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

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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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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Phrase-level emotion intensity detection of text using lexicon-based unit circle and pipelined neural networks approaches

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Neural Computing and Applications 2025年第5期37卷 3151-3166页

作者： Patil, Bhuvaneshwari Ajit Vadlakunta, Vishnu Vardhan Guruvu, Poojaa Pranathi Mathew, Meenu Prakash, Jay Computer Science and Engineering National Institute of Technology Calicut Kozhikode India

Emotions have a significant impact on how people make decisions. Due to its potential applications in various fields, emotion intensity detection has attracted a lot of attention recently. Several methods have been proposed in the past using natural language processing to recognize emotions from the text. These methods include the keyword-based approach, the lexicon-based approach and the machine learning approaches. Nevertheless, there were a few limitations with the lexicon-based unit circle approach, as it focuses on detecting emotion intensity at the word-level. This research proposes an integrated model by considering the advantages of the lexicon-based unit circle and pipelined neural networks approaches. The model is trained on a large corpus of text data. The proposed approach aims to determine emotion intensity, analyzing text at the phrase-level, thereby achieving higher performance in terms of accuracy and F1-score compared to other models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural networks

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Enhancing sustainable 5G powered agriculture 4.0 : Summary of low power connectivity, internet of UAV things, AI solutions and research trends

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Multimedia Tools and Applications 2025年第17期84卷 17389-17433页

作者： Majumdar, Parijata Mitra, Sanjoy Bhattacharya, Diptendu Bhushan, Bharat Department of Computer Science and Engineering Techno College of Engineering Agartala Maheshkhola Madhuban799004 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh Agartala799009 India Department of Computer Science and Engineering National Institute of Technology Agartala Jirania Agartala799046 India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida201310 India

Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-related concerns and to fulfill the increasing food demand of the expanding demographics. Adoption of 5G offers numerous benefits including Ultra-Reliable Low Latency Communication (URLLC) required to reduce communication delays in time-sensitive applications, non-public networks to allocate part of the frequency spectrum on demand, and network slicing to fulfill the diverse requirements requested by a specific application etc. for realizing the full potential of Agriculture 4.0 operations. In this paper, a comprehensive review of 5G radio enhancements, enabling technologies, and multi-tier heterogeneous 5G network architecture are presented. The main focus is to concisely cover AI integration with Unmanned Aerial Vehicles (UAVs) leveraging 5G technologies and Low Power Wide Area Networks (LPWANs) for supporting intelligent, long-range, cost-effective and energy-saving data transmission across the network and seamless Agriculture 4.0 operations. Cloud computing architectures that permit centralized management of Agriculture 4.0 operations through sharing of computations, configurable networks, and servers are also studied in detail to reveal the need for edge computation as increasing number of IoT devices allows only to perform complex computations on-site. Extensive survey reveals the recent advances of 5G-enabled Agriculture 4.0 operations, future research opportunities of 5G networks including Energy Harvesting techniques for the deployment of massive to critical IoT applications, and the socio-demographic impacts of 5G networks in Agriculture 4.0 to support ubiquitous connectivity of exponentially increasing IoT devices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Energy efficiency

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Emotion mining for early suicidal threat detection on both social media and suicide notes using context dynamic masking-based transformer with deep learning

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Multimedia Tools and Applications 2025年第13期84卷 11729-11752页

作者： Kodati, Dheeraj Tene, Ramakrishnudu Department of Computer Science and Engineering National Institute of Technology Warangal India

Suicide represents a poignant societal issue deeply entwined with mental well-being. While existing research primarily focuses on identifying suicide-related texts, there is a gap in the advanced detection of mental health states leading to suicidal tendencies. The purpose is to recognize the emotions influencing individuals’ mental states, potentially leading to suicidal behavior. This study seeks to identify specific emotions like anger, depression, anxiety, guilt, fear, stress, and sadness within suicide-related texts sourced from both social media and suicide notes. We introduce a novel suicide severity assessment method emphasizing emotion detection across categories like suicidal ideation, suicide planning, attempted, committed, suicide notes, describing, and cannot dare. By identifying high-intensity negative emotions, our approach enables early detection of potential suicide threats. Existing studies often struggle to capture nuances and semantic relationships, particularly in suicide-related contexts. To address this, we propose a context dynamic masking based on bidirectional long short-term memory along with a multi-head self-attention and convolutional neural network (CoDyn-BMHSA-CNN) model, capturing bidirectional contexts and maintaining variable-length sequences. Our model achieves accuracies of 98.2% and 97.4%, with F1 scores of 95.5% and 93.8% for social media and suicide note datasets, respectively. We perform ablation tests to pinpoint the key components of our proposed models. Furthermore, a thorough comparative analysis evaluates the model’s performance across various contexts and platforms. Experimental results illustrate the superior effectiveness of our model in identifying emotions from text sequences compared to state-of-the-art approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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An efficient schedulability analysis based on worst-case interference time for real-time systems

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science China(Information sciences) 2024年第9期67卷 88-104页

作者： Hongbiao LIU Mengfei YANG Lei QIAO Xi CHEN Jian GONG School of Computer Science and Technology Xidian University China Academy of Space Technology Beijing Institute of Control Engineering

Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.

关键词： five-tuple real-time task model real-time system spacecraft Internet of Things exact schedulability analysis worst-case interference time

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Genetic algorithm based data controlling method using IoT enabled WSNs

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Soft Computing 2025年第5期29卷 2465-2482页

作者： Singh, Samayveer Nandan, Aridaman Singh Sikka, Geeta Malik, Aruna Singh, Pradeep Kumar Department of Computer Science and Engineering National Institute of Technology Punjab Jalandhar India Department of Computer Science and Engineering National Institute of Technology Delhi India Department of Computer Science and Engineering Central University of Jammu J&K Jammu India

Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs) is not only constitute an encouraging research domain but also represent a promising industrial trend that permits the development of various IoT-based applications. These applications span a wide range from industry to education, and from military to agriculture. The IoT device plays a significant role in various IoT-based networks, and the functioning of such network depends upon the battery power. Once the devices are deployed in the hostile environments, replacing batteries becomes impractical. Despite a plethora of research addressing this challenge, IoT networks still face issues. In this paper, a genetic algorithm based data monitoring and controlling method using IoT enabled WSNs is proposed by using movable sinks in IoT enabled HWSNs (OptiGeA). The OptiGeA protocol is designed for the election of cluster heads (CHs) by incorporating factors such as density, distance, energy and heterogeneous node capacity into its fitness function. The investigation of OptiGeA is conducted with single sink, multiple static sinks and multiple movable sinks provide an unbiased comparative assessment. The novel deployment technique and multiple mobile sinks approaches are proposed to reduce the transmission distance between the sink and CH during system operation and address hotspot issue. It is evident that the OptiGeA protocol shows an increment of 10.44% compared to the GAOC, whereas with the inclusion of DDC process the OptiGeA-DDC protocol demonstrates a remarkable increase of 48.33% compared to MS-GAOC. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Genetic algorithms

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Exploring Nonuniform Structure of NSRTDs for Characterizing MACA Rules in the Null-Boundary Condition

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Complex Systems 2025年第1期34卷 91-127页

作者： Hazra, Suvadip Banerjee, Som Dalui, Mamata Chakraborty, Bidesh Department of Computer Science and Engineering Indian Institute of Information Technology Karnataka Dharwad58009 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia721657 India

The cellular automaton (CA), a discrete model, is gaining popularity in simulations and scientific exploration across various domains, including cryptography, error-correcting codes, VLSI design and test pattern generation. This paper examines a two-state three-neighborhood nonuniform finite CA with a specified number of fixed points. We utilize a graph-based tool known as the next state rule minterm transition diagram (NSRTD) to analyze the spatiotemporal behavior of these cellular automata (CAs) across all lengths. We devise methods for synthesizing nonuniform single length cycle multi-attractor CAs (MACAs), a specialized class of irreversible CA with a predefined number of fixed points for any arbitrary length. Despite the method’s exponential worstcase time complexity, it offers the advantage of selecting rules for each CA cell from a set of candidate rules, ensuring the desired number of fixed points. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： cellular automata fixed-point attractor length cycle multi-attractor cellular automata MACA next state rule minterm transition diagram NSRTD

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SuspAct: novel suspicious activity prediction based on deep learning in the real-time environment

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Neural Computing and Applications 2024年第34期36卷 21307-21320页

作者： Kansal, Sachin Jain, Akshat Kumar Biswas, Moyukh Bansal, Shaurya Mahindru, Namay Kansal, Priya Computer Science Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India

In today’s evolving landscape of video surveillance, our study introduces SuspAct, an innovative ensemble model designed to detect suspicious activities in real time swiftly. Leveraging advanced Long-term Recurrent Convolutional Networks (LRCN), SuspAct represents a significant advancement in intelligent surveillance technology. By combining insights from various LRCN models through the Majority Voting ensemble technique, SuspAct enhances its overall robustness, outperforming traditional surveillance methods. Through rigorous experimentation on large-scale datasets, we demonstrate SuspAct’s superiority in proactive crime prevention, showcasing its potential to revolutionize security protocols and contribute substantially to public safety. Our work addresses the challenges posed by the escalating volume of video data and lays a strong foundation for future advancements in intelligent video surveillance technology. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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Heart sound classification method using gammatonegram and SVM

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Multimedia Tools and Applications 2024年 1-37页

作者： Taneja, Kriti Arora, Vinay Verma, Karun Computer Science and Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India

Today cardiovascular diseases have been posing a serious threat to human lives all over the world. Various automated decision-making systems have been proposed by the researchers to help cardiologists to diagnose heart health with more perfection, thus, reducing the frequency of fatalities. This research aims at classifying the Phonocardiogram (PCG) signals captured from the functioning of the cardiac system as normal or abnormal. The methodology proposed here has been validated using the benchmark dataset PhysioNet Computing in Cardiology (CinC) 2016. As of this writing, the studies carried out using contemporary techniques have so far been unable to generate pictorial representation from cardiac audio recordings that match the way the ear processes sound. The present research suggests a unique methodology for the detection of important events in an audio signal using a biologically-inspired depiction of the audio stream through a picture known as Gammatonegram which correlates to the processing of audio in the cochlea membrane of the inner human auditory system. In this study, texture-related features which include Linear Ternary Pattern (LTP), Local Directional Pattern (LDP), Geometric Local Textural Pattern (GLTP), and Local Phase Quantization (LPQ) have been extracted from a visual representation of PCG signal such as Spectrogram, Scalogram, Mel-spectrogram, and Gammatonegram. As compared to the case when researchers were employing other images, it has been noticed that the fusion of the features retrieved from the Gammatonegram has led to an increased overall classification performance metrics. The experiment yielded an overall accuracy of 94.00 % with precision and F1 scores of 91.77 and 93.61 respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Heart

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