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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Kumar Rapuri, Anil Ravva, Vennela Rasala, Raghavendra Ray Adurgam, Sai Dcepak Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering TamilNadu Krishnankoil India SRM Madurai College for Engineering and Technology Dept of Computer Science and Engineering TamilNadu Sivagangai India

ISBN: (纸本)9798350322842

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. Pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge. © 2023 IEEE.

关键词： Image segmentation

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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Decrease the Prompt Uncertainty: Adversarial Prompt Learning for Few-shot Text Classification

Decrease the Prompt Uncertainty: Adversarial Prompt Learning...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Weng, Jinta Zhang, Zhaoguang Yaqi, Jing Niu, Chenxu Huang, Heyan Hu, Yue School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Cyber Engineering Guangzhou University China National Computer Network Emergency Response Technical Team Coordination Center of China China Beijing Institute of Technology School of Computer Science and Technology Beijing China Southeast Academy of Information Technology Putian China

ISBN: (纸本)9781665410205

With few-shot learning abilities, pre-trained language models (PLMs) have achieved remarkable success in classification tasks. However, recent studies have shown that the performance of PLM is vulnerable due to different prompts and the instability of the prompt-based learning process. To address this challenge, we explore appropriate perturbation addition of adversarial training and integrate the global knowledge of the full-parameter fine-tuned pre-trained language model(PLM). Specifically, we propose an adversarial prompt learning model (ATPET) and ATPET with fine-tuning(ATPET-FT), incorporating ATPET with fine-tuning knowledge into the prompt learning process. Through extensive experiments on several few-shot classification tasks and challenging data settings, we demonstrate that our methods consistently improve the robustness while maintaining the effectiveness of PLMs. © 2024 IEEE.

关键词： Adversarial machine learning

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Contagion Classification using Deep Learning Approach 2

Contagion Classification using Deep Learning Approach

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Kacham, Sai Prajwal Angayarkanni, S.A. Rohithshan, S. Mishra, Arpan Vignesh, K. SRM Institute of Science and Technology College of Engineering and Technology Department of Networking and Communication Tamilnadu Kattankulathur603203 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil626126 India

ISBN: (纸本)9798350365269

Potatoes are a major crop globally, but diseases can seriously harm their growth and quality. Recently, there's been a lot of interest in using machine learning to identify potato diseases from pictures. In this study, we've developed a way to automatically classify potato diseases using deep learning methods. By training a model with CNN and SVM algorithms on a dataset of potato disease images, we achieved an accuracy rate of 93.95%. This shows that deep learning is effective in identifying potato diseases. Our method could be a big help to farmers and researchers, making it faster and easier to spot and deal with potato diseases, which could lead to better crop yield and quality. © 2024 IEEE.

关键词： Support vector machines

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Multi-objective load balancing based on adaptive osprey optimization algorithm

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International Journal of Information technology (Singapore) 2024年第6期16卷 3871-3878页

作者： Panneerselvam, Karthick Nayudu, P. Prathap Banu, M. Sheerin Rekha, P.M. School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Rajeev Gandhi Memorial College of Engineering and Technology Andhra Pradesh Nandyal India Department of Information Technology R.M.K. Engineering College Tamil Nadu Kavaraipettai India Department of Information Science and Engineering JSS Academy of Technical Education Karnataka Bangalore India

With the increasing complexity of distributed systems, achieving an optimal distribution of tasks across resources is paramount for enhancing system performance. Therefore, in this study, a novel multi-objective load balancing approach based on adaptive osprey optimization algorithm (AO²) within computing environments is proposed. Our proposed method aims to simultaneously optimize multiple objectives, such as minimizing energy consumption, cost and time, through the application of advanced optimization algorithms. The proposed system initially predicts the load of each virtual machine (VMs). After the prediction process, the tasks are assigned to the VMs. The research involves a comprehensive evaluation comparing the proposed approach with existing load balancing techniques, showcasing its effectiveness in achieving superior results. The findings demonstrate the potential of the multi-objective optimization algorithm to enhance load balancing efficiency in diverse computing scenarios, providing a valuable contribution to the field of distributed systems and resource management. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adaptive osprey optimization algorithm Load balancing Multi-objective and energy consumption Virtual machine

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DeepFake Detection using Convolutional Neural Networks (CNN) and Recurrent Neural Network(RNN) 5

DeepFake Detection using Convolutional Neural Networks (CNN)...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Anjaneyulu, Battula Prasanna Reddy, Chadipiralla Pavan Kunar Reddy, Gangireddy Venkata Ajaykumar Yogitha, Chava Pandiyarajan, Pandiselvam Maheshwaran, Baskaran Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science Engineering Tamilnadu Krishnankoil India Ayya Nadar Janaki Ammal College Department of Bio-Technology Tamilnadu Sivakasi India

ISBN: (纸本)9798350389609

To analyse the DeepFakes, which are AI-generated synthetic media that impersonate real people and pose substantial threats to digital content security, privacy, and authenticity. This research investigates a unique technique to DeepFake detection that employs Recurrent neural networks (RNN) are used for temporal sequence analysis and convolutional neural networks (CNN) for extracting spatial features. The suggested method seeks to improve detection accuracy across a wide range of DeepFake datasets by combining the strengths of CNNs and RNNs. The experimental results show that this hybrid architecture is excellent at identifying DeepFakes with good precision and recall.) © 2024 IEEE.

关键词： Convolutional neural networks

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Plant Leaf Disease Identification based on Machine and Deep Learning Methods 3

Plant Leaf Disease Identification based on Machine and Deep ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kalaiarasi, P. Ramalakshmi, R. Kannan, V. Bhuvanesh, A. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are labor-intensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models. © 2025 IEEE.

关键词： Deep learning

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MIWE: Multimodal Indexing of Web Entities Incorporating Semantic Artificial Intelligence

MIWE: Multimodal Indexing of Web Entities Incorporating Sema...

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International Conference on Data science, Computation and Security, IDSCS 2023

作者： Aravind Krishnan, A. Deepak, Gerard Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819709748

Web indexing is of critical importance to Web 3.0 owing to the reason that it is not just expanding but growing exponentially. This paper proposes a multimodal indexing mechanism that indexes web images, videos, pages, and documents. The strategy encompasses paradigms like TF-IDF, ontology generation, and label extraction from different datasets. The datasets are subjected to the LSTM for classification for the web image and web video datasets and an LR classifier is used to classify the web document and web page dataset. Subsequently, the formalization of knowledge graphs and enhancement using Google’s knowledge graph API, context frees, microformat chain derivations, the encompassment of Wikidata and NELL knowledge stores, and web crawler agents increases the quantity of auxiliary knowledge, and the convergence to optimality. Semantic relevance is calculated using the NPMI measure. The MIWE shows improved performance when examined against other similar models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Web crawler

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Power and Area Optimized Memristor-Based Majority Identifier for Digital Applications 3

Power and Area Optimized Memristor-Based Majority Identifier...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Raj, P. Michael Preetam Vittapu, Sravan K. Chinnasamy, P. Gupta, Manu Samatha, K. Sasivijaya, K. Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions Department of Electronics and Communication Engineering Hyderabad India Kalasalingam Academy of Research and Education Dept. of Computer Science and Engineering Tamil Nadu India Sreenidhi Institute of Science and Technology Department of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798331518592

In earlier developments, CMOS technology played a great role in realizing majority identifiers. There are huge drawbacks that affect the circuit's performance. To overcome the drawbacks of CMOS-based majority Identifiers, novel memristor-based majority and minority Identifiers have been proposed in this work. The features offered by memristor have motivated us to use the component and achieve greatly improved results compared to CMOS-based majority identifier circuits. After observing the behavior of the basic memristor-based majority identifier circuit, we have decided to design and implement a memristor-based majority and minority identifier circuit which can be used in many automation systems to increase quality and productivity. © 2024 IEEE.

关键词： CMOS integrated circuits

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