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Motion Signal-based Recognition of Human Activity from Video Stream Dataset Using Deep Learning Approach

Recent Advances in computer science and Communications 2024年第3期17卷 77-91页

作者： Yadav, Ram Kumar Arockiam, Daniel Semwal, Vijay Bhaskar Department of Computer Science and Engineering Amity University Madhya Pradesh Gwalior India Department of Computer Science and Engineering MANIT Bhopal India

Background: Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted outstanding research attention due to the implementation of machine learning and deep learning approaches. Aim: This paper proposes a unique deep learning framework based on motion signals to recognize human activity to handle these constraints and challenges through deep learning (e.g., enhance CNN, LR, RF, DT, KNN, and SVM) approaches. Method: This research article uses the BML (Biological Motion Library) dataset gathered from thirty volunteers with four various activities to analyze the performance metrics. It compares the evaluated results with existing results, which are found by machine learning and deep learning methods to identify human activity. Result: This framework was successfully investigated with the help of laboratory metrics with convolutional neural networks (CNN) and achieved 89.0% accuracy compared to machine learning methods. Conclusion: The novel work of this research is to increase classification accuracy with a lower error rate and faster execution. Moreover, it introduces a novel approach to human activity recognition in the BML dataset using the CNN with Adam optimizer approach. © 2024 Bentham science Publishers.

关键词： Support vector machines

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Deep Learning-Based Cloud Security: Innovative Attack Detection and Privacy Focused Key Management

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IEEE Transactions on computers 2025年第6期74卷 1978-1989页

作者： Ahmad, Shahnawaz Arif, Mohd Mehfuz, Shabana Ahmad, Javed Nazim, Mohd Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Galgotias University School of Computer Science & Engineering Greater Noida203201 India Jamia Millia Islamia Department of Electrical Engineering New Delhi110025 India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida201310 India Noida Institute of Engineering and Technology Department of Computer Science and Engineering School of Computer Science and IT Greater Noida201306 India

Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating advanced security frameworks. Traditional models suffer from high false positives and limited adaptability. To address these challenges, VECGLSTM, an attack detection model integrating Variable Long Short-Term Memory (VLSTM), capsule networks, and the Enhanced Gannet Optimization Algorithm (EGOA), is introduced. This hybrid approach enhances accuracy, reduces false positives, and dynamically adapts to evolving threats. EGOA is employed for its superior optimization capability, ensuring faster convergence and resilience. Additionally, Chaotic Cryptographic Pelican Tunicate Swarm Optimization (CCPTSO) is proposed for privacy-preserving key management. This model combines chaotic cryptographic techniques with the Pelican Tunicate Swarm Optimization Algorithm (PTSOA), leveraging the pelican algorithm’s exploration strength and the tunicate swarm’s exploitation ability for optimal encryption security. Performance evaluation demonstrates 99.675% accuracy, 99.5175% recall, 99.7075% precision, and 99.615% F1-score, along with reduced training (1.79s), encryption (0.986s), and decryption (1.029s) times. This research significantly enhances CC security by providing a scalable, adaptive framework that effectively counters evolving cyber threats while ensuring efficient key management. © 1968-2012 IEEE.

关键词： Cloud security

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Bidirectional Photoelectric Response of Perovskite Oxide-Based Synaptic ReRAM Device

IEEE Electron Device Letters

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IEEE Electron Device Letters 2024年第3期45卷 384-387页

作者： Shrivastava, Saransh Limantoro, Stephen Ekaputra Juliano, Hans Tseng, Tseung-Yuen Hsinchu30010 Taiwan Department of Electrical Engineering and Computer Science Hsinchu30010 Taiwan

Photo sensing capability of an artificial synaptic device make it more valuable for brain-inspired computing systems, which can conquer the von Neumann bottleneck. In this letter, Ba0.7 Sr0.3 TiO3 (BST) based single layered resistive random access memory (ReRAM) device is proposed with its four different photoelectric applications. First, bidirectional photoelectric response (BPR) is achieved via 405 and 633 nm wavelengths stimulation. Second, this novel BPR contributes to the emulation of light potentiation, and light depression behaviors. Third, an important neurological behavior 'experience dependent plasticity (EDP)' is mimicked by frequency modulation of 405 nm light pulse. Fourth, ultra-high (>200%) indices of post-tetanic potentiation and depression (PTP/PTD) are achieved by amplifying the device conductance as a result of two different pulse train stimulations. A convolutional neural network is designed to classify Zalando's article images with a test accuracy of 87.7%. These results provide an opportunity for the development of multi-wavelength dependent multifunctional artificial brain-inspired systems. © 1980-2012 IEEE.

关键词： Neural networks

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RelaxVR: Cybersickness Reduction in Immersive Virtual Reality through Explainable AI and Large Language Models

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IEEE Access 2025年 13卷 84689-84712页

作者： Kundu, Ripan Kumar Hoque, Khaza Anuarul University of Missouri Department of Electrical Engineering Computer Science ColumbiaMO United States

Virtual reality (VR) systems are susceptible to cybersickness, significantly hindering user immersion. Very recently, researchers introduced explainable artificial intelligence (XAI) enabled methods for detecting and explaining cybersickness features. Since XAI methods can identify the dominant features causing cybersickness, we argue that this knowledge can also guide the dynamic adoption of effective cybersickness reduction strategies, unlike state-of-the-art static approaches that rely on fixed methods. This paper introduces a new dataset MazeSick and RelaxVR, an interactive XAI-guided VR cybersickness reduction framework to predict, explain, and reduce cybersickness. Specifically, we propose an innovative XAI-guided cybersickness reduction engine, which selects the most appropriate reduction technique based on XAI-provided feature importance scores from a cybersickness reduction library of different reduction techniques. We also design an interactive dialogue system powered by large language models, enabling users to engage in the VR simulation via voice commands to understand the reasoning of cybersickness and select effective reduction strategies through natural language interaction. We deployed RelaxVR on a consumer-grade VR headset (e.g., HTC VIVE Pro) and validated it through a user study on cybersickness evaluation, where participants were immersed in a custom-built VR Maze simulation. Our results demonstrate that RelaxVR effectively reduces cybersickness with minimal impact on immersion, and 94% of the users agreed that RelaxVR is highly effective for reducing cybersickness and easier to use through the dialogue engine for selecting effective reduction strategies. © 2013 IEEE.

关键词： Virtual reality

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Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 329-343页

作者： Yijing Xie Yichen Zhang Wei-Jen Lee Zongli Lin Yacov A.Shamash IEEE Department of Electrical Engineering University of Texas at ArlingtonArlingtonTX 76019 USA Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904 USA Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794 USA

The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.

关键词： Climate change renewable energy resources resilience smart grids virtual power plants(VPPs)

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Informative task classification with concatenated embeddings using deep learning on crisisMMD

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International Journal of computers and Applications 2025年第2期47卷 123-140页

作者： Jain, Tarun Gopalani, Dinesh Kumar Meena, Yogesh Department of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

Disastrous situations pose a formidable challenge, testing our resilience against nature's fury and the race against time to prevent the loss of human life. It is noted that in such situations that Microblogging platforms like Twitter(now X) have proven to be an invaluable resource for citizens to share the real-time updates in their locality. Building upon this premise, we are proposing a new hybrid robust approach that integrates transfer learning with selective features from Liwc and manually engineered features to achieve state-of-the-art results on the CrisisMMD dataset on the informative task. In our innovative approach, we harnessed the power of Deep learning combined with traditional ML practices to improve the model, we utilized OpenAI embeddings and minute details from the tweets like the sentiment, text polarity, Retweet address, links included etc. We employed a dense-dropout-based concatenate model for further processing. We established baselines of each technique individually as well as commonly used techniques like Tf-Idf, BoW, Glove embeddings. Notably, our proposed model exhibits a significant accuracy improvement, achieving an accuracy of 87.93%, surpassing the highest accuracy of traditional methods of 82.93% obtained with Tf-Idf and ensemble voting. Through this work, we conclude that there is a scope of improvement in disaster-related NLP work with the introduction of transformer-based architecture and Pretrained GPT which include a massive corpus of text improving the semantic coherency which in turn lead to higher evaluation metrics. This work in future can be extended to incorporate different embeddings as well as using techniques like Multi-headed attention as well as RNNs like GST, GRU, etc. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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Harnessing metamaterials for efficient wireless power transfer for implantable medical devices

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Bioelectronic Medicine 2024年第1期10卷 1-8页

作者： Mahmud, Sultan Nezaratizadeh, Ali Satriya, Alfredo Bayu Yoon, Yong-Kyu Ho, John S. Khalifa, Adam Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

Wireless power transfer (WPT) within the human body can enable long-lasting medical devices but poses notable challenges, including absorption by biological tissues and weak coupling between the transmitter (Tx) and receiver (Rx). In pursuit of more robust and efficient wireless power, various innovative strategies have emerged to optimize power transfer efficiency (PTE). One such groundbreaking approach stems from the incorporation of metamaterials, which have shown the potential to enhance the capabilities of conventional WPT systems. In this review, we delve into recent studies focusing on WPT systems that leverage metamaterials to achieve increased efficiency for implantable medical devices (IMDs) in the electromagnetic paradigm. Alongside a comparative analysis, we also outline current challenges and envision potential avenues for future advancements. © The Author(s) 2024.

关键词： Metamaterials

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

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Hidden Hierarchy Based on Cipher-Text Attribute Encryption for IoT Data Privacy in Cloud

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computers, Materials & Continua 2023年第7期76卷 939-956页

作者： Zaid Abdulsalam Ibrahim Muhammad Ilyas Department of Electrical and Computer Engineering Altinbas UniversityIstanbulTurkey Department of Computer Engineering Altinbas UniversityIstanbulTurkey

Most research works nowadays deal with real-time Internetof Things (IoT) data. However, with exponential data volume increases,organizations need help storing such humongous amounts of IoT data incloud storage systems. Moreover, such systems create security issues whileefficiently using IoT and Cloud Computing technologies. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has the potential to make IoT datamore secure and reliable in various cloud storage services. Cloud-assisted IoTssuffer from two privacy issues: access policies (public) and super polynomialdecryption times (attributed mainly to complex access structures). We havedeveloped a CP-ABE scheme in alignment with a Hidden HierarchyCiphertext-Policy Attribute-Based Encryption (HH-CP-ABE) access structure embedded within two policies, i.e., public policy and sensitive *** this proposed scheme, information is only revealed when the user’sinformation is satisfactory to the public policy. Furthermore, the proposedscheme applies to resource-constrained devices already contracted tasks totrusted servers (especially encryption/decryption/searching). Implementingthe method and keywords search resulted in higher access policy privacy andincreased security. The new scheme introduces superior storage in comparisonto existing systems (CP-ABE, H-CP-ABE), while also decreasing storage costsin HH-CP-ABE. Furthermore, a reduction in time for key generation canalso be ***, the scheme proved secure, even in handling IoT datathreats in the Decisional Bilinear Diffie-Hellman (DBDH) case.

关键词： Bilinear Diffie-Hellman(DBDH) Internet of Things(IoT) Ciphertext-Policy Attribute-Based Encryption(CP-ABE) Hidden Hierarchy CP-ABE(HH-CP-ABE)

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