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A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

Computer Modeling in Engineering & Sciences 2024年第7期140卷 1-40页

作者： Xianwei Jiang Yanqiong Zhang Juan Lei Yudong Zhang School of Mathematics and Information Science Nanjing Normal University of Special EducationNanjing210038China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into *** article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 *** Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional *** from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung *** Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence *** addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification *** language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition ***,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among *** to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition ***,suitable datasets and evaluation criteria are also worth pursuing.

关键词： Chinese Sign Language Recognition deep neural networks artificial intelligence transfer learning hybrid network models

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FruitVision: Dual-Attention Embedded AI System for Precise Apple Counting Using Edge computing

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 445-459页

作者： Thakur, Divyansh Kumar, Vikram Indian Institute of Information Technology Una School of Computing Bhadsali 177209 India

In this work, we developed and enhanced an artificial intelligence (AI)-centered hardware framework. This framework integrates the Nvidia Jetson Nano processing unit with a Depth AI camera. Our primary goal was to create an improved version of the YOLOv7 algorithm to quantify apple fruits using edge computing resources. We curated a dataset of 9,000 images of apple fruits to support this effort. Within the enhanced YOLOv7 architecture, we introduced a novel dual attention mechanism called the Global-SE Unified Attention Mechanism (GSEAM). This mechanism was designed to improve the accuracy of object detection by combining spatial and channel-oriented attention mechanisms, significantly enhancing the model.s contextual understanding and object recognition in various settings. The incorporation of GSEAM, along with the Gaussian Error Linear Unit activation function, was a deliberate effort to boost the YOLOv7 architecture.s ability to capture intricate contextual details and hierarchical features. Our system.s performance was rigorously evaluated across six key performance metrics and compared with other pretrained models. We achieved a precision of 99.54%, recall of 98.94%, F1-score of 99.71%, and average precision of 99.13%. This system has proven to be a valuable tool for real-time apple fruit counting, with practical applications for farmers. © 2024 IEEE.

关键词： Gaussian distribution

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An Efficient Framework for Predicting Future Retail Sales Using Ensemble DNN-BiLSTM Technique

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SN Computer Science 2024年第1期5卷 150页

作者： Babu, K. N. Surendra Kodabagi, Mallikarjun M. School of Computing and Information Technology REVA University Bengaluru India

Forecasting retail sales often requires various number of products from different stores. Existing deep or machine learning techniques fall short of producing accurate classification results because of overfitting and two-class problem that affects the performance of evaluation parameters like precision, recall, accuracy and F-measure. Hence there is a need for an efficient prediction framework that addresses the existing problems. This work proposes an efficient framework for predicting retail sales using an ensemble DNN-BiLSTM framework. We suggest creating a base forecaster pool that includes both individual and pooled forecasting techniques for developing this ensemble approach to forecasting retail sales. Instead of focusing on finding the best individual technique, we suggest finding the optimal combination of forecasts. Classification Accuracy, Precision, Recall, and F-measure performance metrics of the experiment utilizing the proposed ensemble approach DNN + BiLSTM surpass the current DNN, CNN, and LSTM classifiers by 98.3%, 98.1%, 97.8%, and 97.94%, respectively. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Ensemble DNN + BiLSTM approach Ensemble framework Machine learning Retail dataset

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Optimizing AES S-Box Implementation:A SAT-Based Approach with Tower Field Representations

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Computers, Materials & Continua 2025年第4期83卷 1515-1531页

作者： Jingya Feng Ying Zhao Tao Ye Wei Feng School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Computing and Information Systems The University of MelbourneMelbourne3010Australia Guangxi Wangxin Information Technology Co. Ltd.Nanning530000China

The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data *** paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations and Boolean Satisfiability(SAT)*** research makes several significant contri-butions to the ***,we have optimized the GF(24)inversion,achieving a remarkable 31.35%area reduction(15.33 GE)compared to the best known ***,we have enhanced multiplication implementa-tions for transformation matrices using a SAT-method based on local *** approach has yielded notable improvements,such as a 22.22%reduction in area(42.00 GE)for the top transformation matrix in GF((24)2)-type S-box ***,we have proposed new implementations of GF(((22)2)2)-type and GF((24)2)-type S-boxes,with the GF(((22)2)2)-type demonstrating superior *** implementation offers two variants:a small area variant that sets new area records,and a fast variant that establishes new benchmarks in Area-Execution-Time(AET)and energy *** approach significantly improves upon existing S-box implementations,offering advancements in area,speed,and energy *** optimizations contribute to more efficient and secure AES implementations,potentially enhancing various cryptographic applications in the field of network security.

关键词： AES S-box SAT optimization tower field hardware implementation area efficiency energy consumption

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A Novel Approach for Enhanced Feature Selection Over Retails Sales Data Using Ensemble Machine Learning Technique

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SN Computer Science 2024年第5期5卷 515页

作者： Babu, K. N. Surendra Kodabagi, Mallikarjun M. School of Computing and Information Technology REVA University Bengaluru India

The connection between manufacturers and retailers in the retail industry, as well as supply chain management, depend heavily on sales forecasting. The efficiency of traditional methodologies and methods for completing a task has been undermined by the exponential growth of digital data. This study suggests an enhanced feature selection for retail sales using the Citadel POS (Point of Sales) Retail dataset using ensemble machine learning techniques. In order to predict sales data and provide in-depth analysis on retail sales and assessment, a variety of machine learning techniques are used for ensemble sales data. These techniques include diversified regression like Random Forest Regression, Gradient Boosting Regression, Linear Regression, and time series LSTM Model. The information used in this study was given from 2013 to 2019 by Citadel POS, a cloud-based solution that assists retail establishments in managing transactions, inventory, customers, vendors, monitor reports, manage sales, and tender data locally. The proposed method outperformed the regression and time series LSTM models with an MAE of 5.53 and an RMSE of 0.652. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Enhanced feature selection Ensemble machine learning Regression models Retail sales analysis Time series model

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Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge computing for Makespan Optimization

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Tsinghua Science and technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Huzhou UniversityHuzhou 313000China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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NeurDB: an AI-powered autonomous data system

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Science China(information Sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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SGGAA: a robust adversarial generation based on synthesizing point cloud and mesh space

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Neural computing and Applications 2025年 1-23页

作者： Hu, Ruihan Yang, Rui Faculty of Computing Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Tianjin University Tianjin China

3D vision recognition offers a significantly more robust tool for achieving machine cognition compared to traditional 2D vision techniques. However, similar to the vulnerabilities present in 2D vision, many 3D vision recognition applications, including point cloud classification models, are susceptible to degradation when confronted with imperceptible outliers. Outliers have a considerable impact on the performance and accuracy of models, compromising their overall effectiveness. Existing 3D point cloud attack and defense strategies neglect outliers and overlook the crucial impact of 3D printability in real-world scenarios. Neglecting this aspect hinders the consideration of potential manipulation or alteration of 3D objects in the physical realm, posing significant implications for the security and robustness of 3D vision systems. This manuscript presents a novel and effective targeted attack framework called synthesizing geometry and geodesic attack (SGGAA). It offers an alternative adversarial training algorithm that bridges the gap between the digital and physical domains. SGGAA introduces a novel generalized distance metric, facilitating the design of powerful and simple techniques for generating adversarial samples and improving the training process. It focuses on the regularizer loss object in synthesizing attack modes for mesh and point cloud spaces, utilizing Hausdorff distance, geometry distance, and geodesic distance losses. Comprehensive evaluations, including hyperparameter tuning, qualitative analysis, and quantitative experiments, were conducted to assess SGGAA’s performance. The results demonstrated its superiority, with high attack success rates, excellent geometric similarity scores, and outperformance of state-of-the-art attack strategies across various 3D defense techniques. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mesh generation

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Road Rage Detection System Using Deep Learning and Computer Vision 3

Road Rage Detection System Using Deep Learning and Computer ...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Lohare, Shaik Tanveer Maaz, Mohammed Razi, Mahammad Nehal, Mohammed Ahmed, Syed Thouheed Rashmi, C. School of Computing and Information Technology Reva University Bengaluru India

ISBN: (纸本)9798331508456

Road rage detection in India's chaotic traffic conditions demands robust, real-time solutions. This paper proposes a 3D CNN-based system that leverages transfer learning from a violence detection model (98 % accuracy on Kaggle datasets) to a road rage-specific model (94 % accuracy on a custom Indian dataset). By incorporating temporal smoothing with a 5-frame sliding window, the system reduces false positives by 15 % compared to existing methods. Experiments demonstrate its efficacy under varying illumination and traffic densities, validated through precision (0.99 for non-violence) and recall (0.99 for violence). The system's deployment in Bangalore highlights its potential for traffic monitoring, law enforcement, and insurance claim verification. Future work includes edge-device optimization and multimodal data integration. © 2025 IEEE.

关键词： Traffic control

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