This document reviews a method for age estimation using deep learning. Specifically, it uses a CNN model called FTDFA that is fine-tuned on face images to extract age features. These features are then input into a Maximum Joint Probability Classifier (MJPCCR) for classification. Experiments on three datasets show the MJPCCR achieves higher accuracy than an SVM classifier, and features from the penultimate layer of the CNN work best. Overall, the proposed method achieves state-of-the-art results for age estimation from facial images.
Comparision of Clustering Algorithms usingNeural Network Classifier for Satel...IJERA Editor
This paper presents a hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The pre-processed image is segmented using the hybrid genetic-Artificial Bee Colony(ABC) algorithm that is developed by hybridizing the ABC and FCM to obtain the effective segmentation in satellite image and classified using neural network . The performance of the proposed hybrid algorithm is compared with the algorithms like, k-means, Fuzzy C means(FCM), Moving K-means, Artificial Bee Colony(ABC) algorithm, ABC-GA algorithm, Moving KFCM and KFCM algorithm.
Proposed algorithm for image classification using regression-based pre-proces...IJECEIAES
This document presents a proposed algorithm for digital image classification that uses regression-based pre-processing and recognition models. The algorithm focuses on optimizing pre-processing results such as accuracy and precision. Simulation results show the proposed method outperforms existing algorithms with higher classification accuracy and precision, as well as higher image matching percentages based on image analytics, compared to K-means, Naive Bayes, and Ada Boost classification approaches. Experimental evaluation of the algorithm using 20 test images in a machine learning simulation tool demonstrates improved performance over other methods.
Multiple Ant Colony Optimizations for Stereo MatchingCSCJournals
The stereo matching problem, which obtains the correspondence between right and left images, can be cast as a search problem. The matching of all candidates in the same line forms a 2D optimization task and the two dimensional (2D) optimization is a NP-hard problem. There are two characteristics in stereo matching. Firstly, the local optimization process along each scan-line can be done concurrently; secondly, there are some relationship among adjacent scan-lines can be explored to promote the matching correctness. Although there are many methods, such as GCPs, GGCPs are proposed, but these so called GCPs maybe not be ground. The relationship among adjacent scan-lines is posteriori, that is to say the relationship can only be discovered after every optimization is finished. The Multiple Ant Colony Optimization(MACO) is efficient to solve large scale problem. It is a proper way to settle down the stereo matching task with constructed MACO, in which the master layer values the sub-solutions and propagate the reliability after every local optimization is finished. Besides, whether the ordering and uniqueness constraints should be considered during the optimization is discussed, and the proposed algorithm is proved to guarantee its convergence to find the optimal matched pairs.
IRJET- Interactive Image Segmentation with Seed PropagationIRJET Journal
The document proposes a method called Adaptive Constraint Propagation Cut (ACPCut) for interactive image segmentation that uses seed propagation to learn a global discriminative structure from limited user inputs. ACPCut adaptively propagates characteristics of user markers across the image to avoid bias while preserving data coherence. Experimental results show that ACPCut achieves state-of-the-art performance in interactive image segmentation in terms of effectiveness and efficiency compared to other methods.
Image Segmentation Using Two Weighted Variable Fuzzy K MeansEditor IJCATR
Image segmentation is the first step in image analysis and pattern recognition. Image segmentation is the process of dividing an image into different regions such that each region is homogeneous. The accurate and effective algorithm for segmenting image is very useful in many fields, especially in medical image. This paper presents a new approach for image segmentation by applying k-means algorithm with two level variable weighting. In image segmentation, clustering algorithms are very popular as they are intuitive and are also easy to implement. The K-means and Fuzzy k-means clustering algorithm is one of the most widely used algorithms in the literature, and many authors successfully compare their new proposal with the results achieved by the k-Means and Fuzzy k-Means. This paper proposes a new clustering algorithm called TW-fuzzy k-means, an automated two-level variable weighting clustering algorithm for segmenting object. In this algorithm, a variable weight is also assigned to each variable on the current partition of data. This could be applied on general images and/or specific images (i.e., medical and microscopic images). The proposed TW-Fuzzy k-means algorithm in terms of providing a better segmentation performance for various type of images. Based on the results obtained, the proposed algorithm gives better visual quality as compared to several other clustering methods.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of...IOSR Journals
This document presents a two-stage methodology for optimizing the location and sizing of distributed generation (DG) resources in transmission and distribution systems to reduce losses. The first stage uses a fuzzy inference system to determine optimal DG locations based on loss and voltage indices. The second stage uses a gravitational search algorithm to compute the optimal DG sizes at the identified locations to minimize losses. The proposed approach is tested on IEEE 5-bus, 14-bus, and a 62-bus Indian practical system, demonstrating reductions in losses and improvements in voltage profiles.
IRJET-Multimodal Image Classification through Band and K-Means ClusteringIRJET Journal
This document proposes a bilayer graph-based learning framework for multimodal image classification using limited labeled pixels. It constructs a simple graph in the first layer where each vertex is a pixel and edge weights encode pixel similarity. Unsupervised learning estimates grouping relations among pixels. These relations form a hypergraph in the second layer, on which semisupervised learning classifies pixels to address challenges of complex relationships and limited labels in multimodal images. The framework effectively exploits the underlying data structure.
Comparision of Clustering Algorithms usingNeural Network Classifier for Satel...IJERA Editor
This paper presents a hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The pre-processed image is segmented using the hybrid genetic-Artificial Bee Colony(ABC) algorithm that is developed by hybridizing the ABC and FCM to obtain the effective segmentation in satellite image and classified using neural network . The performance of the proposed hybrid algorithm is compared with the algorithms like, k-means, Fuzzy C means(FCM), Moving K-means, Artificial Bee Colony(ABC) algorithm, ABC-GA algorithm, Moving KFCM and KFCM algorithm.
Proposed algorithm for image classification using regression-based pre-proces...IJECEIAES
This document presents a proposed algorithm for digital image classification that uses regression-based pre-processing and recognition models. The algorithm focuses on optimizing pre-processing results such as accuracy and precision. Simulation results show the proposed method outperforms existing algorithms with higher classification accuracy and precision, as well as higher image matching percentages based on image analytics, compared to K-means, Naive Bayes, and Ada Boost classification approaches. Experimental evaluation of the algorithm using 20 test images in a machine learning simulation tool demonstrates improved performance over other methods.
Multiple Ant Colony Optimizations for Stereo MatchingCSCJournals
The stereo matching problem, which obtains the correspondence between right and left images, can be cast as a search problem. The matching of all candidates in the same line forms a 2D optimization task and the two dimensional (2D) optimization is a NP-hard problem. There are two characteristics in stereo matching. Firstly, the local optimization process along each scan-line can be done concurrently; secondly, there are some relationship among adjacent scan-lines can be explored to promote the matching correctness. Although there are many methods, such as GCPs, GGCPs are proposed, but these so called GCPs maybe not be ground. The relationship among adjacent scan-lines is posteriori, that is to say the relationship can only be discovered after every optimization is finished. The Multiple Ant Colony Optimization(MACO) is efficient to solve large scale problem. It is a proper way to settle down the stereo matching task with constructed MACO, in which the master layer values the sub-solutions and propagate the reliability after every local optimization is finished. Besides, whether the ordering and uniqueness constraints should be considered during the optimization is discussed, and the proposed algorithm is proved to guarantee its convergence to find the optimal matched pairs.
IRJET- Interactive Image Segmentation with Seed PropagationIRJET Journal
The document proposes a method called Adaptive Constraint Propagation Cut (ACPCut) for interactive image segmentation that uses seed propagation to learn a global discriminative structure from limited user inputs. ACPCut adaptively propagates characteristics of user markers across the image to avoid bias while preserving data coherence. Experimental results show that ACPCut achieves state-of-the-art performance in interactive image segmentation in terms of effectiveness and efficiency compared to other methods.
Image Segmentation Using Two Weighted Variable Fuzzy K MeansEditor IJCATR
Image segmentation is the first step in image analysis and pattern recognition. Image segmentation is the process of dividing an image into different regions such that each region is homogeneous. The accurate and effective algorithm for segmenting image is very useful in many fields, especially in medical image. This paper presents a new approach for image segmentation by applying k-means algorithm with two level variable weighting. In image segmentation, clustering algorithms are very popular as they are intuitive and are also easy to implement. The K-means and Fuzzy k-means clustering algorithm is one of the most widely used algorithms in the literature, and many authors successfully compare their new proposal with the results achieved by the k-Means and Fuzzy k-Means. This paper proposes a new clustering algorithm called TW-fuzzy k-means, an automated two-level variable weighting clustering algorithm for segmenting object. In this algorithm, a variable weight is also assigned to each variable on the current partition of data. This could be applied on general images and/or specific images (i.e., medical and microscopic images). The proposed TW-Fuzzy k-means algorithm in terms of providing a better segmentation performance for various type of images. Based on the results obtained, the proposed algorithm gives better visual quality as compared to several other clustering methods.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of...IOSR Journals
This document presents a two-stage methodology for optimizing the location and sizing of distributed generation (DG) resources in transmission and distribution systems to reduce losses. The first stage uses a fuzzy inference system to determine optimal DG locations based on loss and voltage indices. The second stage uses a gravitational search algorithm to compute the optimal DG sizes at the identified locations to minimize losses. The proposed approach is tested on IEEE 5-bus, 14-bus, and a 62-bus Indian practical system, demonstrating reductions in losses and improvements in voltage profiles.
IRJET-Multimodal Image Classification through Band and K-Means ClusteringIRJET Journal
This document proposes a bilayer graph-based learning framework for multimodal image classification using limited labeled pixels. It constructs a simple graph in the first layer where each vertex is a pixel and edge weights encode pixel similarity. Unsupervised learning estimates grouping relations among pixels. These relations form a hypergraph in the second layer, on which semisupervised learning classifies pixels to address challenges of complex relationships and limited labels in multimodal images. The framework effectively exploits the underlying data structure.
This project is to retrieve the similar geographic images from the dataset based on the features extracted.
Retrieval is the process of collecting the relevant images from the dataset which contains more number of
images. Initially the preprocessing step is performed in order to remove noise occurred in input image with
the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant
Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the
features from the images. After this process, the relevant geographic images are retrieved from the dataset
by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which
are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to
perform reliable recognition, it is important that the feature extracted from the training image be
detectable even under changes in image scale, noise and illumination. The GLCM calculates how often a
pixel with gray level value occurs. While the focus is on image retrieval, our project is effectively used in
the applications such as detection and classification.
This document presents a proposed method for efficient image compression using integer wavelet transform (IWT) employing the lifting scheme (LS). The lifting scheme allows for faster computation compared to conventional discrete wavelet transform by exploiting both high-pass and low-pass filter values. Simulation results using MATLAB show the proposed method achieves superior compression performance in terms of encoding time, decoding time, peak signal-to-noise ratio, and compression ratio for various test images. The integer wavelet transform with lifting scheme provides a robust algorithm for truly lossless image compression by decomposing both approximation and detailed image contents.
Technique to Hybridize Principle Component and Independent Component Algorith...IRJET Journal
This document presents a hybrid approach to face recognition that combines Principal Component Analysis (PCA) and Independent Component Analysis (ICA). It uses a score-based fusion process to combine the two techniques. The proposed system extracts features using both PCA and ICA in parallel. It then classifies images using two separate neural networks. Finally, it combines the classification results from the two networks using a score-based strategy. The experiment results on the ORL face database show that the hybrid system achieves higher score values and lower error rates than using either PCA or ICA alone, demonstrating the effectiveness of the combined approach.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Adaptive threshold for moving objects detection using gaussian mixture modelTELKOMNIKA JOURNAL
Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
A fast fpga based architecture for measuring the distance betweenIAEME Publication
This paper presents an FPGA-based architecture for measuring the Manhattan distance between two RGB color images. The architecture takes RGB pixel values from each image as input and calculates the absolute difference between corresponding pixel values. It sums all the absolute differences and divides by the total number of pixels to obtain the normalized Manhattan distance. The architecture was implemented on a Xilinx Spartan 3 FPGA and can operate at 171.585 MHz, faster than software solutions. Experimental results demonstrating distance calculations on sample image pairs are presented. The FPGA implementation allows real-time Manhattan distance measurement for applications like image retrieval.
Hybrid method for achieving Pareto front on economic emission dispatch IJECEIAES
In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multiobjective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.
Optimal neural network models for wind speed predictionIAEME Publication
The document presents two neural network models - multilayer perceptron (MLP) and radial basis function (RBF) networks - for wind speed prediction. It evaluates these models on real wind speed data collected over one year from wind farms in Coimbatore, India. The experimental results show that the RBF and MLP models can improve wind speed prediction accuracy compared to other approaches, according to statistical metrics like coefficient of determination, mean absolute error, root mean square error, and mean bias error.
Medial Axis Transformation based Skeletonzation of Image Patterns using Image...IOSR Journals
1) The document discusses extracting the medial axis transform (MAT) of an image pattern using the Euclidean distance transform. The image is first converted to binary, then the Euclidean distance transform is used to compute the distance of each non-zero pixel to the closest zero pixel.
2) The medial axis transform represents the core or skeleton of an image pattern. There are different algorithms for extracting the skeleton or medial axis, including sequential and parallel algorithms. The skeleton provides a simple representation that preserves topological and size characteristics of the original shape.
3) The document provides background on medial axis transforms and different skeletonization algorithms. It then describes preparing the binary image and applying the Euclidean distance transform to extract the MAT and skeleton
Genetic Algorithm Processor for Image Noise Filtering Using Evolvable HardwareCSCJournals
This document describes a genetic algorithm processor for image noise filtering using evolvable hardware. It proposes an evolvable hardware architecture that uses a genetic algorithm and virtual reconfigurable circuit to evolve image filters without prior information. The genetic algorithm is used to generate configuration bits that control the connections and functions of processing elements in the virtual reconfigurable circuit. This allows the filter to adapt its design to different noise types through evolution. Experimental results show the evolved hardware filter more effectively removes Gaussian and salt-and-pepper noise compared to conventional filters. Future work could explore applying this approach to additional noise models and image processing tasks.
This document summarizes a research paper that proposes a new method called the "selective rotation based preliminary plan" for squeezing color images using directional wavelet transforms. The method first determines the direction of maximum energy in an image and rotates the image in that direction. It then applies a conventional 2D wavelet transform for decomposition. The method outperforms JPEG2000 according to experimental results. It concentrates more energy in low frequency subbands and provides better reconstructed image quality at higher compression ratios compared to JPEG2000.
IRJET- Performance Analysis of Optimization Techniques by using ClusteringIRJET Journal
This document discusses optimization techniques for clustering algorithms. It introduces fuzzy bee colony optimization (FBCO) and compares its performance to other swarm algorithms like fuzzy c-means (FCM) and fuzzy particle swarm optimization (FPSO). FBCO is motivated by the natural behaviors of bee colonies and aims to avoid local minima problems. The document provides background on clustering, describes the FCM and FPSO algorithms, and proposes a FBCO algorithm to improve clustering performance.
Validation Study of Dimensionality Reduction Impact on Breast Cancer Classifi...ijcsit
A fundamental problem in machine learning is identifying the most representative subset of features from
which we can construct a predictive model for a classification task. This paper aims to present a validation
study of dimensionality reduction effect on the classification accuracy of mammographic images. The
studied dimensionality reduction methods were: locality-preserving projection (LPP), locally linear
embedding (LLE), Isometric Mapping (ISOMAP) and spectral regression (SR). We have achieved high
rates of classifications. In some combinations the classification rate was 100%. But in most of the cases the
classification rate is about 95%. It was also found that the classification rate increases with the size of the
reduced space and the optimal value of space dimension is 60. We proceeded to validate the obtained
results by measuring some validation indices such as: Xie-Beni index, Dun index and Alternative Dun
index. The measurement of these indices confirms that the optimal value of reduced space dimension is
d=60.
Real-time traffic sign detection and recognition using Raspberry Pi IJECEIAES
This document presents a real-time traffic sign detection and recognition system developed using a Raspberry Pi 3 processor. The system uses a Raspberry Pi camera to record real-time video and the TensorFlow machine learning algorithm to detect and identify traffic signs based on a dataset of 500 labeled images across 5 sign classes. The system's accuracy, delay, and reliability were evaluated during testbed implementation considering different environmental and sign conditions. Results showed the system achieved over 90% accuracy on average with a maximum detection delay of 3.44 seconds, demonstrating reliable performance even for broken, faded, or low-light signs. This real-time traffic sign recognition system developed with affordable hardware has potential to increase road safety.
This document presents a method for speeding up fractal image compression using genetic algorithms. Fractal image compression explores self-similarity in images to compress them but the search for similar domain blocks is time-consuming. The proposed method uses genetic algorithms to more efficiently search for similar domain blocks. Experimental results on several test images show that the genetic algorithm approach can compress images faster than standard fractal compression with acceptable image quality and compression ratios. Key parameters like population size and error threshold that affect the genetic search are also analyzed.
DETECTION OF HUMAN BLADDER CANCER CELLS USING IMAGE PROCESSINGprj_publication
Bladder cancer presents a spectrum of different diatheses. A precise assessment for
individualized treatment depends on the accuracy of the initial diagnosis. In this method the
performance of the level set segmentation is subject to appropriate initialization and optimal
configuration of controlling parameters, which require substantial manual intervention. A
new fuzzy level set algorithm is proposed in this paper to facilitate medical image
segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy
clustering. The Spatial induced fuzzy c-means using pixel classification and level set
methods are utilizing dynamic variational boundaries for image segmentation. The
controlling parameters of level set evolution are also estimated from the results of clustering.
The fuzzy level set algorithm is enhanced with locally regularized evolution. Such
improvements facilitate level set manipulation and lead to more robust segmentation.
Performance evaluation of the proposed algorithm was carried on medical images
Disease Classification using ECG Signal Based on PCA Feature along with GA & ...IRJET Journal
This document describes a method for classifying ECG signals to detect cardiovascular diseases using principal component analysis (PCA), genetic algorithms, and artificial neural networks. PCA is used to extract features from ECG signals. A genetic algorithm is then used to select optimal features and train an artificial neural network classifier. The method is tested on datasets from Physionet.org to classify ECG signals as normal or indicating conditions like bradycardia or tachycardia with high accuracy. The goal is to develop an automated system for ECG analysis and heart disease diagnosis.
This document presents a traffic sign recognition system using a convolutional neural network (CNN) model. The authors train the CNN model on a German traffic sign dataset containing over 50,000 images across 43 classes. The proposed CNN architecture contains 4 VGGNet blocks with convolutional, max pooling, dropout and batch normalization layers. The model is trained for 45 epochs and achieves 96.9% accuracy and 11.4% test loss on the test set, outperforming other baseline models. The trained CNN model can accurately classify traffic sign images to assist with applications like self-driving cars.
IRJET- A Novel Gabor Feed Forward Network for Pose Invariant Face Recogni...IRJET Journal
The document proposes an Analytic Gabor Feed Forward Network (AGFN) for pose invariant face recognition. AGFN uses a single hidden layer to efficiently extract Gabor features from raw face images without computationally expensive convolutions. Features from multiple orientations and scales are fused at the output layer. The network is trained using total error rate minimization to find a globally optimal solution without iterations. Experiments on several public face datasets showed the AGFN approach achieved accurate face recognition while being computationally efficient.
IRJET- Crowd Density Estimation using Novel Feature DescriptorIRJET Journal
This document proposes a new texture feature-based approach for crowd density estimation using Completed Local Binary Pattern (CLBP). The approach divides images into blocks and further divides blocks into cells to compute CLBP features for each cell. A multi-class Support Vector Machine (SVM) classifier is trained to classify each block into one of four crowd density categories (Very Low, Low, Medium, High). Experiments on the PETS 2009 dataset show the proposed CLBP descriptor achieves 95% accuracy and outperforms other texture descriptors for crowd density estimation.
IRJET- Mango Classification using Convolutional Neural NetworksIRJET Journal
This document presents research on classifying different types of mangoes using convolutional neural networks (CNNs). The researchers collected a dataset of over 5000 mango images across 5 classes. They used transfer learning with the Inception v3 CNN model pre-trained on ImageNet, removing the final classification layer and retraining a new one for the mango classes. The CNN achieved over 99% accuracy on the test set at classifying mango types, demonstrating that CNNs can effectively perform fine-grained image classification of mangoes and distinguish between similar types.
Realtime face matching and gender prediction based on deep learningIJECEIAES
Face analysis is an essential topic in computer vision that dealing with human faces for recognition or prediction tasks. The face is one of the easiest ways to distinguish the identity people. Face recognition is a type of personal identification system that employs a person’s personal traits to determine their identity. Human face recognition scheme generally consists of four steps, namely face detection, alignment, representation, and verification. In this paper, we propose to extract information from human face for several tasks based on recent advanced deep learning framework. The proposed approach outperforms the results in the state-of-the-art.
This project is to retrieve the similar geographic images from the dataset based on the features extracted.
Retrieval is the process of collecting the relevant images from the dataset which contains more number of
images. Initially the preprocessing step is performed in order to remove noise occurred in input image with
the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant
Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the
features from the images. After this process, the relevant geographic images are retrieved from the dataset
by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which
are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to
perform reliable recognition, it is important that the feature extracted from the training image be
detectable even under changes in image scale, noise and illumination. The GLCM calculates how often a
pixel with gray level value occurs. While the focus is on image retrieval, our project is effectively used in
the applications such as detection and classification.
This document presents a proposed method for efficient image compression using integer wavelet transform (IWT) employing the lifting scheme (LS). The lifting scheme allows for faster computation compared to conventional discrete wavelet transform by exploiting both high-pass and low-pass filter values. Simulation results using MATLAB show the proposed method achieves superior compression performance in terms of encoding time, decoding time, peak signal-to-noise ratio, and compression ratio for various test images. The integer wavelet transform with lifting scheme provides a robust algorithm for truly lossless image compression by decomposing both approximation and detailed image contents.
Technique to Hybridize Principle Component and Independent Component Algorith...IRJET Journal
This document presents a hybrid approach to face recognition that combines Principal Component Analysis (PCA) and Independent Component Analysis (ICA). It uses a score-based fusion process to combine the two techniques. The proposed system extracts features using both PCA and ICA in parallel. It then classifies images using two separate neural networks. Finally, it combines the classification results from the two networks using a score-based strategy. The experiment results on the ORL face database show that the hybrid system achieves higher score values and lower error rates than using either PCA or ICA alone, demonstrating the effectiveness of the combined approach.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Adaptive threshold for moving objects detection using gaussian mixture modelTELKOMNIKA JOURNAL
Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
A fast fpga based architecture for measuring the distance betweenIAEME Publication
This paper presents an FPGA-based architecture for measuring the Manhattan distance between two RGB color images. The architecture takes RGB pixel values from each image as input and calculates the absolute difference between corresponding pixel values. It sums all the absolute differences and divides by the total number of pixels to obtain the normalized Manhattan distance. The architecture was implemented on a Xilinx Spartan 3 FPGA and can operate at 171.585 MHz, faster than software solutions. Experimental results demonstrating distance calculations on sample image pairs are presented. The FPGA implementation allows real-time Manhattan distance measurement for applications like image retrieval.
Hybrid method for achieving Pareto front on economic emission dispatch IJECEIAES
In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multiobjective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.
Optimal neural network models for wind speed predictionIAEME Publication
The document presents two neural network models - multilayer perceptron (MLP) and radial basis function (RBF) networks - for wind speed prediction. It evaluates these models on real wind speed data collected over one year from wind farms in Coimbatore, India. The experimental results show that the RBF and MLP models can improve wind speed prediction accuracy compared to other approaches, according to statistical metrics like coefficient of determination, mean absolute error, root mean square error, and mean bias error.
Medial Axis Transformation based Skeletonzation of Image Patterns using Image...IOSR Journals
1) The document discusses extracting the medial axis transform (MAT) of an image pattern using the Euclidean distance transform. The image is first converted to binary, then the Euclidean distance transform is used to compute the distance of each non-zero pixel to the closest zero pixel.
2) The medial axis transform represents the core or skeleton of an image pattern. There are different algorithms for extracting the skeleton or medial axis, including sequential and parallel algorithms. The skeleton provides a simple representation that preserves topological and size characteristics of the original shape.
3) The document provides background on medial axis transforms and different skeletonization algorithms. It then describes preparing the binary image and applying the Euclidean distance transform to extract the MAT and skeleton
Genetic Algorithm Processor for Image Noise Filtering Using Evolvable HardwareCSCJournals
This document describes a genetic algorithm processor for image noise filtering using evolvable hardware. It proposes an evolvable hardware architecture that uses a genetic algorithm and virtual reconfigurable circuit to evolve image filters without prior information. The genetic algorithm is used to generate configuration bits that control the connections and functions of processing elements in the virtual reconfigurable circuit. This allows the filter to adapt its design to different noise types through evolution. Experimental results show the evolved hardware filter more effectively removes Gaussian and salt-and-pepper noise compared to conventional filters. Future work could explore applying this approach to additional noise models and image processing tasks.
This document summarizes a research paper that proposes a new method called the "selective rotation based preliminary plan" for squeezing color images using directional wavelet transforms. The method first determines the direction of maximum energy in an image and rotates the image in that direction. It then applies a conventional 2D wavelet transform for decomposition. The method outperforms JPEG2000 according to experimental results. It concentrates more energy in low frequency subbands and provides better reconstructed image quality at higher compression ratios compared to JPEG2000.
IRJET- Performance Analysis of Optimization Techniques by using ClusteringIRJET Journal
This document discusses optimization techniques for clustering algorithms. It introduces fuzzy bee colony optimization (FBCO) and compares its performance to other swarm algorithms like fuzzy c-means (FCM) and fuzzy particle swarm optimization (FPSO). FBCO is motivated by the natural behaviors of bee colonies and aims to avoid local minima problems. The document provides background on clustering, describes the FCM and FPSO algorithms, and proposes a FBCO algorithm to improve clustering performance.
Validation Study of Dimensionality Reduction Impact on Breast Cancer Classifi...ijcsit
A fundamental problem in machine learning is identifying the most representative subset of features from
which we can construct a predictive model for a classification task. This paper aims to present a validation
study of dimensionality reduction effect on the classification accuracy of mammographic images. The
studied dimensionality reduction methods were: locality-preserving projection (LPP), locally linear
embedding (LLE), Isometric Mapping (ISOMAP) and spectral regression (SR). We have achieved high
rates of classifications. In some combinations the classification rate was 100%. But in most of the cases the
classification rate is about 95%. It was also found that the classification rate increases with the size of the
reduced space and the optimal value of space dimension is 60. We proceeded to validate the obtained
results by measuring some validation indices such as: Xie-Beni index, Dun index and Alternative Dun
index. The measurement of these indices confirms that the optimal value of reduced space dimension is
d=60.
Real-time traffic sign detection and recognition using Raspberry Pi IJECEIAES
This document presents a real-time traffic sign detection and recognition system developed using a Raspberry Pi 3 processor. The system uses a Raspberry Pi camera to record real-time video and the TensorFlow machine learning algorithm to detect and identify traffic signs based on a dataset of 500 labeled images across 5 sign classes. The system's accuracy, delay, and reliability were evaluated during testbed implementation considering different environmental and sign conditions. Results showed the system achieved over 90% accuracy on average with a maximum detection delay of 3.44 seconds, demonstrating reliable performance even for broken, faded, or low-light signs. This real-time traffic sign recognition system developed with affordable hardware has potential to increase road safety.
This document presents a method for speeding up fractal image compression using genetic algorithms. Fractal image compression explores self-similarity in images to compress them but the search for similar domain blocks is time-consuming. The proposed method uses genetic algorithms to more efficiently search for similar domain blocks. Experimental results on several test images show that the genetic algorithm approach can compress images faster than standard fractal compression with acceptable image quality and compression ratios. Key parameters like population size and error threshold that affect the genetic search are also analyzed.
DETECTION OF HUMAN BLADDER CANCER CELLS USING IMAGE PROCESSINGprj_publication
Bladder cancer presents a spectrum of different diatheses. A precise assessment for
individualized treatment depends on the accuracy of the initial diagnosis. In this method the
performance of the level set segmentation is subject to appropriate initialization and optimal
configuration of controlling parameters, which require substantial manual intervention. A
new fuzzy level set algorithm is proposed in this paper to facilitate medical image
segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy
clustering. The Spatial induced fuzzy c-means using pixel classification and level set
methods are utilizing dynamic variational boundaries for image segmentation. The
controlling parameters of level set evolution are also estimated from the results of clustering.
The fuzzy level set algorithm is enhanced with locally regularized evolution. Such
improvements facilitate level set manipulation and lead to more robust segmentation.
Performance evaluation of the proposed algorithm was carried on medical images
Disease Classification using ECG Signal Based on PCA Feature along with GA & ...IRJET Journal
This document describes a method for classifying ECG signals to detect cardiovascular diseases using principal component analysis (PCA), genetic algorithms, and artificial neural networks. PCA is used to extract features from ECG signals. A genetic algorithm is then used to select optimal features and train an artificial neural network classifier. The method is tested on datasets from Physionet.org to classify ECG signals as normal or indicating conditions like bradycardia or tachycardia with high accuracy. The goal is to develop an automated system for ECG analysis and heart disease diagnosis.
This document presents a traffic sign recognition system using a convolutional neural network (CNN) model. The authors train the CNN model on a German traffic sign dataset containing over 50,000 images across 43 classes. The proposed CNN architecture contains 4 VGGNet blocks with convolutional, max pooling, dropout and batch normalization layers. The model is trained for 45 epochs and achieves 96.9% accuracy and 11.4% test loss on the test set, outperforming other baseline models. The trained CNN model can accurately classify traffic sign images to assist with applications like self-driving cars.
IRJET- A Novel Gabor Feed Forward Network for Pose Invariant Face Recogni...IRJET Journal
The document proposes an Analytic Gabor Feed Forward Network (AGFN) for pose invariant face recognition. AGFN uses a single hidden layer to efficiently extract Gabor features from raw face images without computationally expensive convolutions. Features from multiple orientations and scales are fused at the output layer. The network is trained using total error rate minimization to find a globally optimal solution without iterations. Experiments on several public face datasets showed the AGFN approach achieved accurate face recognition while being computationally efficient.
IRJET- Crowd Density Estimation using Novel Feature DescriptorIRJET Journal
This document proposes a new texture feature-based approach for crowd density estimation using Completed Local Binary Pattern (CLBP). The approach divides images into blocks and further divides blocks into cells to compute CLBP features for each cell. A multi-class Support Vector Machine (SVM) classifier is trained to classify each block into one of four crowd density categories (Very Low, Low, Medium, High). Experiments on the PETS 2009 dataset show the proposed CLBP descriptor achieves 95% accuracy and outperforms other texture descriptors for crowd density estimation.
IRJET- Mango Classification using Convolutional Neural NetworksIRJET Journal
This document presents research on classifying different types of mangoes using convolutional neural networks (CNNs). The researchers collected a dataset of over 5000 mango images across 5 classes. They used transfer learning with the Inception v3 CNN model pre-trained on ImageNet, removing the final classification layer and retraining a new one for the mango classes. The CNN achieved over 99% accuracy on the test set at classifying mango types, demonstrating that CNNs can effectively perform fine-grained image classification of mangoes and distinguish between similar types.
Realtime face matching and gender prediction based on deep learningIJECEIAES
Face analysis is an essential topic in computer vision that dealing with human faces for recognition or prediction tasks. The face is one of the easiest ways to distinguish the identity people. Face recognition is a type of personal identification system that employs a person’s personal traits to determine their identity. Human face recognition scheme generally consists of four steps, namely face detection, alignment, representation, and verification. In this paper, we propose to extract information from human face for several tasks based on recent advanced deep learning framework. The proposed approach outperforms the results in the state-of-the-art.
IRJET- American Sign Language ClassificationIRJET Journal
This document describes a system for classifying American Sign Language (ASL) gestures into their corresponding English alphabet letters and then converting those letters to audio using text-to-speech. The system uses convolutional neural networks (CNNs) with transfer learning using the VGG16 model to classify ASL images. It achieved 0.99 precision on classification. The CNN model was trained on a dataset of ASL alphabet gestures. Once a gesture is classified, the letter is converted to audio using the gTTS library to play the sound of the classified letter. The system provides a pipeline from image classification to audio output to help facilitate communication between deaf and hearing communities.
IRJET- Segmentation and Representation of Data Dependent Label Distribution L...IRJET Journal
This document discusses a method for age estimation using deep convolutional neural networks (CNNs) with distribution-based loss functions. Two CNN models with different architectures are trained separately on different datasets and then fine-tuned on a competition dataset. Distribution-based loss functions like KL divergence are used to exploit the uncertainty in age labels from multiple annotators. The results show this method achieves better age estimation performance than human-level benchmarks, with a mean absolute error of 0.3057 years.
Comparative Study of Pre-Trained Neural Network Models in Detection of GlaucomaIRJET Journal
The document presents a comparative study of various pre-trained neural network models for the early detection of glaucoma from fundus images. Six pre-trained models - Inception, Xception, ResNet50, MobileNetV3, DenseNet121 and DenseNet169 - were analyzed based on their accuracy, loss graphs, confusion matrices and performance metrics like precision, recall, F1 score and specificity. The DenseNet169 model achieved the best results among the models based on these evaluation parameters.
IRJET- Handwritten Decimal Image Compression using Deep Stacked AutoencoderIRJET Journal
This document proposes using a deep stacked autoencoder neural network for compressing handwritten decimal image data. It involves training multiple autoencoders in sequence to form a deep network that can compress the high-dimensional input images into lower-dimensional encoded representations while minimizing information loss. The autoencoders are trained one layer at a time using scaled conjugate gradient descent. Testing on the MNIST handwritten digits dataset showed the deep stacked autoencoder achieved compression by encoding the 400-dimensional input images down to a 25-dimensional representation while maintaining good reconstruction accuracy, as measured by minimizing the mean squared error at each layer.
CenterAttentionFaceNet: A improved network with the CBAM attention mechanismIRJET Journal
The document proposes a new face detection model called CenterAttentionFaceNet that combines the CenterFaceNet architecture with a CBAM attention mechanism. The CBAM attention mechanism includes channel attention and spatial attention modules. It is inserted after each block of the CenterFaceNet backbone model to help focus on important regions and features of the input. The proposed model is tested on the WIDER FACE dataset and achieves superior performance compared to the original CenterFace model and other state-of-the-art methods, with detection accuracy rates of 94.3%, 93.6%, and 88.4% on the easy, medium, and hard sets respectively.
This document proposes a method for remote sensing image retrieval using convolutional neural networks with weighted distance and result re-ranking. It has two stages: 1) An offline stage where a pre-trained CNN is fine-tuned on labeled images to extract features for the retrieval dataset. 2) An online stage where the fine-tuned CNN extracts features from a query image and calculates weighted distances to retrieved images, giving more preference to images from similar classes to the query. Experiments on two datasets show the method improves retrieval performance compared to state-of-the-art methods.
Human Activity Recognition Using AccelerometerDataIRJET Journal
This document discusses human activity recognition using accelerometer data from smartphones. It uses a dataset containing accelerometer readings from 36 participants performing activities like walking, jogging, sitting, and standing. The data is preprocessed and a 2D convolutional neural network is used to classify the activities. The model achieves an accuracy of 96.75% on the test data based on a confusion matrix. Some activities like upstairs and downstairs are confused, likely due to data imbalance during preprocessing. In conclusion, neural networks can effectively perform human activity recognition from smartphone sensor data.
IRJET- Sketch-Verse: Sketch Image Inversion using DCNNIRJET Journal
The document describes a system that uses deep convolutional neural networks to convert face sketch images to photorealistic images. It first constructs a semi-simulated dataset from a large dataset containing face sketches and corresponding photos. It then trains a model using techniques like deep residual learning and perceptual losses. The trained model is able to take face sketches as input and generate photorealistic images as output. An evaluation of the system found a conversion rate of around 70% for test images. The authors aim to improve the model's robustness through additional data augmentation and training.
Car Steering Angle Prediction Using Deep LearningIRJET Journal
This document discusses two deep learning models for predicting steering angles of a self-driving car from camera images. The first model uses 3D convolutional layers followed by LSTM layers to incorporate temporal information. The second model uses transfer learning with pre-trained CNN models. Both models were tested in simulation and showed potential for real-world autonomous driving applications by accurately predicting steering angles from camera images alone.
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
IRJET- Face Recognition of Criminals for Security using Principal Component A...IRJET Journal
This document presents a face recognition system using principal component analysis to identify criminals at airports. The system is trained on images of known criminals collected from law enforcement agencies. It uses PCA for dimensionality reduction to generate eigenfaces from the training images. During testing, it generates an eigenface from the input image and calculates the Euclidean distance between this eigenface and the eigenfaces of the training images. It identifies the criminal as the one corresponding to the training image with the minimum distance, alerting authorities. The document outlines the methodology, including preprocessing steps like subtracting the mean face, and reviews prior work applying PCA and other algorithms to face recognition.
Creating Objects for Metaverse using GANs and AutoencodersIRJET Journal
This paper proposes a method to create objects for use in the metaverse using generative adversarial networks (GANs) coupled with an autoencoder. Specifically:
1) A GAN model called DCGAN is used to generate new images of human faces from a dataset of over 5000 images.
2) The lower quality images produced by the GAN are then upscaled using an autoencoder model to improve image quality.
3) The higher quality generated images can then be used as virtual objects that are more connected to the real world in augmented and virtual reality applications of the metaverse.
BREAST CANCER DETECTION USING MACHINE LEARNINGIRJET Journal
1) The document presents a study on using machine learning techniques for breast cancer detection from mammography images.
2) It proposes a methodology using CNN-based feature extraction with transfer learning and Gaussian kernel-based segmentation for classification.
3) The proposed method achieved 96% accuracy in classifying mammography images as cancerous or non-cancerous, an improvement over prior methods.
Improving face recognition by artificial neural network using principal compo...TELKOMNIKA JOURNAL
This document presents a method for improving face recognition using artificial neural networks and principal component analysis. It discusses:
1) Extracting features from face images using PCA to reduce dimensionality before training neural networks.
2) Training two neural network models - a feedforward backpropagation network and an Elman network - on feature sets of 40 and 50 dimensions.
3) The feedforward backpropagation network achieved 98.33-98.8% accuracy while the Elman network achieved 98.33-95.14% accuracy, showing the proposed method effectively recognizes faces.
Dataset Pre-Processing and Artificial Augmentation, Network Architecture and ...INFOGAIN PUBLICATION
Training a Convolutional Neural Network (CNN) based classifier is dependent on a large number of factors. These factors involve tasks such as aggregation of apt dataset, arriving at a suitable CNN network, processing of the dataset, and selecting the training parameters to arrive at the desired classification results. This review includes pre-processing techniques and dataset augmentation techniques used in various CNN based classification researches. In many classification problems, it is usually observed that the quality of dataset is responsible for proper training of CNN network, and this quality is judged on the basis of variations in data for every class. It is not usual to find such a pre-made dataset due to many natural concerns. Also it is recommended to have a large dataset, which is again not usually made available directly as a dataset. In some cases, the noise present in the dataset may not prove useful for training, while in others, researchers prefer to add noise to certain images to make the network less vulnerable to unwanted variations. Hence, researchers use artificial digital imaging techniques to derive variations in the dataset and clear or add noise. Thus, the presented paper accumulates state-of-the-art works that used the pre-processing and artificial augmentation of dataset before training. The next part to data augmentation is training, which includes proper selection of several parameters and a suitable CNN architecture. This paper also includes such network characteristics, dataset characteristics and training methodologies used in biomedical imaging, vision modules of autonomous driverless cars, and a few general vision based applications.
Similar to IRJET- A Review on Data Dependent Label Distribution Learning for Age Estimation using CNN (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.
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.
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
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%.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
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