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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： E. Gothai R.R. Rajalaxmi R. Thamilselvan Sridhar K Department of Computer Technology Kongu Engineering College Perundurai Tamilnadu India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India MSc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Banks play a pivotal role in generating significant profits through loan operations. However, the challenge lies in accurately identifying genuine loan applicants who are likely to repay their loans. Manual processes are prone to misconceptions, prompting the development of a loan prediction system using deep learning. This system aims to automate the selection of eligible candidates, benefiting both bank staff and applicants by drastically reducing the loan approval timeframe. Two key machine learning algorithms such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed in this work for applicant selection. SVM, a popular supervised learning algorithm, creates a decision boundary or hyperplane to segregate classes in n-dimensional space. Conversely, ANN is inspired by biological neural networks, which mimic the organization of neurons in the input layer, hidden and output layers of the human brain. The integration of ANN and SVM in an ensemble model enhances accuracy compared to traditional methods. This collaborative approach ensures precise predictions of loan repayment likelihood based on applicant data and past loan eligibility records. The model's performance is measured using accuracy. The outcomes of the research were promising and the loan prediction system defines the minimum and maximum loan amounts that can be sanctioned to an applicant. By leveraging the strengths of both ANN and SVM, the ensemble model serves as a robust tool for accurately identifying worthy loan applicants, streamlining the loan approval process, and minimizing the risk of default. The accuracy of the algorithm is improved by this method to 91%.

关键词： Support vector machines Renewable energy sources Accuracy Machine learning algorithms Supervised learning Artificial neural networks Nearest neighbor methods Brain modeling Vectors Biological neural networks

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A Survey on Anomaly Detection with Few-Shot Learning 8th

A Survey on Anomaly Detection with Few-Shot Learning

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Junyang Wang, Changbo Hong, Yifan Mi, Rui Zhang, Liang-Jie Wu, Yirui Wang, Huan Zhou, Yue College of Computer Science and Software Engineering Shenzhen University Shenzhen China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Informatics Huazhong Agricultural University Wuhan China Hohai University Nanjing China Department of Obstetrics and Gynecology Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031779534

The primary objective of anomaly detection is to identify abnormal or unusual patterns within a dataset, where the number of normal samples typically exceeds that of abnormal samples. Due to the scarcity of labeled abnormal samples, traditional methods face challenges when dealing with anomaly detection. To overcome these limitations, few-shot learning has emerged as a promising solution. By leveraging a limited number of labeled anomaly samples, few-shot learning enables the construction of models that enhance anomaly detection performance and generalization. This paper provides a comprehensive investigation of anomaly detection, covering its definition, fundamental principles, methods, and challenges. Furthermore, it introduces few-shot learning as a solution and explores its principles, applications, and technical categorization, including meta-learning, transfer learning, generative models, prototypical learning, and siamese networks. The paper explores the utilization of few-shot learning in anomaly detection across diverse data types This paper delves into significance across different domains. Additionally, it addresses the challenges faced by few-shot learning in the field of anomaly detection and proposes future directions for development. This comprehensive analysis aims to provide profound insights and guidance for prospective research and application in anomaly detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Graph Variational Multi-view Clustering

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Yan, Wenbiao Zhu, Jihua Chen, Jinqian Cheng, Haozhe Zheng, Qinghai Xi'an Jiaotong University School of Software Engineering Xi'an710049 China Fuzhou University College of Computer and Data Science Fuzhou350108 China

Multi-view clustering (MVC) aims to extract consensus information from multi-source data and has developed rapidly. While generative model-based methods perform well by leveraging predefined priors, they often overlook inter-instance relationships, which are essential for high-quality clustering. To address this issue, we propose Graph Variational Multi-view Clustering (GVMVC), which integrates graph information into the generative process. Specifically, we treat both the original multi-view features and graph information from each view as observed data, guiding the learning of latent representations. The key principles of this approach are: 1) enhancing discriminative feature learning through graph integration, and 2) ensuring consistent multi-view learning via graph-based constraints. Extensive experiments show that GVMVC outperforms state-of-the-art methods across various datasets and metrics. Code is available at https://***/WenB777/***. © 1991-2012 IEEE.

关键词： Variational techniques

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A Survey on Multi-Behavior Sequential Recommendation

arXiv

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arXiv 2023年

作者： Chen, Xiaoqing Li, Zhitao Pan, Weike Ming, Zhong The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

People usually have the explicit or implicit desire to get the information they need and are most interested in from massive information, which has led to the creation of personalized recommender systems. Recommender systems are set up to address the issue of information overload in traditional information retrieval systems such as search engines, and have been a significant area of research focused on recommending information that is of most interest to users. There is a sequential nature to the behavior of a person interacting with a system, such as examining one item of clothing before examining others. The problem of taking this sequential nature into account in delivering recommendation is known as sequential recommendation (SR). The traditional sequential recommendation problem merely takes into account a single type of behavior of the users, while in real scenarios users tend to engage in multiple types of behaviors, such as examining and adding clothes to cart before purchasing them, leading to the proposal of multi-behavior sequential recommendation (MBSR). MBSR considers both sequentiality and heterogeneity of user behaviors, which can achieve state-of-the-art recommendation through suitable modeling. Hence, MBSR is a relatively new and worthy direction for in-depth research, for which some related works have been proposed. This survey aims to shed light on the MBSR problem. Firstly, we introduce MBSR in detail, including its problem definition, application scenarios and challenges faced. Secondly, we detail the classification of MBSR, including neighborhood-based methods, matrix factorization-based methods and deep learning-based methods, where we further classify the deep learning-based methods into different learning architectures based on RNN, GNN, Transformer, and generic architectures as well as architectures that integrate hybrid techniques. In each method, we present related works based on the data perspective and the modeling perspective, as well

关键词： Search engines

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Forecasting Price Prediction for Vegetables and Fruits Using Recurrent Neural Network

Forecasting Price Prediction for Vegetables and Fruits Using...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： E. Gothai R.R. Rajalaxmi R. Thamilselvan Harshath S M Department of Computer Technology Kongu Engineering College Perundurai Tamilnadu India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India MSc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Unpredictable fruit and vegetable prices create significant challenges for farmer livelihoods. This research proposes an innovative approach using recurrent neural networks (RNNs) to predict both minimum and maximum prices. By analyzing historical market data, these models can forecast potential price ranges, empowering farmers to make informed decisions about planting, harvesting, and sales strategies. This enhanced foresight helps farmers navigate market volatility and improve income stability.

关键词： Performance evaluation Recurrent neural networks Navigation Livelihood Predictive models Stability analysis Data models Forecasting Long short term memory Consumer electronics

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Based on feature reconstruction and feature intersection, the Deep CTR Prediction Model

Based on feature reconstruction and feature intersection, th...

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Big Data, Information and computer Network (BDICN), International Conference on

作者： Ning Bian Li Wang College of Computer and Software Engineering University of Science and Technology Liaoning Anshan China

Collecting a lot of feature data can enhance the prediction impact for the recommender system’s CTR prediction task. The second-order feature cross factorization machine model, which is the most popular, has the ability to automatically learn implicit features based on existing features. The factorization machine model, however, has the following issues: (1) The encoding is fixed, and the same feature represents the same in various contexts. (2) The encoding type is single, and the feature is represented by a single weight vector, which cannot completely express the information of the feature. To harvest possible feature information, recommender systems employ neural networks. A novel method of feature intersection is created by the Compressed Interaction Network (CIN) component suggested in xDeepFM. In order to address the issue of encoding fixation in the factorization machine, the original feature encoding is reconstructed in this paper using xDeepFM in conjunction with the feature refinement network (FRNet). Additionally, different feature representations are expressed in various contexts for the same feature. To address the issue of a single encoding type in factorization machines, the field-aware interactive machine (FIM) component provides feature information at the domain and feature levels, respectively. The model is known as the Field-aware Feature Refinement Depth Factorization Machine (FRDFFM). On the Criteo dataset and the Avazu dataset, respectively, the suggested model was evaluated, and it produced promising results.

关键词： Computational modeling Neural networks Predictive models Feature extraction Encoding Data models Data mining

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Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids

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计算机科学 2022年第6期49卷 44-54页

作者： Ran WANG Jiang-tian NIE Yang ZHANG Kun ZHU College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 211106China School of Computer Science and Engineering Nanyang

As a typical industrial Internet of things(IIOT)service,demand response(DR)is becoming a promising enabler for intelligent energy management in 6 G-enabled smart grid systems,to achieve quick response for supply-demand ***-ever,existing literatures try to adjust customers’load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption *** this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the *** approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical *** on the extracted information,the residential appliances can be scheduled effectively and ***,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning *** simulation experiments are conducted with real-world *** results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio(PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR *** the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.

关键词： Demand response(DR) Customer clustering Deep learning 6G-enabled industrial Internet of things(IIOT) Smart srid(SG)

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A Real-Time Hand Sign Language Recognition System for Threatening Situations Using Deep Learning 12

A Real-Time Hand Sign Language Recognition System for Threat...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Shwany, Zardasht Abdulaziz Abdulkarim Ismail, Shayda Khudhur Khoshnaw, Karwan Hoshyar Khalid Mustafa, Twana Saeed Karabatak, Murat Othman, Nashwan Adnan Erbil Polytechnic University Physiotherapy Department Erbil Iraq Ministry of Higher Education and Scientific Research Network Department Erbil Iraq Erbil Polytechnic University Department of Information and Communication Technology Engineering Erbil Iraq Firat University Department of Software Elazig Turkey College of Science Knowledge University Department of Computer Science Erbil Iraq Knowledge University Department of Computer Engineering Erbil Iraq Al-Kitab University Department of Computer Engineering Altun Kupri Iraq

ISBN: (纸本)9798350330366

Hand sign language has been done as physical movements in natural languages that humans have used since ancient times, along with letters, words, and spoken language. This paper presents a real-time method to identify threatening signs by criminals during interrogation, which increases their criminal rank and helps to reach conclusions. The proposed method is to install a camera of appropriate quality in front of the offender, record hand gestures in a specific area of the hand, apply some image processing techniques, such as contrast enhancement techniques, to the image to facilitate recognition as input, and then classify the images using a convolutional neural network (CNN) for a specific problem with high specifications for that topic by and using AlexNet. The accuracy of the presented method is more than 94.2% in real-time testing. © 2024 IEEE.

关键词： Convolutional neural networks

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Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

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computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel Alsolai Mohammed Rizwanullah Mashael Maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

关键词： Soil classification earth sciences machine learning parameter optimization metaheuristics

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Life Expectancy Prediction Based on Particle Swarm Optimization Support Vector Machine Regression

Life Expectancy Prediction Based on Particle Swarm Optimizat...

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Electrical engineering, Big Data and Algorithms (EEBDA), IEEE International Conference on

作者： Zhaodong Guo Yuanxi Que Xiang Li College of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China College of Computer Science Nankai University Tianjin China

This study delves into the application of Particle Swarm Optimization (PSO) algorithms in the parameter optimization of Support Vector Regression (SVR) models to enhance the accuracy of predicted average life expectancy. An adaptive PSO algorithm augmented with random perturbations is introduced. Through a fitness-function-guided dynamic adjustment mechanism, this method circumvents the premature convergence issue typical of conventional PSO, locating the optimal penalty parameter C and kernel parameter for the SVR model. These parameters are then incorporated into the SVR model for training and regression prediction, achieving optimal data fitting and generalization effects. Comparative analysis reveals that the PSO-enhanced SVR model outperforms the traditional SVR across various performance metrics. Notably, there is a marked improvement in the coefficient of determination, a significant re-duction in root mean square error, and mean absolute percentage error, affirming the advancements of the improved PSO-SVR model in prediction accuracy and reliability.

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