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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Vallabhaneni, Nagalakshmi Panneer, Prabhavathy Sundaram, Venkatesan Meenakshi School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Computer Science and Engineering National Institute of Technology Puducherry India

ISBN: (纸本)9798350325188

Yoga is a popular practice that aims to improve one's health and well-being through physical postures, breathing exercises, and meditation. The growing popularity of yoga has prompted researchers to focus on automating the process of classifying yoga poses. Using deep convolutional neural networks (CNNs) and transfer learning, we present a deep learning-based method for correctly categorizing yoga postures in this research. The primary goal of this study is to develop an efficient method for automatically recognizing and categorizing yoga poses in given photographs. The impressive success of deep CNNs in picture classification tasks is exploited here to accomplish this goal. We also use transfer learning, a method for customizing pre-trained CNN models for our unique yoga posture categorization task. This paper presents a Transfer Learning based Method for Yoga Poses classification using Deep Convolutional Neural Networks (TLMYPC-DCNN). The proposed model uses the Yoga Posture Dataset for classification of images. Initially, this model starts with pre-processing the input images by splitting input data into three groups such as training set, validation set and test data. The deep learning CNN model will be trained using the training data, and then its parameters will be tweaked using the validation data. Finally, the test data will be used to assess how well the data performs. A convolutional neural network (CNN) model called MobileNetV2 will be applied to the model photos. The experimental results show that the suggested TLMYPC-DCNN based technique with transfer learning is effective at accurately and reliably classifying yoga poses. © 2023 IEEE.

关键词： Deep Convolutional Neural Networks MobileNetV2 Transfer Learning Yoga Pose classification

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Novel Framework of Segmentation 3D MRI of Brain Tumors

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computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI Rician noise speckle noise segmentation deep learning

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Adaptive Context Based Road Accident Risk Prediction Using Spatio-Temporal Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2872-2883页

作者： Bhardwaj, Nishit Pal, Anupriya Bhumika Das, Debasis Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Rajasthan 343020 India Samsung Semiconductor India Research Karnataka560048 India Verint Systems Karnataka560005 India

Traffic accidents are common urban events that pose significant risks to human safety, traffic management, and economic stability;consequently, the research community is paying increasing attention toward accident risk prediction. However, accident risk prediction is a challenging problem because accident occurrences are sparse and influenced by multiple contextual factors (e.g., POI, road structure, road type, hour of the day, and month). Therefore, in this article, we propose a novel architecture named Topographic-Weighted Context Category (TWCCnet) that adapts heterogeneous contextual category weights based on spatial-temporal correlations across sectors. Specifically, the framework consists of two parallel components: one uses convolution and stacked bidirectional gated recurrent unit (Bi-GRU) to capture spatial-temporal relationships between neighborhood sectors, while the other uses multiple graph convolution network (GCN) over resemblance graphs to capture spatial-temporal relationships between semantic sectors. At last, temporal attention is utilized on top of parallel components to learn important spatiotemporal features that have a substantial impact on traffic accidents. The extensive experiments on two publicly available citywide datasets, i.e., New York City and Chicago demonstrate the effectiveness of the proposed approach and showed (7.21, 10.7) Root Mean Squared Error (RMSE), (34.09, 20.75) Mean Average Precision (MAP), and (0.19, 0.09) Recall approximately over both datasets, respectively, outperforming baseline as well as state-of-the-art models. © 2020 IEEE.

关键词： Convolution

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Benaddi, Lamya Ouaddi, Charaf Naimi, Lahbib Souha, Adnane Jakimi, Abdeslam Ouchao, Brahim Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction. © 2024 IEEE.

关键词： Customer satisfaction

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Channels engineering in Magnetic Recording: From Theory to Practice

IEEE BITS the Information Theory Magazine

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IEEE BITS the Information Theory Magazine 2023年第3期3卷 6-49页

作者： Garani, Shayan Srinivasa Vasic, Bane Indian Institute of Science Department of Electronics Systems Engineering Bengaluru560012 India University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Information theory, coding theory, and signal processing have significantly shaped magnetic read/write channels engineering through a chronological sequence of innovations and research advancements, cognizant of the underlying physical processes. In this magazine article, we provide an overview of magnetic recording technologies leading to the channels engineering aspects, central to this article. We survey important coding and signal processing algorithms along with some design architectures that have made it to practice within hard disk drives for magnetic recording channels. The push toward realizing ultrahigh densities (> 4 Tb/in2>) on magnetic disks with ultrahigh throughput rates (>10 Gb /s) necessitates the development of native two-dimensional (2-D) coding and signal processing algorithms and architectures within the framework of 2-D magnetic recording (TDMR) along with read head engineering and changes to the recording physics. We also provide an overview of novel channels engineering solutions covering all aspects of TDMR channels driven from a systems science perspective. The innovations and research advances described in this article may serve a broad engineering audience in other areas as well. © 2021 IEEE.

关键词： Magnetic recording

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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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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A Practical Embedded System Utilizing Artificial Intelligence Technology 4

A Practical Embedded System Utilizing Artificial Intelligenc...

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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Molapo, Makhabane Tu, Chunling Du Plessis, Deao Du, Shengzhi Tshwane University of Technology Computer Systems Engineering Pretoria South Africa Tshwane University of Technology Computer Science Department Pretoria South Africa

ISBN: (纸本)9798350387988

Thereis a clear and present lethal clash between South Africa's wildlife and its expanding population. Some of its main effects are harm to people and property. Fatalities, crop damage, livestock threads, and habitat devastation. We are inspired by the need for data-driven approaches in artificial intelligence in today's technologies. We have built an embedded system called 'SmartFarmApp' by leveraging datasets for object detection. This system capably serves as the backbone of computer vision models by focusing on detecting and classifying livestock according to their classes using the Florence 2 AI (Artificial Intelligence) model. We have livestock of interest and wild animals as a threat to our livestock and if wild animals are around this class label the message for security concern must be raised. This system utilizes the Florence 2 AI vision-language model developed by Microsoft that excels in various vision tasks, including object detection where we use object detection Application Programmable Interfaces (APIs) to do detection for this system in a realistic outdoor environment. However, Florence 2 as vision tasks it does not support Object tracking, so we improved Florence 2 model to work by allowing tracking. Florence 2 model feeds our system, while built-in Raspberry Pi devices equipped with cameras feed this system in real-time using the mobile application to remotely view daily activities in our farms. © 2024 IEEE.

关键词： Livestock

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An optimal hybrid cascade regional convolutional network for cyberattack detection

An optimal hybrid cascade regional convolutional network for...

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作者： Alqahtani, Ali Khan, Surbhi Bhatia Department of Networks and Communications Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Data Science School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Cyber-physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero-day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search-cascade regional convolutional neural network (hybrid TS-Cascade R-CNN) to detect cyberattacks. The proposed model combines the hybrid transit-search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards. © 2023 John Wiley & Sons Ltd.

关键词： Zero-day attack

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Enhanced Growth Optimizer and Its Application to Multispectral Image Fusion

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computers, Materials & Continua 2024年第11期81卷 3033-3062页

作者： Jeng-Shyang Pan Wenda Li Shu-Chuan Chu Xiao Sui Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The growth optimizer(GO)is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social ***,the original GO algorithm is constrained by two significant limitations:slow convergence and high mem-ory *** restricts its application to large-scale and complex *** address these problems,this paper proposes an innovative enhanced growth optimizer(eGO).In contrast to conventional population-based optimization algorithms,the eGO algorithm utilizes a probabilistic model,designated as the virtual population,which is capable of accurately replicating the behavior of actual populations while simultaneously reducing memory ***,this paper introduces the Lévy flight mechanism,which enhances the diversity and flexibility of the search process,thus further improving the algorithm’s global search capability and convergence *** verify the effectiveness of the eGO algorithm,a series of experiments were conducted using the CEC2014 and CEC2017 test *** results demonstrate that the eGO algorithm outperforms the original GO algorithm and other compact algorithms regarding memory usage and convergence speed,thus exhibiting powerful optimization ***,the eGO algorithm was applied to image *** a comparative analysis with the existing PSO and GO algorithms and other compact algorithms,the eGO algorithm demonstrates superior performance in image fusion.

关键词： Growth optimizer probabilistic model Lévy flight image fusion

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