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Supervised and unsupervised learning for characterising the industrial material defects

International Journal of Business Intelligence and Data Mining 2022年第2期21卷 233-246页

作者： Radha, P. Selvakumar, N. Raja Sekar, J. Johnsonselva, J.V. Department of Computer Applications Mepco Schlenk Engineering College Sivakasi India Department of Mechanical Engineering Mepco Schlenk Engineering College Sivakasi India Department of Computer Science and Engineering Mepco Schlenk Engineering College Sivakasi India

The ultrasonic-based NDT is used in industries to examine the internal defects without damaging the components since the materials used in the industrial standard components must be 100% perfect. The ultrasonic signals are difficult to interpret, and the domain expert has to concentrate at every sampling point to identify the defect. Hence, the existing ultrasonic-based NDT method is improved by applying IoT, machine learning, and deep learning techniques to process the ultrasonic data. This work integrates NDT and IoT to analyse the properties of materials using deep learning-based supervised model and filter outliers using unsupervised model like density-based clustering method. After analysing the different categories of defects, the notifications are sent to various stakeholders to either repair or replace the defective components through their mobile using advanced communication techniques to avoid expensive experimentation or maintenance. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Nondestructive examination

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An Effective Detection of Ransomware Using Improved Long Short-Term Memory 3

An Effective Detection of Ransomware Using Improved Long Sho...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Vijayalakshmi, V. Fallahhusein, Muhamed Balamurugan, D. Suresh, T. Muthu Krishnan, K. Lakshmi Bangaru Arts and Science College Department of Computer Science with Artificial Intelligence Melmaruvathur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Computer Science and Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Electrical and Electronics Engineering Mamallapuram India

ISBN: (纸本)9798331508456

Ransomware detection focuses on recognizing the unusual system activities which indicate an unauthorized data access and restriction. It continuously monitors the system behavior continuously to detect potential threats before significant damage occurs. By identifying anomalies and patterns, it effectively distinguishes ransomware attacks from normal operation. However, Ransomware attacks cause a serious threat because of far-reaching effects and its zero-day variants are even more hazardous. This research proposes Improved Long Short-Term Memory (LSTM) to detect the ransomware effectively and accurate detection of Ransomware. The proposed improved LSTM learns the sequential patterns in system behavior and detects subtle abnormalities over time. The enhanced model improves detection accuracy by learning past data and adapts to new attack variants including zero-day threats. By identifying anomalies, the proposed method enables timely detection and reduces the impact of ransomware attacks. To select the appropriate features, the Dung Beetle Optimization (DBO) is used to optimize the feature subset for better detection performance. The proposed improved LSTM obtains a better accuracy of 95% when compared to existing method like cost sensitive Pareto Ensemble classifier (CSPE-R). © 2025 IEEE.

关键词： Zero-day attack

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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YOLO-O2E:A Variant YOLO Model for Anomalous Rail Fastening Detection

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computers, Materials & Continua 2024年第7期80卷 1143-1161页

作者： Zhuhong Chu Jianxun Zhang Chengdong Wang Changhui Yang Department of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Institute of Flexible Electronics(Future Technologies) Xiamen UniversityXiamen361000China College of Mechanical Engineering Chongqing University of TechnologyChongqing400054China

Rail fasteners are a crucial component of the railway transportation safety *** fasteners,distinguished by their high length-to-width ratio,frequently encounter elevated failure rates,necessitating manual inspection and *** inspection not only consumes time but also poses the risk of potential *** the advancement of deep learning technology in rail fasteners,challenges such as the complex background of rail fasteners and the similarity in their states are *** have proposed an efficient and high-precision rail fastener detection algorithm,named YOLO-O2E(you only look once-O2E).Firstly,we propose the EFOV(Enhanced Field of View)structure,aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial ***,The OD_MP(ODConv and MP_2)and EMA(EfficientMulti-Scale Attention)modules mentioned in the algorithm can acquire a wider spectrum of contextual information,enhancing the model’s ability to recognize and locate ***,we collected and prepared the GKA dataset,sourced from real train *** testing on the GKA dataset and the publicly available NUE-DET dataset,our method outperforms general-purpose object detection *** the GKA dataset,our model achieved a mAP 0.5 value of 97.6%and a mAP 0.5:0.95 value of 83.9%,demonstrating excellent inference ***-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings,addressing the industry gap in rail fastener detection.

关键词： Rail fastening detection deep learning anomalous rail fastening variant YOLO feature reinforcement

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Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

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computers, Materials & Continua 2023年第2期74卷 2725-2738页

作者： Mohammad Alamgeer Amal Al-Rasheed Ahmad Alhindi Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemMecca24382Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data *** same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall *** this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)*** proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental ***,data pre-processing and correlation matrix(CM)based feature selection processes are carried *** addition,deep belief network(DBN)model is applied for the effective prediction of rainfall ***,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO ***,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN *** demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct *** simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

关键词： Data mining rainfall prediction deep learning correlation matrix hyperparameter tuning metaheuristics

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Implementation of Cross Layer Design with Localization Techniques in Wireless Sensor Networks Using Deep Learning 4

Implementation of Cross Layer Design with Localization Techn...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Sreethar, S. Srinivas, Regulavalasa Satya S Chanakyan, C. Prasath, S. Senthilkumar, G. Natrayan, L. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Tamil Nadu Chennai India MVJ College of Engineering Department of Mechanical Engineering Karnataka Bengaluru India Mohamed Sathak A J college of Engineering Department of Mechanical Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Poonamallee Chennai India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This study addresses the integration of wireless sensor networks, machine learning models, and go-layer architecture to embellish industrial processes in small-scale manufacturing. A strong monitoring infrastructure is produced by the incorporation of various sensors, proximity, temperature, pressure, vibration, UWB, and RFID. Cross-layer arrangement, along with localization methodologies, gives spatial context to sensor information, enhancing safety, predictive maintenance, and asset monitoring. K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), Artificial Neural Networks (ANN), and Decision Trees (DT) act as predictive models, exhibiting outstanding overall performance across accuracy, precision, keep in mind, and F1 Score criteria. Practical deployment in a small-scale firm, displaying CNC machines and forging stations, illustrates the machine's versatility. Results suggest the models' success in forecasting replies relying completely on dynamic sensor data, allowing actual-time selection-making. The system's usefulness supports enhanced safety measures, simpler maintenance processes, and optimal asset management. In addition to offering practical insights to organisations navigating the challenges of Industry 4.0, this research adds to the theoretical framework of move-layer layout and machine learning integration. The suggested system emerges as a complete solution for intelligent operational improvement and performance in modern day industrial settings. © 2024 IEEE.

关键词： Predictive maintenance

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ForeScanGuard: Proactive Monitoring and Detection for Sustainable Forest Conservation 7

ForeScanGuard: Proactive Monitoring and Detection for Sustai...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Krishnan, R. Santhana Balamurugan, S. Rekha, M. Jose, R. Arul Haribabu, K. Muthu, A. Essaki Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India Velammal College of Engineering And Technology Department of Mathematics Madurai India P.S.R.R College of Engineering Department of Computer Science And Engineering Sivakasi India RajaRajeswari College of Engineering Department of Electrical And Electronics Engineering Bangalore India Scad College of Engineering And Technology Department of Mechanical Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

In response to the increasing environmental threats, ForeScanGuard emerges as a vital forest monitoring system. Integrating advanced sensors and a proactive anomaly detection algorithm, it employs a 5-channel fire sensor for early fire detection, ultrasonic and vibration sensors for identifying tree cutting activities, and a BMP120 sensor to confirm tree falls. GPS modules attached to trees allow real-time location tracking, transmitted via Wi-Fi to a cloud server. The ForeScanGuard Algorithm analyzes sensor data for anomalies like fire outbreaks, illegal logging, and tree falls. Its proactive alerts facilitate rapid responses from forest officials, ensuring effective forest management. ForeScanGuard, with multiple sensors and cloud-based analytics, represents a significant advancement in smart forest monitoring, promoting conservation efforts through real-time anomaly detection and informed decision-making. This comprehensive solution enhances ecosystem protection, fostering a sustainable and proactive approach to forest conservation. © 2024 IEEE.

关键词： Deforestation

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Smooth Reference Command Generation and Control for Transition Flight of VTOL Aircraft Using Time-Varying Optimization

Smooth Reference Command Generation and Control for Transiti...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Kim, Jinrae Bullock, John L. Cheng, Sheng Hovakimyan, Naira Department of Mechanical Science and Engineering The Grainger College of Engineering University of Illinois Urbana-Champaign United States Department of Electrical and Computer Engineering The Grainger College of Engineering University of Illinois Urbana-Champaign United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Vertical take-off and landing (VTOL) aircraft pose a challenge in generating reference commands during transition flight. While sparsity between hover and cruise flight modes can be promoted for effective transitions by formulating 1-norm minimization problems, solving these problems offline pointwise in time can lead to non-smooth reference commands, resulting in abrupt transitions. This study addresses this limitation by proposing a time-varying optimization method that explicitly considers time dependence. By leveraging a prediction-correction interior- point time-varying optimization framework, the proposed method solves an ordinary differential equation to update reference commands continuously over time, enabling smooth reference command generation in real time. Numerical simulations with a two-dimensional Lift+Cruise vehicle validate the effectiveness of the proposed method, demonstrating its ability to generate smooth reference commands online. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： VTOL/STOL aircraft

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Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

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IEEE Access 2024年 12卷 82443-82468页

作者： Sana, Laraib Nazir, Muhammad Mohsin Yang, Jing Hussain, Lal Chen, Yen-Lin Ku, Chin Soon Alatiyyah, Mohammed Alateyah, Sulaiman Abdullah Por, Lip Yee Lahore College for Women University Department of Computer Science Punjab Lahore54000 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia The University of Azad Jammu and Kashmir Department of Computer Science and IT Neelum Campus Azad Kashmir13230 Pakistan King Abdullah Campus The University of Azad Jammu and Kashmir Department of Computer Science and IT Azad Kashmir Muzaffarabad13100 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Prince Sattam Bin Abdulaziz University College of Sciences and Humanities-Aflaj Department of Computer Science Al-Kharj16278 Saudi Arabia Qassim University College of Computer Department of Computer Engineering Buraydah52571 Saudi Arabia

The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. This study demonstrates the power of our new methods for detecting and stopping attacks on Internet of Things (IoT) networks. This improved detection offers

关键词： Intrusion detection

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GeoRefineNet: A Multistage Framework for Enhanced Cephalometric Landmark Detection in CBCT Images Using 3D Geometric Information 17th

GeoRefineNet: A Multistage Framework for Enhanced Cephalome...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Viriyasaranon, Thanaporn Ma, Serie Choi, Jang-Hwan Department of Computational Medicine Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Artificial Intelligence Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9789819609000

The precise detection of cephalometric landmarks on two-dimensional (2D) radiographs or three-dimensional (3D) computed tomography (CT) images is a fundamental step in various medical fields, especially in research on orthodontics and maxillofacial surgery. Deep learning-based detectors have demonstrated remarkable accuracy in 2D cephalometric analysis, whereas conventional single-view approaches are limited by their reliance on information from a single perspective. This study proposes GeoRefineNet, a novel multistage framework that leverages information from multiple CT scans acquired at various angles. By incorporating geometric knowledge through a 3D heatmap reconstruction process, GeoRefineNet improves robustness, accuracy, and adaptability to various cephalometric configurations. The proposed framework predicts 3D landmark positions on CT images, effectively addressing challenges associated with high-dimensional input data and limited training examples. GeoRefineNet surpasses the existing state-of-the-art models in the 2D and 3D domains, as demonstrated by its superior performance on numerical and clinical datasets. These findings indicate that GeoRefineNet offers a promising avenue for improving the accuracy and reliability of cephalometric landmark detection fostering further advances in clinical diagnosis and treatment planning. Our code is available at https://***/Thanaporn09/***. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computerized tomography

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