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2023 IEEE International Conference on Electrical, Electronics, Communication and Computers, ELEXCOM 2023

作者： Ali, Md. Akkas Dhanaraj, Rajesh Kumar Sharma, Anupam Kumar Balusamy, Balamurugan Geetha, V. Grover, Veena School of Computing Science and Engineering Galgotias University U.P Greater Noida India Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh Pune India Shiv Nadar University India School of Applied Sciences Reva University Dept. of Computer Science Bangalore India Noida Institute of Engineering and Technology U.P Greater Noida India

ISBN: (纸本)9798350305111

Agriculture has progressed to an advanced stage worldwide and into an economical source of sophisticated agricultural practices. It is a significant milestone in the history of agriculture. The broad embrace of information and today's advanced technology has paved the way for this evolution. But pests cause substantial harm to farming, which has serious financial, ecological, and societal repercussions. That emphasizes the significance of computerized systems for early pest detection. Agricultural issues have recently emerged as a significant focus for machine learning-based research. This study offers an efficient Internet of Things (IoT)-enabled pest detection system that uses pest sound analytics in large agricultural fields. The system is built on a model called a Graph-based Pyramid Attention-multi module deep convolutional neural network (GPA-Net). The GPA-Net platform comprises a variety of essential subcomponents, a couple of which are the CSPNet, CTA, and SPA. The GPA-Net model has been trained using the sounds of the pests. The outcomes of the demonstrations revealed that the recommended GPA-Net model successfully identified the pest and outscored the state-of-the-art models YOLOv5, Faster RCNN, and ResNet-50 with 99.75% accuracy and an F1 score of 98.99%. © 2023 IEEE.

关键词： Internet of things

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MPTA-Net: Multi-Scale Perception Network with Triple-View Attention for Polyp Segmentation 4

MPTA-Net: Multi-Scale Perception Network with Triple-View At...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Chen, Zhihong Yao, Lisha Liu, Yue Qiu, Bingjiang Han, Chu Tang, Haoyang Du, Yujia Liu, Zaiyi Fang, Gang Guangzhou University Institute of Computing Science and Technology Guangzhou China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China South China University of Technology School of Medicine Guangzhou China Department of Radiology Guangzhou China Jiangxi College of Applied Technology School of Information Engineering Ganzhou China Guangdong Provincial People's Hospital Guangdong Academy of Sciences Guangdong Cardiovascular Institute Guangzhou China Southern Medical University Medical Research Institute Guangzhou China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China

ISBN: (纸本)9798350394375

Accurate segmentation of polyps in endoscopic images is pivotal for the early detection and diagnosis of colorectal cancer (CRC). Nonetheless, the varied appearance of polyp foregrounds and the intricate background interference significantly diminish the efficacy of pixel-level predictions. To address this issue, we propose a multi-scale perception network with triple-view attention (MPTA-Net) for polyp segmentation. The model primarily revolves around two core principles. The first integrates multiscale spatial information into the network's hierarchical encoding process to better preserve detailed polyp semantic information. The second introduces a triple-view attention mechanism that analyzes feature maps from three distinct perspectives to focus on and localize semantically relevant areas, thereby enhancing segmentation accuracy. Experimental results reveal that the proposed model outperforms existing mainstream segmentation models, effectively addressing under-segmentation, over-segmentation, and edge blurriness issues in polyp segmentation, thereby achieving better identification of challenging areas. © 2024 IEEE.

关键词： Semantics

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Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

Deep Learning-Driven Cryptanalysis in Modern Encryption Syst...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Venkateswaran Radhakrishnan Vijayakumari Rodda S. Sowmiya Abdur Rahman Sarker R. Palanikumar R. Asokan Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Dhofar Sultanate of Oman Department of Computer Science Krishna University Rudravaram Machilipatnam Sri Eshwar College of Engineering COIMBATORE Applied Statistics and Data Science Jahangirnagar University Dhaka Bangladesh Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of ECE Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

With the rapid advancements in artificial intelligence, deep-learning-based cryptanalysis has emerged as a promising approach to evaluating the security of modern encryption algorithms. Traditional cryptanalysis methods, such as linear and differential attacks, often require extensive mathematical modelling and computational effort. In contrast, deep learning enables automated feature extraction and pattern recognition, potentially identifying cryptographic weaknesses more efficiently. This paper presents an advanced neural network-based cryptanalysis framework targeting modern encryption schemes, including AES, RSA, and lightweight block ciphers. We employ deep learning architectures such as Convolutional Neural Networks (CNNs) and Transformer models to analyse ciphertext-plaintext relationships and recover cryptographic keys. Additionally, we explore the impact of data preprocessing, hyperparameter optimization, and model interpretability on cryptanalysis accuracy. Our experiments demonstrate that deep learning can achieve significant improvements in key recovery probability compared to conventional approaches. However, we also highlight the computational challenges and practical limitations of deep-learning-based cryptanalysis. This study provides insights into the applicability of AI-driven techniques in cryptanalysis and their implications for cryptographic security.

关键词： Deep learning Ciphers Accuracy Computational modeling Visual analytics Neural networks Transformers Encryption Pattern recognition Convolutional neural networks

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Deep Learning for Ocular Disease Classification: A Study on ODIR-5K Dataset

Deep Learning for Ocular Disease Classification: A Study on ...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Suresh Rasappan S. Ahamed Nishath Sathish Kumar Kumaravel Regan Murugesan Kala Raja Mohan Nagadevi Bala Nagaram Mathematics Department University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

This research investigates the utilization of deep learning methodologies for classifying ocular dis-eases using the ODIR-5K dataset. A tailored convolutional neural network (CNN) architecture is developed to categorize fundus images into five distinct classes: diabetes, glaucoma, cataract, age-related macular de-generation, and normal. Notably, experimental outcomes achieve a 96.32 % accuracy rate, underscoring the effi-cacy of deep learning in ocular disease diagnosis.

关键词： Deep learning Glaucoma Technological innovation Accuracy Systematics Feature extraction Convolutional neural networks Medical diagnosis Optimization Diseases

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Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

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Computer Systems Science & Engineering 2023年第3期44卷 2277-2292页

作者： Ashit Kumar Dutta Mazen Mushabab Alqahtani Yasser Albagory Abdul Rahaman Wahab Sait Majed Alsanea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhKingdom of Saudi Arabia Physical Therapy Department Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl Ahsa31982HofufKingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational *** LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning *** recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for *** the different processes involved in ML based LMS,feature selection and classification processesfind *** this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for *** key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in *** pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal ***,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)*** design of GSO and MFO techniques result in reduced computation complexity and improved classification *** presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several *** simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

关键词： Learning management system data mining performance monitoring machine learning feature selection

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Producing a Standard Dataset of Speed Climbing Training Videos Using Deep Learning Techniques

arXiv

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arXiv 2023年

作者： Xie, Yufei Li, Shaoman Lin, Penghui College of Computing and Information Technologies National University Manila999005 Philippines Rock Climbing Sports Centre Jiangxi College of Applied Technology Ganzhou341000 China

This dissertation presents a methodology for recording speed climbing training sessions with multiple cameras and annotating the videos with relevant data, including body position, hand and foot placement, and timing. The annotated data is then analyzed using deep learning techniques to create a standard dataset of speed climbing training videos. The results demonstrate the potential of the new dataset for improving speed climbing training and research, including identifying areas for improvement, creating personalized training plans, and analyzing the effects of different training methods. The findings will also be applied to the training process of the Jiangxi climbing team through further empirical research to test the findings and further explore the feasibility of this *** Codes 68T45 © 2023, CC BY-NC-ND.

关键词： Deep learning

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Pneumonia Detection in Chest X-ray using InceptionV3 and Multi-Class Classification 3

Pneumonia Detection in Chest X-ray using InceptionV3 and Mul...

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3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Pandeeswaran, C. Abed Mohammed, Mazin Glan Devadhas, G. Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq Vimal Jyothi Engineering College Department of Electronics and Instrumentation Engineering Chemperi670632 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The lung is a imperative internal organs in human physiology. The abnormality in the lung will cause severe respiratory problems. Pneumonia is a severe lung infection, and early screening and treatment are essential to heal the illness. This research aims to implement a pre-trained InceptionV3 scheme to detect pneumonia in chest X-ray pictures. This scheme consists of the following phases;(i) Image collection and resizing, (ii) Deep-features extraction using InceptionV3, (iii) Feature reduction with firefly algorithm, (iv) Multi-class classification, and (v) Validation. A four-class classifier is employed in the proposed scheme to classify the X-ray into normal, mild, moderate, and severe classes using 5-fold cross-validation. The experimental outcome of the K-Nearest Neighbor (KNN) classifier confirms that this scheme offered a classification accuracy of 85.18% on the considered image database. © 2022 IEEE.

关键词： Classification (of information)

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DL-HAR: Deep Learning-Based Human Activity Recognition Framework for Edge computing

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Computers, Materials & Continua 2020年第11期65卷 1033-1057页

作者： Abdu Gumaei Mabrook Al-Rakhami Hussain AlSalman Sk.Md.Mizanur Rahman Atif Alamri Research Chair of Pervasive and Mobile Computing King Saud UniversityRiyadh11543Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11543Saudi Arabia Information and Communication Engineering Technology School of Engineering Technology and Applied ScienceCentennial CollegeTorontoCanada

Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to *** learning has gained momentum for identifying activities through sensors,smartphones or even surveillance ***,it is often difficult to train deep learning models on constrained IoT *** focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing,which we call *** goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on less-powerful edge devices for *** idea is to conduct the training of the model in the Cloud and distribute it to the edge *** demonstrate how the DL-HAR can perform human activity recognition at the edge while improving efficiency and *** order to evaluate the proposed framework,we conducted a comprehensive set of experiments to validate the applicability of *** results on the benchmark dataset show a significant increase in performance compared with the state-of-the-art models.

关键词： Human activity recognition edge computing deep neural network recurrent neural network Docker

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UNet with Two-Fold Training for Effective Segmentation of Lung Section in Chest X-Ray 3

UNet with Two-Fold Training for Effective Segmentation of Lu...

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3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Gnanasoundharam, J. Abed Mohammed, Mazin Glan Devadhas, G. Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq Vimal Jyothi Engineering College Department of Electronics and Instrumentation Engineering Chemperi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Segmentation and evaluation of the Region of Interest (ROI) in medical imaging is a prime task for disease screening and decision-making. Due to accuracy, Convolutional-Neural-Network (CNN) based ROI segmentation has been widely employed in recent years to evaluate a class of medical images recorded using chosen modality. The proposed work aims to demonstrate the segmentation performance of the UNet scheme with a one-fold and two-fold training process. To experimentally verify the merit of the proposed scheme, segmentation of the lung section from the chest X-ray is studied. This research includes the following parts;(i) Resizing the test image and image mask to pixels, (ii) Training the UNet with one-fold and two-fold approaches, (iii) Extracting the ROI, (iv) Comparing the ROI with the mask to compute the image metrics and (v) Validating and confirming the segmentation performance of UNet. The performance of UNet is then verified with UNet+ and UNet++. The investigational ending substantiates that the proposed approach helps to get better Jaccard (>95%), Dice ((>97%), and Accuracy (>98%) in two-fold training compared to other methods considered in this study. © 2022 IEEE.

关键词： Decision making

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Exploring the Role of Internet of Things in Wireless Sensor Networks for Industry 4.0 Applications

Exploring the Role of Internet of Things in Wireless Sensor ...

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Pioneering Developments in Computer Science & Digital Technologies (IC2SDT), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N.R T. Sujatha S. Renuga Chithra R.S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Application Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Ponjesly College of Engineering Kanyakumari India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings.

关键词： Wireless sensor networks Protocols Wireless networks Sensor phenomena and characterization Routing Software Sensor systems Power grids Fourth Industrial Revolution Machinery

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