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Identification of Human Activity from Video Streaming Smartphone data Using Intensified VGG16

International Journal of Engineering, Transactions B: Applications 2025年第6期38卷 1340-1352页

作者： Yadav, R.K. Daniel, A. Semwal, V.B. Department of Computer Science and Engineering Amity University Gwalior India Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering Maulana Azad National Institute Technology Bhopal India

Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. HAR is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. The paper aims to propose a VGG16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset;further, we compare the evaluated results with existing results. The proposed methods achieved 99.88% accuracy with excellent precision, recall, and F_measure values. Comparing the evaluated result with existing outcomes over the WISDM and UCI-HAR datasets. The new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. Furthermore, this research uses Tensor Processing Units (TPU) available on Kaggle to improve classification accuracy while reducing error rates and speeding up execution. ©2025 The author(s).

关键词： Smartphones

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Dimensional-noise-aware battery lifetime prediction via an EM-TLS framework

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Progress in Natural science:Materials International 2025年第1期35卷 146-155页

作者： Chenlong Yu Ting Lu Guohua Liu Xiaoang Zhai Wuyan Deng Jiayu Wan Yang Liu Xin Li School of Computer Science and Technology Donghua University Future Battery Research Center Global Institute of Future Technology Shanghai Jiaotong University Data Science Research Center Duke Kunshan University

Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early charging/discharging cycles to the remaining useful lifetime. While most existing techniques train the prediction model through minimizing the prediction error only, the errors associated with the physical measurements can also induce negative impact to the prediction accuracy. Although total-least-squares(TLS) regression has been applied to address this issue, it relies on the unrealistic assumption that the distributions of measurement errors on all input variables are equivalent, and cannot appropriately capture the practical characteristics of battery degradation. In order to tackle this challenge, this work intends to model the variations along different input dimensions, thereby improving the accuracy and robustness of battery lifetime prediction. In specific, we propose an innovative EM-TLS framework that enhances the TLS-based prediction to accommodate dimension-variate errors, while simultaneously investigating the distributions of them using expectation-maximization(EM). Experiments have been conducted to validate the proposed method based on the data of commercial Lithium-Ion batteries, where it reduces the prediction error by up to 29.9 % compared with conventional TLS. This demonstrates the immense potential of the proposed method for advancing the R&D of rechargeable batteries.

关键词： Battery lifetime prediction Dimension-variate measurement error Energy storage Expectation-maximization Total-least-squares regression

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High-speed encrypted traffic classification by using payload features

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Digital Communications and Networks 2025年第2期11卷 412-423页

作者： Xinge Yan Liukun He Yifan Xu Jiuxin Cao Liangmin Wang Guyang Xie School of Cyber Science and Engineering Southeast University Nanjing Huafei Data Technology Co. Ltd. School of Computer Science and Communication Engineering Jiangsu University

Traffic encryption techniques facilitate cyberattackers to hide their presence and activities. Traffic classification is an important method to prevent network threats. However, due to the tremendous traffic volume and limitations of computing, most existing traffic classification techniques are inapplicable to the high-speed network environment. In this paper, we propose a High-speed Encrypted Traffic Classification(HETC) method containing two stages. First, to efficiently detect whether traffic is encrypted, HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted flows. Second, HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest model. The experimental results show that HETC can achieve a 94% F-measure in detecting encrypted flows and a 85%–93% F-measure in classifying fine-grained flows for a 1-KB flow-length dataset, outperforming the state-of-the-art comparison methods. Meanwhile, HETC does not need to wait for the end of the flow and can extract mass computing features. The average time for HETC to process each flow is only 2 or 16 ms, which is lower than the flow duration in most cases, making it a good candidate for high-speed traffic classification.

关键词： Traffic classification Flow analysis Information entropy Machine learning Randomness test

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Toward Precise Quantification of Energy Expenditure in Wheelchair Users

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IEEE Access 2025年 13卷 72189-72201页

作者： Alam, Iftikhar Ali, Zulfiqar Khalid, Wassauf Ali, Gauhar ELAffendi, Mohammed City University of Science and Information Technology Department of Computer Science Peshawar25000 Pakistan Prince Sultan University EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Riyadh11586 Saudi Arabia

Modern electronic devices like smart bands, smartwatches, smartphones, and treadmills are widely used to track exertion metrics, also called energy expenditure, such as step counts, running, time, and distance. However, these devices often fail to meet the needs of individuals with mobility impairments, such as wheelchair users, for whom such metrics are hard to evaluate. This research introduces a tailored model to track and quantify exertion data for manual wheelchair users. The existing Heart Intensity Metric (HIM), which relies on parameters such as heart rate, weight, age, and time (exercise duration), is adapted with a revised Activity Intensity Assessor (AIA). The model incorporates critical factors for wheelchair users, including heart rate, adjusted movement status (1 for movement and zero for no movement), and inclination status, with new parameters, such as Metabolic Equivalent of Task (MET), and wheelchair speed. The revised AIA is then adapted for the energy expenditure formula to calculate calorie-burning estimation specifically for manual wheelchair users. The revised approach minimizes false positives commonly produced by existing approaches for manual wheelchair users, especially in scenarios involving non-movement exercises like upper limb activities. Unlike prior models, the proposed AIA ensures precise energy expenditure calculations, even during stationary activities, and reflects a zero-calorie expenditure when no exercise occurs. Results are statistically verified and demonstrate that traditional formulas yield inaccurate calorie estimations for wheelchair users, while the revised model aligns better with physiological realities. This work provides a practical framework for designing electronic tools that effectively track energy expenditure/total energy (ET), also known as exertion efforts, and estimate calories burnt by manual wheelchair users. The scope of this study is limited to examining energy expenditure exclusively for manual wheelcha

关键词： Wheelchairs Heart rate Software Legged locomotion Monitoring Manuals Estimation Accuracy Hardware Adaptation models

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Novel Reliability Indicators From the Perspective of data Center Networks

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IEEE Transactions on Reliability 2025年第1期74卷 2459-2472页

作者： Zhuang, Hongbin Li, Xiao-Yan Lin, Cheng-Kuan Liu, Ximeng Jia, Xiaohua Fuzhou University College of Computer and Data Science Fuzhou350108 China National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong

Modern large-scale computing systems always demand better connectivity indicators for reliability evaluation. However, as more processing units have been rapidly incorporated into emerging computing systems, existing indicators (e.g., -component edge connectivity and -extra edge connectivity) have gradually failed to provide the required fault tolerance. In addition, these indicators require, for example, that the faulty network should have at least components (or that each component should have at least nodes). These fault assumptions are not flexible enough to deal with diversified structural demands in practice circumstances. In order to address these challenges simultaneously, this article proposes two novel indicators for network reliability by utilizing the partition matroid technique, named matroidal connectivity and conditional matroidal connectivity. We first investigate the accurate values of (conditional) matroidal connectivity of k -ary n -cube Qnk, which is an appealing option as the underlying topology for modern parallel computing systems. Moreover, we propose an O(kn-1) algorithm for determining structural features of minimum edge sets whose cardinality is the conditional matroidal connectivity of Qnk. Simulation results are presented to verify our algorithm's correctness and further investigate the distribution pattern of edge sets subject to the restriction of partition matroid. We also present comparative analyses illustrating the superior edge fault tolerance of our findings in relation to prior research, which even exhibits an exponential enhancement when k ≥ 4. © 1963-2012 IEEE.

关键词： Fault tolerance

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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

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SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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FaVEL: Fact Validation Ensemble Learning 24th

FaVEL: Fact Validation Ensemble Learning

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24th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2024

作者： Qudus, Umair Pekarou, Franck Lionel Tatkeu da Silva, Ana Alexandra Morim Röder, Michael Ngomo, Axel-Cyrille Ngonga Data Science Group Department of Computer Science Paderborn University Paderborn Germany

ISBN: (纸本)9783031777912

Validating assertions before adding them to a knowledge graph is an essential part of its creation and maintenance. Due to the sheer size of knowledge graphs, automatic fact-checking approaches have been developed. These approaches rely on reference knowledge to decide whether a given assertion is correct. Recent hybrid approaches achieve good results by including several knowledge sources. However, it is often impractical to provide a sheer quantity of textual knowledge or generate embedding models to leverage these hybrid approaches. We present FaVEL, an approach that uses algorithm selection and ensemble learning to amalgamate several existing fact-checking approaches that rely solely on a reference knowledge graph and, hence, use fewer resources than current hybrid approaches. For our evaluation, we create updated versions of two existing datasets and a new dataset dubbed FaVEL-DS. Our evaluation compares our approach to 15 fact-checking approaches—including the state-of-the-art approach HybridFC—on 3 datasets. Our results demonstrate that FaVEL outperforms all other approaches significantly by at least 0.04 in terms of the area under the ROC curve. Our source code, datasets, and evaluation results are open-source and can be found at https://***/dice-group/favel. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge graph

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Einocchio: Efficiently Outsourcing Polynomial Computation with Verifiable Computation and Optimized Newton Interpolation

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1981-1992页

作者： Pei, Xintao Chen, Yuling Long, Yangyang Sang, Haiwei Luo, Yun Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

Cloud computing, as a promising service platform, has gained significant popularity in addressing emerging data privacy issues in applications such as machine learning and data mining. Researchers have proposed the verifiable computing that allows the cloud users to delegate their computation tasks to the cloud server. Then, the cloud server computes the cryptographic proofs that verify the correctness of the results, a process that is generally faster ompared to local manual computation. However, performing computation tasks or verifying the correctness of encrypted data, such as multivariate polynomial functions, remains a significant challenge. To solve this problem, we propose Einocchio: A verifiable computation scheme that combines the efficient Pinocchio system with homomorphic encryption, which allows the public verification of the computational results on the server side while ensuring data confidentiality and the results. Compared with the existing solutions, Einocchio does not reveal the client's input. Furthermore, we extrapolate Einocchio by optimizing the Pinocchio's quadratic arithmetic program component using a differential optimization method, which reduces the computational workload owing to the conversion from quadratic to linear complexity, thereby increasing the efficiency of the quadratic arithmetic program preprocessing stage. Security analysis demonstrates that Einocchio achieves IND-CPA security. Finally, the performance evaluation confirmed its effectiveness and suitability for cloud computing environments. Compared to the corresponding scheme based on Newton interpolation, Einocchio achieves a threefold greater computational efficiency, with the generation of interpolation polynomials for 50 data inputs occurring in a mere 0.31 ms, while simultaneously reducing the number of computations. © 2005-2012 IEEE.

关键词： Cloud platforms

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PLC-MDT: A Framework for Detecting Anomalies with Digital Twins of Industrial Control Systems

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IEEE Sensors Journal 2025年第10期25卷 17739-17749页

作者： Xu, Zidong Zhang, Zhenyong He, Tuzeqiu Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

The attack events (such as the Stuxnet and BlackEnergy) that targeted the Industrial Control System (ICS) have validated its vulnerability to cyber intrusions. The prevention of ICS from cyberattacks is undoubtedly important and practical as it operates dangerous equipment and produces products that people need. Traditional anomaly detection methods, which rely heavily on SCADA data, are often unreliable due to the limitation of such data in accurately reflecting real-time system states under attacks. Therefore, we propose PLC-MDT, a novel anomaly detection method based on digital twins (DT), exploiting the modern communication technology and computing resources of ICS. PLC-MDT does not depend solely on SCADA data, instead, it leverages the interaction between DT and real-world ICS, utilizing both DT and field-site data to perform anomaly detection. Specifically, for PLC-MDT, the prediction model in DT is trained based on historical data, and the predicted values are used as input to the mechanism model in DT to generate simulated data. Networked structure of ICS guarantees real-time data collected from field sites for DT. Therefore, PLC-MDT detects attacks by monitoring the difference between the predicted and real-time data, revealing control channel corruption when the difference exceeds a threshold. The effectiveness of PLC-MDT is demonstrated through experiments on real-world systems, showing its potential for real-time detection of anomalies in ICS. © 2001-2012 IEEE.

关键词： SCADA systems

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