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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8123 条记录，以下是141-150 订阅

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Design and Implementation of Deep Learning CN-RNN Algorithm for Crop Analysis 5

Design and Implementation of Deep Learning CN-RNN Algorithm ...

引用

5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： Vidyarthi, Vrij Mohan Bhandari, Narendra Singh Goyal, Shweta Pundhir, Shivali Soni, Mukesh Mittal, Amit Tula's Institute Department of EEE U.K. Dehradun India Graphic Era Hill University School of Agriculture Bhimtal India Graphic Era Deemed to be University Deptt. of Electrical Engg. Dehradun India Tula 's Institute Deptt. of Computer Sci.& Engg. Dehradun India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade pune India Graphic Era Hill University Dept of Allied Sciences Bhimtal India

ISBN: (纸本)9798350367171

this research study presents the design and implementation of a deep learning model for analyzing various crops in the farming sector, focusing on parameters such as temperature and humidity. the study begins with data extraction from available crops using a Convolutional Neural Network (CNN). To enhance detection and classification, a combination of CNN and Recurrent Neural Network (RNN), referred to as CN-RNN, is employed. the study measures accuracy, precision, and recall using the CN-RNN method and compares these metrics with those obtained from the existing CNN method. Results demonstrate that the CN-RNN method yields improved values in accuracy, precision, and recall, indicating its superior performance in the analysis of agricultural crops. © 2024 IEEE.

关键词： Convolutional neural networks

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Data Mining and Text Mining Approach: Query based Information Retrieval using Genetic Algorithm 5

Data Mining and Text Mining Approach: Query based Informatio...

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5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： Ananthi, S. Venkateswaran, R. Vijaya, G. Arun, J. Sathya, R. Sri Eshwar College of Engineering Advanced Research Centre for Future Networks and Digital Twin Coimbatore India College of Computing & Information Sciences University of Technology and Applied Sciences Faculty-Cyber and Information Security Salalah Oman Sri Krishna College of Engineering and Tech Dept. of CSE Coimbatore India Sri Eshwar College of Engineering Dept. of ECE Coimbatore India Kongunadu College of Engg and Tech Department of IT Namakkal India

ISBN: (纸本)9798350367171

Information Retrieval system facilitates the retrieval efficiency of the user's requirement from a collection of information resources. In this research work, Genetic Algorithm (GA) with various fitness functions are implemented for retrieving the relevant document for the given textual query. To increase the efficiency of the retrieval system extended keywords are added to the retrieval system using query reformulation method. Furthermore, the selection procedure namely fitness proportionate selection is used to identify the best fit individual among the chosen population. this selection algorithm assumes that, for the chosen population, the fittest individuals have a greater chance of endurance than weaker ones. the performance of the proposed system is evaluated with three different datasets solely consists of 20,322 documents are analyzed using various matching functions and their results are examined. © 2024 IEEE.

关键词： Content based retrieval

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Abnormal Network Traffic Detection Based on Multi-Pooling Cascaded MPCNN 5

Abnormal Network Traffic Detection Based on Multi-Pooling Ca...

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5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： He, Kefeng Shi, Yarong China Engineering Group Gansu Electric Power Design Institute Co. Ltd. Lanzhou China

ISBN: (纸本)9798331533694

Traditional network traffic detection methods based on machine learning have been widely used, but the accuracy of identifying and detecting abnormal traffic is not high. Compared with machine learning methods, deep learning has made significant progress in fields such as text processing, video generation, and other fields, bringing new ideas to abnormal traffic detection. this paper adopts an abnormal traffic classification method based on multi-pooling cascaded convolutional neural network (MPCNN). the accuracy of extensive experiments is 0.9914, the precision is 0.9984, and the F1 score is 0.9962. these experiments achieved the company's goal of accuracy greater than 0.98. It is used for real-time monitoring of daily network traffic to recognize abnormal traffic virtually, alert topic timely, and prevent potential network threats. © 2025 IEEE.

关键词： Convolutional neural networks

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Proceedings - 5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

Proceedings - 5th International Conference on Smart Systems ...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

ISBN: (纸本)9781665474672

the proceedings contain 288 papers. the topics discussed include: a novel converter design for energy management in electrical vehicles;a novel hybrid islanding detection method to improve the performance of PV grid connected system;a review on optical amplifiers for future optical networks;an adaptive-based predicted nodes behavior algorithm in flying ad hoc networks;a study of routing mechanisms in vehicular ad hoc networks for reliability;optimizing cluster head formation using re-election algorithm in wireless sensor networks;improving data integrity for gray hole attack detection by using a hash signature algorithm in WSN;and mobile sink based data-gathering method to minimize the network delay.

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An Enhanced Wheat Leaf Disease Classification Using Pre-Trained InceptionV3 Model 5

An Enhanced Wheat Leaf Disease Classification Using Pre-Trai...

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5th international conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Saini, Archana Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331530013

Wheat is widely recognized for having a high nutritional content since it includes essential proteins, fiber, carbs, and vitamins and minerals. this crop is a crucial energy source for both humans and other creatures. Food production is also affected by various diseases. After providing an extensive overview of the categorization of wheat leaf diseases, this paper highlights the need for early and accurate identification to protect wheat output globally. Spectral analysis, machine learning, and image processing advances have made substantial progress in automating the classification of wheat leaf disease. a summary of the accuracy and loss throughout several training and validation epochs. this Inception V3 model applies different epochs such as 15, 20, 25, and 30. Empirical evidence suggests that the model performs optimally around epoch30, which also has the lowest loss (0.2513 in the training phase and 0.2069 in the validation phase) and the highest accuracy (0.9075 in the training phase and 0.9218 in the validation phase). © 2024 IEEE.

关键词： classification CNN deep learning diseases image processing Inception V3 leaf Wheat

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Automated Detection and Prediction of Brain Tumor using ML 5

Automated Detection and Prediction of Brain Tumor using ML

引用

5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： Shanthini, M. Monica, R. Kiran Srinivas, V. Hari Harann, S.V. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350367171

this research study focuses on developing an advanced machine learning system for the accurate classification of brain tumors using MRI scans. Traditional manual diagnosis consumes more time in deciding the type of brain tumor as there are very little changes in every type. Utilizing computer vision's feature extraction techniques, including texture analysis and shape characterization, the proposed system aims to differentiate between various tumor types such as meningioma, glioma, pituitary, and non-tumor cases. the models used to validate the accuracy of the brain tumor classification system are Logistic Regression, Convolution Neural Network (CNN), and Visual Geometry Group(VGG), which are simple yet effective way to train and deploy in the radiology system to enhance diagnostic accuracy in neurooncology, and improve patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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AI-Driven Legal Automation to Enhance Legal Processes with Natural Language processing 5

AI-Driven Legal Automation to Enhance Legal Processes with N...

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5th international conference on IoT Based Control networks and Intelligent Systems, ICICNIS 2024

作者： Nithya, M. Harini, S. Kavyadharshini, S. Srinidhi, K. Chennai India

ISBN: (纸本)9798331518097

the legal sector often faces delays and inefficiencies due to the overwhelming volume of information, the labor-intensive nature of research, and high service costs. this paper introduces a novel framework for AI-driven legal automation, which employs Natural Language processing (NLP) and Machine Learning (ML) to streamline critical legal tasks. the system is designed to generate precise legal summaries, draft and validate documents, and respond accurately to complex legal queries while safeguarding data privacy. It also facilitates legal research by efficiently identifying precedents and arguments, providing users with tailored support. Comparative analysis reveals the proposed approach's superiority in accuracy and operational efficiency compared to existing solutions. A detailed evaluation methodology, supported by mathematical models and expert validation, underscores the framework's reliability. Furthermore, the paper highlights the potential of this AI-driven solution to democratize access to legal resources, particularly for under-served communities. Future work will focus on extending system capabilities to adapt to diverse legal jurisdictions and enhance usability. © 2024 IEEE.

关键词： Data privacy

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An Efficient and Energy Data Transmission Using Intelligent-Based Techniques in Wireless sensor networks 5

An Efficient and Energy Data Transmission Using Intelligent-...

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5th IEEE international conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Vasudevan, I. Sarah, Subaniya Visal Chetankumar, B.R. Chakraborty, S. Dilipcharaan, R.M. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Dept.of Cse Chennai India Madras Christian College Dept. of Ba Chennai India

ISBN: (纸本)9798350379884

the aim of the research is to use AI approaches to secure, energy and reliably transmit data on ad hoc networks. Ad hoc networks are becoming standard fare in contemporary communication infrastructures, wireless technology, and mobile devices. Protecting sensitive information during transmission is no simple task, especially in ad hoc networks due to their inherent instability and lack of central authority. the existing network can explain all the unknowns and complexity settings in Ad-HoC, conventional encryption techniques, and routing protocols. the proposed artificial intelligence-driven technique for ensuring safe and strong data transmission in various ad hoc network situations works by running comprehensive simulations and evaluations. For similarity metrics like packet delivery ratio, end-to-end delay, and attack resilience, our findings show that the proposed framework outperforms conventional approaches an effectively without any delay and improve the speed of the data transmission. © 2024 IEEE.

关键词： Data transfer

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Machine Learning Approaches for Early Detection and Classification of Cervical Cancer Using Image processing 5

Machine Learning Approaches for Early Detection and Classifi...

引用

5th international conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Kumaran, S. Roshini, M. Rohini, G. Saveetha Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798331530013

Traditional machine learning (ML) techniques have limitations that make it difficult for existing algorithms to diagnose cervical cancer. these limitations include lower accuracy and an inability to handle complicated variations in histopathological images. these systems usually rely on manual evaluations, decision trees, logistic regression, and basic neural networks;these methods are not well suited to handling the nuances of precancerous lesions and picture quality fluctuations. By combining innovative techniques, the proposed hybrid system solves these problems. Specifically, it integrates Convolutional Neural networks (CNNs) for hierarchical feature extraction, Vision Transformers (ViTs) for contextual information and ensemble methods that combine Gradient Boosting Machines (GBMs) and Support Vector Machines (SVMs) for robust classification. Further improving model robustness is self-supervised learning. the methodology outperforms the existing systems in terms of accuracy (97.0%), precision (94.5%), recall (95.2%), and F1-score (94.8%), indicating improved performance and fewer false positives and negatives. © 2024 IEEE.

关键词： Cervical cancer Classification Diagnostic Automation Early detection Histopathology Transfer learning

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Glaucoma Detection through Synergistic Combination of Image processing and Deep CNN using MATLAB 5

Glaucoma Detection Through Synergistic Combination of Image ...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Subbarayudu, B. Yadalam, Harshitha Seepiri, Jeevana Bhogathi, Jayalakshmi Dept. ECE of Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India

ISBN: (纸本)9798350361155

this study introduces an innovative Glaucoma detection framework that significantly outperforms existing methodologies by employing a hybrid approach combining advanced image processing and Convolutional Neural networks (CNN). the key to our approach is the precise segmentation of the optic cup using state-of-the-art image processing algorithms, which allows the CNN to accurately identify glaucomatous changes from retinal images. Our Glaucoma detection method had 95.45% sensitivity, 93.59% specificity, and 94.44% accuracy, surpassing other contemporary techniques such as the SVM Classifier, which showed an accuracy of 90%, and a Convolutional Neural Network approach by other researchers with an accuracy of 91.52%. these results affirm the potential of our method as a highly reliable tool for early Glaucoma diagnosis in clinical practice, promoting better patient outcomes through earlier treatment interventions. this study introduces an opportunity for enhancing glaucoma screening and early treatments through using of MATLAB software. © 2024 IEEE.

关键词： MATLAB

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