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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Bano, Mehnaz Matta, Priya Chandel, Shivali Tula's Institute Department of Computer Science and Engineering Dehradun India Tula's Institute Department of Computer Applications Dehradun India

ISBN: (纸本)9798350387490

The Content Based Image Retrieval (CBIR) is basically used to extract images from the collection of images using several image matching and extraction methods. As it is now with the advancement and ongoing consistent technology and negligible cost of the electronic, digital and virtual sensors, a huge collection of image databases is processed by different types of applications. This type of database of images extends the requirement that we should provide effective search methods for the retrieval of images that can fulfill the demands of end-user. The texture, color, or shape of a picture is the most basic feature used to symbolize and index the images. These distinct elements of photographs are retrieved and used as a measure to find image similarities. A search procedure is initiated for relevant images that fulfill the user's information need using a sketch drawing or a query image given by the user that has similar features. The relevance feedback can be used as an optional step to retrieve and enhance the CBIR process. In this paper, after extracting semantic characteristics from photos, an analysis of the content of the images is performed with respect to features connected to the low level. In this paper, an overview of traditional CBIR methods (e.g.: Color histogram-based, texture based and shape based) are discussed. This paper's primary goal is to study the comprehensive details on the various computational techniques used for content-based image retrieval. To further enhance the efficiency of the image retrieval process, their limitations are also addressed. © 2024 IEEE.

关键词： Content based retrieval

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Enhancing Security in the Internet of Things: A Trust-Based Protocol for Resilient Communication

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SN computer science 2024年第1期5卷 4页

作者： Althaf Ali, A. Hussain, Mohammed Muzaffar Subramaneswara Rao, A. Lavanya, S. Feroz Khan, A.B. Department of Computer Applications Madanapalle Institute of Technology & Science (MITS) Andhra Pradesh Madanapalle India Department of Computer Science and Engineering C. Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India Department of Software Applications Thiruthangal Nadar College Tamil Nadu Chennai India Department of Computer Science Syed Hameedha Arts and Science College Tamil Nadu Kilakarai India

The quick increasing in the Internet of Things (IoT) devices has raised significant security concerns, particularly in the face of reactive jamming attacks. This paper proposes a trust-based protocol named Trust-Based Protocol for Resilient Communication (TBRC) that incorporates a robust key management technique to improve the security of IoT systems and mitigate the impact of reactive jamming attacks. To assess the effectiveness of our protocol, the work is compared with existing schemes such as CKMT, EDDK, and CEMT. Through extensive simulations under realistic conditions, we analyze key performance metrics, including energy consumption, packet loss, computational time, and throughput. The comparative analysis demonstrates that our proposed protocol outperforms CKMT, EDDK, and CEMT in terms of computational efficiency, throughput, and delay, making it a more effective solution for secure IoT communication. By leveraging a robust key management technique, our protocol provides a secure and resilient communication framework for IoT environments, effectively countering reactive jamming attacks. The integration of this technique strengthens the overall security posture of IoT networks, ensuring reliable and secure communication between devices. This work contributes to advancing IoT security by introducing a trust-based protocol with a robust key management technique that effectively mitigates the impact of reactive jamming attacks. The comparative evaluation against existing schemes validates the superiority of our proposed approach in terms of security, efficiency, and reliability. The results of our evaluation highlight the improved performance of our protocol, positioning it as a promising solution for real-world IoT deployments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DDoS IoT Security Trusted metrics

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Unmasking Hypertension Through a Fusion of Human Lifestyle Data using Machine Learning Algorithms 6

Unmasking Hypertension Through a Fusion of Human Lifestyle D...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Vishnu Priya, V. Nisanth, G. Nivethaa, R.P. Vijay, Srinidhi Department of Computer Science and Engineering Kongu Engineering College Erode Perundurai India Department of Computer Applications Kongu Engineering College Erode Perundurai India Department of Computer Technology UG Kongu Engineering College Erode Perundurai India

ISBN: (纸本)9798331522667

Hypertension, also referred to as high blood pressure, is a condition arising from the consistently high blood pressure against artery walls. The volume and output of blood from the heart primarily control blood pressure under stiff or tight arterial conditions. High pulse pressure leads to increased blood pressure, since the pumping of more blood makes the arteries inactive. The World Health Organization has a comprehensive guideline in which an evidence-based pharmacological approach has been recommended for managing adult patients with hypertension, not neglecting to mention lifestyle modification in order to support medicinal treatment. Machine learning may revolutionize the way hypertension treatment is projected for the management of personalized treatment routines for patients based on their characteristics. ML provides great promise in seeking ideal medication and predicting responses thereof against massive sets of data for hypertensive patients. The techniques allow for the determination of the individual risk for hypertension and ensure that treatment protocols are optimal. Compared to methods used to predict hypertension in the past, ML algorithms show a marked improvement in performance. © 2025 IEEE.

关键词： Personalized medicine

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Spine Disease Detection in Spinal X-Ray Images using Machine Learning 13

Spine Disease Detection in Spinal X-Ray Images using Machine...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Khenwar, Medha Chauhan, Dushyant Sharma, Nandini Gupta, Priyanka Gla University Department of Computer Engineering and Applications Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Millions of individuals globally experience spinal ailments. This stresses the importance of swift detection for achieving better treatment results. Traditional diagnosis techniques can be lengthy. They might lack precision. So here the aim is to design a powerful yet budget-friendly system that employs a deep neural network which has been trained on a vast X-ray image database. The system aims at carrying out X-ray scans of spinal vertebrae with improved accuracy. The method proposed utilizes a lite deep neural network to gather information. This is done from numerous levels to aid. In the detection of spinal health conditions, the autonomous system for finding spine illnesses consist crucial elements. These include photo filtering tools attribute excavation methods and efficient classification algorithms. Moreover, Clinical information and patient past records are added to the analyses. This augments the diagnostic precision and dependability of the system. © 2024 IEEE.

关键词： Budget control

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Integrative Remote Sensing Approaches Using Generative Adversarial Networks for Urban Heat Island Analysis and Mitigation

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 681-698页

作者： Sundar, Ganapathy Patchaiammal, Pitchaimuthu Venkateshwarulu, Balaji Vijayan Kumar, Thangavel Pradeesh Rajamannar, Kesavamoorthy Tripathi, Rajesh Kumar Department of Computer Applications Sindhi College Chennai India Department of Information Science and Engineering HKBK College of Engineering VTU Belagum Bengaluru Nagawara India VIT School of Agricultural Innovations and Advanced Learning (VAIAL) VIT Vellore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India

The phenomenon of urban heat islands (UHI) presents a critical challenge for urban sustainability, exacerbating local temperatures, increasing energy demands, and impairing public health. Traditional methods for addressing UHI are often resource-intensive and slow. This study introduces a novel approach, utilizing a Hybrid CycleGAN-SVM (HCGS) model that leverages the synergy of Generative Adversarial Networks (GANs) and Support Vector Machines (SVMs) to efficiently analyze and mitigate UHI effects through high-resolution satellite imagery and temperature data. The model incorporates Enhanced Vision Transformers (EViTs) for superior feature extraction, adept at capturing intricate spatial and spectral urban patterns. The CycleGAN component of the model generates high-quality synthetic imagery, enhancing the dataset and addressing class imbalances, thereby bolstering the SVM classifier’s ability to precisely pinpoint heat-prone urban areas. Implemented in Google Colab, the HCGS model demonstrated exceptional performance, achieving a classification accuracy of 0.98. This indicates its potential as an effective tool for urban heat mitigation, offering actionable insights for urban planning and policy-making. By integrating advanced machine learning techniques with remote sensing data, the HCGS model paves the way for innovative climate adaptation strategies, fostering more sustainable and resilient urban environments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Enhanced Vision Transformers EViTs GANs Generative Adversarial Networks HCGS model Hybrid CycleGAN-SVM Satellite imagery Support Vector Machines SVM UHI effect Urban heat island

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Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

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computers, Materials & Continua 2024年第1期78卷 1253-1274页

作者： Sangeeta Yadav Preeti Gulia Nasib Singh Gill Piyush Kumar Shukla Arfat Ahmad Khan Sultan Alharby Ahmed Alhussen Mohd Anul Haq Department of Computer Science&Applications Maharshi Dayanand UniversityRohtak124001India University Institute of Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia

Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

关键词： Fall detection activity recognition human pose estimation accuracy

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Brain Tumor Detection using Optimized Stochastic Gradient Descent Function

Brain Tumor Detection using Optimized Stochastic Gradient De...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Mishra, Annu Chaturvedi, Ravi Prakash Sharma, Hirdesh Kumar, Prashant Asthana, Sameer Parashar, Mayank Sharda School of Engineeringand Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineeringand Technology Department of Computer Science and Applications Greater Noida India JEMTEC Department of Computer Science and Engineering Greater Noida India ITS Engineering College Department of Computer Science and Engineering Greater Noida India GNIOT Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

The learning rate of a CNN determines the efficiency of the neural network. In brain tumor detection, the learning rate and the optimization function plays a key role in deciding the final output. The Optimized Stochastic Gradient Descent (OSGD) optimizer giver better result in terms of Accuracy, Recall and other determining factors. We have also compared the results obtained with other optimizers and it could be clearly stated that our proposed model. The OSGD optimizer controls the maxima and minima, and move the near optimum solution buy controlling the learning rate. So via this method we can control our right *** we detect our object in les error. © 2024 IEEE.

关键词： Stochastic systems

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An Enhanced Career Prospect Prediction System for Non-computer Stream Students in Software Companies 1st

An Enhanced Career Prospect Prediction System for Non-comput...

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1st International Conference on Computational Intelligence for engineering and Management applications, CIEMA 2022

作者： Abraham, Biku Ambili, P.S. Department of Computer Applications Saintgits College of Engineering Kottayam India School of Computer Science and Applications REVA University Bangalore India

ISBN: (纸本)9789811984921

The stakeholders of the present education system have a key focus on the chances of on-campus employability of their wards. Since software companies play prominent role as campus recruiters, there is a low demand for the traditional engineering branches. The need to enhance skills for software related employment is a challenge to the private sector professional institutions for the existence of non-computer streams in this scenario. In this study, the authors being academic experts in the similar field, attempt to support institutions, students, curriculum designers, and the faculty by providing insight to better employment possibility in campus. Data set for the study is from the same institution for which the placement prediction was completed. The methodology models the problem as a sequential event prediction problem and employs deep learning techniques. The system extracts data from data set with 19 attributes. The results indicate that this predictive approach provides enhancement methods by which a non-computer stream student can ensure full-time employment in software related jobs through in-campus selection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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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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Copy Move Forgery Detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

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computer Systems science & engineering 2023年第5期45卷 1577-1593页

作者： R.Dhanya R.Kalaiselvi Department of Computer Applications Noorul Islam Centre for Higher EducationNagercoil629180India Department of Computer Science Engineering Noorul Islam Centre for Higher EducationNagercoil629180India

A severe problem in modern information systems is Digital media tampering along with fake *** though there is an enhancement in image development,image forgery,either by the photographer or via image manipulations,is also done in *** researches have been concentrated on how to identify such manipulated media or information manually along with automatically;thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced ***,high complexity affects the developed ***,it is complicated to resolve the issue of the speed-accuracy *** tackling these challenges,this article put forward a quick and effective Copy-Move Forgery Detection(CMFD)system utilizing a novel Quad-sort Moth Flame(QMF)Light Gradient Boosting Machine(QMF-Light GBM).Utilizing Borel Transform(BT)-based Wiener Filter(BWF)and resizing,the input images are initially pre-processed by eliminating the noise in the proposed *** that,by utilizing the Orientation Preserving Simple Linear Iterative Clustering(OPSLIC),the pre-processed images,partitioned into a number of grids,are ***,as of the segmented images,the significant features are extracted along with the feature’s distance is calculated and matched with the input ***,utilizing the Union Topological Measure of Pattern Diversity(UTMOPD)method,the false positive matches that took place throughout the matching process are *** that,utilizing the QMF-Light GBM visualization,the visualization of forged in conjunction with non-forged images is *** extensive experiments revealed that concerning detection accuracy,the proposed system could be extremely precise when contrasted to some top-notch approaches.

关键词： Borel transform based wiener filter(BWF) orientation preserving simple linear iterative clustering(OPSLIC) keypoint features block features outlier detection

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