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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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Analytically Determining the Robustness of Binarized Neural Networks 23

Analytically Determining the Robustness of Binarized Neural ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Alzahrani, Sahar M. Schewe, Sven Huang, Chao Huang, Xiaowei University of Liverpool Department of Computer Science Liverpool United Kingdom College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of Southampton Department of Computer Science Southampton United Kingdom

ISBN: (纸本)9798350374889

Binarized neural networks (BNNs) are a class of deep neural networks (DNNs) known for their minimal computational requirements during inference, making them ideal for low-performance environments. Despite their efficiency, BNNs are as vulnerable to safety and security issues as other types of DNNs, and hence analyzing their robustness is essential. In contrast to general DNNs, BNNs are relatively small and straightforward, making it feasible to assess their robustness analytically. We propose a method for rigorously analyzing BNN robustness based on 0-1 Integer Linear Programming (0-1 ILP), which utilizes the dual functionality of boolean variables within BNNs as both boolean (with values of 'true' and 'false') and real-valued variables constrained to binary values (0 and 1). By leveraging this duality, we formalize the robustness problem as a linear programming problem augmented with boolean reasoning. We minimize the distance to a differently classified image using either the L1-norm (binarized and non-binarized inputs) or the Lœ-norm (non-binarized inputs). We have implemented our approach using the Z3 solver and evaluated it on the MNIST and FashionMNIST datasets. Our results demonstrate the capability to construct optimal adversarial examples with minimal deviations from the ground truth images. We achieved a one-pixel difference between the original and perturbed images for all binarized images. We also identified minimal perturbations on grayscale images using the L1 and Lœ-norms. For the latter, we simplified the analysis to determine the existence of epsilon-close adversarial examples across a range of epsilon values. © 2024 IEEE.

关键词： Integer linear programming

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An intrusion detection system using ranked feature bagging

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1213-1219页

作者： Azhagiri, M. Rajesh, A. Karthik, S. Raja, K. Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of IT College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

An Intrusion Detection System monitors the network for any malicious attacks. It is an ideal tool for protecting extensive business networks from any kind of attack. In this paper, an Intrusion Detection System using Bagging and ranked features has been proposed. The sample datasets for training the ensemble of classifiers in the bagging approach are obtained by generating subsets of the features ranked according to their importance in the classification process. OneR algorithm has been used to rank the features. The decision tree has been used as the base classifier in the bagging approach. The proposed system was able to predict attacks with an accuracy of 99.71% with a minimal number of features and outperformed other similar approaches discussed in the literature. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bagging CIC-IDS 2017 dataset Decision tree Ensemble Feature selection Intrusion detection system OneR feature selection

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Online Prediction to Facilitate a Flipped and Adaptive Classroom 53

Online Prediction to Facilitate a Flipped and Adaptive Class...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Cheng, Qiong College of Computing and Informatics University of North Carolina at Charlotte Department of Computer Science CharlotteNC United States

ISBN: (纸本)9798350336429

In a flipped and adaptive learning environment, adaptability in time is the key to handling constant change and student individuality for their success. To maximize the learning experience, an urgently demanding task is to identify lower-performance students on the fly for targeted timely interventions. Existing state-of-the-art works focus on a regular course setting, where timelines in adaptability is not necessarily a high priority. These works apply batch-based (offline) learning algorithms or ensemble methods, which need the entire training data collected before prediction. These methods, without the whole learning process into consideration, may affect the accuracy of prediction results. In response to the urgent need, we propose to apply an online predictive learning method to handle incoming student data throughout the time steps of a course semester and predict low-performing students for each time step, with a goal to minimize the overall classification error. We built up our experimental design on the multiple learning theories, designed and executed four surveys, and conducted predictive analysis on student data. In the process of feature engineering, we conducted a series of correlation and cause-effect regression analyses and further quantified the determinant factors of predicting student performance. We further developed a framework for identifying low-performing students on the fly and comparing and analyzing deep online learning and diverse traditional batch-based (offline) predictive modeling methods. Our comparative analysis indicates that the online predictive learning approach is encouraging. It outperforms all batch-based (offline) methods overall;prediction results on low-performing students at a time step help identify their problem patterns situated in the context of the whole course progress to design and conduct timely interventions. The innovative study set up a stage for us to deeply understand the learning process, identify main determ

关键词： active learning adaptive learning Machine learning online deep learning

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Knowledge augmented significant language model-based chatbot for explainable diabetes mellitus prediction 19

Knowledge augmented significant language model-based chatbot...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Elfayoumi, Mazen AbouElazm, Mohamed Mohamed, Omar Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user’s understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT-3.5, and Gemma2. This approach augments the chatbot’s responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures. © 2025 IEEE.

关键词： Prediction models

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Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

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computers, Materials & Continua 2023年第10期77卷 583-599页

作者： Fadhil Mukhlif Norafida Ithnin Roobaea Alroobaea Sultan Algarni Wael Y.Alghamdi Ibrahim Hashem Information Assurance and Security Research Group(IASRG) Faculty of ComputingUniversiti Teknologi Malaysia(UTM)Johor BahruJohor81310Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia College of Computing and Informatics Department of Computer ScienceUniversity of SharjahSharjah27272United Arab Emirates

The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several *** every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese *** of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare *** time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will *** prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a *** short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term *** an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for *** provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring *** developed a data analysis methodology,including data preparation and dataset *** examine two popular algorithms,LSTM and Decision tree on the official ***,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the *** issues and challenges are discussed for future *** is expected that the results these methods provide for the Health Scenario would be reliable and credible.

关键词： Healthcare framework AI COVID-19 machine&deep learning LSTM RNN decision tree

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

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computers, Materials & Continua 2023年第12期77卷 3697-3722页

作者： Fares Alharbi Reem Alshahrani Mohammed Zakariah Amjad Aldweesh Abdulrahman Abdullah Alghamdi Department of Computer Science College of Computing and ITShaqra UniversityShaqra15526Saudi Arabia Department of Computer Science College of Computers and ITTaif UniversityP.O.Box 11099Taif21944Saudi Arabia College of Computer and Information Science King Saud UniversityRiyadhSaudi Arabia

Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels,optical fiber,and blockchain *** Internet of Things(IoT)is a network of connected,interconnected *** issues occasionally arise due to the amount of data ***,they have been primarily addressed by blockchain and smart contract *** there are still security issues with smart contracts,primarily due to the complexity of writing the code,there are still many challenges to consider when designing blockchain designs for the IoT *** study uses traditional blockchain technology with the“You Only Look Once”(YOLO)object detection method to accurately locate and identify license *** YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising,they have received limited research ***-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network(RCNN)built with the tensor flow core open source *** method works reasonably well for identifying any license *** Automatic License Plate Recognition(ALPR)approach delivered outstanding results in various ***,with a recognition rate of 96.2%,our system(UFPR-ALPR)surpassed the previously used technology,consisting of 4500 frames and around 150 ***,a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR ***,the license plate’s characters were complicated for standard methods to identify because of the shifting lighting *** proposed model,however,produced beneficial outcomes.

关键词： Intelligent transportation system blockchain technology license plate recognition privacy YOLO deep learning technique ALPR

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IDCE:Integrated Data Compression and Encryption for Enhanced Security and Efficiency

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computer Modeling in Engineering & sciences 2025年第4期143卷 1029-1048页

作者： Muhammad Usama Arshad Aziz Suliman A.Alsuhibany Imtiaz Hassan Farrukh Yuldashev Department of Cyber Security Pakistan Navy Engineering CollegeNational University of Sciences and TechnologyKarachi75350Pakistan Department of Computer Science Main CampusIqra UniversityKarachi75500Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100170Uzbekistan

Data compression plays a vital role in datamanagement and information theory by reducing ***,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and *** the exponential growth of digital data,robust security measures are *** encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key *** their individual benefits,both require significant computational ***,performing them separately for the same data increases complexity and processing *** the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and *** on 128-bit block sizes and a 256-bit secret key *** combines Huffman coding for compression and a Tent map for ***,an iterative Arnold cat map further enhances cryptographic confusion *** analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.

关键词： Chaotic maps security data compression data encryption integrated compression and encryption

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Integrating Edge Intelligence with Blockchain-Driven Secured IoT Healthcare Optimization Model

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computers, Materials & Continua 2025年第5期83卷 1973-1986页

作者： Khulud Salem Alshudukhi Mamoona Humayun Ghadah Naif Alwakid Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computing School of Arts Humanities and Social SciencesUniversity of RoehamptonLondonSW155PHUK

The Internet ofThings(IoT)and edge computing have substantially contributed to the development and growth of smart *** handled time-constrained services and mobile devices to capture the observing environment for surveillance *** systems are composed of wireless cameras,digital devices,and tiny sensors to facilitate the operations of crucial healthcare ***,many interactive applications have been proposed,including integrating intelligent systems to handle data processing and enable dynamic communication functionalities for crucial IoT ***,most solutions lack optimizing relayingmethods and impose excessive overheads for maintaining devices’***,data integrity and trust are another vital consideration for nextgeneration *** research proposed a load-balanced trusted surveillance routing model with collaborative decisions at network edges to enhance energymanagement and resource *** leverages graph-based optimization to enable reliable analysis of decision-making ***,mobile devices integratewith the proposed model to sustain trusted routes with lightweight privacy-preserving and *** proposed model analyzed its performance results in a simulation-based environment and illustrated an exceptional improvement in packet loss ratio,energy consumption,detection anomaly,and blockchain overhead than related solutions.

关键词： Smart cities load balancing blockchain health systems edge computing

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