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International Conference on Inventive Systems and Control (ICISC)

作者： D.K. Shareef V. Jyothsna Department of Computer Science and Engineering School of Computing MOHAN BABU UNIVERSITY (Erstwhile Sree Vidyanikethan Engineering College (Autonomous)) Tirupati Andhra Pradesh India Department of Data Science School of Computing MOHAN BABU UNIVERSITY (Erstwhile Sree Vidyanikethan Engineering College (Autonomous)) Tirupati Andhra Pradesh India

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

This research study presents a comprehensive survey of deep learning methods applied in order to improve the security along with accuracy of the mobile sink position prediction in Vehicular Pattern Wireless Sensor Networks (V-WSNs). V-WSNs are pivotal in modern wireless communication, especially in dynamic environments like urban traffic systems. The survey begins with an overview of V-WSNs, highlighting their unique characteristics, applications, and challenges, with a particular focus on mobile sink position prediction. It then delves into the nuances of routing protocols in V-WSNs, discussing the advantages and limitations of existing methods, and underscores the necessity for deep learning-based solutions. The paper further explores the integration of deep learning in WSNs, examining its applications, benefits, and inherent challenges. The significance of accurate and secure routing is analyzed, followed by an in-depth discussion on the integration of deep learning for enhancing these aspects in V-WSN routing. The paper also addresses the current challenges and open issues in mobile sink prediction and secure, accurate routing, paving the way for future research directions. A comparative analysis of various deep learning approaches and their practical applications in real-world scenarios is presented, offering insights into the effectiveness of these techniques. The survey concludes by summarizing the key findings and reaffirming the importance of integrating deep learning for secure and accurate mobile sink position prediction in V-WSNs.

关键词： Deep learning Surveys Wireless communication Wireless sensor networks Technological innovation Accuracy Routing

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A GTFS data acquisition and processing framework and its application to train delay prediction

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International Journal of Transportation Science and Technology 2023年第1期12卷 201-216页

作者： Jianqing Wu Bo Du Zengyang Gong Qiang Wu Jun Shen Luping Zhou Chen Cai School of Computing and Information Technology University of WollongongNSW 2522Australia SMART Infrastructure Facility University of WollongongNSW 2522Australia School of Civil Mining and EnvironmentalUniversity of WollongongNSW 2522Australia Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China School of Electrical and Information Engineering University of SydneyNSW 2006Australia CSIRO’s Data61 NSW 2015Australia

With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed Specification (GTFS), with standard open-source data in both static and real-time formats, is being widely used in public transport planning and operation management. However, compared to other extensively studied data sources such as smart card data and GPS trajectory data, the GTFS data lacks proper investigation yet. Utilization of the GTFS data is challenging for both transport planners and researchers due to its difficulty and complexity of understanding, processing, and leveraging the raw data. In this paper, a GTFS data acquisition and processing framework is proposed to offer an efficient and effective benchmark tool for converting and fusing the GTFS data to a ready-to-use format. To validate and test the proposed framework, a multivariate multistep Long Short-Term Memory is developed to predict train delay with minor anomaly in Sydney as a case study. The contribution of this new framework will render great potential for broader applications and deeper research.

关键词： General transit feed specification Delay prediction Train delay Long short-term memory data fusion

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A Comprehensive Survey on Deep Learning for Enhanced Node Position Prediction in Vehicular Ad-Hoc Networks

A Comprehensive Survey on Deep Learning for Enhanced Node Po...

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Ubiquitous computing and Intelligent Information Systems (ICUIS), International Conference on

作者： B.Deena Divya Nayomi V. Jyothsna Department of Computer Science and Engineering School of Computing MOHAN BABU UNIVERSITY (Erstwhile Sree Vidyanikethan Engineering College (Autonomous)) Tirupati Andhra Pradesh India Department of Data Science School of Computing MOHAN BABU UNIVERSITY (Erstwhile Sree Vidyanikethan Engineering College (Autonomous)) Tirupati Andhra Pradesh India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

This research study presents a thorough examination of the application of deep learning techniques in enhancing node position prediction within Vehicular Ad-Hoc Networks (VANETs). As VANETs become increasingly integral to the development of intelligent transportation systems, accurate and efficient node position prediction emerges as a critical component for ensuring optimal network functionality, safety, and traffic management. This paper explores the performance and adaptability of multiple deep learning models in the dynamic and complicated VANET environment, such as Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs). We provide a detailed analysis of the existing literature, highlighting the strengths and limitations of different deep learning approaches in handling the high mobility, variable density, and recurrently changing topology of vehicular networks. Furthermore, the survey discusses the integration of contextual information, such as environmental factors and road conditions, into the prediction models, enhancing their accuracy and reliability. We also explore the implications of advanced methodologies like Transfer Learning and Federated Learning in this domain. In addition to providing insights into future research paths, the paper finishes with an assessment of the difficulties encountered in the practical application of these models, including data privacy and computing limits. The goal of this thorough investigation is to provide a roadmap for future developments in node location prediction in VANETs, making it an invaluable tool for scholars and professionals working in the subject of intelligent transportation systems.

关键词： Deep learning Surveys Accuracy Network topology Transfer learning Vehicular ad hoc networks Topology Safety Security Vehicle dynamics

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Image-Based Soil Health Analysis Using Deep Learning for Precision Agriculture 9

Image-Based Soil Health Analysis Using Deep Learning for Pre...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Saranya, V.S. Mangali, Saikiran Srinija, K. Medabalimi, Galeiah Devi, Meena Venkata Ramana, N.R. Lakkshmanan, Ajanthaa School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603203 India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Mlrit Department: Data science Dundigal India Sagi Ramakrishnam Raju Engineering College Sagi Ramakrishnam Raju Marg Chinna Amiram Andhra Pradesh Bhimavaram India Kcg college of technology Department of electrical and electronics engineering Chennai600097 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India

ISBN: (纸本)9798350377972

Soil health is a critical component of agricultural productivity and sustainability. Traditional methods of assessing soil health, including physical sampling and laboratory testing, are labor-intensive, time-consuming, and costly. This paper proposes a novel image-based approach for soil health analysis using deep learning techniques. By utilizing aerial images captured by drones or satellites equipped with multispectral and thermal sensors, the system non-invasively assesses key soil health indicators such as moisture content, nutrient levels, organic matter, and erosion patterns. The proposed method integrates image processing techniques with machine learning models, particularly Convolutional Neural Networks (CNNs), to extract relevant soil features from the images. These CNNs are trained to predict soil health metrics, and the system's performance is evaluated through accuracy, mean squared error (MSE), and F1-score, showing significant improvements over traditional methods. This scalable solution offers a real-time tool for precision agriculture, aiding farmers and agronomists in making informed, data-driven decisions regarding soil health management. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Empirical Study of Query Performance Across GPU DBMSes

A Comprehensive Empirical Study of Query Performance Across ...

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作者： Suh, Young-Kyoon An, Junyoung Tak, Byungchul Na, Gap-Joo School of Computer Science and Engineering Department of Data Convergence Computing Kyungpook National University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of Artifcial Intelligence Research Laboratory Electronics and Telecommunications Research Institute Korea Republic of

In recent years, GPU database management systems (DBMSes) have rapidly become popular largely due to their remarkable acceleration capability obtained through extreme parallelism in query evaluations. However, there has been relatively little study on the characteristics of these GPU DBMSes for a better understanding of their query performance in various contexts. Also, little has been known about what the potential factors could be that affect the query processing jobs within the GPU DBMSes. To fill this gap, we have conducted a study to identify such factors and to propose a structural causal model, including key factors and their relationships, to explicate the variances of the query execution times on the GPU DBMSes. We have also established a set of hypotheses drawn from the model that explained the performance characteristics. To test the model, we have designed and run comprehensive experiments and conducted in-depth statistical analyses on the obtained empirical data. As a result, our model achieves about 77% amount of variance explained on the query time and indicates that reducing kernel time and data transfer time are the key factors to improve the query time. Also, our results show that the studied systems should resolve several concerns such as bounded processing within GPU memory, lack of rich query evaluation operators, limited scalability, and GPU under-utilization. © 2022 ACM.

关键词： Graphics processing unit

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

Circular Decomposition and Cross-Modal Recombination for Mul...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

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Analysis on n-level quantum systems by means of a coordinate transformation

Analysis on n-level quantum systems by means of a coordinate...

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IEEE Conference on Decision and Control

作者： Huilong Xu Zibo Miao Qing Gao School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The structural decomposition of two-level quantum systems has recently been established, related to various applications in quantum information science. As an extension to previous work on two-level quantum systems, this paper aims to investigate the structural properties of n-level quantum systems. We develop algebraic conditions under which the attainment of quantum back-action evading (BAE) measurements, the generation of decoherence-free (DF) subspaces and quantum non-demolition (QND) variables, as well as the existence of steadystate solutions for the Lindblad equations, can be realized. In particular, a specific coordinate transformation is explicitly constructed to facilitate the achievement of above-mentioned objectives, accompanied by an illustrative numerical example.

关键词： Quantum system Atmospheric measurements Quantum information science Particle measurements

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Hybrid Machine Learning Approach: SVM with ANN Kernel for HSI Medical Image Enhancement 2

Hybrid Machine Learning Approach: SVM with ANN Kernel for HS...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Aravindhan, K. Revathi, K. Priyanka, S. Priyadharshini, S. Keerthana, P.K. Muthulakshmi, K. REVA University School of Computing and Information Technology Karnataka Bengaluru India SNS College of Engineering Department of Information Technology Tamil Nadu Coimbatore India SNS College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Manipal Institute of Technology Department of Information Technology Karnataka Bengaluru India

ISBN: (纸本)9798331534950

In medicine image processing provides a perceptive study on disease diagnosis and identification as well as treatment preparation. Since it can acquire complete spectral information over a wide spectrum of wavelengths, especially hyperspectral imaging (HSI) has shown great potential for the non-invasive characterization of tissue. On the other hand, due to their high-dimensional character as well as the presence of noise HSI medical images present a great challenge for processing and enhancement. Even if traditional methods are effective since they sometimes struggle to manage the complexity of these datasets, more sophisticated approaches are needed and demanded. The proposed a hybrid machine learning model including a support vector machine (SVM) classifier and an artificial neural network (ANN) kernel inside the scope of this work to improve and classify HSI images. The ANN kernel is designed to control the inherently complex and non-linear elements in HSI data. Acting as a mapping tool, the artificial neural network kernel transforms the input space into a feature space easily managed in which the SVM can operate with higher accuracy. Main goals are to lower image noise and increase contrast to improve diagnosis correctness. The method was evaluated with a readily available general public HSI medical image collection. In the experimental results, image quality and classification accuracy show appreciable rise. Comparatively to the traditional SVM methods, the signal-to-noise ratio (SNR) increased by 15%;hence, the general classification accuracy was found to be 94.8%. Moreover, the better images show more clear tissue limits and spectral features, which helps clinical interpretation of the results. The results imply that the SVM with ANN kernel model provides a strong solution for the enhancement and classification of HSI medical images, so opening a new path for the improvement of medical diagnostics. © 2024 IEEE.

关键词： Support vector machines

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Robust Attack Identification Strategy to Prevent Document Image Forgeries by using Enhanced Learning Methodology

Robust Attack Identification Strategy to Prevent Document Im...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Prabu, R.T. Balaji, A. Dhanalakshmi, K.M. Diwakaran, S. Prathima, Ch. Vijayakumar, S. Department of ECE Saveetha School of Engineering SIMATS Chennai India Department of ECE Rajalakshmi Institute of Technology Chennai India Department of ECE S.A Engineering College Chennai India Department of ECE Kalasalingam Academy of Research and Education Srivilliputhur India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati India Department of ECE Paavai Engineering College Namakkal India

ISBN: (纸本)9798350305708

Forgery in images is a long-standing issue that spans back to ancient times. Throughout history, images have been employed as evidence or documentation of events. In the present day, the accessibility of image preprocessing tools and affordable hardware has facilitated the creation of manipulated images with ease, often driven by ulterior motives such as disseminating false information or pursuing personal gains unlawfully. In recent years, the spotlight has turned to Artificial Neural Networks (ANNs), sparking significant interest in the realm of image forgery identification. Nevertheless, prevailing image forgery techniques rooted in ANNs are often specialized, geared towards identifying distinct types of forgeries. Consequently, a pressing need arises for an approach adept at efficiently and accurately uncovering unfamiliar forms of image manipulation. This research fills that gap by presenting a robust deep learning-based system designed specifically to spot fakes while working with double image compression. The model's training hinges upon discerning disparities between an original image and its recompressed iterations. To enhance the model's performance, the Ant Lion Optimization (ALO) technique is employed to fine-tune the features of the input data. Noteworthy for its efficiency, the proposed model stands out as a lightweight solution. Empirical results underscore its prowess, showcasing swifter effectiveness compared to prevailing state-of-the-art technique. Encouragingly, the model achieves an impressive overall validation accuracy of 95.87%, reinforcing its efficacy in identifying image forgeries. © 2023 IEEE.

关键词： Ant Lion Optimization (ALO) technique Artificial Neural Networks Document Image Forgeries Image Forgery Detection

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