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

作者： Ashwinkumar, V.K. Loganathan, V. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

Phishing attacks, a prevalent form of social engineering and cyber threats, exploit human vulnerabilities to deceive internet users into divulging sensitive data for deceptive intentions. Among these, URL-based phishing attacks pose significant risks to individuals and organizations by manipulating trust in URLs to steal data or inject malware. This paper seeks to address this issue by employing machine learning models and deep neural networks trained on a dataset containing both malicious and normal URLs. Extracting relevant features from URL and website content, the models are evaluated and compared based on their predictive performance. This research contributes to enhancing cybersecurity measures against URL-based phishing attacks by leveraging advanced machine learning techniques for detection and prevention. © 2024 IEEE.

关键词： Websites

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VisQAid: Visual Question Answering for Visually Impaired 6

VisQAid: Visual Question Answering for Visually Impaired

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Anjana, S. Arya, J. Jayaraj, Sandra V Raj, Sreelakshmi T Manmadhan, Sruthy Nss College of Engineering Department of Computer Science and Engineering Palakkad India

ISBN: (纸本)9798350381689

Visual Question Answering for the visually impaired is an emerging and important research area. This field centers on creating technologies so visually impaired people can interact with their surroundings and learn by asking questions about visual content. The goal is to empower them to gain greater independence through this technology.A Visual Question Answering system has been developed to assist visually impaired people in understanding visuals. Using the VizWiz dataset, the proposed model learns from pictures and questions asked by visually impaired people. By training the system with Contrastive Language-Image Pre-training on this data, it can now respond to many visual questions in plain language. The results of the project shows the performance of the Visual Question Answering (VQA) system on the VizWiz *** accuracy rate of 72.9% shows the ability to answer visual questions from those who have sight challenges. © 2024 IEEE.

关键词： Contrastive Learning

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

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Multimedia Tools and Applications 2025年第18期84卷 19223-19253页

作者： Anusha, N. Tapas Bapu, B.R. S, Selvakumaran Vijayaraj, A. Ramesh Kumar, C. P, Raji Department of IoT School of Computer science and Engineering VIT Tamil Nadu Vellore India Department of Electronics and Communication Engineering S.A.Engineering College Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Information Technology R.M.K Engineering College R.S.M Nagar Tamil Nadu Kavaraipettai India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India

Wireless sensor network (WSN) is one of the essential components of a multi-hop cyber-physical system comprising many fixed or moving sensors. There are many common attacks in WSN, which can quickly harm a WSN system. In this manuscript, Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) with war strategy optimization (WSO) algorithm is introduced for detecting intrusion in WSN. Initially, the input data is attained from the NSL-KDD and CICIDS2017 datasets. The data is then passed onto Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) based pre-processing. After that, the filtered data is sent to the DIWGAN, which detects the intrusion and classifies the fraud data in the network. Additionally, the WSO algorithm is introduced to enhance the classification parameters of DIWGAN. By then the performance of the proposed DIWGAN-WSO-IDS methodology is evaluated using Python Platform. Also, the performance of the proposed methodology is calculated using various metrics like accuracy, precision, recall, f-measure, mean square error, and attack detection rate. Thus, the proposed technique has attained 29.87%, 31.7%, and 26.8% higher accuracy, 23.54%, 20.09%, and 26.98% higher recall, 12.76%, 10.96%, and 6.7% higher precision. And 6.85%, 4.56%, and 8.56% lower MSE than the existing techniques like BFSGSRF, MHICA-SSA, and CNN-LSTM methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wireless sensor networks

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Hybrid Color Texture Features Classification Through ANN for Melanoma

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Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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Automated Insurance Claims Using Block Chain 10

Automated Insurance Claims Using Block Chain

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sagar, Jukanti. Varun Siddharth, Poshala. Sai, B. Nikhil Seshadri, U. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350384369

This paper proposes an advanced system for automated insurance claims processing, integrating Natural Language Processing (NLP) and blockchain technology. Unlike existing systems that rely on rule-based automation and centralized databases, our proposed system enhances efficiency, data security, and fraud detection. NLP is employed to extract nuanced information from claims, while blockchain ensures decentralized and immutable record-keeping. The objectives of this project encompass efficiency optimization, heightened data security, cost reduction, trust fortification, and streamlined processes through the convergence of Natural Language Processing (NLP) and blockchain technology. By automating data extraction, minimizing human errors, and reducing operational costs, this innovative system aims to revolutionize the insurance claims process, ultimately fostering greater trust and transparency. © 2024 IEEE.

关键词： Cost reduction

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Evaluation of Reliability in Request-segregated Clouds through Remiadiations Predicting Hardware Failure Scheme

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Recent Patents on engineering 2025年第1期19卷 112-127页

作者： Sharma, Rohit Yadav, Vibhash Singh, Raghuraj Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology for Handicapped U.P. Kanpur208024 India Department of Information Technology Rajkiya Engineering College Banda210201 India Department of Computer Science and Engineering Harcourt Butler Technical University Kanpur208002 India

Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system more reliable and fault-tolerant. Objective: This research aims to analyze a predictive hardware failure model based on machine learning that anticipates the required remediations for undiagnosed failures in a cloud computing system serving multiclass requests. Methods: The model is tested on a carefully designed cloud data center that categorizes incoming requests as web, compute, storage, and dedicated server requests. To demonstrate improved reliability, a carefully designed test case is run on ReliaCloud-NS, which is a simulator for creating a CCS and computing its reliability. Results: The work found that using this model considerably enhanced the reliability of cloud computing systems when compared to not using the model. Conclusion: Although various estimation methods are patented to evaluate the system reliability of a cloud computing network, the emphasis of this study was mostly on improving the reliability of request-segregated clouds upon failing hardware resources like CPU, memory, bandwidth, and hard disc. Moreover, the prediction model might potentially be expanded to other system resources such as GPUs, software, and database packages. © 2025 Bentham science Publishers.

关键词： Cloud platforms

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Mood Melody Matchmaker System Using Deep Learning Model 9

Mood Melody Matchmaker System Using Deep Learning Model

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Mishra, Susmita Pradhan, Chittaranjan Jananee, V. Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India School of Computer Engineering KIlT Deemed To Be University Bhubaneswar India Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

Accessing a wide variety of music is made simple by modern music services. Many users rely on recommendation algorithms to select the ideal song for any given circumstance. Due to its frequent use for relaxation, mood regulation, stress, and illness management, as well as support for mental and physical activities, music has a profound influence on individuals. Music therapy employs a range of therapeutic environments and methods to enhance overall well-being. The article suggests a mood melody matching related to the listener's feelings. Machine learning and deep learning methods are employed to create a recommendation system designed to assist individuals in choosing music suitable for various life circumstances. Efficiently recognizing the ideal track for the user's current mood, the system delivers songs tailored to their emotional state, eliminating the need for users to spend time searching or seeking out music themselves. © 2024 IEEE.

关键词： Python

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A cloud based interactive framework for emergency medical data sharing 4th

A cloud based interactive framework for emergency medical da...

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4th Annual International Conference on data science, Machine Learning and Blockchain Technology, AICDMB 2023

作者： Taranum, Ayesha Manishankar, S. Padmaja, K. Balarengadurai, C. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysore India Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Kodakkara Thrissur India Department of Computer Science and Engineering GSSS Institue of Engineering and Technology for Women Karnataka Mysore India

ISBN: (纸本)9781032426853

Pandemic bringing a change in the medical system and medical infrastructure. This requires a complete revamping of medical data collections and storage. In such a scenario there has to be a system which enables the efficient transport of a patient to a distant hospital with minimal human support. In such a remote telehealth service, the inclusion of a system which is capable of sending and receiving medical data like live image of patients, X-Rays and multimedia images would result in better and fast pre-hospitalization. In such an emergency situation, the information has to be exchanged between the ambulance and the doctor in the least time possible. An efficient image compression algorithm will ensure the faster communication. The proposed framework is a combination of mobile application an interactive OpenTok API, cloud based platform Heroku and Firebase. The proposed system ensures a dedicated high speed data sharing environment based on cloud and mobile API to make an interactive framework for Healthcare Industry. © 2024 The Author(s).

关键词： Image compression

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An Enhanced Analysis of Cloud-Based Deep Learning Model in Smart Healthcare Management 2

An Enhanced Analysis of Cloud-Based Deep Learning Model in S...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Khanna, Tejaswi Mathur, Sonali Ims Engineering College Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350371055

Cloud-based Deep learning of models has been widely applied in intelligent healthcare management to deliver improved diagnostics and analytics for advanced patient outcomes. The aim of this research is to assess the cloud based Totally Deep system's performance more thoroughly and familiarize oneself with the model across a range of healthcare scenarios. The model can be assessed based on its capacity to predict outcomes largely from an affected person's clinical history and records, as well as its ability to accurately diagnose conditions unique to each affected person. Accuracy, time efficiency, and scalability could also be used to evaluate the version's performance. The findings of this study will help to enhance more intelligent predictive healthcare analytics for improved patient outcomes in real-world packages. © 2024 IEEE.

关键词： Broadly Cloud-Based Evaluation Healthcare Knowledge

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