The document discusses techniques for automated fabric defect detection. It begins with an introduction to the importance of automated defect detection systems for quality control in the textile industry. It then categorizes fabric defect detection techniques into three groups: statistical, spectral, and model-based approaches. The majority of the document describes various statistical approaches that have been used for defect detection, including methods based on morphological operations, thresholding, fractal dimension, edge detection, co-occurrence matrices, autocorrelation functions, eigenfilters, local linear transforms, histograms, and local binary patterns. Spectral and model-based approaches are also briefly mentioned. The goal of the review is to evaluate and compare different computer vision-based defect detection algorithms.
IRJET- Real Time Vision System for Thread Counting in Woven FabricIRJET Journal
This document presents a real-time vision system for automatically counting threads in woven fabrics. It begins with an introduction to woven fabrics and the traditional manual method of counting threads, which is time-consuming and prone to errors. It then describes a proposed automated system using image processing techniques like blob detection and feature matching to track fabric motion and recognize warp and weft counts in real-time with high accuracy. The system is tested on denim fabric and is able to accurately count the number of warp and weft threads in the sample image. The automated approach provides an improvement over manual counting by reducing labor costs and eliminating human errors.
IRJET- Defect Detection in Fabric using Image Processing TechniqueIRJET Journal
This document discusses using image processing techniques for defect detection in fabric. It proposes an automated approach using computer vision to identify defects, which can help minimize costs and improve quality control in the textile industry. Currently, fabric inspection is often done manually by inspectors, which is time-consuming and expensive. The paper outlines a system that uses image acquisition, preprocessing, feature extraction, detection and classification to automatically identify fabric defects from digital images of fabrics. Wavelet transforms are discussed as an effective technique for feature extraction that can characterize normal and defective textures in fabrics. An automated system could help reduce labor costs and waste while improving production efficiency.
1) The study uses Failure Mode and Effects Analysis (FMEA) to analyze and prioritize causes of broken filament defects in a direct spin drawing yarn production process.
2) FMEA identified the 10 highest risk causes, which were mostly related to detection methods and machine parameter settings.
3) Improvements to detection methods and frequencies reduced the defective rate from 3.35% to 1.76%. Machine parameters were suggested to further optimize using design of experiments.
IRJET- Melanoma Detection using Feed Forward Neural Network and Therapeutic S...IRJET Journal
This document describes a proposed method for detecting melanoma using a feed forward neural network and suggesting appropriate treatment. The method involves preprocessing skin images using median filtering to remove noise, segmenting the affected skin cells using an improved k-means clustering algorithm, extracting features using texture and color analysis, and classifying images as melanoma or nevus using a neural network classifier. The results will be tested on a medical image dataset to evaluate the accuracy of the proposed melanoma detection system.
A REVIEW OF DETECTION OF STRUCTURAL VARIABILITY IN TEXTILES USING IMAGE PROCE...Journal For Research
As a result of globalization & also increasing competition, it has become very important for any industry to develop solutions regarding the quality of products. Effective monitoring and control, better data predictions, quick response to query is necessary for effective Quality Control. For a long time the fabric defects inspection process is still carried out with human visual inspection. However, they cannot detect more than 60% of the overall defects for the fabric if it is moving at a faster rate and thus the process becomes insufficient and costly. Therefore, automatic fabric defect inspection is required to reduce the cost and time waste caused by defects. Studies have been carried out in this area, where in different inspection systems for detection of defects and properties of fibers, yarns and fabrics have been looked upon. The purpose of this paper is to categorize and describe the same. In this paper an attempt has been made to present the survey on these different inspection systems for detection of defects and properties in various areas of textiles and its role in the overall quality control.
IRJET- Color and Texture based Feature Extraction for Classifying Skin Ca...IRJET Journal
This document presents a method to classify skin cancer images as malignant or benign using color and texture feature extraction with support vector machine (SVM) and convolutional neural network (CNN) classifiers. The method segments skin cancer images from the ISIC dataset using active contour modeling. Color features are extracted using histogram analysis in HSV color space. Texture features like mean, variance, skewness and kurtosis are calculated statistically. Both SVM and CNN are used to classify the images based on these features, and CNN achieves higher average accuracy than SVM. The CNN approach is therefore proven more effective for skin cancer classification using color and texture features.
This document summarizes the development of an automated drapability tester that quantifies the draping behavior of reinforcement fabrics. The tester combines force measurement with optical analysis to detect defects like gaps, loops, and wrinkles during forming. It uses cameras and laser scanning to capture these defects, allowing drapability effects to be quantified. Test results on non-crimp fabrics and woven fabrics show how the tester can measure forces, gap widths, fiber misalignment, and sample deformation at different forming levels. The automated tester provides detailed drapability data to support composite part and process design.
IRJET - Detection of Skin Cancer using Convolutional Neural NetworkIRJET Journal
This document presents a method for detecting skin cancer using convolutional neural networks. The proposed method involves collecting skin images, preprocessing them by removing noise and segmenting regions of interest, extracting features like asymmetry, border, color, and diameter, performing dimensionality reduction using principal component analysis, calculating dermoscopy scores, and classifying images as malignant or benign using a convolutional neural network (CNN) model. The CNN model achieves 92.5% accuracy in classification. The document provides background on skin cancer and challenges with traditional biopsy methods. It describes the system architecture including data collection, preprocessing, segmentation, feature extraction, and classification steps. Key aspects of CNNs like convolutional, ReLU, pooling, and fully connected layers are also overviewed
IRJET- Real Time Vision System for Thread Counting in Woven FabricIRJET Journal
This document presents a real-time vision system for automatically counting threads in woven fabrics. It begins with an introduction to woven fabrics and the traditional manual method of counting threads, which is time-consuming and prone to errors. It then describes a proposed automated system using image processing techniques like blob detection and feature matching to track fabric motion and recognize warp and weft counts in real-time with high accuracy. The system is tested on denim fabric and is able to accurately count the number of warp and weft threads in the sample image. The automated approach provides an improvement over manual counting by reducing labor costs and eliminating human errors.
IRJET- Defect Detection in Fabric using Image Processing TechniqueIRJET Journal
This document discusses using image processing techniques for defect detection in fabric. It proposes an automated approach using computer vision to identify defects, which can help minimize costs and improve quality control in the textile industry. Currently, fabric inspection is often done manually by inspectors, which is time-consuming and expensive. The paper outlines a system that uses image acquisition, preprocessing, feature extraction, detection and classification to automatically identify fabric defects from digital images of fabrics. Wavelet transforms are discussed as an effective technique for feature extraction that can characterize normal and defective textures in fabrics. An automated system could help reduce labor costs and waste while improving production efficiency.
1) The study uses Failure Mode and Effects Analysis (FMEA) to analyze and prioritize causes of broken filament defects in a direct spin drawing yarn production process.
2) FMEA identified the 10 highest risk causes, which were mostly related to detection methods and machine parameter settings.
3) Improvements to detection methods and frequencies reduced the defective rate from 3.35% to 1.76%. Machine parameters were suggested to further optimize using design of experiments.
IRJET- Melanoma Detection using Feed Forward Neural Network and Therapeutic S...IRJET Journal
This document describes a proposed method for detecting melanoma using a feed forward neural network and suggesting appropriate treatment. The method involves preprocessing skin images using median filtering to remove noise, segmenting the affected skin cells using an improved k-means clustering algorithm, extracting features using texture and color analysis, and classifying images as melanoma or nevus using a neural network classifier. The results will be tested on a medical image dataset to evaluate the accuracy of the proposed melanoma detection system.
A REVIEW OF DETECTION OF STRUCTURAL VARIABILITY IN TEXTILES USING IMAGE PROCE...Journal For Research
As a result of globalization & also increasing competition, it has become very important for any industry to develop solutions regarding the quality of products. Effective monitoring and control, better data predictions, quick response to query is necessary for effective Quality Control. For a long time the fabric defects inspection process is still carried out with human visual inspection. However, they cannot detect more than 60% of the overall defects for the fabric if it is moving at a faster rate and thus the process becomes insufficient and costly. Therefore, automatic fabric defect inspection is required to reduce the cost and time waste caused by defects. Studies have been carried out in this area, where in different inspection systems for detection of defects and properties of fibers, yarns and fabrics have been looked upon. The purpose of this paper is to categorize and describe the same. In this paper an attempt has been made to present the survey on these different inspection systems for detection of defects and properties in various areas of textiles and its role in the overall quality control.
IRJET- Color and Texture based Feature Extraction for Classifying Skin Ca...IRJET Journal
This document presents a method to classify skin cancer images as malignant or benign using color and texture feature extraction with support vector machine (SVM) and convolutional neural network (CNN) classifiers. The method segments skin cancer images from the ISIC dataset using active contour modeling. Color features are extracted using histogram analysis in HSV color space. Texture features like mean, variance, skewness and kurtosis are calculated statistically. Both SVM and CNN are used to classify the images based on these features, and CNN achieves higher average accuracy than SVM. The CNN approach is therefore proven more effective for skin cancer classification using color and texture features.
This document summarizes the development of an automated drapability tester that quantifies the draping behavior of reinforcement fabrics. The tester combines force measurement with optical analysis to detect defects like gaps, loops, and wrinkles during forming. It uses cameras and laser scanning to capture these defects, allowing drapability effects to be quantified. Test results on non-crimp fabrics and woven fabrics show how the tester can measure forces, gap widths, fiber misalignment, and sample deformation at different forming levels. The automated tester provides detailed drapability data to support composite part and process design.
IRJET - Detection of Skin Cancer using Convolutional Neural NetworkIRJET Journal
This document presents a method for detecting skin cancer using convolutional neural networks. The proposed method involves collecting skin images, preprocessing them by removing noise and segmenting regions of interest, extracting features like asymmetry, border, color, and diameter, performing dimensionality reduction using principal component analysis, calculating dermoscopy scores, and classifying images as malignant or benign using a convolutional neural network (CNN) model. The CNN model achieves 92.5% accuracy in classification. The document provides background on skin cancer and challenges with traditional biopsy methods. It describes the system architecture including data collection, preprocessing, segmentation, feature extraction, and classification steps. Key aspects of CNNs like convolutional, ReLU, pooling, and fully connected layers are also overviewed
IRJET- Detection & Classification of Melanoma Skin CancerIRJET Journal
This document discusses methods for detecting and classifying melanoma skin cancer. It begins with an introduction to skin cancer and the importance of detecting melanoma early. It then reviews literature on existing techniques for melanoma detection using image processing and machine learning. The proposed system uses image segmentation, feature extraction using the ABCD criteria, principal component analysis to select key features, and support vector machine classification to determine whether images contain cancerous or non-cancerous lesions. The system aims to provide an accurate and fast evaluation of skin lesions to help in melanoma diagnosis.
1) The document presents a method for detecting skin lesions using support vector machines (SVM). It involves preprocessing images, segmenting the skin lesion region, extracting features related to shape, color, and texture, and classifying lesions as melanoma or non-melanoma using an SVM classifier.
2) Features extracted include asymmetry, border irregularity, compactness, color ratios in HSV, RGB and LAB color spaces, and texture features from the gray-level co-occurrence matrix.
3) An SVM classifier is used for classification as it can accurately classify data by finding the optimal separating hyperplane that maximizes the margin between the classes. The method achieved efficient classification of lesions.
Human Skin Cancer Recognition and Classification by Unified Skin Texture and ...IOSR Journals
This document presents a novel method for automatically segmenting skin lesions in macroscopic images using iterative stochastic region merging based on discrete wavelet transformation. It aims to address challenges like illumination variation, presence of hair, irregular skin color variation, and multiple unhealthy skin regions. The method divides an input image into regions, extracts features like color, texture, skewness and kurtosis, then classifies the image using knowledge-based classification. Experimental results on 60 real images show the proposed method achieves lower segmentation error than level set active contours, skin lesion segmentation, and multidirectional gradient vector flow methods.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
IRJET- DNA Fragmentation Pattern and its Application in DNA Sample Type Class...IRJET Journal
This document proposes a framework for classifying DNA sample types using DNA fragmentation patterns. It involves several steps: (1) applying Gaussian blurring and bilateral filtering to reduce noise from images of fragmentation patterns, (2) extracting the region of interest, (3) calculating gray-level co-occurrence matrix features such as contrast and correlation, (4) using a k-nearest neighbors classifier to classify samples, and (5) segmenting images based on the classification. The results showed near 100% accuracy in classifying hundreds of DNA samples as different types based on their fragmentation patterns.
This document presents a method for detecting melanoma skin cancer using image processing and machine learning techniques. Images of skin lesions are first segmented using active contour models. Features like color, size, shape and texture are then extracted from the segmented images. Texture is analyzed using local binary patterns (LBP). The extracted features are used to classify images as melanoma or non-melanoma using a support vector machine (SVM) classifier. The goal is to develop an automated system for early detection of melanoma to help reduce death rates from this dangerous form of skin cancer.
11.[37 46]segmentation and feature extraction of tumors from digital mammogramsAlexander Decker
This document summarizes a proposed computer-aided detection (CAD) system for segmenting tumors and extracting features from digital mammograms to help radiologists diagnose breast cancer. The proposed CAD system includes five stages: 1) collecting input images and extracting regions of interest, 2) enhancing the regions of interest, 3) segmenting the tumors, 4) filtering noises, and 5) extracting texture and statistical features. Texture features like Haralick features extracted from gray-level co-occurrence matrices are calculated to classify tumors as benign or malignant. The system is tested on mammogram images from the Mammographic Image Analysis Society database to evaluate the segmentation and feature extraction methods.
Ghaziabad, India - Early Detection of Various Types of Skin Cancer Using Deep...Vidit Goyal
This presentation discusses using convolutional neural networks (CNNs) for intelligent skin cancer detection. CNNs can help address issues like limited doctor availability in rural areas and the high cost of cancer detection. Research shows CNNs can achieve 91% accurate cancer diagnosis compared to 79% for experienced physicians. The presentation then explains how CNNs work, discussing concepts like convolutional layers, pooling layers, activation functions, and transfer learning. It describes applying a CNN model trained on ImageNet to a skin cancer dataset in order to recognize 3 common cancer types. The goal is developing an automatic early-stage cancer detection system using CNNs and cloud computing to reduce human effort and costs while improving accuracy.
Skin Cancer Detection using Image Processing in Real Timeijtsrd
Machine learning is a fascinating topic its astonishing how a small change in the evaluation values may result in an unfathomable number of outcomes. The goal of this study is to develop a model that uses image processing to identify skin cancer. We will later use the model in real life through an android application. Sunami Dasgupta | Soham Das | Sayani Hazra Pal "Skin Cancer Detection using Image-Processing in Real-Time" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46384.pdf Paper URL : https://www.ijtsrd.com/computer-science/artificial-intelligence/46384/skin-cancer-detection-using-imageprocessing-in-realtime/sunami-dasgupta
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
Image Classification And Skin cancer detectionEman Othman
The document discusses using a CNN model to classify skin cancer images as either benign or malignant. It first prepares the skin cancer image data by reducing noise to make detection easier. It then builds a CNN model with four convolutional layers, two max pooling layers, two dropout layers, and one flatten layer and two dense layers. When tested on a skin cancer dataset, the model achieved 80.3% accuracy. With more effort, the authors aim to build a more efficient model that achieves higher accuracy.
Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning ...Zabir Al Nazi Nabil
Industrial pollution resulting in ozone layer depletion has influenced
increased UV radiation in recent years which is a major environmental risk factor for invasive skin cancer Melanoma and other keratinocyte cancers. The incidence of deaths from Melanoma has risen worldwide in past two decades.
Deep learning has been employed successfully for dermatologic diagnosis. In
this work, we present a deep learning based scheme to automatically segment
skin lesions and detect melanoma from dermoscopy images. U-Net was used
for segmenting out the lesion from surrounding skin. The limitation of utilizing
deep neural networks with limited medical data was solved with data augmentation and transfer learning. In our experiments, U-Net was used with spatial
dropout to solve the problem of overfitting and different augmentation effects
were applied on the training images to increase data samples. The model was
evaluated on two different datasets. It achieved a mean dice score of 0.87 and a
mean jaccard index of 0.80 on ISIC 2018 dataset. The trained model was assessed on PH² dataset where it achieved a mean dice score of 0.93 and a mean
jaccard index of 0.87 with transfer learning. For classification of malignant
melanoma, a DCNN-SVM model was used where we compared state of the art
deep nets as feature extractors to find the applicability of transfer learning in
dermatologic diagnosis domain. Our best model achieved a mean accuracy of
92% on PH² dataset. The findings of this study is expected to be useful in cancer diagnosis research.
Published at IJCCI 2018. Source code available at https://github.com/zabir-nabil/lesion-segmentation-melanoma-tl
This document discusses ways to improve productivity in wet processing, which includes bleaching, dyeing, printing, and finishing of textiles. It identifies several key factors that affect productivity, including selection and procurement of dyes and chemicals, machine modifications, process standardization, and use of information technology. Using concentrated chemicals instead of diluted ones, directly importing dyes, and identifying the optimal levels of dye and chemical usage can help reduce costs and improve productivity in wet processing.
Design of woven fabrics using DYF1.0 specialized software codeIOSRjournaljce
DYF1.0 software code for design of woven fabrics is presented. The need for fast calculation of the parameters of a new fabric is discussed. The module for designing of fabrics and its six menus are presented in details. The parameters needed for setting the winding, warping, sizing and weaving of the new fabric, are presented in terms of their calculation using the DYF1.0 software code.
IRJET- Analysis of Skin Cancer using ABCD TechniqueIRJET Journal
This document describes a proposed method for analyzing skin cancer using the ABCD technique. It begins with an introduction to skin cancer and melanoma. The proposed method involves preprocessing the skin lesion image using filters to reduce noise, segmenting the lesion from the image, extracting features using the ABCD parameters of asymmetry, border, color, and diameter, and then identifying malignant melanoma based on the feature analysis. If melanoma is detected early using this technique, it could help reduce healthcare costs by lowering the need for biopsies. The method aims to accurately detect melanoma for early treatment when survival rates are highest.
IRJET- Skin Cancer Prediction using Image Processing and Deep LearningIRJET Journal
This document discusses using deep learning and image processing to develop a model for skin cancer detection. It begins with an introduction to the rising problem of skin cancer cases and importance of early detection. Next, it describes the process of visual inspection and dermoscopy images currently used by dermatologists. The document then reviews literature on existing methods for skin cancer detection using machine learning approaches like convolutional neural networks (CNNs). Deeper CNN models that can learn from limited data are highlighted. Finally, the document outlines the fundamentals of different types of skin cancer and concludes by acknowledging guidance received to complete the project.
Detection of erythemato-squamous diseases using AR-CatfishBPSO-KSVMsipij
Nowadays, one of the most important usages of machine learning is diagnosis of diverse diseases. In this work, we introduces a diagnosis model based on Catfish binary particle swarm optimization (CatfishBPSO), kernelized support vector machines (KSVM) and association rules (AR) as our feature selection method to diagnose erythemato-squamous diseases. The proposed model consisted of two stages. In the first stage, AR is used to select the optimal feature subset from the original feature set. Next, based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy and CatfishBPSO is a promising tool for global searching, a CatfishBPSO based approach is employed for parameter determination of KSVM. Experimental results show that the proposed AR-CatfishBPSO-KSVM model achieves 99.09% classification accuracy using 24 features of the erythemato-squamous disease dataset which shows that our proposed method is more accurate compared to other popular methods in this literature like Support vector machines and AR-MLP (association rules - multilayer perceptron). It should be mentioned that we took our dataset from University of California Irvine machine learning database.
Design and Manufacturing of Receiving GaugeIRJET Journal
This document summarizes the design and manufacturing of a receiving gauge. The receiving gauge is used to inspect parts for dimensional accuracy by checking dimensions precisely according to standards. It was designed based on the specifications and tolerances of the part being inspected. The receiving gauge provides accurate and precise inspection in a time-saving and cost-effective manner compared to a coordinate measuring machine. It is suitable for use in mass production environments. The receiving gauge design is presented, including diagrams of the gauge and how it is used to inspect parts. Advantages such as reduced inspection time and cost are highlighted.
IRJET- Process Parameter Optimization for FDM 3D PrinterIRJET Journal
This document discusses optimizing process parameters for 3D printing on a fused deposition modeling (FDM) 3D printer. It begins with an introduction to FDM 3D printing technology and describes the methodology used. Specimens were prepared with variations in layer thickness, shell thickness, and fill density. The document discusses the different process parameters, the manufacturing process which includes modeling, printing, and finishing specimens, and uses a Taguchi design of experiments approach to optimize the parameters to maximize strength and minimize surface roughness.
FABRIC DEFECT DETECTION BASED ON IMPROVED FASTER RCNNijaia
In the production process of fabric, defect detection plays an important role in the control of product
quality. Consider that traditional manual fabric defect detection method are time-consuming and
inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the
manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention
module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can
infer the attention map from the intermediate feature map and multiply the attention map to adaptively
refine the feature. This method improve the performance of classification and detection without increase
the computation-consuming. The experiment results show that Faster RCNN with attention module can
efficient improve the classification accuracy.
IRJET- Fabric Defect Classification using Modular Neural NetworkIRJET Journal
The document describes a study on classifying fabric defects using a modular neural network approach. 164 fabric images were analyzed to extract wavelet transform coefficients as features. A modular neural network with one hidden layer of 8 processing elements was found to accurately classify defects 92.65% of the time when trained on the images. The algorithm is presented as an effective alternative to traditional fabric defect analysis methods for evaluating fabric quality.
IRJET- Detection & Classification of Melanoma Skin CancerIRJET Journal
This document discusses methods for detecting and classifying melanoma skin cancer. It begins with an introduction to skin cancer and the importance of detecting melanoma early. It then reviews literature on existing techniques for melanoma detection using image processing and machine learning. The proposed system uses image segmentation, feature extraction using the ABCD criteria, principal component analysis to select key features, and support vector machine classification to determine whether images contain cancerous or non-cancerous lesions. The system aims to provide an accurate and fast evaluation of skin lesions to help in melanoma diagnosis.
1) The document presents a method for detecting skin lesions using support vector machines (SVM). It involves preprocessing images, segmenting the skin lesion region, extracting features related to shape, color, and texture, and classifying lesions as melanoma or non-melanoma using an SVM classifier.
2) Features extracted include asymmetry, border irregularity, compactness, color ratios in HSV, RGB and LAB color spaces, and texture features from the gray-level co-occurrence matrix.
3) An SVM classifier is used for classification as it can accurately classify data by finding the optimal separating hyperplane that maximizes the margin between the classes. The method achieved efficient classification of lesions.
Human Skin Cancer Recognition and Classification by Unified Skin Texture and ...IOSR Journals
This document presents a novel method for automatically segmenting skin lesions in macroscopic images using iterative stochastic region merging based on discrete wavelet transformation. It aims to address challenges like illumination variation, presence of hair, irregular skin color variation, and multiple unhealthy skin regions. The method divides an input image into regions, extracts features like color, texture, skewness and kurtosis, then classifies the image using knowledge-based classification. Experimental results on 60 real images show the proposed method achieves lower segmentation error than level set active contours, skin lesion segmentation, and multidirectional gradient vector flow methods.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
IRJET- DNA Fragmentation Pattern and its Application in DNA Sample Type Class...IRJET Journal
This document proposes a framework for classifying DNA sample types using DNA fragmentation patterns. It involves several steps: (1) applying Gaussian blurring and bilateral filtering to reduce noise from images of fragmentation patterns, (2) extracting the region of interest, (3) calculating gray-level co-occurrence matrix features such as contrast and correlation, (4) using a k-nearest neighbors classifier to classify samples, and (5) segmenting images based on the classification. The results showed near 100% accuracy in classifying hundreds of DNA samples as different types based on their fragmentation patterns.
This document presents a method for detecting melanoma skin cancer using image processing and machine learning techniques. Images of skin lesions are first segmented using active contour models. Features like color, size, shape and texture are then extracted from the segmented images. Texture is analyzed using local binary patterns (LBP). The extracted features are used to classify images as melanoma or non-melanoma using a support vector machine (SVM) classifier. The goal is to develop an automated system for early detection of melanoma to help reduce death rates from this dangerous form of skin cancer.
11.[37 46]segmentation and feature extraction of tumors from digital mammogramsAlexander Decker
This document summarizes a proposed computer-aided detection (CAD) system for segmenting tumors and extracting features from digital mammograms to help radiologists diagnose breast cancer. The proposed CAD system includes five stages: 1) collecting input images and extracting regions of interest, 2) enhancing the regions of interest, 3) segmenting the tumors, 4) filtering noises, and 5) extracting texture and statistical features. Texture features like Haralick features extracted from gray-level co-occurrence matrices are calculated to classify tumors as benign or malignant. The system is tested on mammogram images from the Mammographic Image Analysis Society database to evaluate the segmentation and feature extraction methods.
Ghaziabad, India - Early Detection of Various Types of Skin Cancer Using Deep...Vidit Goyal
This presentation discusses using convolutional neural networks (CNNs) for intelligent skin cancer detection. CNNs can help address issues like limited doctor availability in rural areas and the high cost of cancer detection. Research shows CNNs can achieve 91% accurate cancer diagnosis compared to 79% for experienced physicians. The presentation then explains how CNNs work, discussing concepts like convolutional layers, pooling layers, activation functions, and transfer learning. It describes applying a CNN model trained on ImageNet to a skin cancer dataset in order to recognize 3 common cancer types. The goal is developing an automatic early-stage cancer detection system using CNNs and cloud computing to reduce human effort and costs while improving accuracy.
Skin Cancer Detection using Image Processing in Real Timeijtsrd
Machine learning is a fascinating topic its astonishing how a small change in the evaluation values may result in an unfathomable number of outcomes. The goal of this study is to develop a model that uses image processing to identify skin cancer. We will later use the model in real life through an android application. Sunami Dasgupta | Soham Das | Sayani Hazra Pal "Skin Cancer Detection using Image-Processing in Real-Time" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46384.pdf Paper URL : https://www.ijtsrd.com/computer-science/artificial-intelligence/46384/skin-cancer-detection-using-imageprocessing-in-realtime/sunami-dasgupta
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
Image Classification And Skin cancer detectionEman Othman
The document discusses using a CNN model to classify skin cancer images as either benign or malignant. It first prepares the skin cancer image data by reducing noise to make detection easier. It then builds a CNN model with four convolutional layers, two max pooling layers, two dropout layers, and one flatten layer and two dense layers. When tested on a skin cancer dataset, the model achieved 80.3% accuracy. With more effort, the authors aim to build a more efficient model that achieves higher accuracy.
Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning ...Zabir Al Nazi Nabil
Industrial pollution resulting in ozone layer depletion has influenced
increased UV radiation in recent years which is a major environmental risk factor for invasive skin cancer Melanoma and other keratinocyte cancers. The incidence of deaths from Melanoma has risen worldwide in past two decades.
Deep learning has been employed successfully for dermatologic diagnosis. In
this work, we present a deep learning based scheme to automatically segment
skin lesions and detect melanoma from dermoscopy images. U-Net was used
for segmenting out the lesion from surrounding skin. The limitation of utilizing
deep neural networks with limited medical data was solved with data augmentation and transfer learning. In our experiments, U-Net was used with spatial
dropout to solve the problem of overfitting and different augmentation effects
were applied on the training images to increase data samples. The model was
evaluated on two different datasets. It achieved a mean dice score of 0.87 and a
mean jaccard index of 0.80 on ISIC 2018 dataset. The trained model was assessed on PH² dataset where it achieved a mean dice score of 0.93 and a mean
jaccard index of 0.87 with transfer learning. For classification of malignant
melanoma, a DCNN-SVM model was used where we compared state of the art
deep nets as feature extractors to find the applicability of transfer learning in
dermatologic diagnosis domain. Our best model achieved a mean accuracy of
92% on PH² dataset. The findings of this study is expected to be useful in cancer diagnosis research.
Published at IJCCI 2018. Source code available at https://github.com/zabir-nabil/lesion-segmentation-melanoma-tl
This document discusses ways to improve productivity in wet processing, which includes bleaching, dyeing, printing, and finishing of textiles. It identifies several key factors that affect productivity, including selection and procurement of dyes and chemicals, machine modifications, process standardization, and use of information technology. Using concentrated chemicals instead of diluted ones, directly importing dyes, and identifying the optimal levels of dye and chemical usage can help reduce costs and improve productivity in wet processing.
Design of woven fabrics using DYF1.0 specialized software codeIOSRjournaljce
DYF1.0 software code for design of woven fabrics is presented. The need for fast calculation of the parameters of a new fabric is discussed. The module for designing of fabrics and its six menus are presented in details. The parameters needed for setting the winding, warping, sizing and weaving of the new fabric, are presented in terms of their calculation using the DYF1.0 software code.
IRJET- Analysis of Skin Cancer using ABCD TechniqueIRJET Journal
This document describes a proposed method for analyzing skin cancer using the ABCD technique. It begins with an introduction to skin cancer and melanoma. The proposed method involves preprocessing the skin lesion image using filters to reduce noise, segmenting the lesion from the image, extracting features using the ABCD parameters of asymmetry, border, color, and diameter, and then identifying malignant melanoma based on the feature analysis. If melanoma is detected early using this technique, it could help reduce healthcare costs by lowering the need for biopsies. The method aims to accurately detect melanoma for early treatment when survival rates are highest.
IRJET- Skin Cancer Prediction using Image Processing and Deep LearningIRJET Journal
This document discusses using deep learning and image processing to develop a model for skin cancer detection. It begins with an introduction to the rising problem of skin cancer cases and importance of early detection. Next, it describes the process of visual inspection and dermoscopy images currently used by dermatologists. The document then reviews literature on existing methods for skin cancer detection using machine learning approaches like convolutional neural networks (CNNs). Deeper CNN models that can learn from limited data are highlighted. Finally, the document outlines the fundamentals of different types of skin cancer and concludes by acknowledging guidance received to complete the project.
Detection of erythemato-squamous diseases using AR-CatfishBPSO-KSVMsipij
Nowadays, one of the most important usages of machine learning is diagnosis of diverse diseases. In this work, we introduces a diagnosis model based on Catfish binary particle swarm optimization (CatfishBPSO), kernelized support vector machines (KSVM) and association rules (AR) as our feature selection method to diagnose erythemato-squamous diseases. The proposed model consisted of two stages. In the first stage, AR is used to select the optimal feature subset from the original feature set. Next, based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy and CatfishBPSO is a promising tool for global searching, a CatfishBPSO based approach is employed for parameter determination of KSVM. Experimental results show that the proposed AR-CatfishBPSO-KSVM model achieves 99.09% classification accuracy using 24 features of the erythemato-squamous disease dataset which shows that our proposed method is more accurate compared to other popular methods in this literature like Support vector machines and AR-MLP (association rules - multilayer perceptron). It should be mentioned that we took our dataset from University of California Irvine machine learning database.
Design and Manufacturing of Receiving GaugeIRJET Journal
This document summarizes the design and manufacturing of a receiving gauge. The receiving gauge is used to inspect parts for dimensional accuracy by checking dimensions precisely according to standards. It was designed based on the specifications and tolerances of the part being inspected. The receiving gauge provides accurate and precise inspection in a time-saving and cost-effective manner compared to a coordinate measuring machine. It is suitable for use in mass production environments. The receiving gauge design is presented, including diagrams of the gauge and how it is used to inspect parts. Advantages such as reduced inspection time and cost are highlighted.
IRJET- Process Parameter Optimization for FDM 3D PrinterIRJET Journal
This document discusses optimizing process parameters for 3D printing on a fused deposition modeling (FDM) 3D printer. It begins with an introduction to FDM 3D printing technology and describes the methodology used. Specimens were prepared with variations in layer thickness, shell thickness, and fill density. The document discusses the different process parameters, the manufacturing process which includes modeling, printing, and finishing specimens, and uses a Taguchi design of experiments approach to optimize the parameters to maximize strength and minimize surface roughness.
FABRIC DEFECT DETECTION BASED ON IMPROVED FASTER RCNNijaia
In the production process of fabric, defect detection plays an important role in the control of product
quality. Consider that traditional manual fabric defect detection method are time-consuming and
inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the
manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention
module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can
infer the attention map from the intermediate feature map and multiply the attention map to adaptively
refine the feature. This method improve the performance of classification and detection without increase
the computation-consuming. The experiment results show that Faster RCNN with attention module can
efficient improve the classification accuracy.
IRJET- Fabric Defect Classification using Modular Neural NetworkIRJET Journal
The document describes a study on classifying fabric defects using a modular neural network approach. 164 fabric images were analyzed to extract wavelet transform coefficients as features. A modular neural network with one hidden layer of 8 processing elements was found to accurately classify defects 92.65% of the time when trained on the images. The algorithm is presented as an effective alternative to traditional fabric defect analysis methods for evaluating fabric quality.
Crack Detection in Ceramic Tiles using Zoning and Edge Detection Methodsijtsrd
The quality control process in ceramic tile industry plays crucial role to enhance the quality standards. At present the quality analysis is mostly done manually. Manual inspection is not so efficient and it is labour intensive. Defect detection accuracy is lower due to human mistakes and unforgiving mechanical condition. To vanquish these issues, an automated inspection system for crack tiles that depends on image processing methods is presented. The tiles are examined using image processing concept using matlab software. The processing is very less compared to that of manual inspection. The automated inspection system can replace the manual ceramic tile detection system more efficiently and with better accuracy. Bhagyashree R K | S. A. Angadi"Crack Detection in Ceramic Tiles using Zoning and Edge Detection Methods" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15724.pdf http://www.ijtsrd.com/computer-science/other/15724/crack-detection-in-ceramic-tiles-using-zoning-and-edge-detection-methods/bhagyashree-r-k
This document discusses crack detection in concrete structures using deep learning techniques. It begins by describing traditional manual inspection and image processing methods for crack detection, noting limitations such as being time-consuming, inaccurate, and unable to handle complex image data. The document then introduces convolutional neural networks (CNNs) as a deep learning technique for crack detection, which can automatically learn features from image data without predefined feature extraction. It provides details on common CNN architecture components like convolution, activation and pooling layers. The document concludes by outlining the process of developing a CNN model for crack detection, including collecting a dataset, training the model, and evaluating the trained model's performance using classification metrics.
Automatic fabric defect detection employing deep learningIJECEIAES
A major issue for fabric quality inspection is in the detection of defaults, it has become an extremely challenging goal for the textile industry to minimize costs in both production and quality inspection. The quality inspection is currently done manually by professionals; hence the need for the implementation of a fast, powerful, robust, and intelligent machine vision system in order to achieve high global quality, uniformity, and consistency of fabrics and to increase productivity. Consequently, the automatic inspection control process can improve productivity and enhance product quality. This article describes the approach used in developing a convolutional neural network for identifying fabric defects from input images of fabric surfaces. The proposed neural network is a pre-trained convolutional model ‘DetectNet’, it was adapted to be more efficient to the fabric image feature extraction. The developed model is capable of successfully distinguishing between defective fabric and non-defective with 93% accuracy for the first model and 96% for the second model.
A REVIEW ON LATENT FINGERPRINT RECONSTRUCTION METHODSIRJET Journal
This document reviews several methods for reconstructing latent fingerprints from minutiae points. It begins with an introduction to fingerprint features and representation. It then summarizes 10 research papers on latent fingerprint reconstruction methods. These include approaches using deep learning networks, fusion of minutiae and pore features, progressive feedback mechanisms, orientation field and phase reconstruction, and other techniques. The document concludes that while reconstruction methods have improved, there remains a performance gap when matching reconstructed prints to originals. The purpose is to provide a comparative analysis of existing latent fingerprint reconstruction methods.
Identification and Rejection of Defective Ceramic Tile using Image Processing...IJMTST Journal
Manual Ceramic Tile inspection process is tedious if human operator is employed to look for defective tiles and their elimination. The plain ceramic tiles often have the following types of defects viz Cracks, Blobs and pin holes [1]. The fatigue of human operator deteriorates the quality of the tile being produced. In this paper a novel and simple automatic tile defect identification and elimination system is proposed. The proposed system is built around MATLAB and ARDUINO. The systems performance is evaluated in terms of accuracy and time taken for detection. The Proposed system promises superior performance when compared to the other existing system
A New Deep Learning Based Technique To Detect Copy Move Forgery In Digital Im...IRJET Journal
This document proposes a new deep learning technique to detect copy move forgery in digital images. It uses a VGG16 CNN model to extract feature vectors from image blocks. Euclidean distance is used to measure similarity between feature vectors and detect matching blocks, indicating potential forgery. The proposed method is evaluated on the CoMoFoD dataset and achieves higher F1-scores than ResNet50 and EfficientNet models, detecting forged regions more accurately.
IRJET- Fabric Defect Detection using Discrete Wavelet TransformIRJET Journal
This document describes a system for detecting defects in fabric images using discrete wavelet transform and a K-nearest neighbor classifier. The system takes an image using a camera, converts it to grayscale, applies discrete wavelet transform to decompose the image, and then uses a KNN classifier to classify the image as defective or defect-free based on extracted features. The system is able to detect common fabric defects like vertical yarn missing, horizontal yarn missing, and stains. It is implemented using MATLAB software and uses a basic hardware setup of a camera, motor, and lighting. Test results showed the system could accurately detect different types of synthetic fabric defects in real-time images.
Fingerprint Based Gender Classification by using Fuzzy C- Means and Neural Ne...IRJET Journal
The document presents a method for classifying gender using fingerprint images. It proposes extracting features from fingerprints like ridge count and thickness using Gabor filters and binarization. A fuzzy C-means algorithm and neural network are used to classify gender based on the extracted features. The method is evaluated on a dataset of 400 fingerprints (200 male, 200 female) and achieves over 90% accuracy in classifying gender. It concludes there is still room for improvement by exploring additional feature extraction and classification techniques to increase accuracy of fingerprint-based gender classification.
Analyze Gear Failures and Identify Defects in Gear System for Vehicles Using ...IOSR Journals
This document summarizes a research paper that analyzes gear failures and identifies defects in gear systems for vehicles using digital image processing. The paper proposes a gear defect recognition system that uses computer vision and local thresholding techniques to identify possible defects in gears. The recognizer processes digital images of gears, applies restoration and thresholding techniques to generate binary images, counts the number of teeth to determine if it matches expected specifications, and can identify defective areas. Experimental results on plastic gear images demonstrate the system's ability to detect defects like differences in tooth counts and surface blemishes. The paper concludes that future work could apply machine learning to make defect detection more robust and accurate over time.
Implementation of Lane Line Detection using HoughTransformation and Gaussian ...IRJET Journal
This document summarizes a research paper that implements lane line detection in images and videos using the Hough transform and Gaussian smoothing. The methodology section outlines the steps taken, which include converting the image to grayscale, applying Gaussian smoothing for noise reduction, using Canny edge detection to extract edges, and applying the Hough transform to detect lane lines. Key algorithms discussed are Gaussian smoothing, Canny edge detection, Hough transformation, grayscale conversion, and defining a region of interest. The implementation section demonstrates applying these techniques to detect lane lines, including masking the image, edge detection, and identifying the lane lines.
Review of three categories of fingerprint recognition 2prjpublications
This document reviews three categories of fingerprint recognition techniques: correlation-based, minutiae-based, and pattern-based. Minutiae-based matching is the most popular as minutiae points require less storage than images but it is more time-consuming than other methods. The correlation-based method matches entire fingerprint images and handles poor quality prints better but is computationally expensive. Pattern-based matching compares fingerprint swirl/loop patterns but requires consistent image alignment. Challenges include enhancing low-quality images and improving feature extraction, matching, and alignment algorithms.
Review of three categories of fingerprint recognitionprjpublications
This document reviews three categories of fingerprint recognition techniques: correlation-based, minutiae-based, and pattern-based. Minutiae-based matching is the most popular as it uses ridge endings and bifurcations, but it is time-consuming. Pattern-based matching uses a virtual core point and pattern points for alignment. Correlation-based matching superimposes images and computes pixel correlations but is computationally expensive. Challenges include handling low quality images and improving feature extraction and matching accuracy and speed.
Review of three categories of fingerprint recognition 2prj_publication
This document reviews three categories of fingerprint recognition techniques: minutiae-based matching, pattern based matching, and correlation-based matching. Minutiae-based matching is the most popular and analyzes ridge endings and bifurcations. Pattern based matching uses fingerprint patterns like loops and whorls. Correlation-based matching overlays images and computes pixel correlations. The document also discusses challenges with fingerprint identification like low quality images and evaluates different enhancement and feature extraction methods.
IRJET-Implementation of CAD system for Cancer Detection using SVM based Class...IRJET Journal
This document presents a proposed CAD system for cancer detection using SVM classification. The system aims to automatically detect, segment, and classify breast masses in mammograms. It first extracts the region of interest from mammograms and performs segmentation using fuzzy C-means clustering. It then extracts texture and geometric features from segmented masses. Feature selection is used to select the most important features, which are then classified as benign or malignant using an SVM classifier. The proposed system seeks to develop a fully automated CAD system for breast cancer detection and classification without manual intervention.
The document describes a skin cancer detection mobile application that uses image processing and machine learning. The application analyzes skin images for characteristics of melanoma like asymmetry, border, color, diameter and texture. It trains a model using the MobileNet-v2 architecture on datasets containing thousands of images. The trained model achieves 70% accuracy in detecting melanoma and differentiating normal and abnormal skin lesions when tested on new images. The application has potential to help identify skin cancer in early stages and assist medical practitioners.
Survey on Different Methods for Defect DetectionIRJET Journal
This document discusses various methods for defect detection in images and products. It begins with an introduction to digital image processing and its applications such as enhancement, restoration, and segmentation. Defect detection is important for quality control in manufacturing. Traditionally, human inspection was used but it has disadvantages. The document then surveys statistical approaches like autocorrelation functions and co-occurrence matrices. Spectral approaches including Fourier transforms, wavelet transforms, and Gabor filters are also covered. Model-based approaches using autoregressive models are summarized as well. The advantages and disadvantages of each method are compared. In conclusion, the document states that combining approaches may provide better results than individual methods for defect detection.
A Review Paper on Personal Identification with An Efficient Method Of Combina...IRJET Journal
This document summarizes a review paper on personal identification using an efficient method to combine left and right palmprint images. It discusses combining left and right palmprint images at the matching score level by obtaining three matching scores - from left palmprint matching, right palmprint matching, and a crossing match between the left query and right training images. Multibiometric techniques like this can improve accuracy over single biometrics. The paper also reviews various palmprint identification methods including line-based, coding-based, representation-based, and SIFT-based approaches.
Similar to A Review on Fabric Defect Detection Techniques (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.