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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Tensor Extended Kalman Filter and its Application to Traffic Prediction

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 13813-13829页

作者： Chang, Shih Yu Wu, Hsiao-Chun Kao, Yi-Chih San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Taoyuan32003 Taiwan National Yang Ming Chiao Tung University Information Technology Service Center Hsinchu30010 Taiwan

Traffic prediction is a very important mechanism in intelligent transportation systems for applications including routing planning and traffic control. In order to infer multifarious traffic information, one/two-relational traffic data in the vector/matrix form need to be expanded to multi-relational traffic data in an arbitrary tensor form. However, none of the existing approaches is capable of performing traffic prediction by characterizing and tracking the inherent nonlinear dynamics which are often encountered in realistic time-series analysis. Although the extended Kalman filter (EKF) has been proven to be quite promising in inferring nonlinear dynamics from time series, but the current EKF approach still suffers, unfortunately, from a serious drawback that state variables have to be represented in vector form. In fact, the characteristics of multi-relational states in practice can never been manifested accurately in practice and the performance of an EKF would be greatly restricted thereby. In this work, we introduce a new tensor extended Kalman filter (TEKF) approach to accommodate arbitrary input, output, and state variables all in arbitrary tensor forms. We also propose a new tensor-based expectation-maximization (EM) algorithm to estimate the nonlinear state-transition and observation-model mappings. The computational and memory complexities of the proposed TEKF approach are also studied in this paper. Finally, numerical experiments are conducted to evaluate the traffic prediction performance of the proposed new TEKF approach over the simulated and realworld traffic datasets in comparison with three other existing deep-learning prediction methods. © 2000-2011 IEEE.

关键词： Tensors

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector Machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： Dimensionality reduction

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Label-Free Biomolecule Detection With InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate

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IEEE Access 2025年 13卷 89667-89684页

作者： Hashim Elshafie Abdulrahman Saad Alqahtani Azath Mubarakali Adarsh Rag S M. Venkatesh P. Parthasarathy Department of Computer Engineering College of Computer Science King Khalid University Abha Saudi Arabia College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Electronics and Communication Engineering CMR Institute of Technology Bengaluru Karnataka India

This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of $2.5 \times 10$ cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of $3.5 \times 10 ^{3}$ , outperforming configurations without overlap ( $2 \times 10 ^{3}$ ), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection.

关键词： Biosensors Logic gates Sensitivity Tunneling Dielectric constant Performance evaluation Metals Biological system modeling TFETs Electrostatics

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Effective Short Packet Communication for Resource Allocation in Wireless Powered Iot Networks 9

Effective Short Packet Communication for Resource Allocation...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Selvi, K. Kanchana, D. Sankar, R. Princy, B Anni Saravanan, S. Balamurugan, A. R M K Engineering College Department of Information Techology Tamil Nadu India SRM Institute of Science and Technology Department of Computer Applications Tamil Nadu India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu India Jeppiaar Institute of Technology Department of Aritificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350397376

The Internet-of-things (IoT) is a technology that might be used to link huge amounts of equipment and gadgets in the future. This research uses a hybrid access point (HAP) to explore short packet communication (SPC), in which the IoT devices broadcast their short data packets to the HAP using the energy they gather. As compared to regular wireless networks, SPC has a lower transmission rate and a higher packet error rate. With unlimited block length codes, the allocation of resources in the literature is no longer the best it could possibly be. Based on a user's unique effective amount of information r, their transmission time and packet error rate are tuned to maximize or decrease total effective throughput or total transmission time. Nonconvex issues need the development of efficient ways for unacceptable solutions. These algorithms outperform current benchmarks and are on par with an exhaustive search for the optimum solution in terms of performance. © 2023 IEEE.

关键词： Resource allocation

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Deep Learning based Smart American Sign Language Detection in Alphabets 6

Deep Learning based Smart American Sign Language Detection i...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： RukumaniKhandhan, C. Vijayakumar, P. Sri Sabari, C. Arun, G. Chandrasekaran, N. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - PG Erode Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai India Kongu Engineering College Department of Computer Technology - PG Tamil Nadu Perundurai India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thuduppathi India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350304480

Disabled people are of different kinds like physically challenged people, mentally challenged etc. To help the people who cannot convey their thoughts and who cannot hear others thoughts, sign language is being used to communicate with them. Sign language is something that is used to communicate through body movements mainly through hands and *** Sign Language(ASL) is an authorized sign language that is taught and used widely. ASL is difficult to teach and learn for them because it is completely new and they have no knowledge about it. In this research paper the ASL is recognized and converted into text through data mining techniques. The study uses a dataset of ASL alphabet images and applies various data mining techniques, including feature selection, classification, and clustering. The features extracted from the images include shape, size, color, and texture. The use of neural networks, decision trees, and support vector machines speeds up the data categorization process. Techniques such as k-means clustering and hierarchical grouping are used to facilitate entity grouping. The results show that the best performance is achieved using a combination of feature selection and classification techniques. The decision tree algorithm with the selected features achieves an accuracy of 96.5% on the test dataset. The system can accurately recognize all the ASL alphabets and convert them into text or speech. The system developed can be used to improve communication between deaf and hearing individuals and can be extended to recognize other ASL gestures and phrases. Future research can focus on real-time detection and recognition of ASL gestures using data mining techniques. CNN algorithm is used to recognize the image based on the images collected. The accuracy rate gets low and the detection is inaccurate. Therefore, VGG16 algorithm is used to recognize the images and it is unique in convolutional models. © 2023 IEEE.

关键词： ASL CNN Gestures VGG16

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AgriLLM:Harnessing Transformers for Framer Queries

arXiv

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arXiv 2024年

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering India Department of Data Science & Computer Applications India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. Copyright © 2024, The Authors. All rights reserved.

关键词： Agriculture

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A Cutting Edge Deep Learning Models for Paddy Leaf Disease Detection and Classification

A Cutting Edge Deep Learning Models for Paddy Leaf Disease D...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： A. Dhanalakshmi K. Ponmozhi A. Ajmal Department of Computer Applications Kalasalingam Academy of Research and Education KrishnanKovil Tamil Nadu India Department of Data Science Vellore Institute of Technology(ICER) Bangalore Karanataka India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The world’s main grain crop is rice (Oryza sativa). It provides the majority of the world’s population with nourishment and energy. Numerous rice leaf diseases are a major threat to the world’s agricultural output because they deteriorate crop quality in addition to lowering crop yield. Traditional methods of classifying and managing diseases mostly rely on laborious and subjective manual examination procedures. However, recent advancements in computer vision methods have made it possible to construct automated systems that can recognize illnesses from picture and video data. The paper emphasizes the potential application of cutting-edge models like Detectron2 to address issues with agricultural leaf disease detection. Together with other cutting-edge models, Detectron2 provides a promising means of enhancing the accuracy and effectiveness of disease diagnosis and control techniques. The use of automated disease detection systems holds the potential to reduce subjectivity in the evaluation of illnesses, optimize the allocation of resources in agriculture, and reduce dependence on human labour. These systems are able to continually learn from and adapt to changing disease patterns by incorporating complex algorithms and machine learning techniques, ensuring robust and stable agricultural operations.

关键词： Deep learning computer vision Adaptation models Computational modeling Scalability Sociology Agriculture

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Efficient survivable mapping algorithm for logical topology in IP-over-WDM optical networks against node failure

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Journal of Supercomputing 2023年第5期79卷 5037-5063页

作者： Cheng, Dun-Wei Chang, Jo-Yi Lin, Chen-Yen Lin, Limei Huang, Yanze Thulasiraman, Krishnaiyan Hsieh, Sun-Yuan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan701 Taiwan College of Mathematics and Informatics and the Key Laboratory of Network Security and Cryptology Fujian Normal University Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Mathematics and Physics Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science University of Oklahoma 660 Parrington Oval NormanOK73019 United States Department of Computer Science and Information Engineering National Chi Nan University No. 1 Daxue Rd Nantou County Puli Township54561 Taiwan

The survivable mapping problem in an IP-over-WDM network with a logical graph and physical graph is the problem of finding a mapping of a logical network in a physical layer so that any failure in physical topology does not break the logical topology’s connection. Determining whether a survivable mapping against failure exists is an NP-complete problem, and therefore many heuristic algorithms have been proposed in the literature. In this paper, a heuristic mapping design strategy is proposed to enable the lightpaths to more efficiently endure a node failure. We individually consider the logical topology by building a Hamiltonian cycle and then deal with the mapping problem for disjoint physical paths. Experimental results demonstrate that the proposed algorithm can provide efficient survivable mapping in IP-over-WDM networks. The simulation shows that the survivability of previous methods (SNF and SMART-Node) is around 60–80%, but our proposed algorithm can build survivable mapping in all kinds of situations. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Heuristic algorithms

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OSIS-NET Deep Learning Framework for Enhanced Osteoporosis Detection and Classification Using MRI Imaging

OSIS-NET Deep Learning Framework for Enhanced Osteoporosis D...

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International Conference on Inventive Computation Technologies (ICICT)

作者： C. Anbarasi J. Revathi G. Arutjothi R. Nandhakumar Vishwa Priya. V M. Yogeshwari Department of Computer Science and Information Technology Vels Institute of Science Technology & Advanced Studies (VISTAS) Women(Autonomous) Chennai PG and Research Department of Computer Science and Applications Vivekanandha College of Art and Sciences for Women(Autonomous) Elayampalam Tamilnadu Department of Data Analyst Saveetha liberal arts and Science College

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Osteoporosis is a major public health issue due to its association with increased fracture risk and reduced quality of life. Early diagnosis is crucial for effective treatment and prevention. This study introduces OSIS-NET, an advanced deep learning-based framework for detecting and classifying osteoporosis using MRI imaging. The model integrates convolutional neural networks (CNNs) with machine learning techniques to enhance accuracy, computational efficiency, and clinical reliability. The next stage applies deep learning (DL) based Dilated leaky ShuffleNet to process images for extracting textural and shape features from them. The selected ST features undergo an evaluation process using ML models which includes SVM, KNN, DT, and RF for detecting normal and abnormal patterns. The system reaches an excellent accuracy level that exceeds 99.76 % for normal cases and 99.89% for abnormal cases. The model achieves 0.86% superior accuracy when compared to GoogleNet alongside 0.54% and 0.16 % and 1.07 % superior results than ShuffleNet, AlexNet and MobileNet respectively. The study addresses model complexity, validation methodologies, and performance improvement strategies. Compared to traditional approaches, OSIS-NET demonstrates superior classification accuracy. We discuss the framework's real-world applications, limitations, and future directions for optimizing its robustness and clinical deployment.

关键词： Support vector machines Radio frequency Deep learning Osteoporosis Accuracy Magnetic resonance imaging Nearest neighbor methods Feature extraction Convolutional neural networks Medical diagnostic imaging

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