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Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

computer Systems science & Engineering 2022年第8期42卷 423-438页

作者： Balakrishnan Natarajan Pushpalatha Krishnan Department of Master of Computer Applications Sona College of TechnologySalem–636005India Department of Computer Applications SRM Institute of Science and TechnologyRamapuramChennai600089India

In existing methods for segmented images,either edge point extraction or preservation of edges,compromising contrast images is so sensitive to *** Degeneration Threshold Image Detection(DTID)framework has been proposed to improve the contrast of edge filtered ***,DTID uses a Rapid Bilateral Filtering process for filtering edges of contrast *** filter decomposes input images into base layers in the DTID *** minimal filtering time,Rapid Bilateral Filtering handles high dynamic contrast images for smoothening edge *** the DTID framework,Rapid Bilateral Filtering with Shift-Invariant Base Pass Domain Filter is insensitive to *** Shift-Invariant Filtering estimates value across edges for removing outliers(i.e.,noise preserving base layers of the contrast image).The intensity values are calculated in the base layer of the contrast image for accurately detecting nearby spatial locations using Shift-Invariant base Pass Domain Filter(SIDF).At last,Affine Planar Transformation is applied to detect edge filtered contrast images in the DTID framework for attaining a high quality of the *** normalizes the translation and rotation of *** this,Degeneration Threshold Image Detection maximizes average contrast enhancement quality and performs an experimental evaluation of factors such as detection accuracy,rate,and filtering time on contrast *** analysis shows that the DTID framework reduces the filtering time taken on contrast images by 54%and improves average contrast enhancement quality by 27%compared to GUMA,HMRF,SWT,and *** provides better performance on the enhancement of average contrast enhancement quality by 28%,detection accuracy rate by 26%,and reduction in filtering time taken on contrast images by 30%compared to state-of-art methods.

关键词： Rapid bilateral filtering edge preserved filtering affine planar transformation key pixel point localization shift-invariant base pass domain filter

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Fake News Detection Review: Concepts, Tools, Research gaps, and Possible solutions 7

Fake News Detection Review: Concepts, Tools, Research gaps, ...

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Yakkundi, Sapana Patil, Rudragoud Goudar, Swetha Indudhar Department of Computer Science and Engineering KLS Gogte Institute of Technology Karnataka Belagavi India Department. of Master of Computer Applications KLS Gogte Institute of Technology Karnataka Belagavi India

ISBN: (纸本)9798331519513

The increasing number of social platforms means that there is a rapid expansion of online data. However, not all of this data can be trusted, as some users intentionally manipulate information to spread false news for personal or political reasons. Fake news can significantly impact public perception and societal outcomes. Its widespread influence was especially noticeable during the 2016 United States election cycle, leading to heated discussions about its role and consequences. Given the changing nature of news, real-time data processing has become essential for effectively identifying and countering misinformation. This research provides a comprehensive examination of the datasets, tools, and techniques utilized for detecting and combating fake news. Many researchers have developed models based on specific datasets, limiting their scope to particular domains. A thorough review of advanced models for real-time fake news detection highlights existing research gaps. The analysis of the review reveals that numerous datasets used in the literature are outdated, and there is a methodological challenge associated with the use of traditional machine learning approaches. With the continuous evolution of news, there is an urgent demand for utilizing sophisticated deep learning models for the automated, real-time verification of misinformation. This research presents promising approaches to tackle the challenge of detecting fake news. © 2024 IEEE.

关键词： Adversarial machine learning

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A Mobile App for Seed Grading by Incorporating Deep Learning Approach with Bio Inspired Optimization Technique 7

A Mobile App for Seed Grading by Incorporating Deep Learning...

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7th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2023

作者： Mundada, Monica R. Sowmya, J.B. Anurag, G. Mani, G.M. Narayana, Satya Ramaiah Institute of Technology Department of Master of Computer Applications Bengaluru India Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Ramaiah Institute of Technology Department of Computer Science Bengaluru India

ISBN: (纸本)9798350343144

One of the main production stumbling blocks that hinders the growth of healthy crops is seed quality. The ability to cultivate more nutrient-dense, higher-yielding crops that can help feed a growing population is made possible by high-quality seeds, which are crucial for ensuring food security. As a result, it is crucial to quickly and accurately identify high-quality seeds. Manually identifying the type of wheat requires skill and time. It gets difficult to manually differentiate seeds from an array when they all seem so identical. This prompted us to develop an app that would automate the process of determining seed quality. With the corn seed data set, a convolutional neural network is trained. By eliminating the thick layers, the characteristics of this neural network are recovered. The moth flame optimization technique, a bio-inspired algorithm, is used to reduce noise and extraneous data. The KNN classifier is used to assess the fitness of the features that have been chosen. Lastly, the KNN classifier is used to categorize the seeds based on the fewest attributes © 2023 IEEE.

关键词： Convolutional neural networks

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Machine Learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0 3

Machine Learning Classifiers for Predicting the Risk of Card...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumari, Palak Kumar, Ashish Chatterjee, Kakali Master of Computer Applications Patna Women's College Patna India National Institute of Technology Patna Dept. of Computer Science and Engineering Patna India

ISBN: (纸本)9798331528201

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body's, brain's, or heart's blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives. © 2024 IEEE.

关键词： Nearest neighbor search

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Smart question answering system using vectorization approach and statistical scoring method

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Materials Today: Proceedings 2023年 80卷 3719-3725页

作者： Manjunath, T.N. Yogish, Deepa Mahalakshmi, S. Yogish, H.K. Information Science & Engineering B M S Institute of Technology &Management Karnataka Bangalore560064 India K S School of Engineering & Management Computer Science & Engineering Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of Master of Computer Applications M S Ramaiah Institute of Technology Karnataka Bangalore560054 India

In this digital era, internet users are increasing day by day to extract information related to various fields such as health, economic, educational, politics and daily needs from Information Retrieval Systems (IRS) such as Google, Ask, Yahoo etc. Information Retrieval (IR) is a conventional method which depends on metadata searching method that is searching of adocuments for information within documents and information about documents. Lot of research in the IR area improves accessing the quality of information. Most of the search engines such as Google, Yahoo, Ask Jeeves depends on keyword based query processing mechanism internally which can't handle natural language questions which results in returning long list of documents for a query. The task of users is to manually explore each document to retrieve correct answer which is a time consuming process. To overcome the problems of conventional search engines, smart question answering system comes to play. The objective of Smart Automated Answering system is to impart accurate and valid answers to user question in natural language rather than set of full documents. The Smart Question answering system which accepts user question in Natural language in specific domain and provide short and precise answer using Natural language processing technique, used for pre-processing, normalized term weighting-TF-IDF with cosine similarity approach for document retrieval and enhanced BM25 ranking function to extract answer. The main focus is on decreasing the response time, designing ranking function and giving the pertinent responses to the users’ intent. The system achieved an overall Precision of 93.2%, Recall of 84.3% and F-measure of 88.5% with 80% of Accuracy. © 2021

关键词： Information retrieval

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MediGlove: Integrating IoT and Machine Learning for Personalized Remote Health Care 2

MediGlove: Integrating IoT and Machine Learning for Personal...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Nanda, K.V. Keerthan Kumar, T.G. Achyutha Prasad, N. Raviprakash, M.L. Anand Babu, J. Naveen Kumar, M.R. Sri Siddhartha Institute Of Technology Master Of Computer Applications Tumkur India Siddaganga Institute Of Technology Information Science And Engineering Tumkur India East West Institute Of Technology Computer Science And Engineering Bangalore India Kalpataru Institute Of Technology Artificial Intelligence And Machine Learning Tiptur India Malnad College Of Engineering Information Science And Engineering Hassan India Vidyavardhaka College Of Engineering Information Science And Engineering Mysore India

ISBN: (纸本)9798350381665

Mobile operating systems have advanced significantly in recent decades, with Android emerging as a widely used open-source platform. Simultaneously, the Internet of Things (IoT) has transformed computer equipment by enabling seamless data transfer over networks without human involvement. This study makes use of intelligent gloves equipped with various sensors, as well as IoT and Android applications, to improve healthcare. In this work, we introduce a pioneering framework that combines IoT and Android technology to revolutionize remote health monitoring called IoT-enabled Smart Gloves for Remote Health Monitoring using an Android-based solution approach (MediGlove). MediGlove uses intelligent smart gloves with robust sensors to continuously collect crucial health data. The solution works with IoT devices to send data quickly to a cloud-based server. The cloud server provides real-time health metric analysis with great precision by utilizing cutting-edge algorithms and machine learning techniques. A simple Android software acts as a dynamic health dashboard, allowing users to track their health in real-time. The framework generates real-time alerts for problematic health markers, allowing prompt medical action. Extensive testing validates the system's efficacy and dependability in health monitoring and the seamless integration of smart gloves with Android applications. © 2024 IEEE.

关键词： Internet of things

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Covid19 Disease Assessment Using CNN Architecture 3

Covid19 Disease Assessment Using CNN Architecture

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3rd International Conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Mary Shiba, C. Mishra, Sumit Kumar Sandhya, S. Vidhya, K. Jaichandran, R. Manjula, G. R.M.K Engineering College Department of Computer Science Chennai India Chandigarh University Department of Computer Science Punjab India Acharya Institute of Technology Department of Master of Business Administration Karnataka India Kpr Institute of Engineering and Technology Department of Computer Science Chennai India Aarupadai Veedu Institute of Technology Department of Computer Science Chennai India

ISBN: (纸本)9798350336238

Recently, the COVID-19 pandemic has emerged as one of the world's most critical public health concerns. One of the biggest problems in the present COVID-19 outbreak is the difficulty of accurately separating COVID-19 cases from non-COVID-19 cases at an affordable price and in the initial stages. Besides the use of antigen Rapid Test Kit (RTK) and Reverse Transcription Polymerase Chain Reaction (RT-PCR), chest x-rays (CXR) can also be used to identify COVID-19 patients. Unfortunately, manual checks may produce inaccurate results, delay treatment or even be fatal. Because of differences in perception and experience, the manual method can be chaotic and imprecise. technology has progressed to the point where we can solve this problem by training a Deep Learning (DL) model to distinguish the normal and COVID-19 X-rays. In this work, we choose the Convolutional Neural Network (CNN) as our DL model and train it using Kaggle datasets that include both COVID-19 and normal CXR data. The developed CNN model is then deployed on the website after going through a training and validation process. The website layout is straightforward to navigate. A CXR can be uploaded and a prediction made with minimal effort from the patient. The website assists in determining whether they have been exposed to COVID-19 or not. © 2023 IEEE.

关键词： COVID-19

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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

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computer Systems science & Engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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Improved AO based Classification Framework for Eye Diseases 2

Improved AO based Classification Framework for Eye Diseases

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Saju, Binju Asha, V. Mathew, Jincy C Sangma, Sengcheng Ghosh, Shouvik Sivaguru, K. Adi Shankara Institute of Engineering & Technology Department of Computer Science - AI Engineering Kerala Kalady India New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350327533

A sensory organ that reacts to visible light, the human eye is a component of the sensory nervous system. A new approach is needed since, although physical detection of the eye condition is still a possibility, it is challenging and time-consuming. The study recommends a hybrid paradigm to classify various eye illnesses. Prior to further processing, pictures are first reduced in size, made grayscale, and noise-free using a combination of the Weiner and Median filters. Histogram equalization based on a Gaussian Mixture is also used to enhance pictures. Picture features are recovered with the aid of a thick convolutional neural network. An improved Aquila optimized ResNet101 model is used for classification. The effectiveness of the models is contrasted with that of related studies in the literature. A F1 score of 98.18%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 98.91%, and precision of 98% were attained by the suggested model. © 2024 IEEE.

关键词： DCNN Diabetic retinopathy Gaussian Mixture IAO ResNet

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IAOMDR: Improved Aquila Optimized Hybrid deep learning Model for Classification of Diabetic Retinopathy 2

IAOMDR: Improved Aquila Optimized Hybrid deep learning Model...

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2nd International Conference on Emerging Trends in Information technology and Engineering, ic-ETITE 2024

作者： Saju, Binju Barwal, Sahil Asha, V. Kumar, Shubham Tressa, Neethu Nikhil, T. Adi Shankara Institute of Engineering & Technology Department of Computer Science-AI Engineering Kalady India New Horizon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350328202

One of the scariest illnesses that causes irreversible blindness is Diabetic Retinopathy (DR). As a result, early exposure to Diabetic Retinopathy can help to preserve vision. The study proposes a hybrid model to classify Diabetic Retinopathy images. Initially, pre-processing of the photographs includes resizing them. Grey scale translation and noise removing using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is as opposed to other studies in literature. The proposed model has acquired F1 score of 98.98%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 99.1%, and precision of 99%. © 2024 IEEE.

关键词： Median filters

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