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Advanced human activity recognition on wearables with multi-scale sparse attention

Multimedia Tools and Applications 2025年 1-30页

作者： Muniasamy, Anandhavalli Asiri, Fatima College of Computer Science Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid University Abha Saudi Arabia

Maintaining a regular daily activity routine is essential for overall health and well-being. Wearable sensors offer a convenient way to track daily activities, but accurately identifying a wide range of activities remains a challenge for existing methods. These methods relied heavily on feature selection approaches in CNNs, which were not only tedious but also struggled to accurately identify activities with similar patterns and, also fails to find the long-range dependencies. The introduction of transformers in CNNs addresses these limitations, enabling more efficient and precise recognition of activities even when patterns are similar. In this paper, a novel Human activity recognition model that combines CNN with a Residual Transformer Network (RTN) is proposed. This model incorporates two key improvements: a sparse attention technique applied at multiple scales to capture complex spatial patterns, and an adaptive fusion. Sparse attention allows the model to concentrate on the most critical parts of the spectrogram at different scales and directions. This approach aids the model in distinguishing between activities with similar overall patterns by capturing subtle differences. Additionally, it enhances the model’s ability to recognize long-range dependencies. An adaptive fusion technique is introduced to combine features from CNN and RTN, with a dynamic scoring function optimally weighting each feature stream. This ensures balanced contributions from spatial patterns and temporal dependencies for accurate activity classification. Experiments conducted on the HARTH, HU-HAR, and HuGaDB datasets demonstrate impressive results with higher accuracies of 94.82%, 98.94%, and 96.33%, respectively. These findings highlight the effectiveness of the proposed method for accurate activity detection and continuous health monitoring applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wearable sensors

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Integrating Bayesian and Convolution Neural Network for Uncertainty Estimation of Cataract from Fundus Images

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computer Modeling in Engineering & sciences 2025年第4期143卷 569-592页

作者： Anandhavalli Muniasamy Ashwag Alasmari Department of Informatics and Computer Systems College of Computer ScienceKing KhalidUniversityAbha61421Saudi Arabia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of ***,the mass disease that needs attention in this context is *** deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty ***,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the *** prepares convolutional neural network(CNN)and BCNN *** proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN *** study then trains them on a dataset of cataract images filtered from the ocular disease fundus images *** deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye *** compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.

关键词： Bayesian neural networks(BNNs) convolution neural networks(CNN) Bayesian convolution neural networks(BCNNs) predictive modeling precision medicine uncertainty quantification

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Proposed Framework for Detection of Breast Tumors

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computers, Materials & Continua 2023年第2期74卷 2927-2944页

作者： Mostafa Elbaz Haitham Elwahsh Ibrahim Mahmoud El-Henawy Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt

computer vision is one of the significant trends in computer *** plays as a vital role in many applications,especially in the medical *** detection and segmentation of different tumors is a big challenge in the medical *** proposed framework uses ultrasound images from Kaggle,applying five diverse models to denoise the images,using the best possible noise-free image as input to the U-Net model for segmentation of the tumor,and then using the Convolution Neural Network(CNN)model to classify whether the tumor is benign,malignant,or *** main challenge faced by the framework in the segmentation is the speckle ***’s is a multiplicative and negative issue in breast ultrasound imaging,because of this noise,the image resolution and contrast become reduced,which affects the diagnostic value of this imaging *** result,speckle noise reduction is very vital for the segmentation *** framework uses five models such as Generative Adversarial Denoising Network(DGAN-Net),Denoising U-Shaped Net(D-U-NET),Batch Renormalization U-Net(Br-UNET),Generative Adversarial Network(GAN),and Nonlocal Neutrosophic ofWiener Filtering(NLNWF)for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio(PSNR)for each level of *** five used methods have been compared with classical filters such as Bilateral,Frost,Kuan,and Lee and they proved their efficiency according to PSNR in different levels of *** five diverse models are achieved PSNR results for speckle noise at level(0.1,0.25,0.5,0.75),(33.354,29.415,27.218,24.115),(31.424,28.353,27.246,24.244),(32.243,28.42,27.744,24.893),(31.234,28.212,26.983,23.234)and(33.013,29.491,28.556,25.011)forDGAN,Br-U-NET,D-U-NET,GANand NLNWF *** to the value of PSNR and level of speckle noise,the best image passed for segmentation using U-Net and classification usingCNNto detect tumor *** experiments proved

关键词： Breast tumor speckle noise GAN model U-Net model neutrosophic

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Different Methods to Estimate Stress-Strength Reliability Function for Modified Exponentiated Lomax Distribution

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2025年第2期6卷 99-106页

作者： Al-Rassam, Raya Salim Mohammed, Khalida Ahmed Rashed, Safwan Nathem Department of Statistics & Informatics College of Computer Science & Mathematics University of Mosul Iraq

When ensuring the reliability of device or the suitability of a material, it is necessary to take into consideration the stress cases in the operating environment. This means that the uncertainty about the reality environmental stress must be taken into as random. The stress-strength (S-S) model treated the stress and strength variables as random. In the simplest form of stress-strength model, y represents the stress put on the unit by the operating environment, and the strength of the unit represented by x. A unit is able to perform its required function if its stress imposed on it is less than the strength of the unit. In this paper, the stress-strength reliability estimation for the modified exponentiated Lomax distribution, which is generalization of the Lomax distribution, with an unknown shape parameter and a known scale parameters is studied using different methods. These methods include the maximum likelihood method, Bayesian estimation method under a quadratic loss function, and the least squares method for complete data. The estimators are compared based on Markov Chain Monte Carlo (MCMC) simulations using R-Studio, evaluated by the mean square error (MSE) criteria. The simulation results show that the maximum likelihood estimators are the best in two cases: the first is when the sample sizes are equal and the second is when the shape parameter of the strength variable is greater than the shape parameter of the stress variable. While least squares estimators are the beast if the strength sample size is smaller than the stress sample size. Finally when the strength sample size is greater than the stress sample size, then the best estimators differ between the maximum likelihood estimators and Bayesian estimators. Bayesian estimators become the best when the shape parameter of stress variable is larger than the shape parameter of the strength variable. © 2025 The Author(s).

关键词： Maximum likelihood estimation

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Advanced deep learning models based on neutrosophic logic for the analysis of brain tumor medical images

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Neutrosophic Sets and Systems 2025年 82卷 924-939页

作者： Atef, Dina El Shahat, Doaa Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt

The categorization of medical photographs poses considerable difficulties owing to noise, uncertainty, and ambiguous information. Conventional deep learning models frequently encounter difficulties in addressing this issue, resulting in diminished diagnostic precision, particularly in the context of low-quality or ambiguous situations. This work presents a hybrid methodology that combines Neutrosophic Set (NS) theory with deep learning models to improve magnetic resonance imaging (MRI) picture classification in uncertain settings. NS theory delineates three domains: True (T), Indeterminate (I) and False (F) to address picture uncertainty and noise, hence enhancing deep learning models' capacity to analyze complex, ambiguous visual data. To assess the methodology, four advanced deep learning models MobileNet, VGG16, DenseNet121 and InceptionV3 were employed, and their efficacy was analyzed on brain tumor medical image datasets. The findings demonstrate that models trained on NS-transformed data, especially DenseNet and inception, produce enhanced results relative to those trained on the original data, attaining notably higher accuracy, precision, and recall. This illustrates that integrating NS theory into deep learning models markedly improves their capacity to categorize uncertain and noisy MRI pictures, offering a reliable method for enhancing diagnostic accuracy in medical imaging. © 2025, University of New Mexico. All rights reserved.

关键词： Convolutional neural networks

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An effective intrusion detection system based on feature selection and regularized long short-term memory classifier

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Multimedia Tools and Applications 2025年 1-24页

作者： Chikkalwar, Sudha Rani Garapati, Yugandhar Department of Computer science and Engineering GITAM Deemed to be University Rudraram Hyderabad India Department of Computer science and Informatics Mahatma Gandhi University Telangana Nalgonda India

In recent days, the expansion of Internet of Things (IoT) and the quick advancement of computer system applications contribute to the current phenomenon of data growth. The field of intrusion detection has expanded considerably as a result of the need to protect networks from various attacks. Previously, security has been provided by combining conventional intrusion monitoring systems with a high number of security measures. However, the ability of combined systems to analyze enormous amounts of data has been restricted. Therefore, in this research, open-source and freely available datasets known as NSL-KDD and CSE-CIC-IDS2018 datasets are analyzed. Those datasets contain a class column that splits the attack detection into two classes (normal and anomalous) and four categories (DoS, probe, R2L, and U2R). Then, feature encoding and scaling are used for pre-processing. After that, a SelectPercentile-based feature selection algorithm is suggested to identify the numerous various intrusion attacks and choose the best features. Later, the Regularized Long Short-Term Memory (LSTM) is employed to classify the data. The result analysis verified that Regularized LSTM surpasses the existing models in the NSL-KDD based on accuracy (99.95%), false alarm rate (0.05%), attack detection rate (99.82%), and f1-measure (99.94%). While in the CSE-CIC-IDS2018 dataset, the Regularized LSTM method achieves better accuracy (99.33%), false alarm rate (0.07%), attack detection rate (99.96), and f1-measure (99.95%). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

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Robust tube-based MPC with smooth computation for dexterous robot manipulation

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science China(Information sciences) 2024年第11期67卷 252-268页

作者： Yu LUO Tianying JI Fuchun SUN Qie SIMA Huaping LIU Mingxuan JING Jianwei ZHANG Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Department of Informatics University of Hamburg

Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system constraints in such scenarios, model predictive control(MPC) has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints. However, in such scenarios, the substantial computational load required to solve the optimal control problem(OCP) at each triggering instant can lead to significant delays between state sampling and control application, hindering real-time performance. To address these challenges, this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks: reaching a given target and tracking a reference trajectory. By predicting the successor state as the initial condition for imminent OCP solving, we can solve the forthcoming OCP ahead of time, alleviating delay effects. Additionally,we establish an upper bound for linearizing the original nonlinear system, reducing OCP complexity and enhancing response speed. Grounded in tube-based MPC theory, the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured. Empirical validation is provided through two numerical simulations and two real-world dexterous robot manipulation tasks, which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.

关键词： dexterous robot manipulation model predictive control smooth computation

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Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder

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computer Modeling in Engineering & sciences 2025年第4期143卷 849-873页

作者： Saad Alahmari Abdulwhab Alkharashi Department of Computer Science Applied CollegeNorthern Border UniversityArar91431Saudi Arabia Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityRiyadh11673Saudi Arabia

The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital *** the development of IoT devices,huge amounts of information,including users’private data,are *** systems face major security and data privacy challenges owing to their integral features such as scalability,resource constraints,and *** challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data,creating an attractive opportunity for *** address these challenges,artificial intelligence(AI)techniques,such as machine learning(ML)and deep learning(DL),are utilized to build an intrusion detection system(IDS)that helps to secure IoT *** learning(FL)is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked *** delivers an effectual tool to defend user confidentiality,mainly in the field of IoT,where IoT devices often obtain privacy-sensitive personal *** study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence(PEFLID-CSAAI)*** main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT ***,the PEFLIDCSAAI technique involves data preprocessing using Z-score normalization to transformthe input data into a beneficial ***,the PEFLID-CSAAI method uses the Osprey Optimization Algorithm(OOA)for the feature selection(FS)*** the classification of intrusion detection attacks,the Self-Attentive Variational Autoencoder(SA-VAE)technique can be ***,the Chameleon Swarm Algorithm(CSA)is applied for the hyperparameter finetuning process that is involved in the SA-VAE model.A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI *** simulated outcomes demonstrated that the PEFLID-CSAAI

关键词： Federated learning internet of things artificial intelligence chameleon swarm algorithm intrusion detection system healthcare IoT devices

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Integration of data science with the intelligent IoT(IIoT): Current challenges and future perspectives

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Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon University University of Electronic Science and Technology of China College of Information Science and Engineering Hohai University Department of Computer Science University of Salerno Graduate School of Informatics and Engineering The University of Electro-Communications

The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence(AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most state-of-the-art research focuses independently on either data science or IIoT, rather than exploring their integration. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions.

关键词： Data science Internet of things (IoT) Big data Communication systems Networks Security Data science analytics

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ToSiM-IoT: Towards a Sustainable Optimisation of Machine Learning Tasks in Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 16829-16840页

作者： Kaushal, Ashish Almurshed, Osama Muftah, Asmail Auluck, Nitin Rana, Omer Indian Institute of Technology Department of Computer Science and Engineering Ropar India Cardiff University School of Computer Science and Informatics United Kingdom Azzaytuna University Libya

With the rise of digital infrastructure and Internet of Things (IoT), a substantial amount of data is continuously generated that needs to be processed efficiently. While modern artificial intelligence (AI) approaches have shown good capabilities in handling large volumes of data, their excessive demands for memory and processing power result in very high utilisation of resources. In this work, we propose ToSiM-IoT, an optimisation framework that introduces a layer selection approach to identify an ideal mix of active, and inactive layers, using a genetic algorithm for model training. Next, we design a pruning mechanism that identifies performance-critical features using heatmap visualisation, during model inference, and eliminates the remaining features. Two machine learning (ML) models - InceptionV3 and VGG16, have been evaluated on an agricultural weed detection scenario, using the DeepWeeds image classification dataset. Experimental results demonstrate that our framework can achieve a significant reduction in model size and training time, while maintaining high accuracy, for both models. Therefore, this approach provides the potential to be efficiently deployed on intelligent IoT systems where computational capabilities are limited. © 2025 IEEE.

关键词： Adversarial machine learning

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