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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40628 条记录，以下是4911-4920 订阅

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IoT-Based Smart Irrigation System for Optimal Water-Resource Utilization in Indian Agriculture 21

IoT-Based Smart Irrigation System for Optimal Water-Resource...

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21st OITS international conference on Information Technology, OCIT 2023

作者： Bhoi, Ashutosh Baliarsingh, Santos Kumar Dev, Prabhu Prasad Ghugar, Umashankar Biswal, Manas Ranjan GITAM Deemed to Be University Department of Computer Science and Engineering Visakhapatnam 530045 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India OP Jindal University Department of Computer Science and Engineering Raigarh 496109 India

ISBN: (纸本)9798350358230

Agriculture holds paramount significance for human civilization. In India, the agricultural sector is the primary source of income, with 70% of farmers and the general populace dependent on it. Conventional irrigation methods like overhead sprinklers and flooding show inefficiency, resulting in significant water wastage and promoting issues such as soil fungus due to excessive moisture. To address these challenges, IoT-based smart irrigation management systems offer a solution by optimizing water resource utilization. Our current study involves developing a smart irrigation system that automates field irrigation through the integration of various sensors like DHT11 (humidity and temperature) and soil moisture with the Raspberry Pi 3B model. We are using MongoDB (database) on VPC to store and process data on the cloud and NodeJS web server to deploy our model. Then machine learning algorithms are implemented to analyze these sensor data and predict the irrigation. We also integrate weather conditions using an open weather API for rain prediction. Data from cloud databases is to be fetched using an Android application. © 2023 IEEE.

关键词： Machine learning

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A Comparative Analysis of the Evolution of DNA Sequencing Techniques along with the Accuracy Prediction of a Sample DNA Sequence Dataset using Machine learning 2

A Comparative Analysis of the Evolution of DNA Sequencing Te...

引用

2nd international conference on Paradigm Shifts in Communications Embedded Systems, Machine learning and Signal Processing, PCEMS 2023

作者： Mohammed, Khizar Baig Boyapati, Sai Venkat Kandimalla, Manasa Datta Kavati, Madhu Babu Saleti, Sumalatha SRM University AP Department of Computer Science Engineering India

ISBN: (纸本)9798350310719

DNA is widely considered the blueprint of life. The instructions required for all life forms, to evolve, breed, and thrive are found in DNA. Deoxyribonucleic acid, more commonly known as DNA, is among the most essential chemicals in living cells. A biological macro-molecule is DNA, also known as deoxyri-bonucleic acid. Life's blueprint is encoded by it. Sequencing of DNA has exponentially progressed due to the immense increase in data production in today's world. By means of this paper, we intend to evaluate the evolution of DNA Sequencing methods and perform a comparative analysis of modern-day DNA sequencing techniques to the ones of the past. We also illuminate the potential of machine learning in this domain by taking an exploratory and predicting the DNA Sequence using a Multinomial Naive Bayes classifier. © 2023 IEEE.

关键词： DNA

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Deep learning model for Classifying leaves of Plants using VGG-16 14

Deep learning model for Classifying leaves of Plants using V...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Abinaya, K. Ishwarya, K. Saraswathi, S. Kasinathan, Prabakaran Nandhini, G. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Semmancheri TamilNadu Chennai600119 India Veltech Rangarajan Dr. Sagunthula R & D Institute of Science and Technology Department of Cse School of Computing TamilNadu India Bharath Institute of Higher Education and Research Biher Department of Computer Science and Engineering Selaiyur Chennai India

ISBN: (纸本)9798350335095

Systematic plant-based categorization has never been difficult. Targeted crop safety is dependent on automated technologies that detect and classify plants. Classical machine learning approaches have been used to categorize leaves using handcrafted features from the shape of plant leaves, which have yielded promising results. However, we concentrate on classifying plant leaves using non-handcrafted traits. To accomplish this, we employ the use of deep learning for extraction of features and categorization. Deep Convolution Neural Networks have recently demonstrated impressive achievements in picture classification and object detection challenges. This paper proposes using the built-in, compact VGG-16 model to detect and classify ten plant species from the Plant dataset. The proposed models are utilized to classify ten other classes. When contrasted with other pre-trained models, VGG -16 fared better in categorizing leaf images. computerized plant species categorization could be beneficial to food engineers, agricultural professionals, researchers, and the general public. © 2023 IEEE.

关键词： Object detection

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A Novel Transfer learning Approach for Eye Tumour Detection using Pre-Trained CNN Models 3

A Novel Transfer Learning Approach for Eye Tumour Detection ...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Prabaharan, G. Rajasekar, Vani Sarveshwaran, Velliangiri B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narasapur India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Erode India Srm Institute of Science and Technology Department of Computational Intelligence TamilNadu Kattankulathur India

ISBN: (纸本)9798350370867

According to a recent study, using retinal imaging to identify the tumour in the eye is difficult. When an image is analyzed, the affected region of the lesion impacted retinal picture is not discovered in the conventional system due to uneven illumination, low contrast, and blurring, which are symptoms of an eye disease. The proposed methodology was created to identify the eye tumour in the earlier stage with enhanced image quality and increased accuracy. The conventional system has a few drawbacks such as lower accuracy and more computational time to analyze the image. Convolutional neural networks (CNN) have recently demonstrated its result of their capacity to learn various category image denotions that aid in image classification in various sectors. These networks have a tremendous ability to extract the proper features from input images with training on millions of images, which produces accurate categorization. The deep learning-based transfer learning approach is suggested for earlier detection of eye tumours. The five pre-trained models such as AlexNet, ResNet, DensNet, VGG 19 and Inception model were used for efficient eye tumour classification. The performance analysis of the proposed approach has shown that higher accuracy of 98.63% and this model shows enhanced performance in better classification. © 2023 IEEE.

关键词： Tumors

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Comparative Analysis of Traditional and Machine learning Based Face Recognition Models 2

Comparative Analysis of Traditional and Machine Learning Bas...

引用

2nd IEEE international conference on Data, Decision and Systems, ICDDS 2023

作者： Shreya, R. Mulgund, Akanksha P Hiremath, Shravani Sri Harshitha, A. Koundinya, Anjan K PES University Department of Computer Science Bengaluru India PES University Department of Computer Science and Engineering Bengaluru India BMS Institute of Technology and Management Department of Information Science and Engineering IEEE Student Branch Counsellor Bengaluru India

ISBN: (纸本)9798350314168

Face recognition technology uses methodologies to process images, register them, and compare and verify it with a set of faces it has already registered and labelled. Recent development in visual recognition showcases the immense power of utilising machine learning techniques like deep neural networks, convolutional neural networks to achieve face recognition. Based on a comprehensive literature review it was found that some of the algorithms that provide great performance metrics in a variety of conditions are MTCNN, FaceNet model, Haar cascade, LBPH, hence it was chosen to be the subject of comparison under various conditions in the paper. A machine learning model was constructed which included MTCNN for face detection and FaceNet for face *** Performance of KNN and SVM classifiers has been studied. The second model uses a combination of Haar cascade for face detection and LBPH for face recognition, a traditional face recognition technique. The results highlight that MTCNN, FaceNet and KNN perform exceptionally well. The performance of the other models are also discussed in the results and discussion section. The research not only guides researchers and practitioners in choosing suitable face recognition techniques but also offers ground for future improvements in accuracy and reliability in the field of algorithm developments. © 2023 IEEE.

关键词： Face recognition

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Classification of Water Apple Leaf Varieties using Transfer learning

Classification of Water Apple Leaf Varieties using Transfer ...

引用

2023 international conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Pratondo, Agus Tambunan, Toufan Novianty, Astri Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350327472

Water apple (Syzygium aqueum) is a fruit that comprises numerous variants, and precise identification of these variants is essential for distinguishing their leaves accurately. This research presents a study on water apple leaf variant classification utilizing the VGG16 deep learning model. The objective was to distinguish between seven distinct water apple leaf variants, namely bol, citra, dalhari, deli merah, king rose, kiojok, and susu. A comprehensive dataset was compiled, consisting of a balanced distribution of 99 to 100 leaf images per variant. The research involved two experiments: one with random weight initialization and the other with pre-trained weights from the ImageNet dataset. The results demonstrated the significant impact of pre-trained weights initialization, as the model achieved an impressive accuracy of 91.69% compared to 47.79% with random weight initialization. The high accuracy, precision, recall, and F1-Score values indicated the model's effectiveness in accurately classifying the water apple leaf variants. This achievement holds promising potential for diverse practical applications in agriculture, botanical research, education, and conservation efforts. © 2023 IEEE.

关键词： Plant diseases

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A Max-Min Security Game for Coordinated Backdoor Attacks on Federated learning

A Max-Min Security Game for Coordinated Backdoor Attacks on ...

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2023 IEEE international conference on Big Data, BigData 2023

作者： Wahab, Omar Abdel Avila, Anderson Polytechnique Montréal Department of Computer and Software Engineering Montréal Canada Montréal Canada INRS-UQO Joint Research Unit on Cybersecurity Gatineau Canada

ISBN: (纸本)9798350324457

We address in this paper the challenge of data poisoning attacks on Federated learning. We consider a particularly challenging attack scenario in which a single poisoning attack is coordinated over a set of clients to complicate its detection. In response, the federated learning server assigns a weight to each client's model update with the aim of mitigating the effects of the poisoning on the global model. To address this challenge, we first design a trust mechanism that enables the federated learning server to assess the trustworthiness of each client on the basis of the client's adherence to the federated learning protocol and the quality of data contributed by the client. Capitalizing on the trust mechanism, we model the interactions between the attacker and federated learning server as a security max-min game. The outcome of the game guides the server on the optimal weight assignment strategy over the set of clients' model updates, so as to minimize the effects of the data poisoning on the global model. Simulations conducted on the MNIST and CIFAR-10 datasets suggest that our proposed solution decreases the coordinated attack success rate, as well as the false positive and false negative percentages compared to two baseline solutions. © 2023 IEEE.

关键词： Coordinated Backdoor Attack Federated learning Federated learning Security Game Theory Poisoning Attack

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A Comprehensive Investigation on Real - Time Object Detection in Deep learning 5

A Comprehensive Investigation on Real - Time Object Detectio...

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5th IEEE international conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Rajeshkumar, M. Arun Raj, L. B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305449

Object Detection technique is extremely related among the computer Vision field. Object detection empowers detecting case in the perception of various objects from videos plus imagery as well as videos or pre-recordings video. This will find out the variety of individuality of imagery more willingly than the recognition of mechanism of objects and will provides a logical as well as perceptive of images in efficient manner deeply related to human vision works. Object detection has recognized with significant notice from researchers for the motive of its very close relationship with image detection. Machine learning algorithms interrelated to Convolutional Neural Networks (CNNs) has been currently investigated in a several times of real time object detection applications through the source of various articles. In this study, we have analyzed about various algorithm implemented in the concept of real-time object detection as well as importance and drawbacks of specific algorithm of the existing works in the form of a comparative table. The results of this investigation indicate that the machine learning algorithms are very successful in detecting the real time object. © 2023 IEEE.

关键词： Object detection

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CNN Transfer learning for Two Stage Classification of Diabetic Retinopathy using Fundus Images 8

CNN Transfer Learning for Two Stage Classification of Diabet...

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Joint 8th international conference on Digital Arts, Media and Technology and 6th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Das, Pranajit Kumar Pumrin, Suree Chulalongkorn University Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350396546

Diabetic retinopathy is a common retina disease caused by diabetes, which is increasing rapidly worldwide along with diabetic Mellitus. It is very difficult to diagnose in the beginning as it is asymptomatic, which leads to blindness. Presently, there are about 422 million diabetes patients, and it is projected that the number will be 552 million in 2030, as per a report from the World Health Organization. With the advancement in Artificial Intelligence, Deep learning, and computer Vision fields, many CNN-powered models have been developed for the detection and classification of DR using color fundus images. Early diagnosis of DR will increase recovery and decrease the possibility of vision loss threats. In this study, we aim to classify Diabetic Retinopathy in two stages, diseased versus healthy images. Three different pre-trained CNN models, namely, VGG16, InceptionV3, and MobileNet were deployed through transfer learning. Messidor and Messidor-2, two publicly available datasets are used in the training and testing of these CNN models. In terms of classification accuracy, the highest result of 84% was obtained using InceptionV3, whereas MobileNet and VGG16 shows 83% and 78% accuracy, respectively. The Highest 86 % precision for the healthy class and 88% sensitivity for the diseased class is shown by VGG16. © 2023 IEEE.

关键词： Image classification

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Algorithm for Detection Foreign Objects in Transmission Lines based on Few-shot learning from UAV Remote Sensing Images 3

Algorithm for Detection Foreign Objects in Transmission Line...

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3rd international conference on computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2023

作者： Zhang, Jing Zhang, Jiyong Wu, Bing Liu, Donghui School of Geomatics and Urban Spatial Information Beijing University of Civil Engineering and Architecture Beijing China State Grid Economic and Technological Research Institute Co. Ltd. Beijing China Economic Research Institute of State Grid Zhejiang Electric Power Company Zhejiang China

ISBN: (纸本)9798350372052

Timely detection of foreign objects on power transmission lines is essential for improving the safety of the power system. To address the challenge of a limited number of samples for foreign objects on power transmission lines, this paper proposes a few-shot learning-based algorithm for detecting power transmission line foreign objects in unmanned aerial vehicle remote sensing images based on the Faster RCNN network. Firstly, a Dual-Awareness Attention Mechanism (DAAM) is employed to capture the spatial relationships between support feature and query feature. Subsequently, a Representation Decoupling Mechanism (RDM) is utilized to address the data distribution differences between base classes and novel classes. Finally, the model is validated on a dataset of foreign objects on power transmission lines. Experimental results demonstrate that the proposed method effectively detects foreign objects on power transmission lines, providing a novel approach for few-shot learning in the detection of foreign objects on power transmission lines. © 2023 IEEE.

关键词： Electric lines

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