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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Pant, Suman Bhat, Niyati Shobana, G. Sindhuja, R. Lekha, D. Srimathi, S. School of CA & IT Shri Guru Ram Rai University India B.E Computer engineering University of Mumbai Mumbai India Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology Kattankulathur India Department of Medical Electronics Saveetha Engineering college India CSE Department R.M.K. College of Engineering and Technology India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Human Gait Recognition (HGR) is a biometric system that is exploited for security purpose over the past few years. The efficiency of gait detection is affected by different features namely walking surfaces, wearing clothes, and carrying a bag. Moreover, detection from divergent views is a complicated problem in HGR. Numerous approaches were proposed for HGR using classical and DL methods but the conventional technique is not appropriate for massive data. This study leverages Ensemble Learning Model for Human Gait Recognition in Human computer Interface (ELMHGA-HCI) technique. The ELMHGA-HCI technique intends to recognize various kinds of human gaits to assist HCI applications. In the ELMHGA-HCI technique, Time Domain (TD) and Frequency Domain (FD) features from the input signals. For gait recognition, an ensemble of three DL models takes place namely Bidirectional Long Short Term Memory (BiLSTM), Gated Recurrent Unit (GRU) and Recurrent Neural Network (RNN). To exhibit the enhancedgait recognition results of the ELMHGA-HCI technique, a widespread simulation analysis is done. The simulation outcomes pointed out the enhanced performance of the ELMHGA-HCI approach over other existing techniques. © 2023 IEEE.

关键词： Activity recognition Deep learning Ensemble learning Gait recognition Human computer interface

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials & Continua 2025年第6期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoNSW 2007Australia School of Data Science and Engineering Indian Institute of Science Education and ResearchBhopal462066India Research Institute of Marine Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyDelhi NCR CampusGhaziabad201204India Battery System Modelling Department CLARIOS VARTA Hannover GmbHHanover30419Germany Science and Innovation Group Bureau of MeteorologyBrisbane4000Australia

Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 *** indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s *** computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction *** GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational *** contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery *** of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep *** findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained *** work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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Attribution rollout: a new way to interpret visual transformer

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Journal of Ambient Intelligence and Humanized Computing 2023年第1期14卷 163-173页

作者： Xu, Li Yan, Xin Ding, Weiyue Liu, Zechao College of Computer Science and Technology Harbin Engineering University Nantong Street Heilongjiang Harbin150001 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130012 China Department of Medicine Harvard Medical School Longwood Avenue BostonMA02115 United States

Transformer-based models are dominating the field of natural language processing and are becoming increasingly popular in the field of computer vision. However, the black box characteristics of transformers seriously hamper their application in certain fields. Prior work relies on the raw attention scores or employs heuristic propagation along with the attention graph. In this work, we propose a new way to visualize model. The method computes attention scores based on attribution and then propagates these attention scores through the layers. This propagation involves attention layers and multi-head attention mechanism. Our method extracts salient dependencies in each layer to visualize prediction results. We benchmark our method on recent visual transformer networks and demonstrate its many advantages over the existing interpretability methods. Our code is available at: https://***/yxheartipp/attr-rollout. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Natural language processing systems

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Wei Ping, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350386226

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Investment Forecasting through the Second-Level Thinking Concept

Enhancing Investment Forecasting through the Second-Level Th...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Dan Zhao Chao Wang Zhenyi Shen The College of Computer Science and Technology Zhejiang University China The Faculty of Science Engineering and Technology Swinburne University of Technology Victoria Australia

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Forecasting models that rely on machine learning often lack a transparent trading logic, which can erode trust when they are applied to real-world trading scenarios. To tackle this problem, a model grounded in an investment concept known as ‘second-level thinking’ is proposed. This approach enhances both the interpretability of the model and the prediction accuracy. The model's first-level thinking process makes a broad prediction based on features related to the macroeconomic environment. These features are used to group samples into clusters, each representing a different state of the macroeconomic environment. For each cluster, an associative memory is constructed to improve interpretability and simplify the prediction process, mirroring the function of human memory. A theorem is developed to explore the relationship between the capacity of the associative memory and the threshold used in the clustering step. This helps establish an appropriate threshold for reliably memorizing patterns within each cluster. The second-level thinking process then refines these predictions by fitting the residuals from the first-level process using technical features, further enhancing prediction performance. Experiments conducted on two datasets of Chinese government bonds demonstrate that our proposed model outperforms others in terms of prediction accuracy and trading performance.

关键词： Associative memory Accuracy Philosophical considerations Government Predictive models Reliability theory Macroeconomics Logic Forecasting Investment

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HRSR-SVM: Hybrid Reptile Search Remora-based Support Vector Machine for forecasting stock price movement

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International Journal of Information technology (Singapore) 2023年第6期15卷 3127-3134页

作者： Shanthini, P. Matilda Parthasarathy, S. Venkatesan, P. Nandhini, S. Department of Mathematics SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai 600089 India Research and Statistics Sri Ramachandra Medical College & RI Sri Ramachandra University Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

The prediction of stock movements remains an arduous process because of the dynamic and volatile nature of the stock market. In recent times, numerous stock prediction approaches are developed to forecast future stock movements. However, they have not achieved satisfactory results in predicting future stock prices. The objective of the paper is to enhance the performance of stock prediction by the Support Vector Machine-based Hybrid Reptile Search Remora (SVM-HRSR) approach. The data gathered from the stock market are utilized to examine the performance of the proposed prediction model which is collected from the National Association of Securities Dealers Automated Quotations (NASDAQ). The unstructured stock market data is structured to increase the prediction performance using the preprocessing steps namely data minimization and min–max normalization. The proposed SVM-HRSR approach accurately determines stock market movements based on the predicted patterns. The performance influencing factors of the Support Vector Machine (SVM) such as kernel parameter and penalty parameter is tuned mechanically using the Hybrid Reptile Search Remora (HRSR) algorithm to improve the prediction accuracy. Some of the indicators such as Mean Absolute Percentage Error (MAPE), Correlation Coefficient (C_c) , Root Mean Square Error (RMSE), Hit Rate (HR) and Symmetric Mean Absolute Percentage Error (SMAPE) estimated the performance of the SVM-HRSR method. The experimental results inherit that the proposed method achieved less error rates as compared to existing methods. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Hybrid Reptile Search Remora algorithm NASDAQ Stock prediction Stock price movement Support Vector Machine

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Distributed Information Bottleneck for a Primitive Gaussian Diamond MIMO Channel

Distributed Information Bottleneck for a Primitive Gaussian ...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Song, Yi Xu, Hao Wong, Kai-Kit Caire, Giuseppe Shamai Shitz, Shlomo Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany University College London Department of Electronic and Electrical Engineering LondonWC1E7JE United Kingdom Technion-Israel Institute of Technology Department of Electrical and Computer Engineering Haifa3200003 Israel

ISBN: (纸本)9781665475549

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and MIMO Rayleigh fading. The channel state is an independent and identically distributed (i.i.d.) process known at the relays but unknown to the destination. The relays are oblivious, i.e., they are unaware of the codebook and treat the transmitted signal as a random process with known statistics. The bottleneck constraints prevent the relays to communicate the channel state information (CSI) perfectly to the destination. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters. © 2023 IEEE.

关键词： Channel state information

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Statins Combined with AAV8-TBG-LOX-1 Reduce the Vascular Lipid-driven Inflammatory Response and Inhibit Atherosclerosis

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当代医学科学（英文） 2024年第6期44卷 1097-1102页

作者： Wen-ping ZHOU Xin-rui FAN Song-hai LI Zhuang-lin ZENG Yu-miao WEI Department of Cardiology The Central Hospital of WuhanTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430014China Key Laboratory for Molecular Diagnosis of Hubei Province The Central Hospital of WuhanTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430014China Sleep and Neuro Imaging Center Faculty of PsychologySouthwest UniversityChongqing 400715China Department of Cardiology Wuhan Fourth HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430033China Department of Emergency Medicine Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Department of Cardiology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Hubei Key Laboratory of Biological Targeted Therapy Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Hubei Engineering Research Center for Immunological Diagnosis and Therapy of Cardiovascular Diseases Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China

Objective:Imbalances in liver lipid metabolism and inflammatory reactions driven by oxidized lipid deposition in blood vessels constitute the core of *** degradation of cholesterol in the liver promotes oxidative modification of lipid particles and their deposition on the blood vessel wall in the peripheral *** blood vessel wall engulfs and processes oxidized low-density lipoprotein(Ox-LDL)as foreign matter through pattern recognition receptors,ultimately forming lipid-encapsulated *** them,endothelial cell oxidized low density lipoprotein receptor 1(LOX1)phagocytosis is an important link in initiating and promoting this mechanism,and hepatocytes,which are the core of lipid metabolism,are unable to process oxidized lipid particles because of the lack of receptors for the uptake of *** objective of this study was to investigate whether continuous clearance of Ox-LDL through the liver metabolic pathway could provide better protection against statins ***:This study used statins combined with an adeno-associated virus(AAV8-TBG-LOX-1)liver-specific transfection system developed by our research group,in which statins reduced the level of LDL and promoted the ectopic expression of LOX-1 in hepatocytes to clear the continuous production of *** ApoE knockout mouse model was used to study the effects of virus transfection and liver uptake and degradation of *** confocal detection,Oil red staining and immunofluorescence staining were used to observe the effects of combined therapy on anti-atherosclerotic ***:Laser confocal microscopy revealed that the recombinant viral vector AAV8-TBG-LOX-1 could specifically transfect hepatocytes and express LOX-1,which mediate hepatocyte phagocytosis and clearance of *** red O staining of the aorta and valvular ring suggested that statins combined with AAV8-TBG-LOX-1 significantly inhibited atherosclerotic *** immunofluorescence sta

关键词： atherosclerosis oxidized low-density lipoprotein statin adeno-associated virus AAV8-TBG-LOX-1

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Neighborhood Modularization‑based Artificial Bee Colony Algorithm for Disassembly Planning with Operation Attributes

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Chinese Journal of Mechanical engineering 2022年第6期35卷 165-177页

作者： Hongfei Guo Linsheng Zhang Yaping Ren Leilei Meng Zhongwei Zhou Jianqing Li GBA and B&R International Joint Research Center for Smart Logistics School of Intelligent Systems Science and EngineeringInstitute of Physical InternetJinan University(Zhuhai Campus)Zhuhai 519070China School of Computer Science Liaocheng UniversityLiaocheng 252059China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

The recycling and remanufacturing of end-of-life products are significant for environmental protection and resource *** is an essential process of remanufacturing end-of-life *** disassembly plans help improve disassembly efficiency and reduce disassembly *** paper studies a disassembly planning problem with operation attributes,in which an integrated decision of the disassembly sequence,disassembly directions,and disassembly tools are ***,a mathematical model is formulated with the objective of minimizing the penalty cost caused by the changing of operation ***,a neighborhood modularization-based artificial bee colony algorithm is developed,which contains a modular optimized ***,two case studies with different scales and complexities are used to verify the performance of the proposed approach,and experimental results show that the proposed algorithm outperforms the two existing methods within an acceptable computational time.

关键词： End-of-life products Disassembly planning Artificial bee colony Neighborhood modularization

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