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Experimental Investigation and Weibull Statistical Analysis on Tensile Behaviour of Carbon/Epoxy Composites

Journal of The Institution of Engineers (India): Series D 2024年第3期105卷 1373-1383页

作者： Amar, Thakur Abhinav Kumar, Amit Yadav, D.K. Department of Production and Industrial Engineering Birla Institute of Technology Ranchi JH Mesra835215 India Department of Mechanical Engineering National Institute of Technology BH Patna800005 India Department of Computer Science and Engineering National Institute of Technology JH Jamshedpur831014 India

This paper is concerned with the experimental and theoretical evaluation of the tensile strength of carbon fabric-reinforced epoxy composites developed using hand lay-up and vacuum bagging techniques. The developed CFRP composite laminates were subjected to tensile tests as per ASTM D3039 at different strain rates of 1 s−1, 10 s−1, and 100 s−1. The statistical analysis of the experimental results was conducted using a two-parameter Weibull distribution function. The results showed an increase in tensile strength with an increase in strain rate for composite laminates produced using both fabrication techniques. The tensile modulus also showed an increasing trend with respect to strain rate, but the increase was marginal, while elongation at break showed a decreasing trend. For all cases of strain rate, the vacuum bag-fabricated composite laminates showed higher tensile strength and modulus values except for elongation at break when compared with the laminates fabricated with hand lay-up. Cumulative probability density data, along with Weibull parameters, were utilised to determine theoretical tensile strength. The projected tensile strength values closely matched the experimental results, with a minimal difference of approximately 6.2% observed in the context of composite laminates fabricated under vacuum bag conditions. Fracture analysis identified distinct failure mechanisms: adhesive failure at the fabric/matrix interface for the hand lay-up method and brittle fracture as the predominant failure mode for laminates produced using vacuum bagging techniques. © The Institution of Engineers (India) 2023.

关键词： Tensile strength

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An Efficient Investigation of Cloud Computing Security with Machine Learning Algorithm 7

An Efficient Investigation of Cloud Computing Security with ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Senthilkumar, R. Yasotha, S. Manochithra, P.M. Senthil, J. Sivakumar, G. Shree Venkateswara Hi Tech Engineering College Computer Science And Engineering Tamilnadu Erode India Sri Eshwar College of Engineering Computer Science And Engineering Tamilnadu Coimbatore India Sri Krishna College of Engineering And Technology M.Tech Computer Science And Engineering Tamilnadu Coimbatore India Erode Sengunthar Engineering College Computer Science And Engineering Tamilnadu Erode India

ISBN: (纸本)9798350359299

Due to developments in technologies like Cloud Computing (CC), the Internet of Things (IoT), etc., the data volume transmitted across communication infrastructures has skyrocketed recently. In order to make network systems susceptible, attackers have increased their determinations. Improving the security of such network systems is very crucial. Using Deep Learning (DL) methods with an Intrusion Detection System (IDS) framework is put into action in this research. The Gated Recurrent Unit (GRU) as well as Support Vector Machine (SVM) are used in this study for attack detection. Therefore, the implementation of SoftMax in the GRU model's last output layer, this study introduces a linear SVM. The NSL-KDD dataset is taken into account to evaluate the proposed IDS framework's performance. In addition, when the number of features increases, current IDSs have poor test accuracy ratings when it comes to identifying new attacks. Each dataset's feature space was reduced using an XGBoost (XGB)-based feature selection approach in this research. After that, 22 detailed characteristics were selected from the NSL-KDD dataset using XGB. The results show that, the proposed IDS paradigm outperformed other approaches. © 2024 IEEE.

关键词： Support vector machines

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Harmful Brain Activity Classification Using Ensemble Deep Learning 66

Harmful Brain Activity Classification Using Ensemble Deep Le...

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66th International Symposium ELMAR, ELMAR 2024

作者： Habijan, Marija Sojo, Robert Tolic, Ivana Hartmann Galic, Irena Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350375411

Electroencephalography (EEG) is a crucial tool for monitoring electrical brain activity and diagnosing neurological conditions. Manual analysis of EEG signals is time-consuming and prone to variability, necessitating automated methods for accurate and efficient interpretation. This paper presents an ensemble learning approach for the automated classification of harmful brain activity from EEG data. The model combines the strengths of ResNet1d GRU, EfficientNetB0, and EfficientNetB1 to predict brain activity from spectrograms and raw EEG signals. By leveraging the unique capabilities of each model and employing a weighted averaging scheme, the ensemble achieves a competitive Kullback-Liebler divergence score of 0.3 on the testing dataset. This approach demonstrates the potential of ensemble learning to improve the accuracy and robustness of automated EEG signal classification, offering a valuable tool for early detection and diagnosis of neurological conditions. © 2024 IEEE.

关键词： Contrastive Learning

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A Machine Learning and Deep Learning-Based Web Application for Crop and Fertilizer Recommendation and Crop Disease Prediction 6th

A Machine Learning and Deep Learning-Based Web Application f...

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Srinidhi, Amuri Jahnavi, Veeramachinani Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819984503

India is a country that is majorly dependent on agriculture and farming for livelihood. The country’s diverse soil lets farmers produce various crops throughout the year. Depending on the characteristics of soil such as the composition of nitrogen, phosphorus, potassium and the pH of the soil and environmental factors such as rain, humidity, and temperature, it is important to know which crops should be grown and what fertilizers to use, to maximize the yield. It is also important to detect any crop diseases at an early stage to prevent major loss. This paper presents an all-in-one platform web application that performs all three tasks, and it uses ML algorithms such as KNN, Decision Tree, Gaussian Naive Bayes, SVM, Logistic Regression, and Random Forest to recommend crops and fertilizers by analyzing soil and environmental factors. For crop recommendation, Random Forest is chosen as it provided the highest accuracy of 99.09%. Random Forest is deployed for fertilizer recommendation as well, as it provided the highest accuracy of 100%. The Deep Learning model ResNet50 is used to detect crop diseases, and the accuracy for training and validation was 95.52% and 87.36%, respectively. Flask framework is used to build the backend of the web application. The web application is enabled to send an SMS of the results to user’s mobile phone using the SMS API, Twilio. The goal of the project is to provide an ideal solution to farmers or the users of the web application, via a text message, which can be accessed even in remote areas and can be understood by the majority of people. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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Grainbee: A Quantum-Resistant Blockchain-Based Ration Distribution System with Hardware Security Modules

Grainbee: A Quantum-Resistant Blockchain-Based Ration Distri...

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

作者： Joni, Sohel Ahmed Rahat, Rabiul Tasnin, Nishat Ghose, Partho Mahbub-Or-Rashid, Md. Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

In developing countries, subsidized food programs aimed at supporting the poor are often undermined by corruption and mismanagement, leading to food waste and unequal distribution. To overcome these challenges, we introduce GrainBee, a blockchain-powered ration distribution system designed to ensure secure, transparent, and efficient food supply distribution. By leveraging blockchain's inherent security and immutability, GrainBee addresses the major security and transparency issues that plague traditional and existing blockchain-based *** proposed system employs a post-quantum secure, blockchain-based approach to combat corruption and fraudulent invoicing, guaranteeing integrity through a distributed, immutable ledger. To further fortify security, we integrate Hardware Security Modules (HSMs), providing an additional layer of protection for sensitive data and transactions. A built-in reservation and scheduling system streamlines the distribution process, while a user-friendly interface and machine translation services bridge language gaps, enhancing usability and accessibility. By harnessing the power of blockchain technology, GrainBee offers a robust solution to the complex challenges of food distribution. © 2024 IEEE.

关键词： Blockchain

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Campus Surveillance: Deep Learning-Based Rules Violation Detection 14

Campus Surveillance: Deep Learning-Based Rules Violation Det...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Deepalakshmi, R. Senthil Murugan, K.R. Arunnachalam, R.S. Mohamed Asif, B. Velammal College of Engineering and Technology Dept of Computer Science Tamil Nadu India

ISBN: (纸本)9798350344837

Cutting edge Innovation had made big affect in areas in Technical, Trade and Mechanical Sectors. Deep learning calculations had created those innovation into great enhancement for the quick, exactness and effectiveness in information. Deep learning Methods could executed in Campus Areas to moves forward its headway in innovation. The paper proposes Deep Learning in college camera which recognizes run the rule violators successfully. Wear ID Card and Wearing Shoes is one of the important rules in Campus, but in today's world limited datasets for the recognizing identity card and shoes. For this issue, Dataset for recognizing the Rule Violators have been made with 5000 pictures. Enhanced deep learning calculations like YOLO for object recognition and Convolutional neural networks to facial detection. YOLO verifies the identity card and Shoes from Pictures and Video. It gives about 80.6% mAP,90.3% precision, and 76.5% recall. © 2024 IEEE.

关键词： Object recognition

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Image Splicing Detection Based on Hybrid CNN-Transfarmer Network 2

Image Splicing Detection Based on Hybrid CNN-Transfarmer Net...

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2nd International Conference on Machine Vision, Image Processing and Imaging technology, MVIPIT 2024

作者： Jiang, Ningjing Zhang, Dengyong Li, Feng School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9798331543037

-Image splicing is widely recognized as the predominant technique in image manipulation. Most current splicing detection methods have low accuracy and poor localization of small-sized splicing areas. Therefore, we design a hybrid CNN-Transformer network for image splicing detection. Firstly, a hybrid structure encoder is constructed, which first uses CNN to extract the input images' local features, and then inputs it to Transformer to calculate long-distance dependencies to obtain global feature representation. Then, a deformable attention enhancement module (DAEM) is designed to make the CNN local features at different scales at each stage more refined and improve the deformation adaptability of the network. Finally, features from the hybrid structured encoder are added to the decoder via skip connections, to make the decoder perform better feature reconstruction. Results on three datasets show that the network can more accurately locate tampered areas and has superior performance than other methods. ©2024 IEEE.

关键词： Network coding

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Healthcare Analytics: Conceptualizing a Research Agenda

Healthcare Analytics: Conceptualizing a Research Agenda

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2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Elragal, Rawan Elragal, Ahmed Habibipour, Abdolrasoul Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

This research recognizes the pressing need for innovative research in healthcare, enabling the transition towards analytics, by explaining how previous studies utilized big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to understand the literature, identify research gaps, and posit research questions for researchers, academic institutions, and governmental healthcare organizations. We intend to explain how contemporary analytics have been used to address healthcare concerns as well as to posit several research questions for future studies based on gaps which we have identified. The study has multi-folds contribution areas: first, it provides a state-of-the-art review to healthcare analytics, second, it posits a research agenda to advance the knowledge in this area further. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Adversarial machine learning

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Detecting Plant Leaf Diseases with Convolutional Neural Networks (CNN) and Deployment via Hugging Face 4

Detecting Plant Leaf Diseases with Convolutional Neural Netw...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： James, T. Prabahar Godwin Pathmanaban, J. Vivekanandan, G. Anantha Karthigeyan, M.S. Deepak, R. Abdul Kalam, J. Sri Sai Ram Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508845

Ensuring food security relies heavily on agriculture, and any threat to crop health can significantly impact local economies and populations. Detecting and addressing plant diseases early poses a challenge for farmers. This study presents a novel approach for identifying leaf diseases in plants, focusing specifically on tomato and potato leaves. A deep learning model using Convolutional Neural Networks (CNN) was developed with 91% accuracy to recognize up to 13 different diseases affecting these plants. The dataset used includes high-resolution images of both healthy and diseased leaves, enabling precise classification. The model is deployed on Hugging Face, making it accessible and scalable for real-time applications in assisting farmers and agricultural specialists with disease detection and prevention. This solution enhances crop health monitoring, leading to improved agricultural productivity and reduced pesticide usage, contributing to precision agriculture and sustainable farming practices. © 2024 IEEE.

关键词： Agricultural technology Convolutional Neural Network Deep Learning Hugging Face Image Classification Plant Disease Detection Precision Agriculture Sustainable Farming Practices Tomato Leaf Disease

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Brain Tissue Segmentation Using Transfer Learning 3rd

Brain Tissue Segmentation Using Transfer Learning

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3rd International Conference on Sustainable Expert Systems, ICSES 2022

作者： Rizvi, Farhan Raza Agarwal, Khushboo Computer Science & Engineering Madhav Institute of Technology and Science Gwalior India

ISBN: (纸本)9789811978739

A brain tumour (BT) is a life-threatening condition produced by aberrant brain cell proliferation that affects human blood cells and nerves. Detecting BTs in a timely and exact manner is critical to preventing complex and unpleasant treatment methods, as it can aid surgeons in preoperative planning. Manually BT detection takes a long time and is extremely reliant on the presence of local experts. As a result, accurate automated techniques for identifying and categorizing diverse forms of brain tumours are urgently needed. However, due to wide differences in size, position, and structure, precise localization and classification of brain tumours is a difficult task. We've proposed an innovative approach to dealing with the problems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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