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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhang, Liyan Qi, Chao Liu, Zengli Zhao, Xuanzhi Rong, Shaowei Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Kunming University of Science and Technology Computing Center Kunming650500 China

ISBN: (纸本)9781510689251

Aiming at the problems of low recognition rate of Chinese dialects and poor robustness in noisy environment, this paper proposes an improved feature extraction algorithm, which combines the Sparrow Search Algorithm (SSA) and Variational Mode Decomposition (VMD) with the improved Gamma-tone Frequency Cepstral Coefficients (GFCC) and Teager Energy Operator Cepstral Coefficient (TEOCC). Firstly, the VMD parameters are optimized by SSA, the dialect signal is decomposed into different modal components, and the correlation coefficients with the original signal are calculated. Then, the component of the dominant signal is screened according to the correlation, which is used to calculate the GFCC characteristics. Subsequently, the improved GFCC features are fused with TEOCC, and Principal Component Analysis (PCA) is used to reduce the dimension of the fusion features. Finally, the Gaussian mixture model-universal background model (GMM-UBM) is used for dialect recognition. The experimental results show that the proposed method can improve the recognition rate compared with the traditional GFCC and single feature in both noise-free and noise-free environments, and verify the effectiveness of the improved algorithm in dialect recognition. © 2025 SPIE.

关键词： Variational mode decomposition

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Multi-Agent System Based on Stacking Technique for Rice Yield Prediction 3

Multi-Agent System Based on Stacking Technique for Rice Yiel...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Seireg, Hayam R. Omar, Yasser M. Elmahalawy, Ahmed Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Menouf Egypt Aastmt Faculty of Computing and Information Technology Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350323511

Artificial intelligence (AI) has expanded its influence across various sectors including education, healthcare, and agriculture. In the agricultural setting, the multiagent system (MAS) is recognized as a powerful tool for optimizing resource allocation, enhancing decision-making processes, and improving overall farm productivity. Accurate prediction of rice yield levels is paramount importance in the agricultural sector. It allows farmers, policymakers, and stakeholders to make informed decisions regarding crop management. Individual machine-learning algorithms (MLA) have been used to predict rice yield levels, but they may not fully exploit the available information. Therefore, the novel system implemented based on stacking techniques to solve complex problems. The objective of this paper is to present an efficient system that leverages MAS based on a novel proposed stacking technique (Extra Trees Classifier (ETC), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA) and Gaussian Naive Bayes (GNB)) for improving the rice yield level prediction within the agricultural environment. Each algorithm brings its unique approach to handling complex relationships, and modeling class separability. The findings from this study provided valuable insights for decision-making in interconnected sectors and facilitating optimal business planning. The dataset incorporated climate variables such as monthly maximum and minimum temperatures and rainfall. The final result of the dataset consists of 1266 rows and 18 features. The results showed that the novel proposed stacking technique achieved the highest prediction accuracy 87% and the best individual Decision tree classifier obtained 77.5%. The novel proposed stacking technique increases the accuracy by 9.5% compared to the best individual MLA. © 2023 IEEE.

关键词： Multi agent systems

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Efficient Intelligent Based Compliance, Detection, Tracking, and Proximity Model for Traffic System

Efficient Intelligent Based Compliance, Detection, Tracking,...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Revadala, Abhinay Reddy, Sri Ram Charan Ram Kumar, J. Kumar, Manoj School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India University of Wollongong Faculty of Engineering and Information Sciences Dubai United Arab Emirates

ISBN: (纸本)9798350375237

The Intelligent Traffic Management System (ITMS) is a cutting-edge solution designed to address multiple aspects of traffic management and safety. This advanced system incorporates various technologies to tackle issues such as helmet compliance, traffic signal violations, number plate identification, cell phone usage tracking, and proximity-based police station alerts. By leveraging the power of artificial intelligence and machine learning algorithms, the ITMS can accurately detect and enforce helmet compliance among motorcyclists, ultimately reducing the risk of head injuries in road accidents. Furthermore, the system utilizes advanced video analytics to detect violations of traffic signals, ensuring that motorists adhere to traffic rules and regulations. Additionally, the ITMS employs automatic number plate recognition (ANPR) technology to quickly and efficiently identify vehicles with illegal or expired license plates, enabling law enforcement agencies to take appropriate action. Moreover, the system includes a cell phone usage tracking feature which uses computer vision techniques to identify drivers who are using their phones while driving, contributing to a decrease in distracted driving incidents. Lastly, by introducing proximity-based police station alerts, the ITMS enables prompt response from law enforcement agencies by automatically notifying the nearest police station in the event of a traffic violation or accident. In summary, the Intelligent Traffic Management System provides a comprehensive and holistic approach to traffic management and safety through its features of helmet compliance, traffic signal violation detection, number plate identification, cell phone usage tracking, and proximity-based police station alerts. © 2024 IEEE.

关键词： Traffic signals

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Privacy preserving dynamic data release based on non-synonymous diverse anatomy

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International Journal of Security and Networks 2023年第2期18卷 75-90页

作者： Yan, Yan Eyeleko, Anselme Herman Mahmood, Adnan Sun, Zichao Dong, Zhuoyue Xu, Fei School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia

The publishing and using of big data brought unprecedented convenience to users. However, it also results in the disclosure of personal privacy information. In order to mitigate the privacy leakage risk of sensitive information during dynamic data updating, this paper envisages a non-synonymous diverse anatomy method for the privacy preserving dynamic data publishing. The envisaged method inherits the advantages of the traditional anatomy method, retains the availability of the original data to the greatest extent, and avoids the loss of information caused by the generalisation process. A series of indicators are designed to evaluate the synonymous linkage between non-numerical sensitive values. A novel grouping mechanism is further proposed to achieve l-diversity anatomy by combining the concept of synonymous linkage and synonymous entropy with the dynamic update procedure. Experimental analysis suggests that the envisaged method can provide better privacy protection effect on the published data. © 2023 Inderscience Enterprises Ltd.

关键词： Sensitive data

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Deep learning on medical image analysis

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 1-35页

作者： Jiaji Wang Shuihua Wang Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Biological Sciences Xi'an Jiaotong-Liverpool UniversitySuzhouJiangsuChina Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia School of Computer Science and Engineering Southeast UniversityNanjingJiangsuChina

Medical image analysis plays an irreplaceable role in diagnosing,treating,and monitoring various *** neural networks(CNNs)have become popular as they can extract intricate features and patterns from extensive *** paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre-trained *** paper also discusses how transfer learning has been applied to different areas within medical image *** comprehensive overview aims to assist researchers,clinicians,and policymakers by providing detailed insights,helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.

关键词： CNN deep learning medical image transfer learning

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Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign Recognition and Voice Alerts 5

Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign R...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Priya, D. Divya Chinnasamy, P. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Jalil, Norazira Binti A. Sangodiah, Anbuselvan Mlr Institue of Technology Department of Computer Science Engineering Hyderabad India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Srivilliputtur India Faculty of Information and Communication Technology Perak Kampar Malaysia Quest International University Faculty of Computing and Engineering Perak Ipoh Malaysia

ISBN: (纸本)9798350379945

Traffic signs secure their safety on the roads and the protection of others around them;everyone must be aware of traffic signs and other aspects of road safety. Traffic sign detection is a highly relevant computer vision problem that forms the basis of numerous applications in sectors like the automotive industry. Between classes, traffic signs differ widely in terms of colour, shape, and the use of pictograms or text. The project's classification of traffic signs in an image into various categories is based on a deep neural network model. IoT devices that capture traffic signs and notify users of them are used to build a model. © 2024 IEEE.

关键词： Automotive industry

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Cyber-Syndrome:Concept,Theoretical Characterization,and Control Mechanism

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Tsinghua science and Technology 2024年第3期29卷 721-735页

作者： Feifei Shi Huansheng Ning Liming Chen Sahraoui Dhelim School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Computing Ulster UniversityNewtownabbeyBT370QBUK School of Computer Science University College DublinDublinD04 V1W8Ireland

The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking(CPST)***,the easy access,the lack of governance,and excessive use has generated a raft of new behaviors within CPST,which affects users’physical,social,and mental *** this paper,we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST *** we characterize the Cyber-Syndrome concept in terms of Maslow’s theory of Needs,from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology,formation,symptoms,and ***,we propose an entropy-based Cyber-Syndrome control mechanism for its computation and *** goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.

关键词： Cyber-Syndrome cyber-physical systems Cyber-Physical-Social-Thinking(CPST)space Maslow’sHierarchy of Needs entropy

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A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

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Using Unmanned Aerial Vehicles for Traffic Flow Analysis at Large Multi-Lane Roundabout

Using Unmanned Aerial Vehicles for Traffic Flow Analysis at ...

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ASEAN Australian engineering Congress, AAEC 2022

作者： Chong, Elizabeth E. M. Bong, Jit-Boon Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Kuching Malaysia

ISBN: (纸本)9789819955466

Traditional traffic turning movement survey of large multi-lane roundabout is a daunting task as the traffic surveyors are unable to track the vehicles’ movement. The direct method to obtain the turning movement requires the survey equipment to be placed at a higher level, usually at masts or high-rise buildings adjacent to the intersection. Indirect methods such as license plate matching may yield inaccurate results. With the advancement of technology, the use of unmanned aerial vehicles (UAV) in various fields has seen an increase, including traffic and transportation, due to its versatility. This paper compares the method of collecting traffic turning movement data using (i) video camera recording;and (ii) UAV to analyse the traffic flow at a 200 m inscribed diameter roundabout in Kuching, Malaysia. The results give an insight into the capability of a single unit UAV in determining the turning movement. Description of the challenges and limitations of UAV application are documented. The differences in the data obtained from methods were considered small and negligible. Due to the battery limitation, the UAV has to be flown down for battery change, which cause approximately 10 min of data loss for an hour’s survey. Continuous data captured with a video camera shows that data loss is within the range of 20% of the total volume. With the expected uptrend of UAV applications in the near future, this kind of research serves as a useful reference for industries and researchers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Video cameras

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AI-Driven Symptom Checker: Leveraging Big Data Technologies for Real-Time Healthcare Triage and Diagnostics 5

AI-Driven Symptom Checker: Leveraging Big Data Technologies ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Banerjee, Augnik Baibhav, Piyush Kumar, Nitin Singh, Aniket Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331530013

The healthcare industry continues to face significant challenges in delivering timely and accurate diagnoses, particularly in remote and underserved areas where access to medical professionals is limited. The work aims to develop an AI-driven symptom checker that leverages big data technologies to offer preliminary diagnoses and support patient triage, ensuring quicker medical response times. Utilizing Hadoop for scalable data storage, Hive for advanced data analysis, and Spark for real-time distributed data processing, the system is designed to efficiently handle large volumes of patient data. Tableau is integrated for intuitive data visualization, allowing healthcare professionals to gain actionable insights and make informed decisions promptly. The proposed architecture enables the swift analysis of patient symptoms in real-time, significantly reducing diagnostic delays and enhancing the quality of care. By focusing on scalable and efficient data processing, the work addresses the need for a robust healthcare solution capable of operating in environments with limited resources. This system has the potential to transform patient care in remote areas by providing real-time analysis, identifying critical cases, and aiding in effective resource allocation. The solution's emphasis on rapid diagnosis and visualization offers a comprehensive approach to improving healthcare outcomes in underserved regions. © 2024 IEEE.

关键词： AI-Driven Symptom Checker Apache Spark Hadoop Healthcare Triage Hive Tableau

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