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Prediction of Lumpy Virus Skin Disease Using Artificial Intelligence 1

5th International Conference on Data and Information sciences, ICDIS 2023

作者： Kholiya, Pankaj Singh Kriti Mishra, Amit Kumar School of Computing DIT University Dehradun India Department of Computer Science and Engineering Jain University Bengaluru India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

Lumpy skin disease (LSD) is a skin infection caused by the lumpy skin disease virus, which belongs to the genus of Capripoxviruses. The disease transmitted by arthropod vectors has high morbidity and low mortality rates. Contagious through arthropod-borne feces and infected insects, it is virtually a non-vectored disease. It takes one to four weeks for viremia to develop. It affects all animals regardless of their age or gender. The use of LSD by cattle in Asia has recently been reported as a terrifying threat. A morbidity rate of 7.1% was reported among cattle for the first time in India for LSD. Clinical manifestations of the disease include fever, anorexia, nodules on the mucus membranes of the mouth, nose, genital, udder, eyes, and rectum, less milk production, infertility, abortion, and even death. First introduced in Bangladesh in July 2019, the disease has since spread to China, Bhutan, India, Nepal, Vietnam, and Myanmar in Southeast Asia. This paper presents a comprehensive overview of LSD outbreaks in Asian countries over the past three years and also sheds light onto the existing methods of disease detection based on artificial intelligence techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Diseases

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Enhanced Underwater Image Restoration: Optimizing Computational Efficiency with U-Net++ and CNN Architectures 15

Enhanced Underwater Image Restoration: Optimizing Computatio...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rayvanth, N. Amruth, S. Jaya Suryaa, E. Singh, Tripty Duraisamy, Prakash University of Wisconsin Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Restoring underwater images poses a significant computational challenge, often requiring more time and resources. U-Net and its variants are not much applied for the task of underwater image restoration so we propose a novel approach to make use of the U-Net++ architecture. The aim here is to develop a U-Net++ model and a CNN model and analyze the quality of the restored images so they are capable of operating efficiently on systems with limited computational resources while significantly reducing the time required for image restoration. The dataset used for the task of underwater image restoration task in this paper is Large Scale Underwater Images (LSUI). This particular U-Net++ is computationally efficient in contrast to other state of the art models that were developed so far and this helps in saving a lot of time and speed up the process of restoring such images which makes it easier for research and analysis in underwater scenarios. From the results obtained, we conclude that the SSIM 0.9994 and PSNR 70.2617 dB values are higher for the U-Net++ model and this architecture can be used for conducting research in underwater environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Content Based Collaborative Filtering Recommendations Using Weighted Word Embeddings 15

Enhancing Content Based Collaborative Filtering Recommendati...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, Pennabadi Devendra Reddy, K. Satya Sampath Gnaneswarachary, P. Reddy, P. Lakshmikanth Venugopalan, Manju Vekkot, Susmitha Nair, Priyanka C. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350370249

In the current era of excessive information, recommendation systems are essential for assisting users to discover relevant content efficiently, ensuring high levels of user satisfaction and engagement. The proposed work focuses on enhancing content-based collaborative filtering using advanced word embed ding techniques like Word2Vec, GloVe, and BERT to capture semantic relationships between items. This study utilizes weighted word embeddings, derived from user-provided ratings, to enhance the recommendation system's capability in identifying and predicting user preferences. By incorporating these weighted embeddings, our approach seeks to improve the precision and relevance of recommendations, eventually increasing user satisfaction. The study conducts a comparative analysis of these word embedding techniques based on cosine similarity scores. Among the three weighted embeddings, GloVe shows the highest cosine similarity score for selected users UserId-68 and UserId-122. © 2024 IEEE.

关键词： BERT collaborative filtering GloVe Recommendation Systems Word embeddings Word2Vec

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A Comprehensive Exploration of Real-Time 3-D View Reconstruction Methods

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5915-5927页

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati781039 India Indian Institute of Technology Kanpur Department of Electrical Engineering 208016 India

Real-time 3-D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, etc. This article thoroughly examines and tests contemporary methodologies addressing challenges in 3-D view reconstruction. The methods being explored in this article are categorized into volumetric and mesh, generative adversarial network based, and open source library based methods. The exploration of these methods undergoes detailed discussions, encompassing methods, advantages, limitations, and empirical results. The real-time testing of each method is done on benchmarked datasets, including ShapeNet, Pascal 3D+, Pix3D, etc. The narrative highlights the crucial role of 3-D view reconstruction in domains such as robotics, virtual and augmented reality, medical imaging, cultural heritage preservation, etc. The article also anticipates future scopes by exploring generative models, unsupervised learning, and advanced sensor fusion to increase the robustness of the algorithms. © 2020 IEEE.

关键词： Generative adversarial networks

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Comparative Analysis of Machine Learning Models for Smart Pothole Detection: Enhancing Road Safety and Maintenance Efficiency 2

Comparative Analysis of Machine Learning Models for Smart Po...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Dhanvanth Prasath, M.P. Avinash, M. Prabu, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

In this paper, we propose a Smart Pothole Detection System designed to enhance road safety and improve maintenance efficiency through machine learning and geospatial analysis. Using smartphone applications, road images with corresponding XYZ coordinates are collected and labeled as either "pothole"or "no pothole". This dataset is utilized to train and evaluate multiple machine learning models, including Support Vector Machine (SVM), Logistic Regression, and Logistic Regression integrated with the AdaBoost ensemble method. These models are compared based on their accuracy, precision, recall, and F1-score, to determine the most effective solution for pothole detection. SVM emerged as the best-performing model, demonstrating high-dimensional data handling capabilities, while the AdaBoost-enhanced Logistic Regression model also performed competitively. Our system holds promise in contributing to real-time, automated pothole detection, which is essential for proactive road safety management and efficient maintenance planning. © 2024 IEEE.

关键词： Logistic regression

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Multiuser Capacity Enhancement in B5G Networks using Hybrid CDMA-NOMA Scheme

Multiuser Capacity Enhancement in B5G Networks using Hybrid ...

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2024 International Conference on Green Energy, computing and Sustainable Technology, GECOST 2024

作者： Sherien, Sabahat Sadia, Haleema Iqbal, Hafsa Ahmed Hablulmateen, Ridha Alaa Ceme Nust Department of Electrical Engineering Islamabad Pakistan School of Electrical Engineering and Computer Science Nust Islamabad Pakistan University of Kufa Department of Electronics and Communication Engineering Najaf Iraq

ISBN: (纸本)9798350357905

The rapid advancement of beyond fifth-generation (B5G) networks has opened a new era of high throughput demands, necessitating novel access schemes to overcome the associated challenges. Non-orthogonal multiple access (NOMA) has emerged as a promising technique, outperforming conventional orthogonal multiple access (OMA) in heterogeneous wireless networks. This paper introduces a new hybrid code division multiple access (CDMA)-NOMA scheme to improve multi-user capacity and power allocation (PA) efficiency in wireless networks. By combining the strengths of CDMA and NOMA, our proposed scheme forms NOMA clusters, each accommodating multiple users with varying channel characteristics. Intra-cluster interference is mitigated through successive interference cancellation (SIC), while inter-cluster interference is eliminated using CDMA spreading codes. We address the challenge of PA, a critical aspect of NOMA networks, by assigning distinct codes to all users within a cell. Through rigorous analysis, we evaluate the bit error rate performance of our hybrid scheme in diverse channel conditions, including the NYUSIM, AWGN, Rician, and Rayleigh channel models. The results presents an innovative solution to enhance B5G network capacity, demonstrating the potential of our hybrid CDMA-NOMA scheme to support more users efficiently while reducing SIC complexity. © 2024 IEEE.

关键词： Code division multiple access

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A Deep Q-Learning Approach to Intrusion Detection and Prevention Systems: Enhancing Cybersecurity through Intelligent Adaptation 4

A Deep Q-Learning Approach to Intrusion Detection and Preven...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Chandre, Pankaj Shinde, Manoj Bapurao Tilwant, Shruti Vilas Ghandat, Arun Bhau Shendkar, Bhagyashree D Dhotre, Prashant Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518981

A Deep Q-Learning approach to Intrusion Detection and Prevention Systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. This method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. The presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. The system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-Learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. The system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. Threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. This intelligent adaptation ensures proactive threat prevention and network resilience. © 2024 IEEE.

关键词： Cybersecurity

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Deep learning on medical image analysis

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 1-35页

作者： Jiaji Wang Shuihua Wang Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Biological Sciences Xi'an Jiaotong-Liverpool UniversitySuzhouJiangsuChina Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia School of Computer Science and Engineering Southeast UniversityNanjingJiangsuChina

Medical image analysis plays an irreplaceable role in diagnosing,treating,and monitoring various *** neural networks(CNNs)have become popular as they can extract intricate features and patterns from extensive *** paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre-trained *** paper also discusses how transfer learning has been applied to different areas within medical image *** comprehensive overview aims to assist researchers,clinicians,and policymakers by providing detailed insights,helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.

关键词： CNN deep learning medical image transfer learning

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Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1026-1036页

作者： Jingyang Mao Xiangyu Meng Derui Ding Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China Division of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803 USA IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership ***,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy ***,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and *** the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation ***-time recursive algorithms are given to find the minimal ellipsoid containing the true ***,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

关键词： Affine model membership functions set-membership filtering stability Takagi-Sugeno fuzzy modeling

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