RPP) such as dSPACE and OPAL RT. This paper aims to bridge the gap between expensive RPP and low-cost MCUs by exploring the feasibility of rapid prototyping (RP) on the low-cost F28379D launchpad. This paper is aimed toward the practitioner or researcher who desires to reduce to practice the MPC methods developed on a RPP to a lower-cost embedded controller. The suitability of a low-cost Texas Instrument (TI) launchpad to perform the same rapid-prototyping functions needed for MPC regulator development in a full-featured, high-end RPP is discussed. Experimental results using the dSPACE 1006 and the F28379D Launchpad showing steady-state performance and dynamic response are illustrated and compared. Comparative results show the F28379D launchpad and dSPACE 1006 offer similar performance markers.

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Deep Networks for Medical Images Classification, a Comparative Study

Deep Networks for Medical Images Classification, a Comparati...

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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Ibrahim, Hussain K. Rokbani, Nizar Wali, Ali Alimi, Adel M. of Sfax University Sfax Tunisia High Institute of Applied Sciences Technology Sousse University of Sousse Tunisia University of Sfax Sfax Tunisia Information Technology Wasit University Wasit Iraq Department of Biomedical Technology College of Applied Medical Sciences in Al-Kharj Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Auckland Park P.O. Box 524 Johannesburg2006 South Africa

ISBN: (纸本)9798350389838

Machine learning (ML) and deep learning (DL) have had a significant impact on healthcare, particularly in the field of medical picture categorization, in recent years. These technologies provide a multitude of advantages that enhance the quality of patient care and increase the effectiveness of healthcare services. The primary rationale for employing (ML) and (DL) in medical picture categorization lies in their remarkable capacity to identify illnesses at an early stage. Automated classification systems has the ability to rapidly analyses and interpret pictures, surpassing the speed of human capabilities. This is particularly vital for effectively handling extensive datasets and becomes critical in emergency scenarios where prompt diagnosis is of utmost importance. The objective of this investigation is to categorize medical images into four distinct categories: normal, COVID-19, lung opacity, and viral pneumonia. In this paper three deep learning models were implemented: the classical CNN, AlexNet and GoogleNet. The comparative analysis showed that AlexNet achieved an accuracy of 96.74%, a processing time of 225 seconds, and a loss of 0.1192. The GoogleNet architecture recorded an accuracy of 93.98%, a processing time of 233 seconds, and a loss of 0.1945 while the classical CNN achieved an accuracy of 94.56%, a processing time of 315 seconds, and a loss of 0.1298. Results showed that the AlexNet has a little advantage in regrads to GoogleNet and classical CNN which report very close performances'. © 2024 IEEE.

关键词： COVID-19

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AoI-Minimal Power and Trajectory Optimization for UAV-Assisted Wireless Networks

AoI-Minimal Power and Trajectory Optimization for UAV-Assist...

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IEEE Conference on Wireless Communications and Networking

作者： Xin Zhang Yun Hu Zheng Chang Geyong Min School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China National Demonstration Center for Experimental Electronic Information and Communications Engineering Education Xidian University Xi’an China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Department of Computer Science University of Exeter Exeter U.K.

In this paper, we consider an Internet of Things (IoT) system where the employed unmanned aerial vehicle (UAV) carries edge computing server to perform data collection and execution for multiple IoT nodes (INs). For such a network, UAV trajectory and uplink transmission power optimization are integrated to minimize the Age of Information (AoI) of the results returned to all ground INs subject to energy consumption limitations. Due to the non-convex nature of the formulated problem, it is divided into two subproblems, which are respectively solved by Lagrangian dual and convex optimization methods, and block coordinate descent method is applied to solve the overall problem. Simulation results show that the proposed algorithm achieves the lowest average AoI of all INs compared with other schemes. The results also reveal the relationship between the average AoI and the number of INs and advantages of the proposed scheme.

关键词： Energy consumption Wireless sensor networks Simulation Wireless networks Autonomous aerial vehicles Information age Internet of Things

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Brain Tumor Classification System Using Decision Tree Algorithm

Brain Tumor Classification System Using Decision Tree Algori...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Sattarpoom Thaiparnit Thanakrit Janchidfah Adil Farooq Dep. Of Information System and Computer Business Faculty of Business Administration Rajamangala University of Technology Suvarnabhumi Suphan Buri Thailand dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Department of Electronic Engineering Maynooth University Ireland

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors.

关键词： Training Technological innovation Accuracy Machine learning algorithms Brain tumors Brain modeling Classification algorithms Decision trees Testing Cancer

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Leaf Guard: Detecting the Diseases in Banana Leaf

Leaf Guard: Detecting the Diseases in Banana Leaf

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Advancements in Computing (ICAC), International Conference on

作者： K. Yathurshan S. Barathy G. Karini P. Sajitha Pradeep Abeygunawardhna Kaushalya Rajapakse Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Engineering| Electrical & Electronic Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research paper introduces “Leaf Guard,” a mobile app designed for disease detection and management in banana trees, specifically tailored for Sri Lanka's agricultural landscape. Through the amalgamation of image processing, deep learning, and customized chemical recommendations, this app proficiently identifies early-stage diseases, offering tailored remedies. Catering to farmers and agricultural experts, ‘Leaf Guard’ provides an intuitive tool, revolutionizing disease management and bolstering sustainable banana cultivation practices. By seamlessly integrating these cutting-edge technologies, this research significantly advances disease detection methods, paving the way for environmentally friendly and economical interventions. The study's findings not only enrich the realm of disease detection technology but also hold promise for future innovations, fostering sustainable approaches to enhance banana cultivation.

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