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Fabric Defect Detection Using Deep Learning 23th

Fabric Defect Detection Using Deep Learning

引用

23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Natesan, P. Thamilselvan, R. Gothai, E. Harini, M. Kasthuri, Chitrasena Nehru Deepankumar, G. Department of Information Technology Kongu Engineering College Perundurai India Department of Computer Science and Engineering Kongu Engineering College Perundurai India

ISBN: (纸本)9783031648359

The identification of fabric imperfections presents a considerable obstacle in the fabric manufacturing sector, owing to the complex configurations and diverse array of defects that may exist. Traditionally, visual inspections by human operators have been employed to identify such flaws, but this approach is both slow and prone to inaccuracies. In order to tackle these problems, this study investigates the benefits of employing Faster R-CNN and a range of YOLOv5 algorithms to accurately identify the fabric flaws. The deep learning object detection algorithms like YOLOv5s and Faster R-CNN have undergone rapid development and have been successfully applied in various industries, demonstrating strong performance. This paper proposes the use of the YOLOv5s algorithm, which exhibits superior accuracy in detecting fabric defects. Experimental results indicate that, compared to both YOLOv5s and Faster R-CNN, the YOLOv5s algorithm achieves higher accuracy in terms of mean Average Precision (mAP) and faster performance in terms of Frames Per Second (FPS). The FPS of YOLOv5s and faster RCNN is calculated and compared. From the results, it is inferred that YOLOv5s algorithm can accurately and rapidly identify the location of defects, making it suitable for real-time applications in the defect detection industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Defects

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Classification and Segmentation of Leaf Images based on Deep Learning for Peanut Plant Disease Detection 4

Classification and Segmentation of Leaf Images based on Deep...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Ebenezer, V. Daniel, Arsha Anna Getzi Jeba Lillipushpam, P. Edwin, E.Bijolin Roshni Thanka, M. Devassy, Rosebel Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300093

Peanut is a key food commodity, and diseases of its leaves can have a negative impact on the quantity and quality of the product. The seed has high levels of potassium, magnesium, calcium, riboflavin, niacin, folic acid, vitamin E, resveratrol, and amino acids. It also contains 36% to 54% oil, 16% to 36% protein, and 10% to 20% carbs. Early, late, and rusty leaf spots, among other frequent illnesses, are recognized by this equipment. Techniques for image augmentation have been used, such as twisting, rotating, and scaling. The variant employs a Multi Class Convolutional neural network with 5 output which include Normal Leaf, Images Without Leaf and the images with the leaf been infected by the diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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Design and Implementation of Solar Powered Smart Irrigation Using IoT 8

Design and Implementation of Solar Powered Smart Irrigation ...

引用

8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Santhosh Kumar, C. Brindasri, S. Krishnamoorthy, J. Vidhya, B. Saranya, G. Madhan, K. Er.Perumal Manimekalai College of Engineering Department of Computer Science and Engineering Hosur India Hindustan Institute of Technology and Science Department of Computer Science and Engineering India Sri Krishna College of Technology Department of ECE Coimbatore India Mailam Engineering College Department of Computer Science and Engineering Mailam India

ISBN: (纸本)9798350347791

The farming is one concerning the indispensable assets due to the fact of advent of ingredients but assumes sizeable portion about the economic regulation about each and every United States on America via which include after Today, coast owners hold a snack troubles within agribusiness appropriate according to the fact of non-appearance about downpours after shortage about water. So, between that task, we wish raise external computerized wind law case due to the fact bank lords as like intention maintain time, value then after on Manual inclusion is wished due to the fact the farmland strategies. By construction use over soil humidity sensor degrees involving ground dampness/mugginess may additionally keep checked. At some also is an harmony the trade yet sends signal in conformity over the microcontroller or relying upstairs that the cloud provision framework works. Robotized cloud government litigation uses valves in accordance in accordance with Engines work continue to be robotized effectively by using construction use of regulators yet no assistance over guide intervention among accordance about flirt apparatus ON then OFF. © 2023 IEEE.

关键词： Internet of things

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A Comprehensive Model Leveraging Convolutional Neural Networks for Online Transaction Fraud Detection

A Comprehensive Model Leveraging Convolutional Neural Networ...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Mishra, Sanskar Goyal, Anjali Sahu, Deepti Sharda University Sharda School of Engineering and Technology Department of Computer Science & Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798331508685

Financial fraud detection is a major challenge faced by financial institutions. For transaction fraud detection, Deep learning and machine learning algorithms are frequently used;however, their efficacy is severely limited by the issue of class imbalance, particularly with regard to the minority fraud class. A thorough study on literature has been done through this research for the examination of the techniques used to handle problems like class imbalance. This work analysed various works on usage of classical machine learning algorithms for transaction fraud detection on unbalanced and balanced datasets produced by under and over sampling. Works on Graph Neural Network that can capture relationships among entities in a graph constructed from the credit card transaction data has also been examined. Furthermore, this work proposes a Convolutional Neural Network (CNN) based approach combined with Adaptive Synthetic Sampling to address the class imbalance problem. The effectiveness has been demonstrated through our preliminary results on AUC, accuracy and other metrics of the approach proposed in learning from the data that is imbalanced as evaluated and compared against other classifiers like Random Forest and K-Nearest Neighbour. The proposed approach achieves an accuracy of 97.5% for fraud detection, outperforming other learners. This study provides insights on leveraging Deep Learning (DL) and Machine Learning (ML) techniques to address data imbalance for improved fraud detection performance. © 2025 IEEE.

关键词： Adaptive Synthetic Sampling Class Imbalance Convolutional Neural Network Credit Card Fraud Detection Financial fraud

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Heuristic Data Placement in Distributed Hierarchical Storage Network Using Deep Reinforcement Learning 22

Heuristic Data Placement in Distributed Hierarchical Storage...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Kaler, Ravi Toshniwal, Durga Indian Institute of Technology Roorkee Computer Science and Engineering Department Uttarakhand India

ISBN: (纸本)9798331508050

Intelligent data placement in hierarchical distributed storage networks (DSNs) has become crucial due to advancements in storage devices, an increase in big data applications, and strict time constraints. Inefficient data placement can lead to significant delays in data movement and increased service latency, impacting the overall performance of a distributed environment. It is a complex online decision-making problem due to its varying access patterns, dynamic network conditions, and ever-changing distributed environment, which traditional network optimization techniques fail to address efficiently. Reinforcement learning is best suited to address this problem as it learns from the environment dynamically and performs action. This paper presents a novel deep reinforcement learning (DRL) framework, soft actor-critic-2 (SAC-2), for heuristic data placement in the distributed network, leveraging the state-of-the-art SAC technique to improve efficiency, reduce latency, and subsequently minimize storage costs. We formulate the problem as a Markov Decision Process (MDP) model and incorporate a prioritized replay buffer for efficient learning. The heuristic data placement technique improves distributed hierarchical storage system efficiency compared to baseline strategies, as demonstrated by extensive experiments on Microsoft Research Cambridge (MSR) network traces. © 2025 IEEE.

关键词： Cloud storage

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Pneumonia Detection using Chest X-ray Images using CNN Algorithm 7

Pneumonia Detection using Chest X-ray Images using CNN Algor...

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Mazumdar, Surya Deepta Kumar, Ashish Sethuraman, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

Pneumonia is a serious health condition where the patient's lungs get infected by bacteria, virus or sometimes fungi. This causes burning sensation in the alveoli present in the lungs. In response to this, body forms fluids in the lungs that can cause trouble in breathing, coughing and even chest pain. The increasing demand of medical care for elderly care and children below five years of age requires advanced healthcare technologies. Premature diagnosis is vital for fast recovery. This research study intends to propose a novel technique for detecting pneumonia in chest x-ray images by training a model to classify the patient's condition as normal or pneumonic. The proposed model applies CNN, a deep learning algorithm to detect the health condition of the patient by analyzing their chest X-ray images. The objective of this research study is to attain the model accuracy as high as possible to provide accurate results to the user. © 2023 IEEE.

关键词： Viruses

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Intelligent Combination of Approaches Towards Improved Bangla Text Summarization 1

Intelligent Combination of Approaches Towards Improved Bangl...

引用

1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Khan, Alam Ishita, Sanjida Akter Zaman, Fariha Ashik, Ashiqul Islam Hoque, Md Moinul Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350323795

Text summarization is a technique to extract the main concept from a large document. It turns a big document into a smaller one without changing the main context. Text summarization have widely research area nowadays. There are two types of text summarization, one generates an extractive summary and the other generates an abstractive summary. Here in this paper, an intelligent model is proposed which can make an extractive summary from a given document. After completing some preprocessing steps on the document, some useful combinations of methods are applied such as Named Entity-based scoring, keyword based-scoring, parts of speech-based scoring, and word and sentence based-analysis to rank the sentences of the passage. These methods combined together generated the final summary. The proposed model is compared with multiple human-made summaries and the evaluation was performed with respect to precision, recall, and F-measures. The model is also compared with the state-of-the-art approaches and found to show its effectiveness with respect to Precision (0.606) and F-measure (0.6177) evaluation measures. © 2023 IEEE.

关键词： Text processing

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The Use of Recurrent Neural Networks in the Optimization of computer science Algorithms 1

The Use of Recurrent Neural Networks in the Optimization of ...

引用

1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Franklin, Ramya G Ronald Doni, A. Poornima, D. Sabasti Prabu, S. Igni Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

A new era of computational efficacy and problem-solving abilities has begun with the combination of Recurrent Neural Networks (RNNs) and computer science methods. It is crucial in modern computing to combine RNNs with methods from the field of computer science. RNNs bring a dynamic learning paradigm, making algorithms more efficient and flexible by allowing them to process sequential input and complicated dependencies. Their usefulness goes beyond one specific area of study, and into others such as optimization issues, recommendation systems, image processing, and natural language processing. RNNs serve a vital role in addressing the severe challenges that algorithms confront, including processing sequential input, identifying temporal connections, and optimizing in high-dimensional environments. By providing dynamic and adaptive modeling, RNNs aid algorithms in navigating these obstacles. Recurrent Neural Network-Based Optimization Algorithm (RNN-OA) is an innovative novel approach presented in this research. RNN-OA is an approach to algorithmic optimization that makes use of RNNs' dynamic learning capability. Mechanisms for selective data processing with attention, regularization methods for model stability, and interpretability improvements for openness are all a part of it. Quickening the process of optimization in many different types of problems is transfer learning and fine-tuning approaches. There are a wide variety of computer science fields where RNN-OA can be useful. Time-series analysis, language modeling, picture recognition, and recommendation systems all benefit greatly from its ability to handle sequential data. The efficacy, scalability, and robustness of the method are thoroughly evaluated by simulating various problem scenarios and data streams. The results of these simulations provide illumination for the benefits and drawbacks of RNN-OA, paving the way for its development toward actual application in algorithmic optimization. © 2023 IEEE.

关键词： Recurrent neural networks

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Enhanced Convolutional Neural Network for Sign Character Recognition with Mixed Pooling and Mish Activation Function

Enhanced Convolutional Neural Network for Sign Character Rec...

引用

2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Sur, Hritajit Mandal, Gourab Das, Koushik Nandia, Apurba Das, Avik Kumar Ghosh, Arijeet Institute of Engineering and Management University of Engineering and Management Cybersecurity & Blockchain Technology Department of Computer Science Engineering Kolkata India

ISBN: (纸本)9798331539948

Communication is an essential part of our daily lives, especially for deaf and dumb people who face difficulty in communicating with others. This paper introduces a method of using sign languages, i.e., to convert various hand gestures to appropriate text. The proposed method will help people with disabilities communicate or express their thoughts and opinions to others. By offering cutting-edge methods for sign character recognition, this neural network model aims to close the communication gap and enhance the quality of life for those it serves. In this work, a dataset of hand gestures is divided into training, validation, and testing sets to apply a convolutional neural network (CNN) that has been used to properly recognise the characters on the image. The CNN is further tested on random test data to obtain a reliable interpretation of the sign language we want. The uniqueness of this model lies in the fact that it makes use of mixed pooling operation and mish activation function which train the data more smoothly and in a strong, robust way, which overall improves the accuracy and precision in recognising sign characters. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Education Accessibility: Portable Microservers for computer-Based Testing in Resource-Constrained Environments 22

Enhancing Education Accessibility: Portable Microservers for...

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22nd International Learning and technology Conference, L and T 2025

作者： Dhuny, Riyad Ying, Fangli University of Technology School of Innovative Technologies and Engineering 11134 Mauritius East China University of Science and Technology Department of Computer Science Shanghai China

ISBN: (纸本)9798331532215

This study explores the potential of LAMP stack open-source applications for embedded computing to enhance education accessibility, particularly in resource-constrained environments. The research focuses on converting a Raspberry Pi (RPI) device into a portable microserver hosting a computer-Based Testing (CBT) system, specifically TCExam. The methodology involves porting TCExam to RPI hardware, testing its operability, and evaluating its performance under various user loads. Experiments were conducted using RPI versions 4 and 5, with Apache JMeter employed for performance testing. Results demonstrate that RPI4 can sustain at least 30 concurrent users, while RPI5 supports over 50 users in a load-testing environment. The study applies Menascé's equation to estimate the theoretical number of supported users, revealing the potential for microservers to accommodate larger class sizes than initially expected. The research highlights the feasibility of using affordable, portable hardware for deploying CBT systems, which is particularly beneficial in areas with limited internet connectivity or power sources. This approach offers educators a secure, offline assessment environment and the flexibility to prepare and test the system before deployment. The study concludes that microservers like RPI can increasingly run applications previously restricted to server rooms, opening new possibilities for improving education quality in developing countries. Future work suggests integrating local Large Language Models for offline use in remote areas, further enhancing the educational toolkit available to educators in resource-constrained environments. © 2025 IEEE.

关键词： Integration testing

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