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

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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Enhancing Reliability in Mobile Ad Hoc Networks (MANETs) Through the K-AOMDV Routing Protocol to Mitigate Black Hole Attacks

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SN computer science 2024年第2期5卷 263页

作者： Kaushik, Sheetal Tripathi, Khushboo Gupta, Rashmi Mahajan, Prerna Amity School of Engineering and Technology Amity University Haryana Gurugram 122413 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Haryana Gurugram 122413 India IITM Janakpuri New Delhi India

A Mobile Ad Hoc Network (MANET) is a self-organize assemblage of mobile nodes without the use of pre-existing infrastructure. They face challenges of security, routing efficiency, and network stability due to dynamic topology and limited resources. The Black Hole Attack on MANETs is a critical concern, affecting communication reliability. This malicious activity involves a node falsely advertising the shortest route to the destination, leading data packets to be routed into a “black hole” where they are dropped and causing severe disruptions. This research focuses on the Ad Hoc On-Demand Multi-Path Distance Vector Routing (AOMDV) protocol, which is preferred for its improved efficiency compared to a single-path routing protocol in MANETs. We observe, investigate, and estimate wireless ad-hoc network route optimization by reducing packet hops between nodes. We suggested a novel strategy in this paper, the K-AOMDV protocol that uses K-means clustering to prevent routing misbehavior. The efficiency of the proposed K-AOMDV (KNN-Ad-hoc on demand multi-path distance vector) routing protocol is calculated using supervised machine learning approach to predict optimal routes with delay and attacks. By employing multiple paths and dynamic route discovery, it ensures robust data delivery even in the presence of malicious nodes. This protocol’s adaptability and multi-path nature effectively minimize the effects of Black Hole Attacks, bolstering the MANETs security. Proposed algorithm has a high accuracy rate of 0.99%, 80% true positives, and 80% recall. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Black hole attack K-AOMDV K-nearest neighbors Machine learning MANET Support vector machine

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Research on Enhanced Multi-head Self-Attention Social Recommendation Algorithm Based on Graph Neural Network

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第7期51卷 754-764页

作者： Teng, Yue Yang, Kai Yue Teng is a postgraduate student at the School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In today’s society, communication among people has become more frequent and extensive due to the rapid development of science, technology, and the Internet. This vast communication occurs in real life and virtual online worlds. However, with the overwhelming amount of information available, people often struggle with finding and accessing the information they need, leading to frustration. To address this challenge, we conducted a study to extract valuable information and insights from users’ social relationships. While existing models can help us understand and analyze users’ social relationships to some extent, there are still significant challenges in extracting valuable insights. Therefore, we conducted in-depth research to improve the GraphRec model and created a new social recommendation model called the CWYGNN model. The CWYGNN model is unique because it can identify deeper connections in users’ social interactions through in-depth learning of the social sequences between users. This is important for understanding users’ behavioral patterns, predicting their needs, and providing personalized services. To validate the effectiveness of the CWYGNN model, we conducted experiments and comparisons on several public datasets. The experimental results show that the CWYGNN model outperforms similar approaches in processing user social relationships and extracting valuable insights. This result provides a new way of thinking and methodology to help us better understand and serve users and ultimately enhance their overall experience. © (2024) International Association of Engineers. All rights reserved.

关键词： Recommender systems

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Crafting a Machine Learning Model for Discerning Synthetic and Authentic Images 2

Crafting a Machine Learning Model for Discerning Synthetic a...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Shah, Misha Jaiswal, Gautam Sharma, Dolly Amity School of Engineering and Technology Amity University Noida India Department of Computer Science and Engineering Amity University Noida India

ISBN: (纸本)9798331541217

This paper shows the development of a Convolutional Neural Network (CNN) model with an attention mechanism and data augmentation for discerning synthetic and authentic images. Given the increase in AI-generated content there is a need for robust methods to identify an image's authenticity. The model employs ensemble learning by using ResNet50 and VGG16 architectures which are combined with a custom CNN. The project has detailed explanations of CNNs, data augmentation, TensorFlow, CUDA, ensemble models and the mathematical concepts underlying these techniques. With a dataset of 2.7 million images, the model aims for high accuracy in classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Interpretation of Sign Language Using Machine Learning 2

Interpretation of Sign Language Using Machine Learning

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Srivastava, Palak Hazela, Bramah Singh, Shikha Asthana, Pallavi Pandey, Deependra Singh, Kamlesh Kumar Singh, Vineet Amity University Amity School of Engineering & Technology Department of Computer Science Engineering Uttar Pradesh India Amity University Amity School of Engineering & Technology Department of Electronics & Communication Engineering Uttar Pradesh India

ISBN: (纸本)9798350364699

Sign language detection using machine learning has emerged as a crucial area of research aimed at bridging communication barriers between individuals with hearing impairments and the broader community. This paper explores the application of machine learning techniques for the automatic recognition and interpretation of sign language gestures. A systematic review of existing methodologies, including traditional machine learning approaches and recent advancements in deep learning architectures, reveals key challenges such as variability in signing styles, regional dialects, and real-time processing requirements. To address these challenges, we propose a novel approach that leverages convolutional neural networks (CNNs) and long short-term memory (LSTM) models, which have demonstrated significant improvements in accuracy and generalization across diverse datasets. Our experimental results show the efficacy of these models on benchmark sign language datasets, achieving high levels of accuracy and robustness in real-world conditions. Furthermore, this study discusses the potential societal impacts of sign language detection systems in education, healthcare, and everyday communication, emphasizing the role of technological innovation in fostering inclusivity. By advancing machine learning methodologies, we contribute to the development of accessible communication solutions for individuals with hearing impairments. © 2024 IEEE.

关键词： Adversarial machine learning

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Predictive Analytics in Sports: Using Machine Learning to Forecast Outcomes and Medal Tally Trends at the 2024 Summer Olympics 4

Predictive Analytics in Sports: Using Machine Learning to Fo...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Moolchandani, Jhankar Chole, Vikrant Sahu, Santosh Kumar, Rahul Shukla, Anshita Kumar, Ashok Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper focuses on using machine learning approaches in predicting the medal projections and analyzing the medal distribution pattern in the 2024 Summer Olympics. Due to the availability of a large number of variables both at the individual athlete level and the country level as well as event specific measures, various statistical models are trained to predict the number of medals and unearthing traits that are associated with success in Olympics. The evaluation of the predictions of the models is done using linear regression and random forest along with support vector classifier, as well as artificial neural network classifiers. Using the research findings, it becomes evident that predictive analytics has the capacity of offering a better understanding of the performance of sport teams and formulation of strategies for future competition events. As is observed from the above findings the random forest model sampled the highest accuracy of 99. 034 percent in the general set out of the four models;thus, it is recommended ideal for complicated set data investigations Based on the results of the experiments, it is clearly observed that the random forest has the highest accuracy of 99. 034. © 2024 IEEE.

关键词： Predictive analytics

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Comparative Analysis of IoT and Blockchain Technologies in Enhancing Security and Efficiency in Healthcare Systems 4

Comparative Analysis of IoT and Blockchain Technologies in E...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Kumar, Ashok Shukla, Anshita Thada, Vikas Chole, Vikrant Moolchandani, Jhankar Sahu, Santosh Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper presents an extensive comparison of the IoT and blockchain technologies in the improvement of security, privacy, and interoperability, as well as energy efficiency in healthcare systems. This is because IoT allows for constant tracking and gathering of data from medical devices and this enhances the welfare of the patients as well as the management of the health facilities. However, this has exposed the Internet of Things (loT) technology with multiple risks in data security and privacy since it uses wireless communication and has limited computational power. Such issues can be addressed through the use of blockchain technology because of its distributed and tamper proof ledger. This research work presents a case of how the integration of IoT with blockchain will lead to a middle of the two worlds, where IoT will be useful for handling real time data while blockchain will handle the issue of security. The study concludes that the integration of blockchains with the IoT offers superior results than the individual implementations in several aspects, thus offering a more secure, efficient, and interoperable environment for today's healthcare applications. Possible future work includes improving the integration of these technologies to minimize the energy consumption, improve the scalability of the system and establish feasible implementation models in actual health care systems. © 2024 IEEE.

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Document retrieval using clustering-based Aquila hash-Q optimization with query expansion based on pseudo relevance feedback

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International Journal of computers and Applications 2024年第7期46卷 496-507页

作者： Inje, Bhushan Nagwanshi, Kapil Kumar Rambola, Radhakrishna Amity School of Engineering and Technology Amity University Jaipur India NMIMS University MPSTME Shirpur Shirpur Dist Dhule India Department of Computer Science and Engineering SoS Engineering and Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

A document retrieval system helps users to retrieve the relevant documents corresponding to their query quickly and easily. In the real world, document retrieval is a difficult task due to high volumes of data, unstructured data, and different formats of data. Even though many research techniques are introduced, major problems like vocabulary mismatch and non-linear matching still need to be solved. In this work, the Aquila hash-q optimizer is the proposed matching technique with the clustering technique to retrieve the document in a time-efficient manner for the user query without collision. First, preprocessing is done by eliminating the stop words from the document, stemming, and grouping documents in a cluster into a single document using Hierarchical Density-based Sampling Spatial Cluster of Applications with Noise (HDBSSCAN) clustering. This clustering algorithm is powerful, robust to noise, and scalable and identifies clusters of documents that are related to each other. Additionally, the sampling technique used in this clustering algorithm increases the clustering speed by reducing the size of the document which improves the performance of document retrieval systems. Secondly, the queries are searched using the Aquila hash-q optimizer matching technique by which the relevant documents are retrieved. The Aquila hash-q optimization works by pre-computing a hash table of the terms in a document collection and then using this hash table to quickly identify the relevant documents from the given query. This can significantly improve the speed of document retrieval, especially for large document collections. Aquila hash-q optimization can improve the accuracy, efficiency, and scalability of document retrieval systems. The effectiveness of the Hierarchical Density-Based Clustering Aquila Optimization approach is determined by various analyses through NPL, LISA, and CACM data in terms of precision @ 5 (0.497), precision @ 10 (0.425), Mean Average Precision (MAP) (0.4

关键词： Clustering algorithms

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Bayesian-Enhanced EEG Signal Analysis for Epileptic Seizure Recognition: A 1D-CNN LSTM Approach 11

Bayesian-Enhanced EEG Signal Analysis for Epileptic Seizure ...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Jain, Mantra Bhardwaj, Harshit Srivastav, Ansh Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

The intricate neurological condition known as epilepsy, which is common across the world, presents consid-erable difficulties in accurately identifying and differentiating between non-epileptic and epileptic activity using electroen-cephalograms (EEGs). To customize successful therapies, it is essential to accurately identify the kinds of epileptic activity. Since epilepsy affects about 50 million people worldwide according to latest update of WHO and is typified by spontaneous seizures, early identification and prediction are vital in enabling people to minimize possible harm. The current seizure prediction methods lacks, with particular emphasis on their limited performance on small training datasets and their disregard for time-series data. To identify small patterns in EEG data, this study combines cutting edge technologies, like Long Short-Term Memory (LSTM) and 1D-CNN (Convolutional Neural Network) leveraging data from five hundred patients acquired from the UCI Machine Learning Repository. To optimize the 1D-CNN LSTM architec-ture and hyper-parameters, Bayesian optimization is employed, allowing for efficient exploration of the parameter space. It's effectiveness is not only limited to enhancing the performance metrics of a particular model but also minimizing the computing power required for fine tuning. The research evaluates the effectiveness of the 1D-CNN LSTM-based model, showcasing its potential as a reliable tool for automated epilepsy detection with accuracy of 99.47% (100%), average sensitivity of 99.45%, and average specificity of 99.57%. This approach, emphasizes the significance of anticipating seizures in advance, attempts to provide epileptics the tools they need to control and avoid seizures in advance, so ultimately enhancing their quality of life for patients. © 2024 IEEE.

关键词： Quality control

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Predicting student Academic Performance using Machine Learning: Analyzing Socio-Economic and Personal Factors from Secondary Education in Portugal 4

Predicting Student Academic Performance using Machine Learni...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Sahu, Santosh Kumar, Ashok Kumar, Rahul Shukla, Anshita Chole, Vikrant Moolchandani, Jhankar Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper aims to predict student academic performance using machine learning algorithms by analyzing socioeconomic and personal factors from secondary education in Portugal. The study utilizes a dataset containing demographics, family background, educational support, extra-curricular activities, and academic records to evaluate their impact on students' success in Mathematics and Portuguese courses. Machine learning models such as Logistic Regression, Decision Trees, and Random Forests were employed to analyze the data. The Random Forest model outperformed others with an accuracy of 85 %, indicating significant potential in predicting student performance using the selected features. © 2024 IEEE.

关键词： Adversarial machine learning

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