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Fast-rCNN Coupled Four-Dense Layered Deep Fully Connected Neural Network-Based Insulator Chain Defect Defection 11th

Fast-RCNN Coupled Four-Dense Layered Deep Fully Connected Ne...

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11th Inter.a.ional Conference on Intelligent Computing and Applications, ICrTC 2023

作者： Shyamala Devi, M. Alex David, S. Vinoth Kumar, S. Sandeep Prasan Kumar, M.G. rohith, S. Department of Computer Science and Engineering Panimalar Engineering College Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9789819717231

Electric power lines may be at risk of a safety hazard as a result of defective insulators. Image detection technique can significantly save maintenance costs and incr.a.e the effectiveness of Insulator Defect Detection. Nevertheless, limited precision and a lengthy detection process are drawbacks of the present insulator fault detection methods. For images with complex backgrounds, it is challenging to detect insulator faults using the conventional methods since they focus on minimal edge detection from images and classifier design. To address this issue, this paper recommends fast-rCNN coupled four-dense layered deep fully connected neural network (Fr-4DCNN) that detects the insulator defects with high accuracy using the Insulator Defect Detection dataset from KAGGLE. The proposed Fr-4DCNN model uses Insulator Defect Detection dataset from KAGGLE with 1800 insulator images. The insulator images are preprocessed with convolutional layers for feature map cr.a.ion followed by the for.a.ion of region proposal network that detects the insulators by bounding box regressor.a.gorithm. The classified insulator is then fed into rOI pooling coupled with four-dense hidden layered deep fully connected neural network having single input and output layer that predicts the rOI for classification of the insulator defect types for each rOI using the bounding box regressor method. The novelty of the proposed Fr-4DCNN exists in the classification of the insulator defects in the form of missing plates in the hanging or.a.tached insulator chain, broking sheds in insulators and the presence of rust in insulators. The dataset for Insulator Defect Detection was divided into training and testing data, and the training data were fitted to both the proposed Fr-4DCNN model and other deep learning models in order to compare the efficiency. results of execution indicate that the proposed Fr-4DCNN model showcases the accuracy of 99.47%, precision of 99.42%, recall of 99.25%, and F1-score of 99.3

关键词： Convolution

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A comparative analysis of chaos theory based medical image steganography to enhance data security 2

A comparative analysis of chaos theory based medical image s...

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2nd Inter.a.ional Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ghosh, Sharmila Saha, Ashim Pal, Tannistha Jha, Anand Kumar Department of Computer Science and Engineering National Institute of Technology Agartala India Department of Information and Technology VelTech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India

The security of patient data is a critical problem for health networks due to the rising popularity of telehealth services and the requirements for clinical data sharing between surgeons, consultants, and medical groups. In contrast to protecting medical data, which includes the cover (i.e., clinical picture), the requirements for protecting other pertinent data are more general. The paper.a.vocates for data encryption before concealment as a robust strategy to uphold patient privacy during medical infor.a.ion exchange. The primary focus of this analysis revolves around a comprehensive exploration of different steganography methods. Through a meticulous examination of various steganographic techniques, including LSB, PVD, and transform domain appr.a.hes, this paper provides a detailed analysis of their strengths and limitations. Objective metrics like PSNr.a.d SSIM are employed to dissect the trade-offs between data security and visual fidelity. The research leads to the conclusion that the diagonal queue-based steganog-raphy methodology, supported by chaotic methods, the Linear Feedback Shift register (LFSr), and the durable rabin encryption system, is the best course of action. This method enables autonomous data transmission among numerous contacts while ensuring the highest level of confidentiality and integrity of patient data inside e-health networks. In summary, this study provides a comprehensive solution that protects patient data and speeds data transmission inside the expanding framework of e-health networks in order to meet urgent data security concerns in telemedicine and healthcare data exchange. © 2024 Elsevier B.V.. All rights reserved.

关键词： Cryptography

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An Enhanced Machine Learning Technique for Text Detection using Keras Sequential model 2

An Enhanced Machine Learning Technique for Text Detection us...

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2nd Inter.a.ional Conference on Electronics and renewable Systems, ICEArS 2023

作者： Deepa, r. Gayathri, S. Chitra, P. Jasmine, J. Jeno Devi, r. renuga Thilagavathy, A. R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Kavaraipettai India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti India

ISBN: (纸本)9798350346640

Optical Character recognition (OCr) has become the well known accepted automated recognition technology that can be used within a variety of functions that converts documents or pictures to modifiable as well as analyzed infor.a.ion. Because they possess a clearly specified form and size, characters that are in print are simple to identify. But is never the situation with handwritten writing. Because each person's handwriting is unique, OCr has trouble r.a.ing the characters. This study provides a two-stage process in this work for the identification and categorization of handwritten digits. Convolutional Neural Networks (CNNs) serve as the basis for the categorization. The recognition of the input number is the initial step in the process. The second step is to determine the language the input number originated from. Python is used to carry out the research work on the MNIST database of handwritten numbers. The simulation's findings demonstrate an efficient perfor.a.ce with a near-perfect identification rate. This study has attained 99.5% training accuracy, 99% testing accuracy, and a training loss of 1.5%. © 2023 IEEE.

关键词： Python

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Predictive Modeling for Medical Insurance Malpractice Using random Forest and XGBoost

Predictive Modeling for Medical Insurance Malpractice Using ...

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2024 Inter.a.ional Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Carmel Mary Belida, M.J. Begum, Almas Alex David, S. Kannan, E. Senthil, K. ruth Naveena, N. Saveetha Institute Of Medical And Technical Sciences Saveetha School Of Engineering Department Of Computer Science And Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Department Of Computer Science And Engineering Chennai India Hindustan Institute Of Technology & Science Department Of Mathematics Chennai India

ISBN: (纸本)9798350352689

The study promotes a thorough research effort to cr.a.e a predictive model specifically for medical malpractice litigation, highlighting the intricate relationships between insurance and medical malpractice claims. The model that has been suggested incorporates sophisticated machine learning methods, particularly the random Forest and XGBoost algorithms, to improve accuracy and forecasting skills. The study emphasizes the need for.a.comprehensive strategy that combines state-of-the-art prediction technology with a well-defined theoretical framework. The article provides a comprehensive overview of the topic by carefully examining the underlying factors that give rise to medical malpractice claims, which might range from poor recordkeeping to communication problems. It highlights the financial strain that healthcare providers and patients experience and suggest ways that insurance companies can better control risks, evaluate claims, and encourage preventive tr.a.ment. The perfor.a.ce of the suggested random Forest and XGBoost models is presented in the results and comments, with XGBoost showing better predicting skills. The residual distribution and the accuracy of the models are clearly shown by the visualizations. To sum up, this study lays the groundwork for the development and application of predictive models and provides insurance companies with impor.a.t infor.a.ion to help them negotiate the intricate ter.a.n of medical malpractice in the healthcare industry. © 2024 IEEE.

关键词： Patient tr.a.ment

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removing Gaussian Image Noise Using Advanced Non-Local Means Filtering Techniques

Removing Gaussian Image Noise Using Advanced Non-Local Means...

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2023 Inter.a.ional Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Suresh, A. Waris, Saiyed Faiayaz Mohankumar, B. Arumuga Arun, r. Saravanan, G. Vellore Institute of Technology School of Computer Science and Engineering Department of Computational Intelligence Vellore India Vignan's Foundation for Science Technology & Research Vadlamudi Department of Computer Science and Engineering Andhrapradesh Guntur India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350317060

Our system is aimed at improving the application of image de-noising processes, even though the presence of noise has a negative impact on image quality. End-user.a.plications suffer.a. a result of the focusing Gaussian noise. Introduce the Non-local means (NLM) filter to take advantage image similarities and analyse data preser.a.ion in de-noising. Even though the NLM appr.a.h eliminates some noises, like salt and pepper, an estimate weighted between the input images and edge preser.a.ion is still calculated. It could experience loss of visual structure, noise reduction, and blurring. This research shows the enhancement of the threshold level by combining stationary wavelet transform with NLM. This type of Advanced NLM method is effective at removing high noisy regions like Gaussian noise levels. The proposed ANLM method improves edge preser.a.ion and image structure similarities. This article uses some noisy images as input, then employs quantitative parameters to perform the measurement. Finally, this method demonstrates the effective removal noise and edge preser.a.ion of weighted refined structures. PSNr, COC, and MSE are the measuring parameters. © 2023 IEEE.

关键词： Mean square error

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Enhanced Monitoring and Security Measures with Video Analysis

Enhanced Monitoring and Security Measures with Video Analysi...

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2025 Inter.a.ional Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Jyothi, V. Esther rakesh, G. Aruna, Vippar.a.Gorintla, Shobana Ayyappa, Yalanati Naidu, U. Ganesh Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Department of Computer Applications A.P. Vijayawada India Department of Computer Science and Engineering Pothavarapadu A.P India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Avadi India B V Raju Institute of Technology Telangana Narsapur India

ISBN: (纸本)9798331509828

In modern surveillance, activities have incr.a.ingly become dependent on the continuous obser.a.ion offered by CCTV systems. Still, with massive amounts of video data generated in a minute, sifting through this infor.a.ion manually to pick up any anomalies would be extremely burdensome and nearly impossible without significant human labor.a.d vigilant watchfulness. CNN and CLIP models changed everything with regards to the working of surveillance systems. It makes use of CNN's capability for video frame processing and analysis to detect and classify these activities in the frames as either nor.a. or suspicious. At the same time, it improves this by adding the CLIP model's capability of understanding textual descriptions and visual content together for better nuance detection in suspicious activities. This methodology transforms video surveillance by br.a.ing video str.a.s into frames and analyzing the behavior.a.d interactions of persons in those frames. The synergy of CNN and CLIP models not only ensures r.a.-time, efficient, and accurate surveillance but also minimizes dependency on extensive manual labor for monitoring. This paper.a.ms to provide a contribution toward developing better security infrastructures in public spaces, transpor.a.ion hubs, and private facilities. The introduction of this anomaly-detection mechanism can hugely improve the capability of implemented surveillance systems with a pr.a.tive response based on r.a.-time detection and response capabilities against suspicious activities. © 2025 IEEE.

关键词： Video analysis

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A Method for Predicting Coronary Heart Disease Using Machine Learning Appr.a.hes 2

A Method for Predicting Coronary Heart Disease Using Machine...

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2nd Inter.a.ional Conference on recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： ramathilagam, A. Pradeepha, S. ram, M. Preethi Lakshmi, B. Sankara Kumar, S. Arun Neels Ponkumar, D. David P.S.R. Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Ramco Institute of Technology Department of Computer Science and Engineering Tamil Nadu Rajapalayam India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350366570

Predicting a patient's heart disease risk is a major difficulty in medical data analysis for preventative measures and mor.a.ity reduction. Early diagnosis is essential for timely tr.a.ment and continuing monitoring by healthcare professionals, however medical staff's limitations in patient surveillance frequently prevent it. Early cardiac problem detection and medical monitoring reduce fatality rates. The accuracy of heart disease detection is uncer.a.n, and clinicians cannot regularly communicate with patients. Machine learning (ML) may help clinicians identify and forecast heart diseases using global healthcare data. This project will employ many feature selection methods to cr.a.e a viable ML system for early heart disease prediction. It is possible to correctly forecast cardiac illness using a variety of machine learning methods, including Logistic regression, random Forest, Naïve Bayes classifier, Decision Tree, and LrND classifier. With the help of nearby technology, these algorithms may detect heart problems and provide tr.a.ment. © 2024 IEEE.

关键词： Logistic regression

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Hybrid Vision Transformer.a.d CNN for Detection of Overripe Coffee Berry Disease (OCBD) in Coffee Plantation 2

Hybrid Vision Transformer and CNN for Detection of Overripe ...

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2nd IEEE Inter.a.ional Conference on Emerging research in Computational Science, ICErCS 2024

作者： Selvanarayanan, raveena Surendran, r. Gomathi, T. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

The article proposes an innovative Hybrid Vision Transformer.a.d CNN architecture for the precise identification of Overripe Coffee Berry Disease (OCBD) in coffee farms. The suggested model adeptly integrates the advantages of Vision Transformers and CNNs, allowing it to capture both the global and local characteristics of coffee berries. Experimental findings indicate that the Hybrid model outperforms conventional CNN-based methods, with an impressive accuracy of 96.7% on a varied dataset. The model's resilience to fluctuating illumination conditions and obstructions makes it a viable alter.a.ive for practical applications in coffee plantation management. © 2024 IEEE.

关键词： Image segmentation

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Fake News Detection Using Logistic regression and LightGBM 2

Fake News Detection Using Logistic Regression and LightGBM

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2nd Inter.a.ional Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Carmel Mary Belinda, M.J. Alex David, S. Kannan, E. ruth Naveena, N. rajathi, K. Mahesh, C. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of computer science and engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of computer science and engineering Chennai India Hindustan Institute of Technology & Science Department of mathematics Chennai India

ISBN: (纸本)9798350368413

The spr.a. of false infor.a.ion via fake news in recent years has raised serious issues and caused social discontent and division. It is critical to cr.a.e efficient techniques for quickly and reliably recognizing bogus news. In the context of identifying fake news, this paper compares two popular machine learning techniques: LightGBM and Logistic regression. Our.a.alysis makes use of a dataset that includes both fake and r.a. news pieces, with 23,481 fraudulent news stories and 21,417 r.a. news stories. Impor.a.t perfor.a.ce parameters including recall, accuracy, precision, F1-score, and Matthews correlation coefficient are taken into account while evaluating these algorithms. Our goal in doing this research is to clarify how effective these strategies are at solving the urgent problem of the spr.a. of false infor.a.ion. © 2024 IEEE.

关键词： Logistic regression

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Analysis of Lung Cancer for Developing Smart Healthcare with the Help of BGWO Based TSA-XGBoost Model

Analysis of Lung Cancer for Developing Smart Healthcare with...

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2023 Inter.a.ional Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Mahaveerakannan, r. Murali Dhar, M.S. Goswami, Sohan Krishna, P. Gopala Jayanthi, K. Kumar, M. Sathish The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Technique Polytechnic Institute Department of Computer Science and Technology West Bengal India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Pollachi Institute of Engineering and Technology Department of Computer Science and Engineering Pollachi Tamil Nadu Coimbatore India Karpagam College of Engineering Department of Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350300857

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is incr.a.ed by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an appr.a.h using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority over.a.pling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method. © 2023 IEEE.

关键词： Internet of things

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