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

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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Enhancing object detection for visually impaired integrating YOLOv8 with spiking EfficientDet

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Multimedia Tools and Applications 2025年 1-29页

作者： Biradar, Vinod Gull, Karuna C. Department of Computer Science and Engineering KLE College of Engineering & Technology Karnataka Chikodi591201 India Department of Computer Science and Engineering S. G. Balekundri Institute of Technology Belagavi Visvesvaraya Technological University Karnataka Belagavi590018 India

Object detection is a significant method in computer vision, mainly for detecting objects belonging to different classes in an image. Its applications are widely spread to video surveillance, human tracking, autonomous vehicles, and assistive technologies for sight-impaired individuals. However, existing methods suffer from accurately identifying an object and its respective classes due to misidentifying or failing to identify smaller objects. Therefore, this research proposes a new object detection model called You Only Look Once version 8 with Spiking EfficientDet (Yv8SED) that improves detection accuracy, reducing wrong classification with particular emphasis on smaller objects. This model is beneficial in assistive devices for the navigation and safety of a visually-impaired person. The Yv8SED method provides excellent object detection performance with reduced time and cost, making it an efficient methodology for different object detection tasks. The segmentation process is improved through SegNet, which efficiently separates the object from the image. Moreover, the Hippopotamus Optimization Algorithm (HOA) is used to optimize error parameters in the proposed method, thereby improving efficiency and robustness in the object detection process. Experimental results proved the efficiency of the proposed method, with mAP at 95% and mean Average Recall (mAR) at 93% on the COCO dataset and mAP and mAR at 95% and 98%, respectively, on the VOC dataset. These results verify the efficacy and reliability of the proposed method for object detection, particularly for enhancing assistive technologies for the blind and visually impaired. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Object detection

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Internet of Things Authentication Protocols: Comparative Study

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computers, Materials & Continua 2024年第4期79卷 65-91页

作者： Souhayla Dargaoui Mourade Azrour Ahmad ElAllaoui Azidine Guezzaz Abdulatif Alabdulatif Abdullah Alnajim Engineering Science and Technology Laboratory IDMS TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknesErrachidia52000Morocco Higher School Essaouira Cadi Ayyad UniversityMarrakesh44000Morocco Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia

Nowadays, devices are connected across all areas, from intelligent buildings and smart cities to Industry 4.0 andsmart healthcare. With the exponential growth of Internet of Things usage in our world, IoT security is still thebiggest challenge for its deployment. The main goal of IoT security is to ensure the accessibility of services providedby an IoT environment, protect privacy, and confidentiality, and guarantee the safety of IoT users, infrastructures,data, and devices. Authentication, as the first line of defense against security threats, becomes the priority ofeveryone. It can either grant or deny users access to resources according to their legitimacy. As a result, studyingand researching authentication issues within IoT is extremely important. As a result, studying and researchingauthentication issues within IoT is extremely important. This article presents a comparative study of recent researchin IoT security;it provides an analysis of recent authentication protocols from2019 to 2023 that cover several areaswithin IoT (such as smart cities, healthcare, and industry). This survey sought to provide an IoT security researchsummary, the biggest susceptibilities, and attacks, the appropriate technologies, and the most used simulators. Itillustrates that the resistance of protocols against attacks, and their computational and communication cost arelinked directly to the cryptography technique used to build it. Furthermore, it discusses the gaps in recent schemesand provides some future research directions.

关键词： Attacks cryptography Internet of Things security authentication

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BEDiff: denoising diffusion probabilistic models for building extraction

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Optoelectronics Letters 2025年第5期21卷 298-305页

作者： LEI Yanjing WANG Yuan CHAN Sixian HU Jie ZHOU Xiaolong ZHANG Hongkai College of Computer Science and Technology Zhejiang University of Technology Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information China Three Gorges University Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University College of Electrical and Information Engineering Quzhou University School of Electronic and Information Engineering Anhui Jianzhu University

Accurately identifying building distribution from remote sensing images with complex background information is challenging. The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds. Building on this concept, we propose a novel framework, building extraction diffusion model(BEDiff), which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion. Our approach begins with the design of booster guidance, a mechanism that extracts structural and semantic features from remote sensing images to serve as priors, thereby providing targeted guidance for the diffusion process. Additionally, we introduce a cross-feature fusion module(CFM) that bridges the semantic gap between different types of features, facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively. Our proposed BEDiff marks the first application of diffusion models to the task of building extraction. Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff, affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.

关键词： A

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Towards characterizing bug fixes through dependency-level changes in Apache Java open source projects

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science China(Information sciences) 2022年第7期65卷 102-120页

作者： Di CUI Lingling FAN Sen CHEN Yuanfang CAI Qinghua ZHENG Yang LIU Ting LIU School of Computer Science and Technology Xi'an Jiaotong University College of Cyber Science Nankai University College of Intelligence and Computing Tianjin University Department of Computer Science Drexel University School of Computer Science and Engineering Nanyang Technological University School of Cyber Science and Engineering Xi'an Jiaotong University

The complexity and diversity of bug fixes require developers to understand bug fixes from multiple perspectives in addition to fine-grained code changes. The dependencies among files in a software system are an important dimension to inform software quality. Recent studies have revealed that most bug-prone files are always architecturally connected with dependencies, and as one of the best practices in the industry,changes in dependencies should be avoided or carefully made during bug fixing. Hence, in this paper, we take the first attempt to understand bug fixes from the dependencies perspective, which can complement existing code change perspectives. Based on this new perspective, we conducted a systematic and comprehensive study on bug fixes collected from 157 Apache open source projects, involving 140456 bug reports and182621 bug fixes in total. Our study results show that a relatively high proportion of bug fixes(30%) introduce dependency-level changes when fixing the corresponding 33% bugs. The bugs, whose fixes introduce dependency-level changes, have a strong correlation with high priority, large fixing churn, long fixing time,frequent bug reopening, and bug inducing. More importantly, patched files with dependency-level changes in their fixes, consume much more maintenance costs compared with those without these changes. We further summarized three representative patch patterns to explain the reasons for the increasing costs. Our study unveils useful findings based on qualitative and quantitative analysis and also provides new insights that might benefit existing bug prediction techniques. We release a large set of benchmarks and also implement a prototype tool to automatically detect dependency-level changes from bug fixes, which can warn developers and remind them to design a better fix.

关键词： empirical software engineering software maintenance software evolution software architecture software design

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A Deep Learning Model-Based Neural Network Framework for Diabetic Foot Ulcer Classification 5

A Deep Learning Model-Based Neural Network Framework for Dia...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Shanmugam, P. Kumar, P.K. Manoj Hussain, Mohammad Manzoor Revathi, R. Deepa, P. Sakthivelu, U. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Subbalakshmi Lakshmipathy College of Science Department of Computer Science Tamil Nadu Madurai India B v Raju Institute of Technology Department of Computer Science and Engineering Medak Telangana Narsapur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Texcity Arts and Science College Department of Computer Science Madukkarai Market Coimbatore India College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350379945

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes, frequently resulting in amputation and having a significant impact on patients' quality of life. Early and accurate DFU classification is essential for prompt and efficient therapy. This study introduces an innovative neural network framework that utilizes deep learning techniques to categorize DFU based on thermograms. The proposed framework employs the pre-trained EfficientNet-B0 deep learning model, well-known for its exceptional performance in image classification tasks. The framework underwent training and assessment using a dataset sourced from Kaggle, consisting of 1866 colored images. Among them, 890 images depict plantar thermograms with negative DFU, whereas 976 images depict foot thermograms with positive DFU. The proposed framework demonstrated a higher accuracy of 98.71, outperforming previous approaches by a wide margin. The findings demonstrate the ability of deep learning to categorize DFU, paving the way for the development of automated testing techniques to assist healthcare practitioners in the timely identification and treatment of this incapacitating ailment. © 2024 IEEE.

关键词： Ability testing

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Enhancing the job scheduling procedure to develop an efficient cloud environment using near optimal clustering algorithm

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International Journal of Cloud Computing 2023年第2-4期12卷 134-147页

作者： Suganya, R. Joseph, Niju P. Rajadevi, R. Ramamoorthy, S. Department of Information Technology Sri Krishna College of Technology Tamil Nadu India Bengaluru India Department of Information Technology Kongu Engineering College Perundurai India Department of Computer Science and Engineering SRM Institute of Science and Technology Kanchipuram Tamilnadu Kattankulathur India

In this internet era, cloud computing and there are various problems in the cloud computing, where the consumers as well as the service providers facing in their day to day cloud activities. Job scheduling problem plays a vital role in the cloud environment. To provide an efficient job scheduling environment, it is necessary to perform efficient resource clustering. In this regard, the proposed system, concentrated on the resource clustering methodology by proposing an efficient resource clustering algorithm named identicalness split up periodic node size (ISPNS) in the cloud environment. The algorithm proposed helps in forming resource clusters with the help of cloud environment. The proposed system is compared with the existing systems to justify the performance of the proposed resource clustering algorithm and it produces near optimal solution for the resource clustering problem which helps to provide an efficient job scheduling in cloud environment. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Clustering algorithms

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A Comprehensive Investigation on Cyclone Prediction Using Deep Learning Technique

A Comprehensive Investigation on Cyclone Prediction Using De...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Priya, A.T.R. Krishna Anuja, R. Devi, R. Sahila Manivannan, I. Sivaprasad Ramya, N.C. Rohini College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350333244

Weather forecasters have long been concerned with cyclones, and over the recent century, scientists have conducted numerous research on axially symmetric formations, operational dynamics, and methodologies. Several scientists have proven that deep learning can offer a fresh approach to overcoming the limitations of TC prediction by employing a pure data-driven system or enhancing numerical methods with DL. This review introduces development based on DL for various techniques in genesis TC predictions, track forecasting models, intensity estimates, severe weather prediction associated with TCs (including such heavy storms and thunderstorms, and their disastrous effects), and severe storm prediction, in addition to enhancing numerical prediction performance, by summarising and analysing the difficulties of tropical cyclone forecasting methods in recent times and maximizing of techniques using DL in these attributes. The chance is that, as of now, neither the DL capability nor the vast amounts of multi-source data were effectively employed to enhance the precision of tropical cyclone prediction. The difficulty is due to the fact that cyclones vary from typical climatic changes and oceanic mechanisms in their use of complex and dynamic frameworks and are easily persuaded by a wide range of factors. Hence, it might be difficult to ensure the observable duration and reliability of cyclone forecasting. © 2023 IEEE.

关键词： Weather forecasting

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Scarlett: Virtual Assistant & Browlett (Browser) 14

Scarlett: Virtual Assistant & Browlett (Browser)

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Dhanush, M. Sai Saritha, G. Sai, A. Virinchi Akarshana, M.N. Methodist College of Engineering and Technology/Computer Science and Engineering Hyderabad India

A personal voice assistant is the software that can perform task and provide different services to the individual as per the individual’s dictated commands. In this modern era, day to day life became smarter and interlinked with technology. We already know some voice assistance like google, Siri. etc. This project works on voice input and give output through voice and displays the text on the screen. The main goal of our voice assistance makes people smart and give computed results instantly. The voice assistance takes the voice input through our microphone (such as Bluetooth and wired microphone) and it converts our voice into computer understandable language gives the required solutions and answers which are asked by the user. This assistance connects with the world wide web to provide results that the user has questioned. Natural Language Processing algorithm helps computer machines to engage in communication using natural human language in many forms. To achieve this we are using modules like web browser, speech recognition, python speech-to-text, and Natural Language Processing (NLP) algorithm. © Grenze Scientific Society, 2023.

关键词： Python

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Offline Signature Forgery Detection Based on Geometric Measures Using Tensorflow Model 2

Offline Signature Forgery Detection Based on Geometric Measu...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Lakshmi, A. Anagha Reddy, G. Siddarth Reddy, M. Sowmya Kathirisetty, Nikhila Vardhaman College of Engineering Department of Information Technology Hyderabad India Vardhaman College of Engineering Department of Computer Science and Data Science Hyderabad India

ISBN: (纸本)9798350370188

The most popular method for identifying people from past signatures is through signatures. By using a TensorFlow model which is a deep learning algorithm, we created a new system to verify signatures on bank checks and other important documents. The main point is that a signature plays a crucial role in banking because it is necessary for money withdrawals. In banks there are no efficient systems to check if a signature is real or fake as it is the most frequently used bio-metric method to verify a person's identity. This can lead to bank fraud. The Project will make it easier to tell whether a signature is genuine or not. Online and offline verification are the two methods of verification. We're going to use various geometric measurements to accomplish offline verification. Python libraries were employed in this case. In the testing phase the model's performance is evaluated. To protect the integrity and legitimacy of handwritten signatures, signature forgery detection is an essential responsi-bility. The capacity to recognize forged signatures with accuracy has grown more important as a result of the rise in digital transactions and the growing reliance on electronic documents. The act of duplicating or copying another person's signature with the purpose to deceive or gain illegal access is known as signature forging. Manually spotting these forgeries can be difficult because expert forgers are adept at closely imitating the visual style and traits of real signatures. Therefore, there has been a lot of interest in the development of automated methods and algorithms to identify fake signatures. © 2024 IEEE.

关键词： Data mining

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