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2023 International Conference on Innovations in engineering and technology, ICIET 2023

作者： Chaitra, B. Meena, K. School of Technology GITAM Deemed to Be University Computer Science and Engineering GITAM Bengaluru India

ISBN: (纸本)9798350309980

Agriculture is the Backbone of India. India has major portion of cultivational Land. Many Crops have lot of demand in abroad. India is one of the major exporters of Food products other than software. But due to erratic climatic conditions, lack of human Resource and improper decision making in choosing crops, farmers are not able to make profits. Also due to urbanization, Agriculture Profession is losing its importance slowly. This problem has invited many Researchers to identify the issues and apply several Machine Learning approaches like Autoregressive Integrated Moving Average (ARIMA), Decision trees, Long Short-Term Memory (LSTM), K Nearest Neighbor (KNN) etc. for accurate Yield and Crop Price Prediction. This helps the Farmers to select suitable crops and obtain maximum yield and Profit. © 2023 IEEE.

关键词： Crops

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Exploring Variants of Extreme Learning Machines for Prediction of Mutual Fund NAV

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Journal of engineering science and technology Review 2024年第2期17卷 97-118页

作者： Das, Smruti Rekha Mishra, Debahuti Rout, Minakhi Department of Computer Science and Engineering GITAM Deemed to be University Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Siksha ‘O’Anusandhan Deemed to be University Odisha Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Odisha Bhuabaneswar India

Investing money through mutual fund benefits the small investors to access equities of big companies with a small amount of capital. It experiences the fluctuation of price along with the performance of stock, which is a major part in making the fund. Here, in this paper variant of Extreme Learning Machines (ELM) are applied to forecast the end-of-year net asset value (NAV) of mutual fund. Various types of ELM such as basic ELM, evolutionary ELM, online sequential ELM and error minimized ELM are explored and applied to historical data of four mutual funds such as SBI mutual fund, UTI mutual fund, Tata Mutual Fund and Kotak Mahindra Mutual Fund for the prediction of NAV. Along with the different ELM based prediction model, this paper has explored on different types of activation functions and the number of nodes in the hidden layer used in variants of ELM. Examining the simulation result of all the models, along with different activation functions and different number of nodes, it is observed that evolutionary ELM outperforms over the other variants of ELM used in this study. © 2024 school of science, DUTH. All Rights Reserved.

关键词： Forecasting

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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A Transfer Learning Framework for Deep Multi-Agent Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2346-2348页

作者： Yi Liu Xiang Wu Yuming Bo Jiacun Wang Lifeng Ma the School of Automation Nanjing University of Science and Technology the Department of Computer Science and Software Engineering Monmouth University

Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1... 详细信息

关键词： Deep agent Framework

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CyberGuard: Detecting Adversarial DDoS Attacks in SDN using WGAN-CNN-GRU 4

CyberGuard: Detecting Adversarial DDoS Attacks in SDN using ...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Goud, Konda Srikar Rao Giduturi, Srinivasa Gitam University Gitam School of Technology Department of Computer Science and Engineering AP Visakhapatnam India Bvrit Hyderabad College of Engineering for Women Telangana Hyderabad India

ISBN: (纸本)9798350343670

Detecting Distributed Denial of Service (DDoS) attacks in Software-Defined Networks (SDN) has become a critical challenge in ensuring network security and availability. Adversarial attacks, which aim to evade traditional detection methods, have emerged as a formidable threat. In this study, we present a novel approach for detecting adversarial DDoS attacks within an SDN environment by leveraging the combined power of Wasserstein Generative Adversarial Networks (WGAN) and Convolutional Neural Networks with Gated Recurrent Units (CNN-GRU). Our proposed methodology capitalizes on the strengths of both WGAN and CNN-GRU to effectively identify subtle adversarial attack patterns. Firstly, we utilize WGAN to synthesize adversarial DDoS attack traffic, exploiting its capability to generate data that mimics legitimate network traffic distribution. Subsequently, we employ a hybrid model of CNN-GRU to process the generated traffic. CNN extracts spatial features from traffic data, while GRU captures temporal dependencies in the network flows. The experimental results demonstrate the effectiveness of our approach in differentiating adversarial DDoS traffic from legitimate traffic. Our model achieves a high detection accuracy by training on synthesized adversarial traffic and real-world network data, even against sophisticated adversarial attack strategies. Furthermore, our approach showcases robustness against unseen adversarial patterns, highlighting its potential for real-world deployment. © 2024 IEEE.

关键词： Adversarial DDoS attacks BOTNET CNN DDoS Deep Learning GRU WGAN

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DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 1-13页

作者： S, Balasubramani D, John Aravindhar Renjith, P.N. Ramesh, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment. © 2023

关键词： K-means clustering

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Improving agility in projects using machine learning algorithm

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Multimedia Tools and Applications 2024年第38期83卷 85987-86005页

作者： Varun, Janani Karthika, R.A. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

All the software products developed will need testing to ensure the quality and accuracy of the product. It makes the life of testers much easier when they can optimize on the effort spent and predict defects for the upcoming modules in the Agile era. The functionality being discussed in this paper is to predict the defects using Random Forest Algorithm. Predictive analytics draws on information from the past to create forecasts about the outcomes of future events. Product team always have the difficulty in delivering the product as per schedule. As we are in the agile era, the requirement keeps changing and team is unsure on upcoming releases. Prediction helps the team to focus on the complex and error prone modules in upcoming releases. The Predictive analytics model designed, can predict defects with an accuracy rate of 88% with the help of historical data. By predicting, testers can focus on the module where there are a greater number of defects predicted by the model and left shift the delivery. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Predictive analytics

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Performance Evaluation of Cement Mortar Prepared with Waste Foundry Sand as an Alternative for Fine Aggregate: A Sustainable Approach

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Iranian Journal of science and technology - Transactions of Civil engineering 2024年第2期49卷 1937-1951页

作者： Tangadagi, Ranjitha B Ravichandran, P.T. Department of Civil Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Civil Engineering GITAM School of Technology GITAM University Karnataka Bangalore India

Concrete can be made cost-effective and environmental friendly using various sources of industrial byproducts. The extensive use of fine aggregates in mortar and concrete has adverse environmental effects as it consumes large quantities of natural resources and causes its depletion. To address these challenges and to make concrete cost effective and environmental friendly, use of waste industrial byproducts have the potential to replace the fine aggregates in concrete and mortar. In this research, waste foundry (WFS) sand is used as an alternative for fine aggregate in mortar. Similarly, for effective utilization of WFS in mortar, this study also attempts use of treated WFS in two forms. WFS treated with 5% concentrated H₂SO₄ is considered chemically treated waste foundry sand (CTFS) and WFS treated with fresh water is considered as water treated foundry sand (WTFS) respectively. After treatment of WFS, CTFS and WTFS is employed in varying proportions like 0%, 10%, 20%, 30%, 40%, and 50% by weight of fine aggregate in cement mortar. This study mainly exhibits feasibility of WFS, WTFS and CTFS on strength, durability, and microstructural properties of cement mortar as per Indian standards. The findings of the tests demonstrated that cement mortar made with WFS performed better when fine aggregate was substituted with 30% of CTFS and WTFS. Similarly, optimum use of WTFS and CTFS is found to be 30–40% subjected to various strength and durability properties. The use of WFS in cement mortar will help the metal/foundry industries from the challenges related to handling and disposal of WFS and help in producing green cement mortar for construction as an innovative construction material. © The Author(s), under exclusive licence to Shiraz University 2024.

关键词： Cement Durability and microstructural analysis Mortar Strength Waste foundry sand

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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