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

作者： Vijayaraj, A. Shreya, S. Deepana, G. Senthilvel, P. Gururama Jebakumar, R. Department of IT R.M.K Engineering College RSM Nagar Kavaraipettai Chennai601 206 India Department of Civil Engineering S.A. Engineering College Thiruverkadu Chennai600077 India Chennai602 105 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350305708

This paper suggests a new approach to enhancing waste classification performance using deep learning algorithms. Specifically, the proposed approach integrates an autoencoder network with a feature selection method in convolutional neural network models. This approach aims to identify valuable features from waste material images while reducing noise and improving the accuracy of the model. Autoencoder networks are utilised to learn a compressed representation of input data, which can be used as a feature vector for classification tasks. The feature selection method is then applied to remove irrelevant features, improving the accuracy of the model. The proposed approach has the potential to provide an efficient and effective solution for waste classification tasks, contributing to sustainable waste management and environmental conservation. Experimental results demonstrate the effectiveness of the proposed approach in enhancing the accuracy of waste classification tasks. © 2023 IEEE.

关键词： autoencoder network sustainable waste management Waste classification

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Practical bipartite consensus for multi-agent systems:A barrier function-based adaptive sliding-mode control approach

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Journal of Automation and Intelligence 2023年第1期2卷 14-19页

作者： Boda Ning Qing-Long Han Xiaohua Ge Centre for Smart Infrastructure and Digital Construction Swinburne University of TechnologyMelbourneVIC 3122Australia School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

This paper is concerned with bipartite consensus tracking for multi-agent systems with unknown disturbances.A barrier function-based adaptive sliding-mode control(SMC)approach is proposed such that the bipartite steady-state error is converged to a predefined region of zero in finite ***,based on an error auxiliary taking neighboring antagonistic interactions into account,an SMC law is designed with an adaptive *** gain can switch to a positive semi-definite barrier function to ensure that the error auxiliary is constrained to a predefined neighborhood of zero,which in turn guarantees practical bipartite consensus tracking.A distinguished feature of the proposed controller is its independence on the bound of disturbances,while the input chattering phenomenon is ***,a numerical example is provided to verify the effectiveness of the proposed controller.

关键词： Barrier functions Practical bipartite consensus Multi-agent systems Signed graphs Sliding-mode control

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A Compact Dual Port Circularly Polarized MIMO Antenna for On-/Off-Body WBAN Applications

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Gao, Guo-Ping Bai, Jin-Ming Jin, Li-Jun Guo, Wen-Di Ma, Ge Hu, Bin Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou730000 China

In this letter, a novel compact dual port circularly polarized multiple-input-multiple-output (MIMO) antenna for On-/Off-Body wireless body area network (WBAN) applications is proposed, radiating unidirectional and omnidirectional radiation patterns by two ports, respectively. By adding a circular arrangement of multiple shorting pins, the frequency points of the TM01/TM02 hybrid mode and TM11 mode of the circular patch are adjusted to the same frequency band, which are used as the resonant mode of two ports, respectively. In order to achieve circular polarization at the unidirectional port, a rectangular slot is etched in the center of the patch by bypassing the omnidirectional radiation port, and two rectangular slots are etched at the edge of the patch. The omnidirectional bandwidth is 70 MHz (3.45 GHz-3.52 GHz), and the unidirectional bandwidth is 190 MHz (3.41 GHz-3.6 GHz). By integrating a decoupling method based on characteristic mode analysis with circularized polarization, not only high port isolation was achieved but also communication degrees of freedom for the unidirectional mode was enhanced, making it suitable for a wider range of applications. © 2002-2011 IEEE.

关键词： Degrees of freedom (mechanics)

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Early Detection of Glaucoma Utilizing Retinal Nerve Fiber Layer (RNFL) Investigation

Early Detection of Glaucoma Utilizing Retinal Nerve Fiber La...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Anandhi, S. Amarnath Kumaran, A. Sivakamasundari, P. Vijayakumar, K. Birnica, Y.J. Senthil Pandi, S. Rajalakshmi Engineering College Department of Cse Chennai India Erode Sengunthar Engineering College Department of Eie Erode Perundurai638057 India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chengalpattu603203 India St. Joseph's Institute of Technology Department of It Chennai India Ssn College of Engineering Department of Ece Chennai6031101 India

ISBN: (纸本)9798331543617

Early detection of glaucoma is essential for preventing permanent vision loss. Glaucoma, a degenerative eye illness that affects the optic nerve, causing vision degeneration without any obvious symptoms in the early stages. One hopeful method for early glaucoma detection is Retinal Nerve Fiber Layer (RNFL) analysis, which assesses the chunkiness of the Nerve Fiber Layer (NFL) in the Retina. Changes in RNFL thickness, particularly thinning, are an early indicator of glaucoma and can be detected before substantial visual injury occurs. Optical Coherence Tomography (OCT) is universally used for RNFL analysis, offering high-resolution images of the retina and enabling precise measurements of RNFL thickness. Through RNFL analysis, clinicians can identify patterns of nerve fiber thinning that correspond to early glaucomatous injury, permitting for timely involvement and treatment. This method can help with glaucoma therapy by reducing disease development and preserving patient's quality of life. Nonstop advancements in imaging technology and automated RNFL assessment tools are further enhance the accuracy and availability of early glaucoma detection. © 2024 IEEE.

关键词： Optical coherence tomography

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Brain Computer Interaction Framework for Speech and Motor Impairment Using Deep Learning

Brain Computer Interaction Framework for Speech and Motor Im...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Somnathe, Abhijit T. Tayubi, Iftikhar Aslam Reddy, Pundru Chandra Shaker Sharma, Nipun Sharma, Vikas Yesubabu, Mannava Electronics and Telecommunication Engineering Smt. Kashibai Navale College of Engineering Pune India King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia School of Computer Sicence and Engineering Manipal University Jaipur Rajasthan Jaipur303007 India Electronics and Communication Engineering Presidency University Karnataka Bangalore India School of Computer Science & Applications Iimt University Uttar Pradesh Meerut India Computing Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India

ISBN: (纸本)9798350357769

Some people may have trouble communicating with others because of motor and speech difficulties brought on by accidents, strokes, or diseases. Paralyzed people often struggle to communicate their demands, which can make even the simplest tasks challenging. People with disorders like dysarthria and amyotrophic lateral sclerosis may have trouble following conversations. The proposed method for automatic detection of everyday basic desires will help those with dysarthria and quadriplegic paralysis has more fulfilling lives. The device accomplishes this by monitoring brain impulses, which it then converts into either perceptible voice commands or messages that can be delivered to a healthcare provider's smart devices, depending on the user's preferences. The suggested method randomly displays an image of one of six basic requirements while using event-related-potentials(ERPs) detected from the Electroencephalogram (EEG) data to determine which need to fulfill. The dataset utilized for train, test, & validation was built using input from 10 participants. The proposed method had a 96.3|% success rate. © 2023 IEEE.

关键词： Brain-Computer Interface CNN Deep Learning EEG ERP

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Enhancing PAN Card Security with OCR Based Information Extraction and Face Recognition for Reliable Identity Verification

Enhancing PAN Card Security with OCR Based Information Extra...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： D N Harshavardhan Reddy Malapati Sai Sravanth Reddy Panyam Sudha Dandapani Computer Science and Engineering With Data Science Sathyabama Institute of Science and Technology Chennai India Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Due to the potential for impersonation, the Indian PAN card is of great importance and thus, it should be encrypted with certain methods. The paper proposes a system that deals for achieving greater security of PAN card by applying a combination of OCR based information extraction and face recognition system as a means of securing identity verification. A set of OCR algorithms is first entrusted with the task to accurately read and translate the text found on the PAN card which includes the, date of birth, name and the PAN number, along with other features. The digitized and translated data is easier to store and retrieve, which makes the verification process quicker. Furthermore, a deep learning face recognition module is added in a bid to match the face of the PAN card holder with the face on the card. The proposed system is based on the architecture of the convolutional neural network (CNN) which has been shown to perform well in systems that need to compare in different amounts of light and at different angles making it reduce both the false acceptance and the false rejection rates. This article emphasizes the need for cutting-edge technologies to be implemented in identity verification systems, as identity fraud can be avoided through the use of Computerized Optical character recognition technology (OCR) combined with face recognition technology that provides advanced scanning capabilities advanced scanning capabilities that assist in safeguarding the PAN card.

关键词： Privacy Translation Face recognition Optical character recognition Information retrieval Fraud Convolutional neural networks Reliability Protection Next generation networking

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DC PROGRAMMING AND ALGORITHM FOR NONCONVEX LOG TOTAL VARIATION IMAGE RECONSTRUCTION

Journal of Applied and Numerical Optimization

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Journal of Applied and Numerical Optimization 2025年第1期7卷 41-54页

作者： Zhang, Benxin Xue, Lei Sun, Shaohua Li, Haixia Zhu, Zhibin School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

In this paper, a nonconvex variational model with log total variation is proposed to investi-gate robust image reconstruction from a certain number of measurements. Due to the form of the new model, the difference of convex functions (DC) programming is presented and a DC algorithm (DCA) is adopted. The specific DCA subproblem can be solved by the alternating direction method of multipliers (ADMM). Theoretically, we prove the sequence generated by the DCA converges to a stationary point. The experimental results demonstrate that the new method outperforms the competitive methods when the measurement is limited or the noise level is significant. © 2025 Journal of Applied and Numerical Optimization.

关键词： Convex optimization

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Named Entity Recognition for Telugu and Kannada on Naamapadam Dataset Using Various Machine Learning and Deep Learning Algorithms

Named Entity Recognition for Telugu and Kannada on Naamapada...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Sree, Kuruva Divya Geethika, Gunti Nair, Aarathi Rajagopalan Gupta, Deepa Veena, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Named Entity Recognition is a crucial task in Natural Language Processing. It involves detecting and categorizing named entities such as individuals, places, and organizations within a text. This process is vital for transforming un-structured text into structured information, which is useful for various applications like information retrieval, question answering, and text summarization. The proposed study investigates bilingual NER for Telugu and Kannada languages using pre-trained language models and embeddings for contextual vectorization. Utilizing the Naamapadam dataset, the research employs IndicFT, MuRIL and IndicBERT embeddings along with machine learning and deep learning algorithms. Notably, utilized only 10% of the considered dataset and achieved results that surpass those reported in the base paper. Among the models tested, MuRIL combined with XGBoost achieved the best performance, with an F1 score of 89.00 for Telugu and 87.00 for Kannada. Performance evaluation demonstrates varying effectiveness across techniques, shedding light on optimal strategies for NER in under resourced languages. These findings contribute to advancing NLP techniques for Indian languages, facilitating broader accessibility and usability in diverse linguistic contexts. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Question answering

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Advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms 4

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohandas, R. Elavarasi, M. Praveen, R.V.S. Chittapragada, Hemanand Nithiya, C. Prabagar, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Bca - It Pallavaram India LTIMindtree Limited Digital Engineering and Assurance Hyderabad India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Madanapalle India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Puduvoyal India Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems. © 2024 IEEE.

关键词： Cyber attacks

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Vision Transformer Approaches for Diabetic Retinopathy

Vision Transformer Approaches for Diabetic Retinopathy

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Soubraylu Sivakumar P. Likhith Kumar Gudeti Neeraj Reddy B. Anandaraj T. Rajesh Kumar P. Palanisamy Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science Engineering Madanapalle Institute of Technology & Science Madanapalle Andhar Pradesh India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Computer Science and Engineering School of Computing K.S.R College of Engineering (Autonomous) Tiruchengode Namakkal India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The focus of this paper is to identify how deep learning can be utilised in identifying diabetic retinopathy, a serious complication of diabetes which causes blindness. In this paper, an end-to-end system is proposed for diagnosing the early signs of disease using CNN to analyze images. Cropped Retinal - CNN - Early Diagnosis of the First Stage of the Disease. The overall performance of the proposed system is determined using accuracy, precision, and usability that are far much better than the conventional method of diagnosing the syndromes. Five models are explored under this study which includes Vision Transformer (ViT), Convolutional Neural Network, MobileNetV2, YOLO and Hybrid model (ViT+CNN). five levels of Diabetic Retinopathy (DR) are explored viz., No, Mild, Moderate, Severe and Proliferative DR. From these five models, the hybrid model achieved a $\mathbf{9 4 \%}$ of accuracy on the DR dataset. More future work includes the expansion of the expressed model and the inclusion of real-time image processing for clinical use.

关键词： YOLO Diabetic retinopathy Computer vision Accuracy Computational modeling Transformers Retina Convolutional neural networks Usability Diseases

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