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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia

ISBN: (纸本)9798350306231

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier. © 2023 IEEE.

关键词： Support vector machines

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Comparative Evaluation between Accelerated RISC- V and ARM AI Inference Machines

Comparative Evaluation between Accelerated RISC- V and ARM A...

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IEEE World Symposium on Communication engineering (WSCE)

作者： Vasileios Christofas Petros Amanatidis Dimitris Karampatzakis Thomas Lagkas Sotirios K. Goudos Konstantinos E. Psannis Panagiotis Sarigiannidis Department of Computer Science International Hellenic University Kavala Greece Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

Embedded AI development has been rapidly im-proving for the past few years and has had a great impact on edge AI networks. However, as neural networks become deeper and deeper it becomes more difficult to execute complicated tasks without sacrificing a good amount of power and performance. In this paper, we make a comparative evaluation between two AI acceleration devices. The first one features a RISC- V 64-bit processor while the other one is ARM powered. These devices are combined with AI co-processors, or ASICs, with computer vision capabilities. Our benchmark consists of a simple classification task split into multiple versions. The results showed that the RISC- V inference machine had 4 times lower consumption while the ARM machine was up to 15 times faster in our largest network. We discuss the results in great detail while keeping our focus on all aspects equally. Finally, we make recommendations based on their usage and application.

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Image Filtering Techniques for Beam Prediction in a Real-world 6G UAV Scenario

Image Filtering Techniques for Beam Prediction in a Real-wor...

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IEEE World Symposium on Communication engineering (WSCE)

作者： Vasileios P. Rekkas Sotirios P. Sotiroudis Panagiotis Sarigiannids Konstantinos E. Psannis George K. Karagiannidis Sotirios K. Goudos ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

Millimeter-wave (mm-wave) and terahertz (THz) communication systems can satisfy the high data rate requirements in 5G, 6G, and beyond networks, but still rely on the use of extensive antenna arrays to guarantee sufficient received signal strength. Many antennas incur high beam training overhead; thus, the narrow beams require adjustment to support highly mobile applications. Deep learning (DL) vision-aided solutions can potentially forecast the optimal beams, leveraging raw RGB images captured at the base station. Image filtering techniques have been widely used in computer vision (CV) to modify and enhance the quality of an image, based on specific rules. This work applies different filters to RGB images for accurate mm-wave/THz beam prediction and feature extraction based on pre-trained convolutional neural networks (CNNs). The assessment of the developed framework is conducted on an actual dataset captured by an unmanned aerial vehicle (UAV) operating in the millimeter-wave (mm-wave) frequency range. The dataset comprises RGB images taken at the base station. Ensemble filtering techniques are also studied, enhancing the beam prediction accuracy of two state-of-the-art (SOTA) DL models.

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The Challenges of Music Deep Learning for Traditional Music 12

The Challenges of Music Deep Learning for Traditional Music

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Moysis, Lazaros Iliadis, Lazaros Alexios Sotiroudis, Sotirios P. Kokkinidis, Kostas Sarigiannidis, Panagiotis Nikolaidis, Spiridon Volos, Christos Boursianis, Achilles D. Babas, Dimitrios Papadopoulou, Maria S. Goudos, Sotirios K. Aristotle University of Thessaloniki Laboratory of Nonlinear Systems - Circuits & Complexity School of Physics Thessaloniki Greece University of Western Macedonia Department of Mechanical Engineering Kozani Greece Aristotle University of Thessaloniki ELEDIA@AUTH School of Physics Thessaloniki54124 Greece University of Macedonia Department of Applied Informatics Thessaloniki Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50131 Greece School of Physics Aristotle University of Thessaloniki Thessaloniki Greece International Hellenic University Department of Information and Electronic Engineering Sindos57400 Greece

ISBN: (纸本)9798350321074

Numerous applications for music listeners, educators, DJs, and musicians have been created over the past decade as the field of Music Deep Learning has expanded. Evidently, the majority of works rely on training their architectures using well-established databases, which consist primarily of Western popular music genres. This creates a deficiency in applications focusing on traditional and regional music, which are vital to preserving culture. As an increasing number of research groups begin to develop works centered on traditional music, several practical challenges for applying deep learning arise. This work discusses these issues and the future of deep learning in the study of traditional music. © 2023 IEEE.

关键词： Deep learning

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning 3

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

ISBN: (纸本)9798350306231

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%;Naive Bayes: 83%. © 2023 IEEE.

关键词： Support vector machines

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Comparison of Biocode Based Machine Learning and Segmentation Model for Automated Prawn Size Prediction for Real Prawn Farm 5

Comparison of Biocode Based Machine Learning and Segmentatio...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Zariful, Muhammad Wafiq Abd Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Fakhrurroja, Hanif Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Basri, Aida Maryam Universiti Technologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei National Research and Innovation Agency Research Center for Smart Mechatronics Bandung Indonesia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Universiti Technologi Brunei School of Applied Science and Matematics Bandar Seri Begawan Brunei

ISBN: (纸本)9798350374407

The aquaculture industry has witnessed a significant surge in technological integration over the past decade, leveraging advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance production efficiency. Monitoring the growth of aquatic cultures, such as fishes and prawns, plays a pivotal role in optimizing production. However, traditional methods, notably in prawn aquaculture in Asia, still heavily rely on stress-inducing techniques like netting and manual measurements. This paper proposes a novel approach using YOLO-based detection algorithm with biocode reference point based on the ArUco design to predict prawn size based on the length and are dimension to analysed the improvement in predicting the size. The propose solution is compared extensively using statistical tests against segmentation model with and without fine-tuning. Initial results show that both YOLOv8 segmentation and YOLOv8 with Segment Anything Model (SAM) give similar results with the actual mean length of 15.7 cm and predicted mean lengths are 20.5 cm and 20.8 cm respectively. However, after fine-tuning the parameter, the accuracy of predicted results improved significantly with 15.8 cm for YOLOv8 segmentation and 16 cm for YOLOv8 with SAM. The mean absolute error of 0.93 cm for YOLOv8 segmentation and 1.10 cm for YOLOv8 with SAM, and the width of 0.48 cm and 0.78 cm respectively. © 2024 IEEE.

关键词： Deep learning

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Simulation of An Intelligent Detection System Virus SARS-CoV-2 in Enclosed Spaces 16

Simulation of An Intelligent Detection System Virus SARS-CoV...

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16th International Conference on Electronics computer and Computation, ICECCO 2021

作者： Moldabayeva, Zhanar S. Schmidt, Peter Seitbattalov, Zhexen Y. Amankeldinov, Tanzharyk A. L.N. Gumilyov Eurasian National University Department of Computer and Software Engineering Nur-Sultan Kazakhstan University of Economics in Bratislava Department of Applied Informatics Bratislava Slovakia

ISBN: (纸本)9781665409452

During the COVID-19 pandemic, methods to determine the presence of coronavirus are very relevant. In this article, a new method has been developed to detect the presence of the SARS-CoV-2 virus in the air. The article analyzes in detail the signs indicating favorable conditions for the spread of the virus. The influence of changes in temperature, humidity, the presence of particles in the air, the level of CO2 and the ventilation of the room on the probability of infection with the virus is *** on the studied parameters, a model was built. It allows to detect the probability of the threat of the spread of the virus. The model was implemented as a fuzzy controller in the Matlab environment. The studied parameters were taken as input parameters for constructing the model. The output parameter was the detector value indicating the presence or absence of a virus in the ***, the built system allows detecting situations in which coronavirus infection is most likely. The system makes a conclusion about the degree of risk of the spread of the virus and, in case of danger, informs about the need for disinfection actions. The use of this system will reduce the cost of processing enclosed spaces, since it will not be carried out according to schedule, but in the case of a system signal. © 2021 IEEE.

关键词： COVID-19

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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2024 International Conference on Electrical and computer engineering Researches, ICECER 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Abbas, Tahir Ateeq, Karamath College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of TIMES Institute Department of Computer Science Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331539733

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems. © 2024 IEEE.

关键词： Prediction models

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AlGaAs soliton microcombs at room temperature

AlGaAs soliton microcombs at room temperature

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Wu, Lue Xie, Weiqiang Xiang, Chao Chang, Lin Yu, Yan Chen, Hao-Jing Yamamoto, Yoshihisa Bowers, John E. Vahala, Kerry J. Suh, Myoung-Gyun T. J. Watson Laboratory of Applied Physics California Institute of Technology PasadenaCA91125 United States Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA93106 United States Physics & Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States

Soliton generation in AlGaAs microresonators at room temperation is reported for the first time. The destabilizing thermo-optic effect is shown to instead provide stability for high soliton repetition rates. The optic... 详细信息

ISBN: (纸本)9781957171258

关键词： Aluminum gallium arsenide

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Detection of Misinformation Related to Pandemic Diseases using Machine Learning Techniques in Social Media Platforms

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Naeem, J. Gul, O.M. Parlak, I.B. Karpouzis, K. Salman, Y.B. Kadry, S.N. Department of Computer Engineering Bahcesehir University Istanbul Turkey Informatics Institute Istanbul Technical University Istanbul Turkey Department of Computer Engineering Galatasaray University Istanbul Turkey Panteion University of Social and Political Sciences Greece Department of Software Engineering Bahcesehir University Istanbul Turkey Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon MEU Research Unit Middle East University Amman11831 Jordan

INTRODUCTION: The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. OBJECTIVES: In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. METHODS: Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies Support Vector Machine (SVM), Decision tree, logistic regression, and neural networks to perform in depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. This ensemble approach significantly improves the model’s overall accuracy and adaptability. RESULTS: Experimental results underscore the efficacy of our methodology, showcasing marked improvements in detection performance compared to individual models. After applying ensemble learning, we achieve an accuracy of 91% for Facebook data, 79% for Instagram data, 80% for Twitter data and 95% for YouTube data. CONCLUSION: Our system not only aids in curbing the dissemination of COVID-19 misinformation but also provides a robust framework for addressing misinformation across various contexts on social media platforms. © 2024 J. Naeem et al., licensed to EAI.

关键词： COVID-19

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