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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kangana, W.M. Allagi, Shridhar Laddi, Mahantesh S G Balekundri Institute of Technology Department of Computer Science and Engineering Belagavi India Kle Institute of Technology Department of Computer Science and Engineering Hubli India Bharatesh Institute of Technology Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798350388916

Stock market Prediction has been a topic of attention for numerous researchers since its beginning. Often traditional statistical methods get conflict to grab the complex, non-linear patterns in stock market data. Due to various factors predicting stock prices is challenging, including a company's financial performance, present assets, politics, general economic conditions, politics, unanticipated occurrence, global situation, etc. The fluctuating behavior of the inventory place market makes it difficult to expect stock prices, as they constantly change. This paper comprehensively reviews machine and deep learning techniques used to predict the stock market and highlights the proper techniques for predicting inventory cost. This paper also covers the performance metrics for measuring the performance of prediction models, research gaps, and issues found in previous work. © 2024 IEEE.

关键词： Marketplaces

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Enhancing SDN Traffic Analysis Through Machine Learning on Preprocessed Controller Flow Statistics and Packet Analysis Data

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SN computer science 2025年第5期6卷 1-22页

作者： Vadivu, D. Sendil Santhosh, Ashwin Rajagopalan, Narendran Pavagada, Kaustub Valsaraj, Aswin Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

As an emerging technology, Software Defined Networks (SDN) has led to several vulnerabilities and risks, making it adoption challenging. Cyber threats in SDN include a wide range of malicious activities intended to exploit the architecture's centralized control and programming ability vulnerabilities. Adequate safety precautions for maintaining the stability of networks are required. Establishing an Intrusion Detection System (IDS) is vital in solving such issues, enabling real-time monitoring and classification of suspicious activity. Very few SDN-based datasets are available for attack detection. Hence, this study aims to generate two SDN-specific datasets, CSOR_SDN (Controller Statistics with Oftcl_Rest application for SDN) and WSCF_SDN (Wireshark Statistics with CicFlowmeter for SDN), from the network environment emulated using the Mininet and RYU controller by utilizing controller statistics and packet analysis data. The dataset generated with normal, DDoS (Distributed Denial of Service), DoS (Denial of Service), and probe attack flows is evaluated with multiple feature selection methods and Machine Learning (ML) classifiers to assess the complexity of the dataset. Correlation Coefficient-based feature selection (CFS) of filter methods is selected out of others with a Decision Tree (DT) classifier, capable of producing 100% results for the CSOR_SDN dataset with the least execution time in attack detection. For the proposed IDS, tenfold stratified cross-validation is performed with confidence interval assessment for the test dataset created on an enterprise network use case to highlight the scalability support of the model. A comparative analysis of both the dataset and its resource utilization is conducted to evaluate its feasibility. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber threat Dataset OpenFlow Ryu SDN Security Traffic analysis

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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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Enhancement of patient's health prediction system in a graphical representation using digital twin technology

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Multimedia Tools and Applications 2024年第17期84卷 1-25页

作者： Sobhana, M. Ch, Smitha Chowdary Koneru, Sowmya Mohan, G. Krishna Kumar, K. Kranthi Department of Computer Science and Engineering V.R. Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Computer Science and Engineering Dhanekula Institute of Engineering & ampTechnology Andhra Pradesh Vijayawada521139 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of IT Vasireddy Venkatadri Institute of Technology Andhra Pradesh Guntur522508 India

The patient health prediction system is the most critical study in medical research. Several prediction models exist to predict the patient's health condition. However, a relevant result was not attained because of poor quality. The IoT-sensed data contains more noise content, which maximizes the complexity of the health prediction. These demerits resulted in low prediction and performance scores. So the proposed work aims to develop a novel Coati-based Recurrent Digital Twin Framework (CbRDTF) to predict the patients' health conditions. The novelty of this research lies in the combined function of the coati optimization and recurrent network with the digital twin for health prediction. Initially, the IoT-sensed data was imported, the data were preprocessed, and meaningful features were selected. Then predict the health condition of the patients and classify the conditions. Here the incorporated coati function at the classification layer extracted the relevant features from the sensed medical data for the robust prediction and also modified the parameters of the recurrent digital twin to improve the prediction and classification accuracy. Finally, the performance was measured;the presented model attained a high exactness score of 99.81% in the prediction accuracy, recall, f-value, and precision;it also, validated the computation time of 611.81 s. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Enhancing Malware Detection: A Comparative Analysis of Ensemble Learning Approaches 1

Enhancing Malware Detection: A Comparative Analysis of Ensem...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Ajvad Haneef, K. Shashank, N.S. Madhu Kumar, S.D. National Institute of Technology Department of Computer Science & Engineering Calicut India National Institute of Technology Department of Computer Science & Engineering Calicut India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392111

Malware represents the greatest threat to cybersecurity and serves as the weapon of choice for carrying out nefarious activities in the digital realm. The proliferation of malware poses significant risks to individuals and organizations. Therefore, malware detection is essential for companies to strengthen their cyberspace, protect sensitive data and minimize financial losses from cyberattacks. Ensemble learning methods have emerged as powerful tools in this domain, leveraging the strengths of multiple models to enhance detection accuracy and reliability. We present a comparative analysis of ensemble learning approaches for malware detection using the EMBER dataset. We evaluate six ensemble models, including Random Forest, AdaBoost, Extra Trees, XGBoost, LightGBM, and Bagging, based on key performance metrics such as accuracy, precision, recall, and F1-score. Our findings reveal that all ensemble models demonstrate strong performance in malware detection, with Bagging ensemble achieving the highest accuracy and overall performance. The results highlight the effectiveness of ensemble learning techniques in enhancing malware detection capabilities. © 2024 IEEE.

关键词： Contrastive Learning

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Classifying the state of knowledge-based question answering: patterns, progress, and prospects

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International Journal of computers and Applications 2025年第1期47卷 93-105页

作者： Sharma, Sumit Jain, Sarika Tiwari, Mukesh Kumar lal, Sohan Department of Computer Science and Engineering Quantum University Roorkee Uttarakhand Roorkee India Department of Computer Applications National Institute of Technology Haryana Kurukshetra India

In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generation of QASs focuses on the types of inquiries that users typically enquire, the features of the data sources that are used, and the formats in which the right responses are produced. Many QASs have been developed since the 1960s when research on the subject first started. There are several cutting-edge frameworks for answering questions that can be used to categorize the QA system according to a number of criteria. The necessity for an extensive survey on QASs comes naturally in order to determine the future focus of research in this field. In this study, QASs are analyzed by the number of criteria used to categorize question-answering (QA) and provide the dimension using a variety of standards to cover all the QAS. We describe the current state of research in each QAS area and make recommendations for its future direction. Based on the dimension criteria outlined in the research methodology, research studies are chosen for additional review and comparison. Constructed some criteria to classify the QASs, we provide the classification and go over the benefits and drawbacks of each class's QAS, as well as the problems with the corresponding study. Results are detailed summarized and compared. As a result of this study, a general architecture of frameworks for QA over knowledge base (KB) is drawn in addition to the comparative analysis of systems, serving as a reference for academics working on related topics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Question answering

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A Novel Deep Reinforcement Learning Approach for Stability-Based Parallel Disassembly Sequence Planning Problem

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SN computer science 2025年第3期6卷 1-19页

作者： Kumar, M. R. Mahesh Ravi, Chandrasekar Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

Parallel Disassembly Sequence Planning (DSP) deals with obtaining the order of disassembling the product with multiple parts disassembling simultaneously. Existing studies derive the product’s disassembly order based on the requirement for liaison data. However, the absence of stability data does not ensure that all the product parts are disassembled without damage. Since the order of retrieval of the product can also be obtained from stability data, this reduces the requirement of an initial precedence graph based on liaison data. The complexity of the problem increases if we adopt different representations for precedence and stability conditions of the product for constraint checking throughout the disassembly process. This paper constructs a stability-based precedence graph based on only the product’s stability data. The developed graph represents the precedence of the stable parts for disassembly, and the same is utilized to formulate the problem as a model-free deep Reinforcement Learning (RL) problem. A novel Exploration–Exploitation (EE) strategy-based Deep Q-Learning (DQL) algorithm is proposed to parallel DSP minimizing direction changes. Our effective reward mechanism accelerates the learning process for all the conditions and objectives of the problem considered. Based on past performance, the proposed algorithm dynamically decides the exploration and exploitation strategy to avoid local optima. We applied the proposed algorithm on five different-scale products and compared the results with uniform-sampling-based model-free deep RL algorithms. It showed significant improvements, particularly in cumulative rewards and disassembling all the parts for large-scale products with early convergence. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep Q-learning Deep Q-Networks Disassembly sequence planning Parallel disassembly Precedence graph Stability graph

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Task scheduling and data replication in cloud with improved correlation strategy

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International Journal of computers and Applications 2023年第11期45卷 697-708页

作者： Rambabu, D. Govardhan, A. Department of Computer Science and Engineering Sreenidhi Institute of Science and Technology Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad Hyderabad India

Cloud providers frequently utilize two tightly coupled resource management strategies like task scheduling & data replication to boost the performance of the system generally, guaranteeing service level agreement (SLA) compliance, as well as protecting their own financial gain. An Improved Correlation strategy-based Task Scheduling and Data Replication in Cloud (ICTSDC) is what this work aims to give. The suggested system's primary phases are as follows: Management of replication and task scheduling. Initial job scheduling will be optimization-based and take into account goals such bottleneck value, migration cost, VM load, enhanced correlation, and replication, respectively. For this, a brand-new extended DMO algorithm called Self-adaptive Dwarf Mongoose Optimization (SADMO) is presented. In the replication management stage, the potential copies must first be identified based on the prior objective. The suggested SADMO model implements the optimization technique for replica placement throughout the replication management process. The outcomes of the ICTSDC technique are evaluated to other methods using a variety of metrics, like bottleneck value, migration cost, Virtual Machine (VM) load, improved correlation, as well as replication efficiency. A lower mean value of 0.324 is gained with the ICTSDC scheme for fitness. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Virtual machine

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Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR)in WSNs

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computer Systems science & engineering 2023年第1期44卷 99-112页

作者： D.Loganathan M.Balasubramani R.Sabitha S.Karthik Department of Electronics and Communication Engineering Info Institute of EngineeringCoimbatore641107India Department of Computer Science and Engineering SNS College of TechnologyCoimbatore641035India

Sensors are considered as important elements of electronic *** many applications and service,Wireless Sensor Networks(WSNs)are involved in significant data sharing that are delivered to the sink node in energy efficient man-ner using multi-hop ***,the major challenge in WSN is the nodes are having limited battery resources,it is important to monitor the consumption rate of energy is very much ***,reducing energy con-sumption can increase the network lifetime in effective *** that,clustering methods are widely used for optimizing the rate of energy consumption among the sensor *** that concern,this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR),which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member *** providing equal rate of energy consumption among nodes,the dimensions of framed clusters are ***,the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base *** evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head ***,the model also provides higher throughput and minimal delay in delivering data packets.

关键词： Wireless sensor networks energy consumption load balanced clustering finest routing

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Hobby Based Learning for Automata Theory and Computability 2

Hobby Based Learning for Automata Theory and Computability

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Pallavi, N. Darshna, A. Naik Manjula, L. Keerthi, P. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Impact Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350306415

In the traditional form of instruction, the teacher and pupil converse face to face. The discussion is started by the instructor, who generally discusses the material from the required textbooks. Many students might not find the concepts intriguing, which would reduce their ability to concentrate and pay attention in class. This makes learning challenging. A novel teaching strategy that allows pupils to learn a subject depending on their interests has been implemented in the classroom. With this approach, students apply their own hobbies to the subject they wish to learn. It focuses on interest-driven learn. This method focuses on interest driven learning where students apply their Personal interest to the subject they want to learn. A prime example of interest-driven learning is hobby-based learning. Modelling enthusiasm and making the topics relevant to their hobby would make the classroom student centric learning and interest driven learning where student would learn the topic self-directed, enjoy learning process, gain deeper insight on the topic and also develop their hobbies. Hence hobby-based learning was introduced to the classroom for the subject Automata Theory and Computability. A comparative study was made on analysis and data obtained from classrooms with hobby based learning and conventional learning handled by the same instructor on the subject Automata Theory and Computability where hobby-based learning gave significantly best feedback than conventional learning. © 2024 IEEE.

关键词： Hobby based learning interest driven learning student centric learning

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