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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Majeed, Asad Iftikhar, Mahnoor Ali, Raja Hashim Arshad, Usama Shabbir, Muhammad Zeeshan Zeeshan Ijaz, Ali Ali, Nisar Aftab, Ali Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant. © 2023 IEEE.

关键词： Computational complexity

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OntoMG: a unique and ontological-based intelligent framework for early identification of myasthenia gravis (MG)

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3847-3853页

作者： Mahajan, Prerna Agarwal, Trapty Vekariya, Daxa Gupta, Rajesh Malviya, Ashwini Anandaraj, S.P. Jain, Garima Anand, D. Department of Computer Science and Information Technology Jain (Deemed to Be University) Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Management Sanskriti University UP Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science Engineering Presidency University Bengaluru India Department of Computer Science and Business Systems (CBS) Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Myasthenia Gravis (MG) is a neuromuscular disease causing extreme muscular fatigue, triggering problems with vision, swallowing, speech, mobility, dexterity, and breathing. However, early detection and prediction of MG is a crucial and quite challenging task for medical practitioners. So, in our proposed work, we are focused on introducing an ontology-based intelligent system for the prognosis of patients affected by this neuromuscular disease. The given paper presents a novel framework that employs the concepts of ontology, semantic web rules, and reasoner for deriving whether the patient is tested positive or negative. The suggested framework's effectiveness is assessed by comparing it to machine learning classifiers using parameters from the NIH repository for Myasthenia Gravis. The experimental results show that the ontological-based framework is able to achieve higher accuracy (80.85%), higher precision (78.37%), and higher recall (76.55%) than the existing classifiers used in the recent studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Machine learning (ML) Myasthenia gravis disease Ontology Reasoners Semantic web rules language (SWRL)

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Covid-19 CT Lung Image Segmentation Using Adaptive Donkey and Smuggler Optimization Algorithm

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computers, Materials & Continua 2022年第4期71卷 1133-1152页

作者： P.Prabu K.Venkatachalam Ala Saleh Alluhaidan Radwa Marzouk Myriam Hadjouni Sahar A.El_Rahman Department of Computer Science CHRIST(Deemed to be University)BengaluruIndia Department of Computer Science and Engineering CHRIST(Deemed to be University)BengaluruIndia Information Systems Department College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadhKSA 84428Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Computer Sciences Department College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electrical Engineering Faculty of Engineering-ShoubraBenha UniversityCairoEgypt

COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and *** such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.

关键词： Adaptive donkey and snuggler optimization.bi-directional long short term memory coronavirus disease 2019 randomly selected propagation semi-supervised learning

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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Use of Virtual Reality to Facilitate Engineer Training in the Aerospace Industry

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Machine Graphics and Vision 2023年第2期32卷 19-44页

作者： Paszkiewicz, Andrzej Salach, Mateusz Wydrzyński, Dawid Woźniak, Joanna Budzik, Grzegorz Bolanowski, Marek Ganzha, Maria Paprzycki, Marcin Cierpicki, Norbert Department of Complex Systems Rzeszow University of Technology Rzeszów Poland Department of Machine Design Rzeszow University of Technology Rzeszów Poland Department of Management Systems and Logistics Rzeszow University of Technology Rzeszów Poland Systems Research Institute Polish Academy of Sciences Warszawa Poland The Faculty of Electrical and Computer Engineering Rzeszow University of Technology Rzeszów Poland

This work concerns automation of the training process, using modern information technologies, including virtual reality (VR). The starting point is an observation that automotive and aerospace industries require effective methods of preparation of engineering personnel. In this context, the technological process of preparing operations of a CNC numerical machine has been extracted. On this basis, a dedicated virtual reality environment, simulating manufacturing of a selected aircraft landing gear component, was created. For a comprehensive analysis of the pros and cons of the proposed approach, four forms of training, involving a physical CNC machine, a physical simulator, a software simulator, and the developed VR environment were instantiated. The features of each training form were analysed in terms of their potential for industrial applications. A survey, using the Net Promoter Score method, was also conducted among a target group of engineers, regarding the potential of use of each training form. As a result, the advantages and disadvantages of all four training forms were captured. They can be used as criteria for selecting the most effective training form. © 2023 faculty of Applied Informatics and Mathematics - WZIM, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Aerospace industry

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Hybrid Machine Learning and Deep Learning CNN Models for Apple Trees Leaves Pathologies Classification 12

Hybrid Machine Learning and Deep Learning CNN Models for App...

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12th International Scientific Conference on computer Science, COMSCI 2024

作者： Matov, Petar Iavorov Lazarova, Milena Kirilova Filipova-Petrakieva, Simona Kirilova Taralova, Ina Technical University of Sofia Faculty of Computer Systems and Technologies Department of Computer Systems 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Technical University of Sofia Faculty of Automation Department of Theoretical Electrical Engineering 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Ecole Centrale de Nantes LS2N UMR CNRS 6004 Nantes France

ISBN: (纸本)9798350392166

Agricultural development in recent years has been based entirely on smart management. Leaves diseases on apple trees is a very common problem. The present work is a continuation of previous research of the author's team to solve it. The recently proposed apple leaves pathology prediction model based on deep learning convolutional neural network (CNN) is extended to hybrid models. The latter are a combination of a deep learning CNN and classical machine learning techniques. To increase the classification accuracy two hybrid models are proposed: CNN+SVM (Support Vector Machines) and CNN+RF (Random Forest). The CNN is based on modified DenseNet architecture and SVM and RF models are integrated as CNN output is fed to SVM and RF models respectively to ensure higher classification accuracy. The suggested hybrid models are evaluated on a data set that comprises 1800 images of pathologies in apple trees' leaves. The experimental results prove the higher accuracies of the hybrid deep + machine learning models compared to the deep learning CNN model. © 2024 IEEE.

关键词： Adversarial machine learning

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Low 3D-HEVC Depth Map Intra Modes Selection Complexity Based on Clustering Algorithm and an Efficient Edge Detection 1st

Low 3D-HEVC Depth Map Intra Modes Selection Complexity Based...

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1st edition of the International Conference on Artificial Intelligence and Green Computing, ICAIGC 2023

作者： Hamout, Hamza Elyousfi, Abderrahmane Computer Systems and Vision Laboratory Faculty of Sciences Ibn-Zohr University Agadir80000 Morocco Department of Computer Science National Engineering School of Applied Sciences Ibn-Zohr University Agadir80000 Morocco

ISBN: (纸本)9783031465833

In 2016, ITU-T Video Coding Experts Group (VCEG) and ISO/IEC MPEG finalize the recent 3-dimensional video coding standard founded on Multi-view texture Videos plus Depth maps (MVD) format called, the 3D extension of High-Efficiency Video Coding (3D-HEVC). In the current design of 3D-HEVC, the conventional intra-prediction modes and Depth Modeling Modes are exploited sophistically to improve the depth map coding performance. However, the coding efficiency improvement is achieved at the cost of computational complexity load, which obstructs 3D-High Efficiency Video Coding standards from practical world applications. In the 3D-HEVC, the depth maps are described by enormous homogeneous regions, and it is observed that for most of the cases, the DMMs and all or the majority of angular intra modes could be skipped for the homogenous area. Thus, we propose an efficient intra-coding model decision for a depth map founded on the clustering algorithm and edge detection to resolve the above-mentioned complexity increase. Experimental results demonstrate that the proposed intra-coding model decision for depth map can decrease the encoding time up to 41.35%, with insignificant loss of rate-distortion performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Edge detection

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Correction To: Photovoltaic system fault detection techniques: a review

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Neural Computing and Applications 2024年第29期36卷 18547-18548页

作者： El-Banby, Ghada M. Moawad, Nada M. Abouzalm, Belal A. Abouzaid, Wessam F. Ramadan, E. A. Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Electrical Engineering of Computer and Control Systems Faculty of Engineering Kafrelsheikh University Kafrelsheikh Egypt

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A Study on DDoS Attacks Detection on IoT Devices Using Machine Learning for Microcontrollers

A Study on DDoS Attacks Detection on IoT Devices Using Machi...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jedidah Mwaura Shunsuke Araki Ken’Ichi Kakizaki Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology FuKuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

In recent years, there has been a proliferation of Internet of Things (IoT) devices, and so has been the attacks on them. In this paper we will propose a methodology to detect Distributed Denial of Service (DDoS) attacks on IoT devices using Machine Learning for Microcontrollers. We will discuss a model which we made for Arduino Nano 33 BLE Sense using Machine Learning for Microcontrollers. Additionally, we will discuss results of our proposal in detecting DDoS attacks on IoT devices. Lastly, we will describe the feasibility of our model on IoT devices.

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systems for Remote Access to FPGA Development Boards: Review 31

Systems for Remote Access to FPGA Development Boards: Review

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31st International Scientific Conference Electronics, ET 2022

作者： Minev, P. Kukenska, V. Varbov, I. Dinev, M. Technical University of Gabrovo Faculty of Electrical Engineering and Electronics Department of Computer Systems and Technology Gabrovo5300 Bulgaria

ISBN: (数字)9781665498784

ISBN: (纸本)9781665498784

This report presents the study results of remote access systems for FPGA development hardware during the period 2008-2021. The possibilities and development of these systems are considered. The study made it possible to define the requirements and the expected final results in the implementation of a new system for remote access to FPGA development hardware, which is in the process of implementation in the 'Automated design' laboratory at the KST department of the Technical University-Gabrovo. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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