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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Shaik, Nasir Hussain Kumar, Neelesh Menon, Rudraksh Sujatha, G. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur India

ISBN: (纸本)9798331518097

Mobile and Commercial devices are at high risk when navigating through websites and clicking on various links. Due to lack of knowledge, Individuals click on various links showing up on legitimate chats leading to clickjacking and phishing attacks. Every time a link shows up, Users cannot go to a site like virustotal and scan and wait for them to detect. This project aims to implement a structured script design and an automated program that scans and flags links as malicious and prevents certain cyberattacks. © 2024 IEEE.

关键词： Bot (Internet)

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Chronic Kidney Disease Prediction 2

Chronic Kidney Disease Prediction

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Rani, Palak Patel, Palak Babu, S. SRM Institute of Science and Technology College Of Engineering and Technology Department Of Computing Technologies Kattankulathur603 203 India

ISBN: (纸本)9798350365269

CKD is a huge health issue that affects millions of people and puts a lot of pressure on healthcare systems. It's important to diagnose it early so it can be treated and managed properly. Machine learning has become a great tool for predictive healthcare analytics, and we've used it to create a model for CKD that's 98% accurate. We collected a dataset with clinical and demographic data from a bunch of different patients, including things like ages, genders, blood pressure, and even serum creatinine and other lab measurements. We preprocessed the data, imputing any missing values, coding the categorical data, and scaling the numerical features. Then, we used random forest and XGBoost - two powerful ensemble algorithms - to build models for predicting CKD. © 2024 IEEE.

关键词： Creatinine

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Wrapper Based Linear Discriminant Analysis(LDA)for Intrusion Detection in IIoT

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Computer Systems Science & engineering 2023年第5期45卷 1625-1640页

作者： B.Yasotha T.Sasikala M.Krishnamurthy Department of Computer Science and Engineering MNM Jain Engineering CollegeChennai600097India Department of School of Computing Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering KCG College of TechnologyChennai600097India

The internet has become a part of every human ***,various devices that are connected through the internet are ***,the Industrial Internet of things(IIoT)is an evolutionary technology interconnecting various industries in digital platforms to facilitate their ***,IIoT is being used in various industrial fields such as logistics,manufacturing,metals and mining,gas and oil,transportation,aviation,and energy *** is mandatory that various industrial fields require highly reliable security and preventive measures against *** detection is defined as the detection in the network of security threats targeting privacy information and sensitive *** Detection Systems(IDS)have taken an important role in providing security in the field of computer *** of intrusion is completely based on the detection functions of the *** an IIoT network expands,it generates a huge volume of data that needs an IDS to detect intrusions and prevent network *** research works have been done for preventing network *** day,the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly *** this regard,this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA(WDLDA).The implemented WDLDA results in a rate of detection accuracy(DRA)of 97%and a false positive rate(FPR)of 11%using the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)dataset.

关键词： Intrusion detection IIoT wrapper support vector machine(SVM) LDA random forest(RF) feature selection

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Deep Fake Detection Based on Hybrid Approach Involving Residual Network Deep Learning Architecture

Deep Fake Detection Based on Hybrid Approach Involving Resid...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shibu, N.V. Muthunagai, R. Prabu, S. Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu India Manakula Vinayagar Institute of Technology Department of Electrical & Electronics Engineering Puducherry India School of Computing Srm Institute of Science and Technologies Department of Computing Technologies Chennai India

ISBN: (纸本)9798331510022

This Deep fake technology aims at generating synthetic video content or morphed video content which is very realistic to the naked eye. It involves the use of deep learning algorithms in the domain of video processing. In most cases, such artificially generated video contents are very difficult to get differentiated from naturally captured video signals. The existing methodologies of detection involved image tamper detection and digital forensics algorithms. These algorithms are ineffective in detecting videos tampered using deep fake technology. The proposed technique involves training and building of deep learning models that works like a black box to detect the artefacts crucial in the detection of video tampering. This paper presents an overview of the technology advancements in the field of deep fake video detection techniques over recent years. This paper also performs a comparative analysis on the performance of hybrid algorithms involving residual network deep learning architecture in the detection of video signals morphed using deep fake technology. The training datasets are obtained from Face forensics to build the model. The algorithm is augmented with image quality assessment, temporal consistency analysis and facial landmarks analysis to improve the detection algorithm. The performance metrics involve the generation of confusion matrix and generation of accuracy measures. The results provide a promising direction towards the construction of better detection models to safeguard against current and future threats. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Learning-Based Sign Language Translator 11th

Deep Learning-Based Sign Language Translator

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11th International Conference on Intelligent computing and Applications, ICRTC 2023

作者： Garg, Hardik Agarwal, Alok Arunachalam, N. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur SRM Nagar Tamil Nadu Chennai603203 India

ISBN: (纸本)9789819717231

According to the 2011 Indian census, 13 lakh people have hearing impairments. According to the Indian National Association of the Deaf, 180 lakh individuals, or about 1% of the population in India, have hearing impairments. Our project was driven by these statistics. Those with speech difficulties and deaf people require a way of connecting with the rest of the world. Some normal people are unable to recognize the symptoms of handicapped people. This results in the objective of our endeavor to convert sign language gestures into text that individuals of average intellect can read. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and Computer Vision 5

A Novel Predictive Analysis and Classification of Land Subsi...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Senthil, G.A. Prabha, R. Arun, M. Sahaya Anselin Nisha, A. Prabu, S. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. The Geographic Information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. This provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. This research study focuses on the use of Predictive Data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. The proposed model makes use of a dataset that includes specific land subsidence circumstances as input. The prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). The proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction. © 2024 The Authors. Published by Elsevier B.V.

关键词： Long short-term memory

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Gaussian weighting—based random walk segmentation and DCNN method for brain tumor detection and classification

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Multimedia Tools and Applications 2025年第8期84卷 4675-4702页

作者： Rani, K. Vijila Sumathy, G. Shoba, L.K. Sivalakshmi, P. Department of ECE Udaya School of Engineering Tamil Nadu Nagercoil India Department of Computational Intelligence SRM Institute Of Science and Technology Kattankulathur CampusChengalpattu Dt Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of ECE R.M.K College of Engineering and Technology Tamil Nadu Chennai India

Cancer is a disease that develops when cells divide abnormally. Cancerous cells can spread to surrounding organ tissues, the bloodstream, and the lymphatic system. This process is known as metastasis, and it shortens the patients' lives. Brain cancer is a type of cancer that develops in the brain and nervous system. Typically, MR (Magnetic Resonance) scan images are used by doctors to assess the condition of tissues that have grown abnormally within the human body. A novel segmentation method with classification is specifically suggested here for assessing the tumor site. To improve the quality of an image, it first uses top hat filtering to perform the morphological process. The brain tumor region is subsequently extracted from the morphologically filtered output using the Gaussian Weighted-Based Random Walk (GWBRW) segmentation technique. Lastly, a classification operation is carried out by the Deep Convolutional Neural Network (DCNN) classifier. Five performance measures are then used to compare the segmented output with the original images. The segmentation-based assessment estimations show that the segmentation achieves higher results, with average values of 22.8160, 0.2133, and 0.9704 for PSNR, Dice, and SSIM, respectively, and lower results, with average values of 19.3997 and 422.0844 for RMSE and MSE. The segmentation results show that the suggested computation enhances the tumor region's characteristics. Eventually, the Deep Convolutional Neural Network classifier successfully classifies 96.1% of the predictions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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Computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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Development of suitability sites for shallow tube wells in Bongao, Tawi-tawi using geographic information system-bases water resources assessment model

Development of suitability sites for shallow tube wells in B...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Trondillo, Mark Jude F. Tuling, Ryan Art M. Lopez, Marvin T. Bejarasco, Jeah A. Davao del Sur State College Philippines Engineering Department Philippines Institute of Computing Engineering and Technology Philippines

ISBN: (纸本)9798331302214

The scarcity and quality of water in the coastal and island province has become a concern specially in agricultural production. The identification of small -scale irrigation project is dependent on the water resources in the specific area and purpose of the infrastructure. The research was purposely conducted and employed a GIS-based model to generate Shallow Tube Wells (STWs) suitability map in Bongao, Tawi-tawi Province. The static water table depth and discharge data were used as factors with corresponding scales and weights of each factor for STW mapping. The results revealed that the government provides 100 units for residential and agricultural purposes, with 66% still functional. Based on the results of the model's geospatial analysis, approximately 10.69 and 9.67% of the total land area of the municipality of Bongao were identified to be moderately suitable and marginally suitable, respectively. A total extent of 3,423.63 hectares was attributed as moderately suitable, while 3,784.55 hectares were identified to be marginally suitable for STW. The adoption and application of the GIS framework were useful in the development of a suitability map. The results showed a scientific basis for municipal planning and project development for STW in the locality. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Irrigation

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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