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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sudha, Nuthalapati Kasetty, Sai Bhargav Madhuri, K. Sai Nikitha, Jajala Margret, Issac Neha Rajakumar, K. Mahindra University School of Engineering Telangana Hyderabad India V.I.T - University School of Computer Science and Engineering Tamilnadu Vellore India BVRIT Narsapur Dept. of Computer Science and Data Science Telangana Hyderabad India VNR VIET Dept. of CSE - AIML and IoT Telangana Hyderabad India

ISBN: (纸本)9798350356816

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation. © 2024 IEEE.

关键词： Image segmentation

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Convolution Neural Network with Unsupervised Machine Learning Approach for Feature Extraction and Brain Tumor Detection in Human beings 1

Convolution Neural Network with Unsupervised Machine Learnin...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Kumar, G. Sathish Nivaashini, M. Maheshwari, G. Uma Rasi, D. Kumar, B. Magesh Arun, R. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India Hindusthan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359756

Brain tumors are atypical progress of cells in the brain or the contiguous tissues. Tumors can be either non-cancerous or cancerous. The prognosis for a person with a brain tumor varies significantly. Benign tumors are generally less aggressive and may be curable with surgery. Malignant tumors can be more challenging to treat and may have a poorer prognosis. The outcome also depends on the stage at which the tumor is diagnosed. Detecting brain tumors using magnetic resonance imaging (MRI) images with convolutional neural networks (CNNs) is a common and effective approach in medical image analysis. In this research, the unsupervised machine learning approach called self-organizing map (SOM) is implemented for effective feature extraction and CNN with ResNet architecture is employed for brain tumor exposure efficiently. The proposed SOMResNet algorithm takes the MRI images as the input and perform feature extraction. Again, the mined essential features are given as the input for SOMResNet to identify the tumor in the human brain. The accuracy of the proposed SOMResNet is compared with softmax, ReLu, Tanh, decision tree classifiers. The accuracy of SOMResNet is 97.5%, Sensitivity is 98.0%, specificity rate is 97.1%, precision rate is 97.3% with 97.6% of F-Score value. The result shows that the SOMResNet algorithm outperforms than the traditional algorithms in brain tumor detection. © 2023 IEEE.

关键词： Feature extraction

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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A Real-Time Video Surveillance-Based Framework for Early Plant Disease Detection Using Jetson TX1 and Novel LeafNet-104 Algorithm 4th

A Real-Time Video Surveillance-Based Framework for Early Pla...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Dhanashekar, K. Siddesh, G.M. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India The Ohio State University Columbus United States

ISBN: (纸本)9789819726134

The precise and timely identification of plant diseases plays a pivotal role in safeguarding crop health and optimizing agricultural yields. This paper introduces an innovative framework designed for plant disease detection using video surveillance-based cameras strategically deployed on the Nvidia Jetson TX1 hardware platform within agricultural farms. Leveraging the capabilities of deep learning and the novel neural network architecture known as LeafNet-104, this framework empowers real-time disease detection on plants. Through the deployment of video surveillance cameras, continuous monitoring and early disease detection become achievable, enabling farmers to take prompt corrective measures and mitigate further crop damage. The Nvidia Jetson TX1, with its robust high-performance computing capabilities, enables on-device processing, rendering it ideal for real-time applications. To assess the efficacy of the proposed framework, comprehensive experiments were conducted on an actual agricultural farm. The dataset encompassed various plant species and disease types. Evaluation metrics such as accuracy, precision, recall, and the F1-score were employed to gauge the performance of the LeafNet-104 algorithm. The experimental findings underscored the framework's capacity to accurately and swiftly detect and classify plant diseases in real time, attaining an impressive accuracy rate of 97.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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Leaf Disease Identification using ResNet

Leaf Disease Identification using ResNet

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Sirenjeevi, P. Karthick, J. Madhu Agalya, K. Srikanth, R. Elangovan, T. Nareshkumar, R. Department of Artificial Intelligence and Data Science Srm Valliammai Engineering College Kattankulathur Chennai India Department of Computer Science and Engineering A.V.C. College of Engineering Mayiladuthurai India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Eachanari Coimbatore India Department of Networking and Communication SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350334357

The study of leaf diseases, as well as their detection and diagnosis, has been the subject of an increasing amount of research and attention as intelligent agricultural systems have become increasingly common and widely used. In order to explore the detection and classification of apple leaf illnesses, we made use of data sets containing examples of apple grey-spot disease, black star disease, cedar rust disease, and healthy leaves. SVM classifier for image segmentation, ResNet and VGG convolutional neural network models were utilized for comparison and improvement respectively. In our prosed method ResNet-18, which had less layers of the ResNet network, achieved greater recognition effects by obtaining an accuracy rate of 98.5 percent. © 2023 IEEE.

关键词： Convolutional neural networks

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American Sign Language Recognition System for Numerical and Alphabets

American Sign Language Recognition System for Numerical and ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sathyanarayanan, D. Reddy, T. Srinivasa Sathish, A. Geetha, P. Arunkumar, J.R. Deepak, S. Prem Kumar Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering Potti Sriramulu Chalivudi Mallikarjuna Rao College of Engineering and Technology vijayawada India Department of Computer Science and Engineering Roever Engineering College Perambalur621220 India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Samayapuram Trichy India Department of Computer Science and Engineering Modern Institute of Technology and research centre Alwar Rajasthan India

ISBN: (纸本)9798350305708

Sign language is a helpful visual technique that can be utilized by individuals who have difficulty communicating verbally. This language is used frequently by people as well. However, the vast majority of individuals don't understand sign language very well, and as a result, they frequently struggle when attempting to communicate with someone who is fluent in it. The community of people who have trouble speaking or hearing will benefit from an initiative called the Sign Language Recognition System. It is extremely challenging for deaf mutes to communicate, which is why we require a linguistic framework to understand what it is that they are attempting to express. In light of this, we present in this research an American Sign Language (ASL) handwriting translator that is built on convolutional neural networks. Instructing computers to recognize specific characters is one way to accomplish this goal. The Support Vector Machine (SVM) and the Convolutional Neural Network are two types of artificial neural networks that are being utilized in our project we have suggest the identification of American Sign Language. In addition, the Decision Tree and Voting Classifier are applied to the dataset in order to do an analysis. We have demonstrated the design and execution of an American Sign Language (ASL) translator that also incorporates fingerspelling as a direct consequence of this. © 2023 IEEE.

关键词： Convolution Neural Network Decision Tree Sign Language Support Vector Machine Voting Classifier

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Augmented Reality and Artificial Intelligence in Sign Language Expression 3

Augmented Reality and Artificial Intelligence in Sign Langua...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kaviyaraj, R. Sathya, P. Nandhini, P. Vinoth Kumar, P. Chelliah, Sureshkumar Ezhilmathi, S. Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Shanmuga Collage of Engineering and Technology Department of Computer Science and Engineering Sankari India St. Joseph's Institute of Technology Omr Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350361186

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion. © 2024 IEEE.

关键词： Augmented reality

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Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9899-9903页

作者： Zhang, Yichi Zhang, Yuchen Zhu, Lipeng Xiao, Sa Tang, Wanbin Eldar, Yonina C. Zhang, Rui University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu611731 China Kash Institute of Electronics and Information Industry Kash844000 China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Chinese University of Hon g Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this correspondence, we propose a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. To maximize the system sum rate, we jointly optimize the digital beamformer, analog beamformer, and positions of sub-arrays, under the constraints of unit modulus, finite movable regions, and power budget. Due to the non-concave/non-convex objective function/constraints, as well as the highly coupled variables, the formulated problem is challenging to solve. By employing fractional programming, we develop an alternating optimization framework to solve the problem via a combination of Lagrange multipliers, penalty method, and gradient descent. Numerical results reveal that the proposed MA-aided hybrid beamforming scheme significantly improves the sum rate compared to its fixed-position antenna (FPA) counterpart. Moreover, with sufficiently large movable regions, the proposed scheme with sub-connected MA arrays even outperforms the fully-connected FPA array. © 1967-2012 IEEE.

关键词： Budget control

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Secured and Privacy-Preserving GPU-Based Machine Learning Inference in Trusted Execution Environment: A Comprehensive Survey 17

Secured and Privacy-Preserving GPU-Based Machine Learning In...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Chaudhuri, Arunava Shukla, Shubhi Bhattacharya, Sarani Mukhopadhyay, Debdeep Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur India Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur India

ISBN: (纸本)9798331531195

With the rapid advancement of machine learning (ML) models and their widespread application across various sectors such as intrusion detection, medical diagnosis, natural language processing, and autonomous driving, these technologies have achieved remarkable success. However, this progress has also raised significant concerns about ensuring the security of ML models and protecting both private training data and model outputs from getting exposed in a shared cloud environment. To address these challenges, researchers have proposed various methodologies to create privacy-preserving, secure, and trustworthy model execution environments to prevent adversarial attacks. This study provides a comprehensive review of Trusted Execution Environment (TEE) implementations across different hardware accelerators. It also offers an overview of modern techniques for preserving privacy and security in execution environments, while identifying critical research gaps that require attention. In essence, this survey is an important resource for researchers, providing insights into recent methodologies and guiding them to focus on pressing research challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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ASAnalyzer: Attention based Sentiment analyzer for Real-world Sentiment Analysis 20

ASAnalyzer: Attention based Sentiment analyzer for Real-worl...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Hussain, Khadim Azhar, Muhammad Lee, Bumshik Iqbal, Asma Affan, Muhammad Ullah Khan, Sajid Comsats University Islamabad Department of Computer Science Sahiwal Pakistan Hong Kong Shue Yan University Department of Applied Data Science Hong Kong Hong Kong Chosun University Department of Computer Science and Software Engineering Korea Republic of Tsinghua University Department of Computer Science China University of Lakki Marwat Department of Computer Science Pakistan

ISBN: (纸本)9798350395785

In this era of big data, a lot of data is produced in various forms every second through various sources. Text data is one of those types that is produced mainly through social media like Twitter, Facebook, YouTube comments, WhatsApp, etc. To know the public's point of view on a specific issue, we can perform sentiment analysis on the text data collected from the above sites. Even though various algorithms have been proposed for sentiment analysis, these algorithms have issues of high time complexity and low context awareness, leading to low classification accuracy. To resolve the above issues, we propose an attention-based sentiment analyzer for real-world sentiment analysis named 'ASAnalyzer', which uses CNN+Attention-Based BiGRU. CNN is used to extract local features from the tweets, and then these features are used by Attention-Based BiGRU to learn the contextual information of the tweets and the long-term dependencies in both directions of the text, which helps to improve accuracy. To validate our algorithm, we used tweet data about the anti-COVID-19 vaccine from Twitter, and the results have shown that our method outperformed other state-of-the-art methods. © 2023 IEEE.

关键词： Classification (of information)

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