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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Han, Jiqun Liu, Xinyu Tan, Dongxu Zhu, Jiehui School of Computer Science and Engineering Northeastern University Shenyang China Guangdong Institute of Intelligent Unmanned System Technology Research and Development Department Guangzhou China

ISBN: (纸本)9798350385557

The attenuation and scattering of light in underwater environments often lead to degradation issues such as image blurring, color cast, and low contrast. Due to the lack of high-quality paired datasets, the performance of deep learning-based methods is limited. To address these challenges, we propose an improved U-Net architecture based on semi-supervised learning for underwater image enhancement. First, a multi-scale input-level fusion module is proposed to allow our network to fully exploit global spatial information from input images, natural light fields, and depth maps. Second, a multi-branch hybrid convolutional attention residual block is designed to improve the network's feature extraction capability and focus on important spatial and channel information in underwater images. Finally, a semi-supervised learning approach is used to fully exploit unpaired datasets, improving the generalization ability of the network model. Through qualitative and quantitative analysis, the proposed network achieves satisfactory image enhancement results in various underwater scenarios. © 2024 IEEE.

关键词： Image enhancement

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A Brief Comparative Study of Metaheuristic Approaches for Hyperparameter Optimization of Machine Learning Model 1

A Brief Comparative Study of Metaheuristic Approaches for Hy...

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1st International Conference on computer Science and Emerging Technologies, CSET 2023

作者： Dilip, Kumar Ghantasala, G. S. Pradeep Rathee, Manisha Kallam, Suresh Bathla, Priyanka Alliance University Department of Computer Science and Engineering Bengaluru India School of Computer and System Sciences Jawaharlal Nehru University India School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350341737

Machine learning models have been successfully applied in numerous fields. Training a model is the most important aspect of machine learning for its successful application for the problem. To improve the training of a machine learning model thereby improve the performance, the selection of features and setting optimal parameters is crucial. Mainly two kinds of parameters are required to deal with, namely internal and external parameters. Internal parameters are model parameters and configurable such as weights of neural networks and their estimation can be done using data set. The hyperparameters such as learning rate, size of layers, number of layers, loss function etc, are external parameters and its values cannot be determined using the data set and it is not the part of the model. Its estimation can be done by the domain expert or using some trial-and-error techniques until it achieves some acceptable values. However, these techniques are highly time-consuming and cannot ensure the optimal values for these hyperparameters. In recent years different metaheuristic techniques have been applied to determine the optimal values of hyper parameters for machine learning models. In this paper we have conducted a brief comparative study of a few popular metaheuristic approaches applied for the hyperparameter optimization for various machine learning models. In this paper various evaluation measures have been considered for comparative analysis of metaheuristic approaches for hyperparameter optimization for deep learning model. © 2023 IEEE.

关键词： Ant colony optimization

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Trust Evaluation-based Machine Learning for WSNs 10

Trust Evaluation-based Machine Learning for WSNs

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kumar, Aditya Singh, Karan Vats, Satvik School of Computer and System Sciences Jawaharlal Nehru University New Delhi India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789380544472

Wireless sensor networks (WSNs) have many different applications in industries such as intrusion detection, healthcare, and environmental monitoring. To ensure their success, it is essential to establish trust among the sensor nodes to enhance trustworthiness, collaboration, and security. However, current trust management methods for large-scale WSNs have several drawbacks, including low dependability, high communication, and memory overhead, which make them ineffective. To address these issues, a new approach has been proposed, which uses trust evaluation-based machine learning for WSNs. This method involves determining trust attributes using a model and then creating a new algorithm based on machine learning to categorize and combine these trust features into a final trust value. This trust value can be used in decision-making to improve the effectiveness of WSNs. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Principal component analysis

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RPRB-Net: A Sign Language Recognition Based on Multi-dimension Attentions

RPRB-Net: A Sign Language Recognition Based on Multi-dimensi...

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2023 International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2023

作者： Yang, Tian Shen, Cong Wang, Xinyue Ma, Xiaoyu Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350306545

Sign Language Recognition (SLR) is the process of translating sign language into text or speech, which can also be viewed as a communication and expression tool between deaf individuals and hearing people. SLR using deep neural network has undergone significant development over the last decade. In this study, we design a feature-enhanced SLR technique based on serval spaces. To aid the network learning features from channel and spatial dimensions, we inject various attention mechanisms into the feature extractor backbone. As a result, we named our approach ResNet + Pyramid-Split-Attention + Recurrent-Criss-Cross-Attention + BiLSTM Network (RPRB-Net). Besides, we employ the BiLSTM architecture to encode high-level representations in temporal sequence. The network is ultimately trained using an end-to-end CTC alignment strategy. Extensive trials show that RPRB-Net achieves WERs of 3.5% and 23.6% on the USTC and RWTH-2014 datasets, respectively. Besides, it implies that RPRB-Net significantly surpasses the cutting-edge SLR approaches. Additionally, numerous ablation experiments have confirmed the efficiency of configuration. © 2023 IEEE.

关键词： Audition

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Heterogeneous system Data Storage and Retrieval Scheme Based on Blockchain 7th

Heterogeneous System Data Storage and Retrieval Scheme Based...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Zhang, Ni Ma, BaoQuan Wang, Peng Lei, XuHua Cheng, YeJian Li, JiaXin Huai, XiaoYong Shen, ZhiWei Song, NingNing Wang, Long National Computer System Engineering Research Institute of China Beijing102200 China School of Computer Science and Technology Xidian University Xi’an710071 China

ISBN: (纸本)9783031281235

In the field of information interaction, when a project involves a large amount of heterogeneous information, it is difficult to transmit and update the required information timely, accurately, reliably and securely in such a complex environment to maintain synchronization. At present, blockchain itself has problems such as high storage pressure of nodes, low access efficiency, and simple query. Therefore, this paper takes this as a starting point and proposes a data mapping method of physical resources based on node attributes and heterogeneous nodes, which provides a general method for the data mapping from actual physical resources to information domain. This method can define the attributes of heterogeneous physical information nodes and support unified expression of various physical information resources on the same platform in the real physical world, then connect heterogeneous physical information nodes and improve the sharing ability of data resources between nodes. When accessing data, the corresponding content can be found in the off-chain database by obtaining the index information of the off-chain location stored on the chain. This method takes advantage of the large space and high access efficiency of the off-chain storage system to share the pressure of on-chain data storage. This paper expounds the design idea of the system, introduces the design objective and method in detail, gives the flow chart of the system operation, and carries out a simple software test and verification. Finally, this paper summarizes the work and prospects the development direction of future work, hoping to provide inspiration for solving such problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Indentation behavior of a semi-infinite piezoelectric semiconductor under a rigid flat-ended cylindrical indenter

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Applied Mathematics and Mechanics(English Edition) 2024年第4期45卷 649-662页

作者： Shijing GAO Lele ZHANG Jinxi LIU Guoquan NIE Weiqiu CHEN State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China School of Mechanical Engineering Shijiazhuang Tiedao UniversityShijiazhuang 050043China Hebei Key Laboratory for Mechanics of Intelligent Materials and Structures Department of Engineering MechanicsShijiazhuang Tiedao UniversityShijiazhuang 050043China State Key Laboratory of Computer Aided Design and Computer Graphics Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceDepartment of Engineering MechanicsZhejiang UniversityHangzhou 310027China

This paper theoretically studies the axisymmetric frictionless indentation of a transversely isotropic piezoelectric semiconductor(PSC)half-space subject to a rigid flatended cylindrical *** contact area and other surface of the PSC half-space are assumed to be electrically *** the Hankel integral transformation,the problem is reduced to the Fredholm integral equation of the second *** equation is solved numerically to obtain the indentation behaviors of the PSC half-space,mainly including the indentation force-depth relation and the electric potential-depth *** results show that the effect of the semiconductor property on the indentation responses is limited within a certain range of variation of the steady carrier *** dependence of indentation behavior on material properties is also analyzed by two different kinds of *** element simulations are conducted to verify the results calculated by the integral equation technique,and good agreement is demonstrated.

关键词： piezoelectric semiconductor(PSC) insulating indenter electromechanical response singular integral equation finite element simulation

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Resilience assessment framework toward interdependent bus-rail transit network:Structure,critical components,and coupling mechanism

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Communications in Transportation Research 2023年第1期3卷 118-129页

作者： Bing Liu Xiaoyue Liu Yang Yang Xi Chen Xiaolei Ma School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Civil&Environmental Engineering University of UtahSalt Lake CityUT84112USA School of Computer Science and Engineering Beihang UniversityBeijing100191China Key Laboratory of Intelligent Transportation Technology and System Ministry of EducationBeijing100191China

Understanding the interdependent nature of multimodal public transit networks(PTNs)is vital for ensuring the resilience and robustness of transportation ***,previous studies have predominantly focused on assessing the vulnerability and characteristics of single-mode PTNs,neglecting the impacts of heterogeneous disturbances and shifts in travel behavior within multimodal ***,this study introduces a novel resilience assessment framework that comprehensively analyzes the coupling mechanism,structural and functional characteristics of bus-rail transit networks(BRTNs).In this framework,a network performance metric is proposed by considering the passengers’travel behaviors under various ***,stations and subnetworks are classified using the k-means algorithm and resilience metric by simulating various disturbances occurring at each station or *** proposed framework is validated via a case study of a BRTN in Beijing,*** indicate that the rail transit network(RTN)plays a crucial role in maintaining network function and resisting external disturbances in the interdependent ***,the coupling interactions between the RTN and bus transit network(BTN)exhibit distinct characteristics under infrastructure component disruption and functional *** findings provide valuable insights into emergency management for PTNs and understanding the coupling relationship between BTN and RTN.

关键词： Complex network Bus-rail transit network Resilience analysis Interdependent coupling relationship Classification analysis

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RTGA: A Redundancy-free Accelerator for High-Performance Temporal Graph Neural Network Inference 24

RTGA: A Redundancy-free Accelerator for High-Performance Tem...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Yu, Hui Zhang, Yu Tan, Andong Lu, Chenze Zhao, Jin Liao, Xiaofei Jin, Hai Liu, Haikun National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9798400706011

Temporal Graph Neural Network (TGNN) has attracted much research attention because it can capture the dynamic nature of complex networks. However, existing solutions suffer from redundant computation overhead and excessive off-chip communications for TGNN inference because they often rely on redundant graph sampling and repeatedly fetching the features and vertex memory. This paper proposes a redundancy-free accelerator, RTGA, for high-performance TGNN inference. Specifically, RTGA proposes a redundancy-aware execution approach with temporal tree into a novel accelerator design to effectively eliminate unnecessary data processing for fewer redundant computations and off-chip communications and also designs a temporal-aware data caching method to improve data locality for TGNN. We have implemented and evaluated RTGA on a Xilinx Alveo U280 FPGA card. Compared with cutting-edge software solutions (i.e., TGN and TGL) and hardware solutions (i.e., BlockGNN and FlowGNN), RTGA improves the performance of TGNN inference by an average of 473.2x, 87.4x, 8.2x, and 6.9x and saves energy by 542.8x, 102.2x, 9.4x, and 8.3x, respectively. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spatio-temporal data

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Strategies for classifying water quality in the Cauvery River using a federated learning technique

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 187-193页

作者： J, Vellingiri K, Kalaivanan P, Gopinath M C, Gobinath Subramaniam, Prabhakar Rontala Rangarajan, Sarathkumar School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Information System and Technology University of KwaZulu-Natal Durban South Africa Victorian Institute of Technology Melbourne Australia

Artificial intelligence methods are emerging techniques used in the field of environmental protection, especially in the analysis of air, water, and soil quality. AI analyzes vast amounts of environmental data to predict pollution and provide decision-makers with the information they need to develop efficient policies. One of the most important problems in environmental analysis is data security, and many organizations are actively working to ensure the secure collection, storage, and utilization of sensitive environmental data. In addition, organizations are focusing on developing strategies to protect their data from malicious attacks, such as cyber-attacks, as well as from accidental misuses, like unauthorized access. For this purpose, we have introduced a novel water quality prediction using the Federated Learning Technique. Federated learning enables multiple parties to collaborate and train a model on their local data without sharing it with others, thereby preserving data privacy. The proposed method is applied to a Cauvery River dataset of water quality parameters, and the results demonstrate that the PSO-optimized federated learning process achieves better prediction accuracy of 87%, a precision of 85%, a recall of 93%, and an 89% F1 score. © 2023

关键词： Machine learning

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A Hybrid WSVM-Levy Approach for Energy-Efficient Manufacturing Using Big Data and IoT

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computers, Materials & Continua 2024年第12期81卷 4895-4914页

作者： Surbhi Bhatia Khan Mohammad Alojail Mahesh Thyluru Ramakrishna Hemant Sharma School of Science Engineering and EnvironmentUniversity of SalfordManchesterM68HDUK University Centre for Research and Development Chandigarh UniversityMohali140307India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering JAIN(Deemed-to-be University)Bangalore560030India Department of Computer Science and Engineering IES College of TechnologyBhopal462044India

In Intelligent Manufacturing,Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors,ensuring more informed decision-making and adaptive system *** also promotes decision making and provides scientific analysis to enhance the efficiency of the operation,cost reduction,maximizing the process of production and so *** methods are employed to enhance productivity,yet achieving sustainable manufacturing remains a complex challenge that requires careful *** study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid Weighted Support Vector-based Lévy flight(HWS-LF)*** objective of the HWS-LF method is to improve the environmental,economic,and social aspects of manufacturing *** this approach,Support Vector Machines(SVM)are used to classify data points by identifying the optimal hyperplane to separate different classes,thereby supporting predictive maintenance and quality control in *** Forest is applied to boost efficiency,resource allocation,and production optimization.A Weighted Average Ensemble technique is employed to combine predictions from multiple models,assigning different weights to ensure an accurate system for evaluating manufacturing ***,Lévy flight Optimization is incorporated to enhance the performance of the HWS-LF method *** method’s effectiveness is assessed using various evaluation metrics,including accuracy,precision,recall,F1-score,and *** show that the proposed HWS-LF method outperforms other state-of-the-art techniques,demonstrating superior productivity and system performance.

关键词： Sustainable manufacturing support vector machine random forest levy flight optimization weighted average ensemble

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