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Revolutionizing Facial Feature Extraction with AI: An Innovative Approach

Revolutionizing Facial Feature Extraction with AI: An Innova...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： B. Lakshmi Jandhyam Lakshmi Alekhya K Parish Venkata Kumar B. Kishore K. Kamala Nyni K. Likhita Dept. of Computer Applications V.R. Siddhartha Engineering Dept of computer science and engineering V.R. Siddhartha Engineering College Department of Computer Applications V.R. Siddhartha Engineering College Department of Computer Applications

This paper provides a technique for detecting human faces & extracting their features based on segmentation of skin tone & facial structure. The system goes through several stages. The method first searches for regions where faces might be present by segmenting skin tone. After performing various morphological operations on skin, it uses aspect ratio of a face to find viable face blocks. face blocks are then used to extract additional features. Therefore, one may identify and validate a person's image using their face features by using this biometric attribute. To enhance facial recognition, numerous researchers have employed Support Vector Machine (SVM), Principal Component Analysis (PCA), a combination of SVM and PCA, PCA with an Artificial Neural Network, and even the conventional PCA-SVM. Due to PCA-ANN's limited dataset limitation, PCA-SVM is superior to PCA-ANN. To extract features, relative location & size are used. If algorithm is unable to identify even one feature in a prospective face block, it rejects face block.

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computer Vision-Hybrid Learning based on Multi Scale Dilated Convolution Module Mechanism Implemented for Object Detection 1

Computer Vision-Hybrid Learning based on Multi Scale Dilated...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Kalpanadevi, D. Kartheeban, K. Mayilvaganan, M. Bamaruckmani, P. Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Koil India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Computer Science PSG College of Arts and Science Coimbatore India Department of Computer Science and Computer Applications Ayya Nadar Janaki Ammal College Sivakasi India

ISBN: (纸本)9781665460842

For smart traffic management and highway regulation, vehicle detection and statistics in highway surveillance video scenes are crucial. The massive installation of traffic surveillance cameras resulted in the collection of a sizable database of traffic video footage for analysis. In order to achieve the best detection from prediction, YOLOV3 (You Only Look Once) can be achieved in this study by including a multi-scale augmentation convolutional module technique. The first camera reads the licence plates of approaching vehicles before blockchain technology is used to store predictions in the cloud. It can display available parking spaces and be embedded in parking spaces. If there aren't any open spots, they are shown similarly. To make sure the automobile stays in the parking spot after it leaves the lot, a second camera scans the licence plate once again and matches it to the time of arrival. The size of vehicle objects varies substantially when viewed from this angle, and it is harder to spot small items that are off the road. It is crucial to successfully address the aforementioned issues and further apply them while dealing with complicated camera scenarios. This article concentrates on the aforementioned issue and offers a workable method so that the outcomes of vehicle detection can be used for tracking numerous objects and vehicle counts. The convolutional network's takes an input must be a fixed size, and its more complex structure necessitates lengthy training sessions and substantial resources. It extracts the feature points that need to be identified, compares them with the ORB algorithm, establishes road detection lines, and counts the number of moving vehicles and the volume of traffic. © 2022 IEEE

关键词： Convolution

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Securing Neural Network in the Cloud: A Systematic Approach of Cloud Security Based on ANN-SVM Model 4

Securing Neural Network in the Cloud: A Systematic Approach ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arunarani, Ar Vijayagopal, V. Kartheesan, L. Dwivedi, Vijay Kumar Rajesh Kumar, A. Nishant, Neerav School of Computing College of Engineering Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Computer Applications Tamilnadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Surguja India N.S.N. College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Karur India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331540364

Many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor Mutual Authentication and Key Agreement (MAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor MAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVM was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVM. © 2024 IEEE.

关键词： Cloud security

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Grouping nodes based PBFT for Preventing Fake Educational Certificates Verification 25

Grouping nodes based PBFT for Preventing Fake Educational Ce...

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26th International Conference on Distributed Computing and Networking, ICDCN 2025

作者： Kumar, Amritesh Bhatia, Nitin Das, Debasis IDRP: IoT and Applications Indian Institute of Technology Jodhpur Rajasthan Jodhpur India Department of Electrical Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur India

ISBN: (纸本)9798400710629

The traditional PBFT consensus mechanism in a blockchain network tolerating 33% node failure faces various challenges, including incorrect data verification, transaction communication complexity, and higher consensus delay in larger networks. In case of appending a block carrying the Educational Certificates (ECs) the impact of node error due to external attacks could lead to production of fake degrees in the system. To address the challenge of fraudulent degree verification, we propose a novel Grouping nodes-based Practical Byzantine Fault Tolerance (GnPBFT) consensus algorithm. We show that the GnPBFT algorithm gives higher probability to prevent fake certificate verification as compared to the PBFT in same network setting. Furthermore, we also show that our scheme is resilient against Sybil attack, with improved performance metrics such as 33% reduction in transaction overhead and 29% decrease in consensus latency. © 2025 Copyright held by the owner/author(s).

关键词： Blockchain

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Detection and Mitigation of Attacks in MANETs: A Comprehensive Survey of Security Techniques 4

Detection and Mitigation of Attacks in MANETs: A Comprehensi...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Mohandas, R. Sivapriya, N. Vaigandla, Karthik Kumar Kirubasankar, K. Chennai Institute of Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai India Cauvery College for Women Department of Computer Applications Tamilnadu Trichy India Balaji Institute of Technology and Science Department of Electronics & Communication Engineering Telangana Warangal India

ISBN: (纸本)9798331529635

MANETs are decentralized and self-organizing networks with no fixed infrastructure. They are very flexible but, at the same time are vulnerable to the huge diversity of security threats. The dynamic topology and the lack of central control along with the reliance on wireless communication expose MANETs to several attacks, which are blackhole, wormhole, Sybil, and DoS attacks. This is a survey paper that covers the most common security threats towards MANETs and probes for different detection and prevention techniques proposed to mitigate those risks. Various methodology are examined, ranging from cryptographic methods and trust-based models to machine learning and intrusion detection systems, comparing the effectiveness, scalability, and adaptability of those approaches in different network environments. The paper further outlines some real strengths and weaknesses of the currently used solutions, indicates potential key challenges, and states the future directions that could form the way for strengthening the security capabilities of MANETs with respect to evolving emerging technologies such as 5G and beyond. The aim is to demonstrate the ongoing state of the attack detection and prevention in MANETs, thereby offering insights into promising research opportunities. © 2024 IEEE.

关键词： Network intrusion

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Fluid Segmentation in Retinal OCT images with Pigment Epithelial Detachment 1

Fluid Segmentation in Retinal OCT images with Pigment Epithe...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Sheeba, T.M. Raj, S. Albert Antony Anand, M. SRM Institute of Science and Technology Faculty of Science and Humanities Department of Computer Applications Chennai Kattankulathur India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (纸本)9798350342796

For the assessment of Pigment Epithelial Detachment(PED), optical coherence tomography (OCT) must automatically and precisely segment the fluid related subretinal fluid. An algorithm for guided fluid segmentation in two stages is suggested. To get the primary curve of the fluid part for the Graph cut approach, the candidate fluid region is automatic determined in the first step. The Gaussian pre-fitting approach is suggested to segment sub-retinal-fluid in the second stage. The suggested algorithm is evaluated using the testing data set, which consists of 55 longitudinal OCT scans from 50 eyes of 50 individuals. DC (Dice Coefficient), Precision, and Recall of the proposed system are calculated. The suggested algorithm can achieve excellent segmentation accuracy without the need to segment the retinal layer. This approach has the potential to enhance clinical PED therapy and may be able to reliably segregate subretinal fluid from OCT images. © 2023 IEEE.

关键词： Graph cut method Local Gaussian Pre-fitting OCT PED Subretinal Fluid Segmentation

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A Deep Review to Find the Most Accurate Method among KNN,RF and MLP for Predicting Alzheimer Disease 4

A Deep Review to Find the Most Accurate Method among KNN,RF ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Subathra, K. Malakar, Antara Suhasini, S. Prakash, Sneha Abed, Sarah Jassim Abdtawfeeq, Tareq Hafdhi Sri Sairam Engineering College Department of Mathematics Chennai44 India The Assam Royal Global University Department of Computer Science & Engineering Guwahati India Cvr College of Engineering Hyderabad India Adi Shankara Institute of Engineering & Technology Department of Computer Applications Kalady India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Debilitating neurodegenerative conditions like Alzheimer's disease (AD) are linked to great pain and eventually death. For the purpose of successful treatment planning and individualized therapy, it is imperative that Alzheimer's disease (AD) and its precursor phases, such as moderate cognitive impairment (MCI), be identified as soon as possible. Single nucleotide polymorphisms, or SNPs, and neuroimaging data, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), have been found to work better together to improve the ability to distinguish between AD/MCI and normal control (NC) participants. But missing data from multimodal sources will always exist, and model learning is complicated by the variety of phenotypic and genotypic biomarkers. Furthermore, most current research uses multistage techniques to forecast illness development and focuses only on classifying brain diseases, ignoring the regression problem. Using the most samples possible inside a k-nearest neighbour, random forest and multi layer perceptron (KNN-RF-MLP) architecture, we provide a unique framework to concurrently solve classification and clinical score regression problems by combining multimodal neuroimaging and genetic data. To impute missing features in partial data, we first apply linear interpolation in our technique. We next employ KNN to extract neuroimaging features from SNPs, MRI, and PET data in order to reduce the variation in phenotypic and genotype data. © 2024 IEEE.

关键词： Positron emission tomography

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Next-Gen Telehealth Ecosystems Using IoT for Virtual Consultations and Support 10

Next-Gen Telehealth Ecosystems Using IoT for Virtual Consult...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kathija Nasreen, A. Priya, S. Gupta, Nukala Sujata Shoba, L.K. Mohankumar, N. Arunkumar, S. Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Deemed To Be University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Chenagalpattu Chennai Tamil Nadu Kattankulathur India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Computer Applications Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The research discussed focuses on improving virtual medical consultations and support using Internet of Things (IoT) technologies in telehealth services. It is predicted that IoT devices can assist with creating a new telehealth environment where patients and doctors can communicate easily for remote monitoring, diagnosis, and treatment. The main objective of this unique approach is to expand access to healthcare, especially in poor and rural areas. Connected medical equipment, smart sensors, and wearable technology all play an important role in this system. These IoT components enable gathering real-time and complete patient data, helping doctors make better, more individualized choices and care plans. Data safety, privacy, and system compatibility are some areas where this integration hits a snag. Sensitive patient data and the integrity of the IoT-enabled systems need robust safety measures. These IoT improvements in telemedicine have had a revolutionary effect. Proactive interventions, continuous health monitoring, and individualized care are ways patients might benefit from improved medical care. Virtual consultations are increasingly used in the medical field, and a more connected and data-driven framework for the patient experience is emerging. © 2024 IEEE.

关键词： Internet of things

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The Use of 6G Communication Technology in Healthcare applications for the Accurate and Better Transmission 4

The Use of 6G Communication Technology in Healthcare Applica...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Omkar, Jamvant Menaka, S. Praveena, J. Varshney, Neeraj Arunkumar, B. Reddy, G. Vinoda Ies College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Information Technology and Management Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Hummanities TN India Gla University Department of Computer Engineering and Applications Mathura India Karpagam Academy of Higher Education Computer Science Engineering Department India Hyderabad Medchal India

ISBN: (纸本)9798350360165

The IIoT is the most important element in the 6th generation networks, or 6G, so it greatly depends on effective data processing and storage. For the cause of ensuring smooth node-to-node communication, 6G provides ultra-low delays, high bandwidth, and higher-level QoS. Intelligent diagnosis, numerous in-hospital, and distant healthcare services are prerequisites to enable healthcare institutions to provide patientcentered care. What is essential is that the system hosting an array of uses, ranging from ambient appropriate living settings with wearable technology-enabled remote patient monitoring to robotic procedures powered by an intelligent networking platform. Therefore, this work addresses the needs of the healthcare sector by providing a reference-layered contact architecture for locations and devices in real-time communication. The unique 6G technology can, therefore, be utilized with great foresight to the future in the healthcare sector, enabling effective treatment and intelligent service integration. Because several networking nodes, including terminal devices, are integrated into effective health service delivery, a backbone structure is thus required for the services that take much time to complete. Therefore, a backbone structure for managing services is required to enable smooth communication by the framework, developed to enable this kind of 6G-enabled advanced healthcare application. © 2024 IEEE.

关键词： Remote patient monitoring

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Fine- Tuning Pre-trained Models for Aspect-Based Sentiment Analysis of Amazon Food Reviews

Fine- Tuning Pre-trained Models for Aspect-Based Sentiment A...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Sridhar, D. Manikandan, K.B. Padma Priya, R.S. Pradeepa, D. Vasumathi, B. Shankar, R. Alliane University Alliance College of Engineering and Design Central Campus Karnataka Bangalore India Hyderabad India Vignan's Foundation for Science Technology and Research Department of Information Technology and Computer Applications Guntur India Reva University School of Computer Science and Applications Bangalore India Brainware University Department of Computational Sciences West Bengal India Bharathiar University Research in Commerce Coimbatore India

ISBN: (纸本)9798331513894

The process of classifying consumer responses to a particular product review as neutral, negative, or positive is known as sentiment analysis. Product owners can utilize sentiment analysis for assistance in them decide whether to continue producing their product, increase production, or eliminate production altogether. As a result, they will gain a deeper comprehension of how customers feel about their products. The method used to determine the opinions expressed in the review regarding a specific feature or aspect of the product is called Aspect Based Sentiment Analysis, or ABSA. Numerous machine learning and natural language processing models are currently available for ABSA. The inability to precisely identify and categories the views expressed towards particular features is a major drawback of the aspect-based sentiment analysis (ABSA) techniques presently in usage for Amazon food assessment tasks. The Hybrid Contextual Aspect-Based Sentiment Analyzer (HCABSA), a hybridized framework for aspect-based sentiment analysis, is developed in this research to accurately assess the sentiment of Amazon food reviews through a number of processes. This research starts by collecting dataset for Amazon food reviews from Kaggle, preprocessing the data to eliminate noise and irrelevant information, and then using POS tagging to identify the specific aspects and sentiment words related to those aspects, which are nouns and adjectives in the review using the Lexicon based approach. In the following stage, the frequency-based candidate extraction method is utilized to pick the characteristics that are used to determine the product's internal and exterior features. The raw data is then transformed into features that the machine learning models can access using the Bag of Words feature extraction process. The Gradient Boosting Decision Tree technique is then used to categories the sentiment as either positive or negative. Using performance metrics like accuracy, precision, recall,

关键词： Decision trees

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