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Global Perspective of Natural Fibre Reinforced Composites: Properties, and Applications

Journal of The Institution of Engineers (India): Series C 2024年第5期105卷 1335-1350页

作者： Tyagi, Ankit Punia, Upender Dadhich, Abhishek Meena, S.L. Department of Mechanical Engineering Poornima College of Engineering Rajasthan Jaipur India Department of Mechanical Engineering DCRUST Haryana Murthal Sonipat India Department of Computer Science Engineering Poornima Institute of Engineering and Technology Rajasthan Jaipur India Department of Mechanical Engineering RTU Kota Rajasthan Kota India

The primary promoters for the advancement of NFRCs (Natural fibre reinforced composites) in many industries are the imperative need to decrease energy consumption and mitigate environmental consequences. Natural fibres provide several advantages in terms of affordability, biodegradability, lightweight nature, superior life-cycle performance, and good mechanical qualities. The present paper initially provides an overview of the different types, benefits and challenges of NFRCs with a brief description. Then after, the article briefly discussed the about DMA (Dynamic mechanical analysis), mechanical behavior, as well as thermal behavior characteristic of NFRCs. Finally, we shortly introduce natural fibres applications in the field of construction work, structures and buildings, automobiles industry, biomedical and pharmaceutical industries, furniture and architecture and in various other industries. Nonetheless, the intrinsic characteristics of natural fibres derived from plants have posed difficulties in the advancement and utilization of NFRCs. These challenges include inconsistent fibre quality, inadequate thermal stability, restricted mechanical properties, susceptibility to water absorption, and a tendency to clump together. © The Institution of Engineers (India) 2024.

关键词： Natural fibers

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Human Emotion Classification for Valence and Arousal via EEG-Based Machine Learning

Human Emotion Classification for Valence and Arousal via EEG...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Pandey, Dharmendra India SRV Media Pvt. Ltd India India Computer science and Engineering Faculty of Engineering and Technology Parul institute of Technology Parul University India Pune India

ISBN: (纸本)9798350374933

In the realm of affective computing, the accurate classification of human emotions through physiological signals, particularly electroencephalogram (EEG), presents a significant opportunity to influence practical outcomes in neuroscience, psychology, and human-computer interaction. This paper introduces a novel machine learning approach to emotion classification that adeptly decodes the emotional dimensions of valence and arousal from EEG signals. Our methodology is comprehensive, involving meticulous data preprocessing, feature extraction, and the implementation of an ensemble of sophisticated algorithms, such as support vector machines and neural networks. The effectiveness of our model is demonstrated through rigorous testing, where it significantly surpasses existing benchmarks in accuracy. Importantly, the results reveal the potential of EEG-based models to revolutionize our understanding of emotional states, enhancing applications in adaptive user interfaces and emotional health monitoring. These applications are crucial for developing technologies that adjust to user emotions in real-time, offering substantial benefits in personalized digital interactions and therapeutic settings, thereby extending the research's relevance beyond academia to directly benefit industry practitioners and clinicians. © 2024 IEEE.

关键词： Support vector machines

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An Ensemble-based ML Model to Predict Cardiac Disease 7

An Ensemble-based ML Model to Predict Cardiac Disease

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jannat Hema, Nur E. Samayeen Zeest, Nubras Mahjabin, Mohammad Ratul Nazrul Islam, Muhammad Military Institute of Science And Technology Department of Biomedical Engineering Dhaka Bangladesh Military Institute of Science And Technology Department of Computer Science And Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359299

In contemporary times cardiovascular disease has emerged as a predominant health concern. It underscores a critical necessity for early and precise predictions to facilitate effective prevention and intervention strategies. This investigation is centered on a meticulous comparative analysis of heart disease prediction which employs advanced machine learning techniques. Three distinct machine learning models such as Random Forest, Light GBM and Gradient Boosting are deployed to analyze and predict heart disease based on an available dataset. The study commences with comprehensive data preprocessing, incorporating sophisticated sampling techniques. Subsequently, the dataset is stratified into training and testing sets, and the three machine learning models are systematically implemented. Evaluation metrics, including accuracy, precision, recall, and F1- score, are employed to assess the predictive capabilities of the models. Three combinations are selected for ensemble modeling based on their better performance for the used dataset. Notably, the ensemble model of Random Forest and XGBoost attained a peak accuracy of 99%. The study highlights the use of machine learning, particularly ensemble model in enhancing diagnostic accuracy for heart disease prediction, holding the potential to significantly contribute to early detection and timely intervention, thereby alleviating healthcare burdens related to cardiovascular health. © 2024 IEEE.

关键词： Forecasting

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On prevention of attribute disclosure and identity disclosure against insider attack in collaborative social network data publishing

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International Journal of Business Intelligence and Data Mining 2023年第1期23卷 14-49页

作者： Kadhiwala, Bintu Patel, Sankita J. Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India

In a collaborative social network data publishing setup, privacy preservation of individuals is a vital issue. Existing privacy-preserving techniques assume the existence of attackers from external data recipients and hence, are vulnerable to insider attack performed by colluding data providers. Additionally, these techniques protect data against identity disclosure but not against attribute disclosure. To overcome these limitations, in this paper, we address the problem of privacy-preserving data publishing for collaborative social network. Our motive is to prevent both attribute and identity disclosure of collaborative social network data against insider attack. For the purpose, we propose an approach that utilises p-sensitive k-anonymity and m-privacy techniques. Experimental outcomes affirm that our approach preserves privacy with a reasonable increase in information loss and maintains an adequate utility of collaborative social network data. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Privacy-preserving techniques

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Alzheimer's stage progression modeling using graph neural network and MRI biomarkers

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Neural Computing and Applications 2025年 1-23页

作者： Shankar, Venkatesh Gauri Sisodia, Dilip Singh Chandrakar, Preeti Department of Computer Science and Engineering National Institute of Technology Raipur Chhattisgarh Raipur India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur India

Stage progression and early detection are the key issues in predicting Alzheimer's disease using MRI. As of now, Alzheimer's existing work generates progression from one stage to multiple stages without the probability of progression. To overcome this problem, the graph network is very feasible for one-to-one stage progression. As for the probability of stage progression concern, this is very important to create feature vectors of biomarkers associated with the stages. The proposed work formulates the graph consisting of stages on nodes and biomarkers on edge. Each stage biomarkers are represented as a feature vector. The feature vector passes through all the stages to the destination stage and gets updated as per biomarkers. Because of many intermediate stages, more than one path is generated while traversing from one stage to another. It is also important to select the optimal path from many paths. The optimal path is selected with the inclusion of max-cut to create optimal path feature vectors. The optimal path feature vectors are multiplied by a weight matrix to produce aggregated results known as logits. After that, the logits are learned, and features are extracted during training by inputting them into a graph neural network (GNN). The proposed model adds a softmax function to GNN, which converts the trained logits into probabilities. These probabilities are scores of stage progression from one stage to another. Finally, quantum approximate optimization is used to tune the parameters and optimize the model solutions. This paper focuses on MRI for Alzheimer's early detection, one-to-one stage progression, and the probability of progression. The model achieved accuracy of 98.10% and F1 score of 97.01% for detecting AD stage. It also reported micro-precision of 99.01% and micro-recall of 99.86%. The model results illustrate the efficacy of the proposed approach. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025

关键词： Graph neural networks

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EfficientViT: An Efficient Vision Transformer for Fire and Smoke Image Classification 1

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Gourisaria, Mahendra Kumar Patel, Ayush V. Chatterjee, Rajdeep Singh, Vijander School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur302034 India Department of ICT and Natural Science Faculty of Information Technology and Electrical Engineering NTNU—Norwegian University of Science and Technology Aalesund Norway

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

Given the variety of fire and smoke, which are distinguished by variances in texture and color, it is extremely difficult to detect fire and smoke from visual imagery. A significant amount of economic and environmental harm has resulted from fire and smoke incidents around the world. In order to prevent property damage and save lives, fires must be detected as soon as possible, for example, forest fires, which frequently occur in the summer yet are easily avoidable. Using cutting-edge deep learning models including Convolutional Neural Network (CNN), VGG16, Vision Transformer (ViT), and MobileViT, we offer a thorough method for classifying fire and smoke in this study. We also provide an innovative model dubbed EfficientViT, which combines the advantages of the MobileViT and Inverted Residual layers. The correct classification of visual images into the categories of fire and smoke is our main goal. We show that EfficientViT beats the other developed models, obtaining an amazing accuracy of 0.92, through a thorough experimentation and evaluation. This demonstrates how well our suggested model works at correctly differentiating between incidents of fire and smoke. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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ETFIDS: An Entropy-Driven, Time-Frequency Analysis Framework for In-Vehicle CAN Signal Intrusion Detection

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IEEE Internet of Things Journal 2025年第12期12卷 21507-21522页

作者： Liu, Wanning Qin, Guihe Liang, Yanhua Song, Jiaru Liu, Qingxin Zhou, Xue Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Changchun130012 China

In recent years, cyberattacks against automobiles have exposed significant security threats to in-vehicle networks. The vulnerability of communication signals to malicious interference and manipulation can lead to serious system failures or abnormal behavior. The existing in-vehicle network intrusion detection methods do not fully exploit the time-frequency characteristics of controller area network (CAN) signals. This limitation reduces their effectiveness in capturing subtle changes and signal complexity. Based on the above motivation, from the perspective of signal perception, we propose an entropy-driven, time-frequency analysis framework for in-vehicle network intrusion detection. The framework integrates a signal sampler, a frequency-domain detector, and a time-domain detector. The signal sampler, as the system’s front-end module, extracts real-time physical signal data streams from CAN messages. The frequency-domain detector identifies frequency components, detecting high-frequency disturbances and cyclic variations. Meanwhile, the time-domain detector captures instantaneous changes and sudden anomalies. It analyzes signal complexity and anomalies through both stream and block detection. Experimental results demonstrate that the proposed method performs well under various attack scenarios, offering superior detection and real-time performance. It effectively senses multiple signal anomalies, providing a robust intrusion detection solution for modern in-vehicle networks. © 2014 IEEE.

关键词： Network intrusion

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Improving Bangla Regional Dialect Detection Using BERT, LLMs, and XAI

Improving Bangla Regional Dialect Detection Using BERT, LLMs...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Paul, Bidyarthi Preotee, Faika Fairuj Sarker, Shuvashis Muhammad, Tashreef Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka1208 Bangladesh Southeast University Department of Computer Science and Engineering Dhaka1208 Bangladesh

ISBN: (纸本)9798331529765

This research addresses the challenge of identifying and categorizing Bangla regional dialects through the application of sophisticated natural language processing techniques. Automated translation, digital content personalization, and speech recognition systems are all improved by precise dialect detection, which is a result of the linguistic diversity in Bangladesh. Few-shot learning techniques were used to compare the performance of transformer-based models, specifically Bangla BERT, with state-of-the-art large language models such as GPT-3.5 Turbo and Gemini 1.5 Pro, and to fine-tune them. The methodology entailed the utilization of a diverse regional Bangla speech samples from the Vasantor dataset, which encompassed regions including Mymensingh, Chittagong, Barishal, Noakhali, and Sylhet. In order to enhance the interpretability of the model, implementation of Local Interpretable Model-agnostic Explanations (LIME) was done. The Bangla BERT model achieved the highest accuracy of 88.74%. GPT-3.5 Turbo's few-shot learning exhibited substantial potential, with an accuracy rate of 64%. These results underscore the significance of hyperparameter optimization and fine-tuning in the enhancement of regional dialect detection models. © 2024 IEEE.

关键词： Speech recognition

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Adaptive Hypergraph Network for Trust Prediction 40

Adaptive Hypergraph Network for Trust Prediction

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Xu, Rongwei Liu, Guanfeng Wang, Yan Zhang, Xuyun Zheng, Kai Zhou, Xiaofang School of Computing Macquarie University Australia School of Computer Science and Engineering University of Electronic Science and Technology of China China Hong Kong University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798350317152

Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users. However, in the real world, interactions between users are usually complicated rather than pairwise only. Hypergraphs offer a flexible approach to modeling these complex high-order correlations (not just pairwise connections), since hypergraphs can leverage hyperedeges to link more than two nodes. However, most hypergraph-based methods are generic and cannot be well applied to the trust prediction task. In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations. AHNTP utilizes Motif-based PageRank to capture high-order social influence information. In addition, it constructs hypergroups from both node-level and structure-level attributes to incorporate complex correlation information. Furthermore, AHNTP leverages adaptive hypergraph Graph Convolutional Network (GCN) layers and multilayer perceptrons (MLPs) to generate comprehensive user embeddings, facilitating trust relationship prediction. To enhance model generalization and robustness, we introduce a novel supervised contrastive learning loss for optimization. Extensive experiments demonstrate the superiority of our model over the state-of-the-art approaches in terms of trust prediction accuracy. © 2024 IEEE.

关键词： Multilayer neural networks

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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Frontiers of computer science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

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