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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Koshariya, Ashok Kumar Manjunath, Manthur Sreeramulu Ananthanath, Gvs Vinayaka, K.S. Sugumar, R. Nishant, Neerav Lovely Professional University School of Agriculture Department of Plant Pathology Punjab Phagwara India Institute of Post Graduate Studies Department of Mca Kmm Andhra Pradesh Tirupati India Sri Venkatramana Swamy College Department of Botany KarnatakaDakshina Kannada Bantwal India Thandalam Saveetha School of Engineering Department of CSE Tamilnadu Chennai India Babu Banarasi das University School of Engineering Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350300093

A fascinating area of agricultural research is the application of machine learning (ML) principles from computer science to the identification of diseases in images of rice plants. The spread of pests and diseases that affect rice has accelerated in recent years. It is essential to recognize symptoms and when and how to effectively control diseases. The primary factor that contributes to yield loss in the agricultural sector is a widespread disease. More often than not, infection acknowledgment and finding happen when the disease has progressed to a super level, achieving a lack of yield, time, and money. A Picture Handling and worked on Deep Learning (DL) model is utilized to identify sicknesses in rice plants is essential until further notice. As a result, the goal of this research is to find out how well the new DL architectures classify the dataset on rice plant disease. In light of notable datasets, tests show that the proposed strategy takes a higher order rate than additional current techniques. © 2023 IEEE.

关键词： Classification (of information)

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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Novel vision transformer and data augmentation technique for efficient detection of monkeypox disease

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Multimedia Tools and Applications 2024年 1-19页

作者： Alarfaj, Aisha Ahmed Ahmad, Salman Hakeem, Abeer M. Alabdulqader, Ebtisam Abdullah PERO, Chiara Alsubai, Shtwai Innab, Nisreen Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Post Graduate Resident Department of Urology Allama Iqbal Teaching Hospital Dera Ghazi Khan Pakistan Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Via Giovanni Paolo II 132 Fisciano Salerno Italy Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Recent technological advancements have paved the way for the optimization of medical processes, particularly automated disease detection. Moreover, the adoption of machine learning (ML) has greatly helped in automating disease detection. Such approaches can detect various diseases early, enabling timely treatment to save countless lives. Early and accurate diagnosis is very important for diseases like monkeypox, to curb its spread. Monkeypox is a viral disease caused by double-stranded DNA and can be transmitted through close contact with infected humans or animals. It’s early identification and accurate lesion diagnosis are critical to contain the disease. This study proposes an automated approach to optimize the diagnosis of monkeypox disease using a novel vision transformer, which is utilized due to its effectiveness for feature extraction. The Proposed approach’s efficiency and accuracy are tested on a public benchmark dataset comprising a variety of skin lesions of different ages and genders. In addition, data augmentation involves rotation, scaling, and flipping thereby enhancing the density of the training data set for better generalization of ML models. Experiments involve binary, as well as, multi-class classification. For the binary class, the proposed model achieves an accuracy of 97.63%, outperforming traditional ML and deep learning (DL) techniques. In the case of multi-class classification with monkeypox, measles, normal, HFMD, cowpox, and chickenpox classes, the proposed model archives an accuracy of 90.61% while precision, recall, and F1 scores are 91.39%, 89.17%, and 90.28%, respectively. Furthermore, the proposed approach shows average accuracy, precision, recall, and F1 scores of 97.54%, 96.19%, 95.16%, and 95.67%, respectively for five-fold cross-validation. Experiments demonstrate that the combination of data augmentation techniques and the vision transformer model significantly optimizes diagnostic performance. In brief, advanced DL architectur

关键词： Medical image processing

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Automated Selection of 2d Axial Knee MRI Slice for Measurement of Parameters Associated with Trochlear Dysplasia

SSRN

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SSRN 2023年

作者： Khokhar, Ridhma Kaur, Manvjeet Sofat, Sanjeev Chouhan, Devendra K. Prakash, Mahesh Department of Computer Science & Engineering Punjab Engineering College Sector-12 Chandigarh India Cyber Security Research Center Punjab Engineering College Sector-12 Chandigarh India Department of Orthopaedics Post Graduate Institute of Medical Education & Research Sector-12 Chandigarh India Department of Radio Diagnosis and Imaging Post Graduate Institute of Medical Education & Research Sector-12 Chandigarh India

Trochlear dysplasia is a knee joint condition where the shape of trochlear groove becomes abnormal resulting in displacement of patella from the usual position. The diagnosis of trochlear dysplasia includes the measurement of certain radiological parameters which are measured on an axial knee joint image which lies at a distance of 3 cm from the joint line. The manual selection of the required axial slice is prone to inter- and intra-observer variations which further leads to variations in the measurement of radiological parameters. In this work, an approach has been proposed for the automated 2D axial slice from the 3D knee MRI data available in DICOM format. The proposed approach uses the U-Net model for the segmentation of femur region in mid-coronal slice for landmark point detection. Further, the DICOM metadata associated with knee MRI scan has been used to find the axial slice lying at a distance of 3 cm from the joint line. A 3D T2-weighted knee MRI dataset of 55 patients from post graduate Institute of Medical Education and Research (PGIMER), Chandigarh has been used to train and test the proposed approach. The segmentation model has been trained using data from 35 patients achieving a validation dice similarity of 97.52%. The automated slice selection approach has been tested on the dataset of all the 55 patients and achieved a good similarity with the expert slice selection. © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Application of Internet of Things and Machine learning in improving supply chain financial risk management System 2

Application of Internet of Things and Machine learning in im...

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2nd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Kafila Kalyan, Nalla Bala Ahmad, Kamal Rahi, Fakhruddin Shelke, Chetan Mahabub Basha, S. Sr University School of Business Telangana Warangal India Department of Management Studies A.P Tirupati517507 India Department of Commerce Uttarakhand Srinagar Garhwal India Government Post Graduate College Department of Commerce Uttarakhand Bageshwar India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Chikkahagade Cross Chandapura Anekal Main Road Karnataka Bengaluru562106 India International Institute of Business Studies Department of Commerce Bangalore India

ISBN: (纸本)9798350381979

The incorporation of the Internet of Things (IoT) and machine learning (ML) methods has attracted significant interest in a variety of sectors in previous years. This article investigates the use of IoT and ML to improve supply chain financial (SCF) threat management. The supply chain is a complicated network with many key players, and financial threat management is important to its sustainability and success. IoT and ML possess inherent advantages in SCF due to their technology properties. They also have significant opportunities to build trust to solve big challenges in SCF, which aids financial development in the Tonkin Gulf area. This paper focuses on introducing the study on using Machine learning innovation in SCF in the Tonkin Gulf area and aims to offer suggestions on how supply chain finance could evolve there using Machine learning. This paper suggests supply chain finance game applications for pertinent investigations as well as blockchain innovation, supply chain banking threats assessment on the IoT and machine learning, and supply chain finance implementation study methodologies in the Tonkin Gulf region. © 2023 IEEE.

关键词： Internet of things

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Hybrid descriptor definition for content based image classification using fusion of handcrafted features to convolutional neural network features

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International Journal of Information Technology (Singapore) 2021年第4期13卷 1365-1374页

作者： Das, Rik Kumari, Khusbu De, Sourav Manjhi, P.K. Thepade, Sudeep Department of Post Graduate Program in Information Technology Xavier Institute of Social Service Ranchi Jharkhand India Department of Computer Applications Yogoda Satsanga Mahavidyalaya Ranchi Jharkhand India Department of Computer Science & Engineering Cooch Behar Government Engineering College Cooch Behar India Department of Mathematics Vinoba Bhave University Hazaribag Jharkhand India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India

The efficacy of content-based image classification is dependent on the richness of the feature vectors extracted from the image data. Traditional feature extraction techniques highlight single low level image characteristics like shape, size, texture, color etc. for feature generation. The process often fails to extract meaningful descriptors since considering a single image characteristic will ignore other rich properties of image contents. Mass adoption of Convolutional Neural Network (CNN) has significantly improved classification performances for content-based image data. Recent literature has documented high level of precision for image classification by carrying out transfer learning with open source pre trained Convolutional Neural Network (CNN). However, the concept of transfer learning experiences major setback in case of limited training data due to overfitting of training instances during finetuning. This work has identified this challenge and has attempted to capture probability distribution of input images to a pre trained Convolution Neural Network by utilizing it as a fixed weight feature extractor. The proposed approach has addressed the overfitting problem by removing the finetuning step involved in transfer learning. Further investigation on robust descriptor definition is carried out by concatenating the pre-trained CNN features to handcrafted features. The hybrid architecture has encouraging outcomes that have outclassed the classification accuracies of state-of-the-art handcrafted techniques. © 2021, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Augmented feature learning Feature fusion Handcrafted features Image classification Pre trained Convolutional Neural Network (CNN) Representation learning

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Intelligent Analysis of Big Data in the Information Flow

Intelligent Analysis of Big Data in the Information Flow

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Information Computing and Processing (ICP), Seminar on

作者： Petr V. Chetyrbok Marina A. Shostak Candidate of Science Engineering and Associate Professor at the Department of Computer Studies and Information Technologies V.I. Vernadsky Crimean Federal University Simferopol Russia Graduate student at the Faculty of Tourism and Service Sochi State University Sochi Russia

The article considers the use of AI methods for creation of a knowledge base in the field of recreation using a national interactive map of local cultural trends. The map contains thousands of brands classified into 9...

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Driving Design and Performance of Dental Restorations through Next-Generation Additive Manufacturing Resins

Driving Design and Performance of Dental Restorations throug...

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Computational Modelling, Simulation and Optimization (ICCMSO), International Conference on

作者： Ashwani Kumar Dhingra Deepak Chhabra Sumit Gahletia Sunita Dhingra Ravinder Kumar Sahdev Sunil Dhingra Ashish Kaushik Mannu Rathee Department of Mechanical Engineering UIET Maharshi Dayanand University Rohtak India Department of Mechanical Engineering Deenbandhu Chhotu Ram University of Science and Technology Sonipat India Department of Computer Science and Engineering UIET Maharshi Dayanand University Rohtak India Department of Mechanical Engineering University Institute of Engineering and Technology Kurukshetra University Kurukshetra Haryana Department of Prosthodontics Post Graduate Institute of Dental Sciences Pt. Bhagwad Dayal Sharma University of Health Sciences Rohtak India

ISBN: (数字)9798350361391

ISBN: (纸本)9798350361407

The advancement of novel and enhanced resin materials is crucial in improving dental product development and enabling the efficient customization of products using emerging technologies like 3D printing. The research explores the impact of AM resins on design improvements, facilitating the production of customized restorations for patients that offer enhanced fit and usefulness. This study intends to demonstrate how developments in AM resins are revolutionizing the design and performance of dental restorations, providing substantial advantages for patients and dental practitioners. This sets the stage for a future in dentistry marked by resin materials and digital manufacturing technologies that must be integrated to establish a materials innovation ecosystem in dental restorations and improve product functionality.

关键词： Technological innovation Biological system modeling Production Three-dimensional printing Product development Product design Dentistry

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Efficiency analysis of mechanical reducer equipment of material handling industry using Sunflower Optimization Algorithm and Material Generation Algorithm

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Materials Today: Proceedings 2022年 50卷 1113-1122页

作者： Sourav Jena Siddharth Jeet Dilip Kumar Bagal Asini Kumar Baliarsingh Dillip Ranjan Nayak Abhishek Barua Department of Mechanical Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India Department of Mechanical Engineering Centre for Advanced Post Graduate Studies BPUT Rourkela Odisha India Department of Electrical Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India Department of Computer Science Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India

Gear reducers are commonly used in cross-industrial applications. These include a range of advanced and basic processes, requiring the delivery of a controlled torque output. Mainly, industrial reducers are used in material handling units as it controls the speed of the machineries such as conveyors, cranes, hoist, mixers etc. For effective operation and to reduce the downtime due to any fault, optimal operating conditions are needed to define. In this paper the effectiveness and yield power are thought of, as the main attributes of the Industrial reducer, and their optimization was performed. As the impacting factors, the viscosity of lubricant, the initial parametric no. of revolutions and the current force intensity on the control unit, were considered. Test tests were performed based on the L27 Taguchi orthogonal array. For maximizing the output power and efficiency of mechanical reducer, recently formulate bio-inspired meta -heuristic algorithms i.e. Material Generation Algorithm and Sunflower Optimization Algorithm were employed besides Taguchi technique for optimization. It was found that current intensity played a significant role in maximizing the output power and efficiency of industrial reducer in accordance to analysis of variance results. Also both Material Generation Algorithm and Sunflower Optimization Algorithm resulted to be precise in providing better output as compared to Taguchi method for maximizing the output power and efficiency of the industrial reducer gearbox.

关键词： Mechanical Reducer Taguchi Method Material Generation Algorithm Sunflower Optimization Algorithm

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High-Speed Optical Convolutional Neural Network Accelerator with 100 Gbaud EO-polymer/Si Hybrid Optical Modulator

High-Speed Optical Convolutional Neural Network Accelerator ...

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OptoElectronics and Communications Conference, OECC

作者： Akito Shinya Koji Kida Hiromu Sato Guo-Wei Lu Shiyoshi Yokoyama Junichi Fujikata Graduate School of Science and Technology for innovation Tokushima University Tokushima Japan Fuculty of Engineering and Design Kagawa University Takamatsu Japan Institute for Materials Chemistry and Engineering Kyushu University Kasuga Japan Division of Computer Engineering The University of Aizu Aizu Japan Institute of Post-LED Photonics Tokushima University Tokushima Japan

Convolutional neural networks are a powerful category of artificial neural networks that can extract hierarchical features of biological data to significantly reduce parametric complexity and increase prediction accuracy. In this paper, we propose high-speed optical convolutional neural network accelerator with 100Gbaud EO-polymer/Si hybrid optical modulator and demonstrated proof of concept both in simulation and experiment.

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