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Fabric Defect Detection using Deep Learning 3

Fabric Defect Detection using Deep Learning

引用

3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Kailasam, Khushi Singh, Jaswant Gopika, G.S. Sreekrishna, M. Supriya, S. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Fabric defect detection plays a crucial role in the textile manufacturing industry's quality control process since it guarantees that customers will receive perfect products. Deep Learning (DL) models have demonstrated a great deal of promise in recent years for accurately identifying fabric defects. The proposed fabric defect identification method combines the best features of two architectures: You Only Look Once (YOLOv4) and Region-based Convolutional Neural Network (R-CNN). While YOLOv4 offers quicker and more effective detection, R-CNN generates region recommendations that allow accurate localization of fabric defects. There are two stages in the proposed system. Initially, region proposals are detected using the R-CNN feature extractor and then a convolutional neural network is used for defect classification. YOLOv4 is used for performing object detection in the second step, where the flaws are concurrently classified and localized. The tests carried out on a fabric defect dataset show that the proposed method outperforms the R-CNN and YOLOv4 models separately. The proposed system is appropriate for real-Time fabric flaw detection applications in a variety of sectors due to its high detection accuracy and quick processing time. This study demonstrates how well R-CNN and YOLOv4 together work for fabric flaw detection, providing hope for future developments in the area. © 2024 IEEE.

关键词： Object detection

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Using textile capacitive sensors to train synchronized movement of hands

Using textile capacitive sensors to train synchronized movem...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Chen, Shih-Hung Wu, Ze-We Liao, Kuo-Cheng Chen, Chi-Chun Ming Young Biomedical Corp. Miaoli Taiwan National Chin-Yi University of Technology Program Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan National Chin-Yi University of Technology Dept. of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

This study introduced a capacitive sensing interactive game platform aimed at promoting emotional stability, which we have named the 'Sunrise and Sunset' game. This game primarily consists of two pieces of regular textile fabric enveloping conductive silver fabric. A microcontroller was employed to extract the sensed capacitive values, and a game named 'Sunrise and Sunset' is designed to complement the slow raising and lowering of both hands. The development of this gaming platform has the potential to offer a novel method of emotional management, particularly in high-stress living environments. It can serve as an effective relaxation tool, aiding individuals in emotional balance, anxiety reduction, and stress alleviation. Simultaneously, this platform can contribute to the promotion of mental well-being, providing an engaging and beneficial means for people to manage their emotions and moods. © 2024 IEEE.

关键词： capacitive sensing emotional stability game relaxation tool silver fabric

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Machine Learning based Anxiety Detection using Physiological Signals and Context Features 2

Machine Learning based Anxiety Detection using Physiological...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Jain, Arushi Kumar, Rajeev National Institute of Technology Hamirpur Dept. of Computer Science and Engineering Hamirpur India

ISBN: (纸本)9798350371314

Anxiety is a common mental disorder that affects millions of people worldwide. Anxiety can be detected using physiological signals such as heart rate, skin conductance, blood pressure, and respiration. Machine learning is the ability to learn from data and forecast future events. Physiological signals can be analyzed and classified into different anxiety levels using machine learning. This paper aims to present the contribution of context features in detection of anxiety using physiological signals via machine learning. It is an imbalanced multiclassification problem using ECG and EDA signals with context features collected from 15 healthy individuals. These signals of sampling rate 700 Hz collected using RespiBAN from the Wearable Stress and Affect Detection (WESAD) dataset are used here with the 6-STAI questionnaire and the context collected about the subject during the experiment. Scores for each subject are calculated from this questionnaire which categorized subjects into different levels of anxiety. Multiple features are extracted from these signals and scaling is done on the extracted features. With the help of different data balancing techniques and different machine learning algorithms, this study will classify anxiety into three classes, namely, low, moderate and high. Classification of anxiety with fusion of multimodal physiological signals gave better results as compared to single modal physiological signals with an accuracy of 89.8% with ECG signal, 85.9% with EDA signal and 96.7% with ECG and EDA both using Gradient Tree Boosting algorithm with LOOCV after random oversampling. While, the best results are achieved when multimodal physiological signals are fused with context features, it gives the accuracy of 97.3% with Gradient Tree Boosting and LOOCV algorithm. © 2024 IEEE.

关键词： Electrocardiograms

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Cryptocurrency with Blockchain technology - A Literature Review 9

Cryptocurrency with Blockchain Technology - A Literature Rev...

引用

9th Somaiya International Conference on technology and information Management, SICTIM 2023

作者： Parab, Leenata J. Nitnaware, Prashant P. Patil, Shweta A. Dept. of Computer Engineering Pillai College of Engineering New Panvel Navi Mumbai India Dept. of Information Technology Engineering Pillai College of Engineering New Panvel Navi Mumbai India

ISBN: (纸本)9798350333299

Blockchain is a new and secured way to store data. Multiple computers connected to form a series of blocks that holds transaction data and the information of the previous block. It is a technology used to develop first cryptocurrency Bitcoin. Blockchain technology is generating new opportunitiesin other industry domains along with financial sector. Thus, in this technology era, Blockchain becomes the future of *** growing popularity of cryptocurrencies as financial assets is an indicator of a revolution in the financial sector. It led to understanding the need-to-know blockchain technology in dept. while investing money in cryptocurrencies. In this article, we will discuss the evolving blockchain technology related to cryptocurrency. We will outline the concept of cryptocurrency with blockchain technology in general along with strength and weaknesses, real world use cases, future trends and more. Cryptocurrencies currently havesignificant commercial value in the financial sector, so knowing the technology behind the implementation of cryptocurrencies will help investors and businesses easily predict financial market trends. This article will provide the general overview ofblockchain technology and its features by studying various research papers, articles, news media and many more. © 2023 IEEE.

关键词： Blockchain

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IoT Enabled Water Flow Management and Monitoring System for Hydroelectric Power Generation

IoT Enabled Water Flow Management and Monitoring System for ...

引用

2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Shaikat, Abu Salman Tasnim, Rumana Hasan, Jahid Zonayed, Md Hussein, Molla Rashied Shithil, Shaekh Mohammad Al Amin, Abdullah Laurentian University of Sudbury Dept. of Bharti School of Engineering and Computer Science Ontario Canada World University of Bangladesh Dept. of Mechatronics Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Mechanical Engineering Dhaka Bangladesh University of Asia Pacific Dept. of Computer Science & Engineering Dhaka Bangladesh University Technology Malaysia Dept. of Mechanical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350369106

Water energy sources play a crucial role, particularly in providing electrical energy through hydroelectric power. Both the water flow rate and the water head determine the potential energy available from water. The hydroelectric system's energy generation directly depends on the water flow rate. A lower flow rate means less water is available to generate energy, even if the head is high. It is essential to increase the potential of the water flow so that it can generate enough electricity. Traditionally, the flow rate is restricted due to the limited capacity of single penstock. This work addresses this issue by introducing multiple penstock-based water flow management systems. Also, conventional water flow monitoring systems often face limitations, like relying on manual measurement, as it only provides periodic data, making it difficult to respond quickly to changes in flow conditions. IoT-based flow sensors leverage IoT technology to monitor water flow and transmit flow data in real-time, enabling operators to detect fluctuating flow conditions and implement timely adjustments to processes, therefore enhancing the overall efficiency. This work aims to design and develop an IoT-enabled prototype hydroelectric power generation system using multiple water flows for enhancing power generation efficiency. The prototype indicates the use of multiple pipes as penstocks in a dam. Additionally, if generating a significant amount of electricity is not required, the water flow can be discontinued, thereby automatically closing each pipeline. This work uses flow sensors, Kitsware software, Arduino IoT Cloud, and an LCD display to generate flow measurement data every ten seconds from multiple pipelines. Moreover, the system automatically generates Excel data and graphically represents multiple water flows, enabling the user to compare the water flow data. The completed work resulted in a hydropower system demonstrating enhanced output and effective compatibility with IoT for

关键词： Penstocks

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Multinomial Naive Bayes Based Machine Learning Analysis of Twitter Sentiment 2

Multinomial Naive Bayes Based Machine Learning Analysis of T...

引用

2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Saravanan, U.K.Balaji Vijay, M. Shreedhar, T. Rajasekar, G. Yashwanth, R. Shakthipriya, P. Sona College of Technology Information Technology Salem India Vmkv Enginerring College Dept. of Computer Science and Engineering Salem India

ISBN: (纸本)9798350347579

Sentiment analysis identifies and categorizes thoughts and feelings expressed in the source text. Social media uses tweets, status updates, and blog columns to generate sensitive data. SA on user-generated data helps understand crowd opinion. Due to slang and spelling errors, Twitter sentiment analysis (TSA) is much more complex than general sentiment analysis. Every day, millions of tweets are generated on various topics. Machine learning algorithms can be used to solve this problem-the proposed machine learning algorithm includes preprocessing, weight calculation, feature extraction, and classification. First, we collected a Kaggle dataset from Twitter sentiment analysis. In the second step, the data preprocessing steps mainly include stop words, emoji, and repeated characters in words and URLs due to their low importance. Then, we used tokenization, normalization, stemming, and lemmatization to achieve better results. In the third step, we computed weights using word2vec to represent the tweets. In the fourth step, we use a feature extraction method consisting of TF-IDF and word embedding to check based on word and noun counts. Finally, TSA can be used to explore sentiment using Multinomial Naïve Bayes (MNB) and Maximum Entropy classification. The proposed machine learning algorithm achieves better results with TSA in terms of recognition accuracy, recall, precision and F-measure. © 2023 IEEE.

关键词： Sentiment analysis

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Simulator for Swarm Feature Selection Algorithms for Classification Problem 2

Simulator for Swarm Feature Selection Algorithms for Classif...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Kulkarni, Mayuri Diwakar Shahade, Makarand Deore, Shailesh Khandekar, Nilesh Bhavsar, Ishwari Garud, Jayesh Ahirrao, Kunal SVKM's Institute of Technology Dept. of Computer Engineering Dhule India SSVPS's B.S. Deore CoE Dept. of Computer Engineering Dhule India

ISBN: (纸本)9798350360523

Swarm Intelligence (SI) algorithms address difficult optimization and decision-making issues using cues from collective behavior. These behaviors are inspired by social creatures like ants, bees, and birds. Using these creatures' behaviors algorithms have demonstrated amazing promise in several fields, such as distributed computing, robotics, and optimization. Assessing SI algorithms' performance under various conditions and scenarios is still a significant difficulty, though. We introduce a new Swarm Intelligence Algorithm Performance Analysis Simulator (SIAPAS) to tackle this issue. This proposed simulator introduces a novel approach to automating the process of selection for Swarm Intelligence (SI) algorithms on datasets. The system empowers users to upload datasets into a specialized simulator, which then orchestrates a comparative analysis using a suite of SI algorithms. The simulator evaluates and selects the most suitable algorithm based on the dataset's characteristics and problem domain. This provides the optimal selected features by the respective algorithms based on measured parameters. © 2024 IEEE.

关键词： Swarm intelligence

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Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

Arrhythmia Classification Using Graph Neural Networks Based ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Han, Seungwoo Tokyo University of Agriculture and Technology Dept. of Electrical Engineering and Computer Science Japan

ISBN: (纸本)9798350386226

With the advancements in graph neural network (GNN), there has been increasing interest in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted ECG features and applied a GNN to classify arrhythmias. Our work was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that proposed method can be considered an approach for arrhythmia classification. © 2024 IEEE.

关键词： Graph neural networks

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EVALUATION OF THE EFFECTIVENESS OF READING ALOUD A PROGRAM CODE IN LEARNING PROGRAMMING BASED ON BRAIN-ACTIVITY MEASUREMENTS 21

EVALUATION OF THE EFFECTIVENESS OF READING ALOUD A PROGRAM C...

引用

21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024

作者： Tanaka, Tetsuo Horiuchi, Ryo Ueda, Mari Dept. of Information and Computer Sciences Faculty of Information Technology Kanagawa Institute of Technology 1030 Shimo-ogino Kanagawa Atsugi243-0292 Japan Dept. of Information Media Faculty of Information Technology Kanagawa Institute of Technology 1030 Shimo-ogino Kanagawa Atsugi243-0292 Japan

ISBN: (纸本)9789898704610

We evaluate the effectiveness of reading aloud a program code in learning programming from a neuroscientific perspective by measuring brain activity using a near-infrared spectroscopy device. The results show that when reading aloud and then reading silently, brain activity increases during reading aloud;a similar trend is observed when the reading order is reversed. In addition, written tests, such as C-language fill-in-the-blank questions, result in higher scores when the participants read aloud than when they read silently. Furthermore, no characteristic differences in brain activity were found between students with good programming skills and those without. Significant differences were found for students with average programming skills. In a future study, we will increase the number of participants to verify the above results and clarify the relation between brain activity and the difficulty level of the program code, reading speed, subvocalization during silent reading, and other factors. Our aim is to establish an efficient learning programming method by employing the reading-aloud technique. © 2024 Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024. All rights reserved.

关键词： Active learning

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Network Intrusion Detection System Using Decision Tree and KNN Algorithm 3

Network Intrusion Detection System Using Decision Tree and K...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Singh, Km Shivani Singh, Khundrapam Johnson National Institute of Technology Dept. of Computer Science and Engineering Manipur Imphal India

ISBN: (纸本)9798350309409

The complexity of attacks is increasing, making it increasingly difficult to effectively discover breaches. A network intrusion detection system is needed to handle the aforementioned problem. An automated software program called a network intrusion detection system alerts administrators when someone attempts to compromise the system by participating in hazardous behaviors. A system or network is protected from harmful invasions by hardware and software firewalls. They function like filters, removing any data that could put the system or network at risk. We aimed to design the best intrusion detection system possible so that it could distinguish between 'normal' and 'attacked' categories of network data with high accuracy. The best accuracy can be obtained by using soft computing techniques like Decision Trees and KNN. We tested a variety of techniques, such as preprocessing the data, feature selection, principal component analysis (PCA) reduction, standardization, and normalization, in order to increase our model's accuracy scores. We also assess the two approaches' results according to their accuracy in order to decide which way is more effective. © 2024 IEEE.

关键词： Decision trees

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