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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Vallabhaneni, Nagalakshmi Panneer, Prabhavathy Sundaram, Venkatesan Meenakshi School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Computer Science and Engineering National Institute of Technology Puducherry India

ISBN: (纸本)9798350325188

Yoga is a popular practice that aims to improve one's health and well-being through physical postures, breathing exercises, and meditation. The growing popularity of yoga has prompted researchers to focus on automating the process of classifying yoga poses. Using deep convolutional neural networks (CNNs) and transfer learning, we present a deep learning-based method for correctly categorizing yoga postures in this research. The primary goal of this study is to develop an efficient method for automatically recognizing and categorizing yoga poses in given photographs. The impressive success of deep CNNs in picture classification tasks is exploited here to accomplish this goal. We also use transfer learning, a method for customizing pre-trained CNN models for our unique yoga posture categorization task. This paper presents a Transfer Learning based Method for Yoga Poses classification using Deep Convolutional Neural Networks (TLMYPC-DCNN). The proposed model uses the Yoga Posture Dataset for classification of images. Initially, this model starts with pre-processing the input images by splitting input data into three groups such as training set, validation set and test data. The deep learning CNN model will be trained using the training data, and then its parameters will be tweaked using the validation data. Finally, the test data will be used to assess how well the data performs. A convolutional neural network (CNN) model called MobileNetV2 will be applied to the model photos. The experimental results show that the suggested TLMYPC-DCNN based technique with transfer learning is effective at accurately and reliably classifying yoga poses. © 2023 IEEE.

关键词： Deep Convolutional Neural Networks MobileNetV2 Transfer Learning Yoga Pose classification

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Differential Evolution Algorithm Based Hyper-Parameters Selection of Convolutional Neural Network for Speech Command Recognition 15

Differential Evolution Algorithm Based Hyper-Parameters Sele...

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15th International Joint Conference on Computational Intelligence, IJCCI 2023

作者： Dhar, Sandipan Sen, Anuvab Bandyopadhyay, Aritra Jana, Nanda Dulal Ghosh, Arjun Sarayloo, Zahra Computer Science and Engineering National Institute of Technology West Bengal Durgapur India Electronics and Telecommunication Indian Institute of Engineering Science and Technology Shibpur Howrah India Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Howrah India School of Computer Science University of Waterloo ON Canada

ISBN: (纸本)9789897586743

Speech Command Recognition (SCR), which deals with identification of short uttered speech commands, is crucial for various applications, including IoT devices and assistive technology. Despite the promise shown by Convolutional Neural Networks (CNNs) in SCR tasks, their efficacy relies heavily on hyperparameter selection, which is typically laborious and time-consuming when done manually. This paper introduces a hyperparameter selection method for CNNs based on the Differential Evolution (DE) algorithm, aiming to enhance performance in SCR tasks. Training and testing with the Google Speech Command (GSC) dataset, the proposed approach showed effectiveness in classifying speech commands. Moreover, a comparative analysis with Genetic Algorithm-based selections and other deep CNN (DCNN) models highlighted the efficiency of the proposed DE algorithm in hyperparameter selection for CNNs in SCR tasks. © 2023 by SCITEPRESS – science and technology Publications, Lda.

关键词： Genetic algorithms

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Contribution Unveiling Cutting-Edge Machine Learning Techniques for Image Segmentation

Contribution Unveiling Cutting-Edge Machine Learning Techniq...

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Shaik, Nazeer Gupta, Ankur Bhati, Sunita Kumar, Jaideep Singh, Jagendra Budhiraja, Ishan Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Applications JECRC University Jaipur India Raj Kumar Goel Institute of Technology AKTU University Lucknow India

ISBN: (纸本)9789819986606

Segregation of images is a critical step in processing images, computer vision, and a variety of other disciplines. The technique involves decomposing an illustration into numerous components or components, every single one that consists of an ensemble of elements with identical features for example African descent, frequency, or consistency. The most important objective of appearance, the intention of fragmentation seems to reduce complexity or customize the mathematical representation of an illustration in a way that is more readily reasonable and more straightforward for assessment. It is widely employed to locate boundaries and features in photos, and this is favorable in an assortment of industries including clinical imaging, object detection, recognition, and autonomous vehicles. Several image segmentation techniques are available, and in incorporating thresholding, zone is an area-based differentiation and corner-based recognition. A threshold segment is a simple and commonly used technique that involves setting a threshold value and dividing the pixels into two classes based on their intensity values. Region-based segmentation involves grouping pixels based on their spatial proximity and similarity in characteristics, while edge-based segmentation involves detecting edges or boundaries in an image and using them to separate different regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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Group Recommendation Method Based on Graph Neural Network

Group Recommendation Method Based on Graph Neural Network

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Lu, Mengye Zhu, Peng Miao, Chun Wang, Youjian Wang, Jiacun Nanjing University of Science and Technology School of Economics and Management Nanjing China Monmouth University Department of Computer Science and Software Engineering NJ United States

ISBN: (纸本)9798350376739

Group activities are becoming more and more common on the Internet in the big data environment. Which makes many scholars focus on how to recommend items or activities to a group. However, conventional recommendation systems grapple with issues like data sparsity and the cold start problem, which limits the relevance and accuracy of recommendation. Therefore, this paper conducts an in-depth exploration of group recommendation methods utilizing graph neural networks and introduces a novel recommendation framework aimed at enhancing the efficacy of group recommendations. Firstly, this paper discusses how to construct an effective graph structure to encode the social interaction and individual characteristics of users. Then, an improved graph neural network architecture is proposed. Finally, extensive experimental evaluation is carried out on actual datasets, and the findings demonstrate that our framework markedly enhances the accuracy of recommendations. © 2024 IEEE.

关键词： Graph neural network Group recommendation Recommendation system

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First-Price Sealed-Bid Auction for Ethereum Gas Auction Under Flashbots

First-Price Sealed-Bid Auction for Ethereum Gas Auction Unde...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Jin, Congying Wang, Taotao Wang, Zhe Shi, Long Zhang, Shengli Shenzhen University College of Electronics and Information Engineering Shenzhen China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing China

ISBN: (纸本)9798350333336

In the block space market of the Ethereum blockchain, miners are the producers who can maximize their revenue - a value called Miner Extrac table Value (MEV) - by including, excluding, or reordering transactions in the blocks they produce. Some special users, arbitrage bots, are aware of MEV and may opt to exploit the existence of MEV to seize arbitrage opportunities in Decentralized Finance (DeFi) applications. These bots usually compete for block space with each other via a gas action process in which bots bid up their gas prices. Due to the lack of symmetric market information, bots blindly issue multiple transactions continuously aiming at the same arbitrage opportunity and they gradually increase the gas prices. Such malicious bidding behavior leads to the problems of transaction congestion and high gas fee in the Ethereum network. Based on the Flashbots project, we propose an Ethereum gas auction model using First Price Sealed-Bid Auction (FPSBA). In our model, the bidding bots no longer need to continue raising their gas bids in response to threats from other bots, and our derived optimal FPSBA bidding strategy under the dominant strategy equilibrium can effectively reduce the bots' bids in gas action. Our simulation results illustrate that our FPSBA can achieve a lower average bidding price for the biding bots compared to the blind continuous bidding strategy dose in the original Ethereum network, thus it can keep the whole gas price level stable. © 2023 IEEE.

关键词： Gases

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A Topic-Guided Self-Attention Network for Daily Mental Wellbeing Prediction Using Mobile Devices

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IEEE Transactions on Affective Computing 2024年第2期16卷 783-798页

作者： Xu, Zeju Liu, Guanzheng Zhao, Guozhen Zhang, Zhiguo Li, Chenzhong Wang, Changhong Shenzhen Campus of Sun Yat-Sen University School of Biomedical Engineering Guangdong Shenzhen518000 China Institute of Psychology CAS Key Laboratory of Behavioral Science Beijing100101 China University of Chinese Academy of Sciences Department of Psychology Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen China Biomedical Engineering School of Medicine Shenzhen518172 China

Prediction of daily mental wellbeing holds profound implications for individual healthcare and societal stability. Previous studies have shown the potential of using individual's multimodal behavioral data collected through mobile devices to predict his/her daily mental wellbeing metrics, such as stress, mood, and anxiety. However, effectively capturing long-range dependencies in behavioral time series data while accurately representing the statistical distribution patterns of various behaviors over a certain period is a significant challenge. In this paper, we propose a daily mental wellbeing prediction model based on a Topic-Guided Self-Attention Network (TGSAN). This model utilizes self-attention mechanism to capture long-range dependencies from the behavioral data collected by mobile devices. We utilize a multi-granularity time encoding method to inject time information of different granularities (i.e., day and hour, or week and day) into the behavioral data, thereby enhancing the sensibility of the self-attention network to capture every individual's habitual cyclicality rhythm. Then, we introduce a neural topic model to analyze the statistical distribution characteristics of various behaviors in the monitoring period as behavioral distribution patterns for different individuals, and further propose a topic attention network to enhance the model's classification performance by guiding the weights of long-range dependencies features from the self-attention network with the derived topic information. Compared to state-of-the-art methods, the proposed TGSAN achieved superior performance on datasets that measure different mental health indicators (stress, mood, and anxiety), with F1 scores outperforming by 4.5% and 2.3% on the Crosscheck and StudentLife datasets, respectively, and accuracy outperforming by 3.3% on the GLOBEM dataset. Our study demonstrates the effectiveness and interpretability of combining self-attention mechanisms with neural topic model, for a b

关键词： Prediction models

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Multiview Deep Learning-based Efficient Medical Data Management for Survival Time Forecasting

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IEEE Journal of Biomedical and Health Informatics 2024年 PP卷 1-13页

作者： Yu, Keping Quan, Lijuan Chakraborty, Chinmay Shen, Yu Guo, Zhiwei Alfarraj, Osama Tolba, Amr School of Computer Science and Information Engineering Bengbu University Bengbu Anhui China Department of Rehabilitation the First Affiliated Hospital Jiangxi Medical College Nanchang University Nanchang China Birla Institute of Technology Mesra India National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China School of Artificial Intelligence Chongqing Technology and Business University Chongqing China Computer Science Department Community College King Saud University Riyadh Saudi Arabia

In recent years, data-driven remote medical management has received much attention, especially in application of survival time forecasting. By monitoring the physical characteristics indexes of patients, intelligent algorithms can be deployed to implement efficient healthcare management. However, such pure medical data-driven scenes generally lack multimedia information, which brings challenge to analysis tasks. To deal with this issue, this paper introduces the idea of ensemble deep learning to enhance feature representation ability, thus enhancing knowledge discovery in remote healthcare management. Therefore, a multiview deep learning-based efficient medical data management framework for survival time forecasting is proposed in this paper, which is named as “MDL-MDM” for short. Firstly, basic monitoring data for body indexes of patients is encoded, which serves as the data foundation for forecasting tasks. Then, three different neural network models, convolution neural network, graph attention network, and graph convolution network, are selected to build a hybrid computing framework. Their combination can bring a multiview feature learning framework to realize an efficient medical data management framework. In addition, experiments are conducted on a realistic medical dataset about cancer patients in the US. Results show that the proposal can predict survival time with 1% to 2% reduction in prediction error IEEE

关键词： Forecasting

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Exploration of soliton structures and modulation instability analysis for the highly dispersive perturbed NLSE with sextic-power law refractive index

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Optical and Quantum Electronics 2025年第1期57卷 1-18页

作者： Abdullah, Eman H. M. Ahmed, Hamdy M. Zaghrout, Afaf A. S. Bahnasy, Amal Ibrahim Ahmed Rabie, Wafaa B. Hashemi, M.S. Bayram, M. Department of Physics and Engineering Mathematics Higher Institute of Engineering El Shorouk Academy Cairo Egypt Department of Mathematics Faculty of Science Al Azhar University Cairo Egypt Department of Physics and Engineering Mathematics Higher Institute of Engineering and Technology Tanta Egypt Department of Mathematics Basic Science Faculty University of Bonab Bonab Iran Computer Engineering Biruni University Istanbul Turkey

This study introduces some novel soliton solutions and other analytic wave solutions for the highly dispersive perturbed nonlinear Schrödinger equation with generalized nonlocal laws and sextic-power law refractive index. This equation models the behavior of complex wave propagation. The improved modified extended tanh function governs the transmission of various solitons with different effects. New optical solitons are constructed as bright solitons, dark solitons, combo bright-dark solitons and singular solitons. Furthermore, exponential solutions, triangular solutions and rational wave solutions are shown. The findings conferred in this article show a crucial advancement in understanding the propagation of waves in non-linear media. The study also includes modulation instability analysis to examine the stability of the derived wave solutions, supplemented by graphical simulations that depict their distinctive features. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Solitons

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Malicious Traffic Detection with Noise Labels Based on Cross-Modal Consistency

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 148-151页

作者： Yuan, Qingjun Niu, Weina Wang, Yongjuan Gou, Gaopeng Lu, Bin Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

To train robust malicious traffic identification models under noisy labeled datasets, a number of learning with noise labels approaches have been introduced, among which parallel training methods have been proved to be effective. Parallel training methods tend to select samples with disagreement to mitigate the risk of self-control degradation. However, it also introduces noisy knowledge into training. In this letter, we try to avoid introducing noisy knowledge by enhancing the consistency of the representations of parallel networks. Meanwhile, the two networks are heterogeneous and introduce information from different modalities, thus mitigating the risk of self-control degradation from multiple perspectives. © 2019 IEEE.

关键词： Feature extraction

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GSAGE2defect: An Improved Approach to Software Defect Prediction based on Inductive Graph Neural Network 35

GSAGE2defect: An Improved Approach to Software Defect Predic...

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35th International Conference on Software engineering and Knowledge engineering, SEKE 2023

作者： Ma, Ju Sun, Yi-Yang He, Peng Zeng, Zhang-Fan School of Computer Science and Information Engineering Hubei University Wuhan China School of Cyber Science and Technology Hubei University Wuhan China

Graph neural network is an effective deep learning framework for learning graph data. Existing research has introduced different variants of graph neural networks into the field of software defects and has achieved promising results. However, the graph neural network model based on the previous research is essentially transductive, is applied to a single fixed graph, and often ignores the direction and weight of the edges when modeling the network. In practice, software systems are dynamically evolving. Furthermore, in software network modeling, the direction and weight of edges are factors that are worth considering. Based on an inductive graph neural network, we proposed an improved defect prediction method named GSAGE2defect. We first constructed the class dependency network of the program and then used node2vec for embedding learning to automatically obtain the structural features of the network. Then we combined the learned structural features with traditional software code features to initialize the properties of nodes in the class dependency network. Next, we fed the dependency network to GraphSAGE for a deeper class representation. Finally, we evaluated the proposed method based on eight open-source programs and demonstrated that GSAGE2defect achieves an average improvement of 2.09%-26.69% over state-of-the-art methods in terms of F-measure. © 2023 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Graph neural networks

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