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AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization

IEEE Transactions on Visualization and computer Graphics 2025年第05期31卷 3408-3417页

作者： Tian, Yang Zhang, Youpeng Yan, Yukang Zhao, Shengdong Ma, Xiaojuan Shi, Yuanchun Guangxi University Department of Computer Science Guangxi Key Laboratory of Multimedia Communications and Network Technology China University of Rochester Department of Computer Science United States City University of Hong Kong School of Creative Media and the Department of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Tsinghua University Department of Computer Science and Technology China

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate © 1995-2012 IEEE.

关键词： Augmented reality

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Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

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Data science and Management 2024年第3期7卷 189-196页

作者： Abhishek Bajpai Harshita Verma Anita Yadav Department of Computer Science Rajkiya Engineering CollegeKannauj209732India Department of Computer Science and Engineering School of EngineeringHarcourt Butler Technical UniversityKanpur208001India

The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and *** address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering *** various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time *** paper presents an approach based on state-of-the-art machine-learning *** this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data *** primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation *** evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop *** proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

关键词： Wireless sensor network Principal component analysis(PCA) Reinforcement learning Data aggregation

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Cyber-attack detection based on a deep chaotic invasive weed kernel optimized machine learning classifier in cloud computing

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Soft Computing 2025年第4期29卷 2015-2030页

作者： Indrasena Reddy, M. Siva Kumar, A.P. Subba Reddy, K. Department of Computer Science and Engineering BVRIT Hyderabad College of Engineering for Women Hyderabad500090 India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Ananthapuramu Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Rajeev Gandhi Memorial College of Engineering and Technology Andhra Pradesh Nandyala India

Nowadays, Cloud Computing has attracted a lot of interest from both individual users and organization. However, cloud computing applications face certain security issues, such as data integrity, user privacy, and service availability. The only better solution to such issues is to identify and prevent cyber threats before they can cause seriously harm the cloud computing system. Several methods have been suggested so far to detect the attack in cloud computing (CC), but no one method attains satisfactory results. To overwhelm these drawbacks, a Cyber Security Attack Detection using Deep Kernel Machine Learning optimized with Chaotic Invasive Weed Optimization algorithm is proposed in this paper for Cloud Computing. Here, the data is amassed from CSE-CIC-IDS2018 and Bot-IoT datasets. Subsequently, a pre-processing step involving redundancy reduction and missing value replacement for uncertainty removal is achieved through the Developed Random Forest with Local Least Squares (DRFLLS) method. By utilizing Entropy-Kurtosis based feature selection approach, the pre-processing data is given to the feature selection to identify the optimal features. The selected features are supplied to the Deep Kernel Machine Learning classifier (DK-ML), which is optimized using the Chaotic Invasive Weed Optimization algorithm (Chaotic-IWOA) for classification. This classification process classifies the data as normal or anomalous categories with anomalies encompassing, like DoS, DDoS, Theft attacks, and normal attacks. The proposed technique is activated in MATLAB. The proposed technique achieves 24.88%, 17.98%, 45.65%, 35.95% better accuracy for CSE-CIC-IDS2018 dataset, and 23.93%, 13.94%, 32.94%, and 29.04% better accuracy for Bot-IoT dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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Multiview data fusion technique for missing value imputation in multisensory air pollution dataset

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3173-3191页

作者： Middya, Asif Iqbal Roy, Sarbani Department of Computer Science and Engineering Jadavpur University Kolkata India

The missing readings in various sensors of air pollution monitoring stations is a common issue. Those missing sensor readings may greatly influence the performance of monitoring and analysis of air pollution data. To address this problem, in this paper, a multi-view based missing value (MV) imputation method called MVDI (Multi-View Data Imputation) is proposed for air pollution related time series data. MVDI combines four models namely LSTM (Long-Short Term Memory), IDS (Inverse Distance Squared), SVR (Support Vector Regressor), and KNN (K-Nearest Neighbors) to estimate MVs. These four models are mainly employed to capture the variations in data from different views of the dataset. Here, different views represent different portions (subsets) of the actual dataset. The estimates of MVs from all the views are combined using a kernel function to get an overall result. The proposed model MVDI is evaluated on real-world air pollution dataset in terms of RMSE, MAE, MAPE, and R2. The experimental results show that MVDI dominates over the baseline methods namely AR (AutoRegressive), ARIMA (AutoRegressive Integrated Moving Average), RFR (Random Forest Regressor), ANN (Artificial Neural Network), LI (Linear Interpolation), NN (Nearest Neighbors), MI (Mean Imputation), CNN (Convolutional Neural Network), ConvLSTM (Convolutional LSTM). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Time series

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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MTUNet + + : explainable few-shot medical image classification with generative adversarial network

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Multimedia Tools and Applications 2025年第11期84卷 9219-9239页

作者： Titoriya, Ankit Kumar Singh, Maheshwari Prasad Singh, Amit Kumar Department of Computer Science and Engineering National Institute of Technology Patna India

Medical imaging, a cornerstone of disease diagnosis and treatment planning, faces the hurdles of subjective interpretation and reliance on specialized expertise. Deep learning algorithms show improvements in automating medical image analysis, reducing radiologists' burden, and potentially enhancing patient outcomes. However, these algorithms require substantial quantities of high-quality labelled data for effective training and refinement. This paper proposes an innovative approach that harnesses few-shot learning (FSL) and generative adversarial networks (GANs) to overcome conventional methods' limitations in medical image classification. FSL, capable of learning from limited labelled examples, holds promise for scenarios where labelled data is scarce. However, the lack of interpretability in existing FSL models impedes their clinical adoption. To tackle this, this paper proposes a explainable FSL network, "MTUNet + +," which integrates an attention mechanism to emphasize relevant regions in medical images. Furthermore, integrating a generative adversarial network, enhances the performance of MTUNet + + by generating synthetic medical images. Systematically eliminating misleading synthetic images improves the reliability and accuracy of medical image classification. Empirical evaluation on benchmark datasets underscores the effectiveness of the approach, achieving 85.19% and 69.28% accuracy for the HAM10000 and Kvasir datasets, respectively. This paper contributes to advancing AI-driven solutions in clinical practice, facilitating enhanced patient care and streamlined workflows within real-world healthcare settings. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Automated image captioning system with deep learning enabled optimized approach

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Multimedia Tools and Applications 2024年 1-21页

作者： Padate, Roshni Kalla, Mukesh Computer Science and Engineering Department Sir Padampat Singhania University Udaipur India Computer science and Engineering Department Fr. Conceicao Rodrigues College of engineering Mumbai India

Image caption-generating systems aim to deliver accurate, coherent, and useful captions. This includes identifying the scene, items, relationships, and attributes of the image's objects. Due to constraints in using all visual information, captioning images can be complex. The proposed Hybrid Chimp Wolf Pack Inception-V3 (HCWPI) -Bidirectional Gated Recurrent Unit (BiGRU) approach uses encoding and decoding units to produce accurate captions for inputted emotion based images. The integration Hybrid Chimp Wolf Pack (HCWP)with Inception-V3 demonstrates a novel approach in encoding part. Initially, the Hybrid, Chip Optimization algorithm (COA) with Wolf Pack Optimization (WPO) namely HCWP incorporated with Inception-V3 is employed to generate appropriate input image reconstructions with fixed-length vectors, displaying the distinctive features of what was accomplished at the stage of encoding. The hyper parameter optimizer of HCWP optimizes Inception-V3 model performance. Descriptive phrases originate via a Bidirectional Gated Recurrent Unit (BiGRU) framework provided within the decoding part. Simulations were run to assess the HCWPI-BiGRU model's improved performance from multiple angles. The experimental results showed that the HCWPI-BiGRU model outperformed than existing approaches on term of blue and Meteor Score such as 0.86and 0.85 respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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A Comprehensive Review of Blockchain and Machine Learning Integration for Peer-to-Peer Energy Trading in Smart Grids

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IEEE Access 2025年 13卷 92756-92782页

作者： Fatima, Sameen Arshad, Muhammad Junaid University of Engineering & Technology Department of Computer Science Lahore54000 Pakistan

The transition from traditional energy or electrical grids to smart energy or electrical grids has significantly transformed energy management. This evolution emphasizes decentralization, efficiency, and sustainability in energy systems. However, it also introduces numerous risks, including cyber-physical system vulnerabilities and challenges in energy trading. The application of blockchain and Machine Learning (ML) offers potential solutions to these issues. Blockchain enhances energy transactions by making them safer, more transparent, and tamper-proof, while ML optimizes grid performance by improving predictions, fault detection, and anomaly identification. This systematic review examines the application of blockchain and ML in peer-to-peer (P2P) energy trading within smart grids and analyzes how these technologies complement each other in mitigating risks and enhancing the efficiency of smart grids. Blockchain enhances security by providing privacy for transactions and maintaining immutable records, while ML predicts market trends, identifies fraudulent activities, and ensures efficient energy use. The paper identifies critical challenges in smart grids, such as unsecured communication channels and vulnerabilities to cyber threats, and discusses how blockchain and ML address these issues. Furthermore, the study explores emerging trends, such as lightweight blockchain systems and edge computing, to overcome implementation challenges. A new architecture is proposed, integrating blockchain with ML algorithms to create resilient, secure, and efficient energy trading markets. The paper underscores the need for global standardization, improved cybersecurity measures, and further research into how blockchain and ML can revolutionize smart grids. This study integrates current knowledge with a forward-looking perspective, providing valuable insights for researchers, policymakers, and stakeholders in the energy sector to collaboratively build a future of efficient and int

关键词： Power management (telecommunication)

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HandRT: Simultaneous Hand Shape and Appearance Reconstruction with Pose Tracking from Monocular RGB-D Video

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5625-5635页

作者： Kalshetti, Pratik Chaudhuri, Parag Indian Institute of Technology Bombay Department of Computer Science and Engineering India

We propose a method to reconstruct a personalized hand avatar, representing the user's hand shape and appearance, from a monocular RGB-D video of a hand performing unknown hand poses under unknown illumination. Our method, HandRT, jointly optimizes hand pose, shape, appearance, and lighting parameters using a physically-based shading model in a differentiable rendering framework incorporating Monte Carlo path tracing. HandRT extends our previous work, Intrinsic Hand Avatar, by relaxing the assumption of a known coarse hand pose and utilizing depth data in the optimization. Specifically, we introduce an articulated registration energy based on iterative closest point over the depth point cloud that enables reconstruction from unknown hand poses via tracking across frames. Thus, we can reconstruct the avatar from arbitrary poses with high accuracy without relying on an off-the-shelf 2D joint detector at each frame. Further, HandRT is capable of precisely tracking the reconstructed avatar from either RGB or RGB-D input. Our evaluation demonstrates that our method outperforms existing hand avatar reconstruction methods on all commonly used metrics while producing significantly accurate mesh compared to state-of-the-art hand mesh recovery methods by a large margin on public and our captured datasets. To facilitate future research, we release our code at https://***/pmkalshetti/handrt © 1979-2012 IEEE.

关键词： Mesh generation

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Building a Robust and Efficient Defensive System Using Hybrid Adversarial Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4470-4478页

作者： Dhanaraj, Rachel Selva Sridevi, M. Goa College of Engineering Computer Engineering Department Goa 403401 India National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets - CelebA, CIFAR-10, and MNIST - were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss. © 2020 IEEE.

关键词： Deep neural networks

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