Recently, Content-Based Image Retrieval is a widely popular and efficient searching and indexing approach used by knowledge seekers. Use of images by e-commerce sites, by product and by service industries is not new nowadays. Travel and tourism are the largest service industries in India. Every year people visit tourist places and upload pictures of their visit on social networking sites or share via the mobile device with friends and relatives. Classification of the monuments is helpful to hoteliers for the development of a new hotel with state of the art amenities, to travel service providers, to restaurant owners, to government agencies for security, etc.. The proposed system had extracted features and classified the Indian monuments visited by the tourists based on the linear Support Vector Machine (SVM). The proposed system was divided into 3 main phases: preprocessing, feature vector creation and classification. The extracted features are based on Local Binary Pattern, Histogram, Co-occurrence Matrix and Canny Edge Detection methods. Once the feature vector had been constructed, classification was performed using Linear SVM. The Database of 10 popular Indian monuments was generated with 50 images for each class. The proposed system is implemented in MATLAB and achieves very high accuracy. The proposed system was also tested on other popular benchmark databases.
Retrieval of Monuments Images Through ACO Optimization ApproachIRJET Journal
This document presents a content-based image retrieval system for retrieving monument images using ant colony optimization for feature selection. It extracts low-level features including shape, texture, and color from images. Shape feature is extracted using morphological gradients. Texture feature uses an improved local binary pattern method. Color feature uses color moments. Ant colony optimization is then used to select the most relevant features. This approach aims to make it easier to identify and retrieve similar monument images from a database.
Authenticate Aadhar Card Picture with Current Image using Content Based Image...ijtsrd
This paper proposes to give review on algorithms which helps to match human face on Aadhar card with their current image using Content based Image Retrieval CBIR .The concert of Content Based Image Retrieval CBIR system is depends on competent feature extraction and accurate repossession of similar images. Content based image retrieval is the work for retrieving the images from the large collection of database on the basis of their own visual content. This paper express the method to obtain better retrieval efficiency from current picture of person which will give maximum matching score with same person Aadhar card photo. Nehali M. Ghosalkar ""Authenticate Aadhar Card Picture with Current Image using Content-Based Image Processing"" 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/ijtsrd25070.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/25070/authenticate-aadhar-card-picture-with-current-image-using-content-based-image-processing/nehali-m-ghosalkar
MMFO: modified moth flame optimization algorithm for region based RGB color i...IJECEIAES
Region-based color image segmentation is elementary steps in image processing and computer vision. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced, an improved algorithm modified moth flame optimization (MMFO) algorithm for RGB color image segmentation which is based on bio-inspired techniques. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different number of clusters is used during the segmentation process.
The document summarizes a research paper that proposes a methodology to extract text from printed image documents and identify the Devanagari script (Hindi language). It first uses morphological operations to extract text from images. The extracted text is then analyzed to identify Hindi text using various techniques. These include segmentation using projection profiles, feature extraction with visual discrimination, and classification with heuristic search. The proposed methodology is compared to other existing techniques and is found to perform better in identifying Hindi text from images based on precision and recall rates.
Content Based Image Retrieval : Classification Using Neural Networksijma
In a content-based image retrieval system (CBIR), the main issue is to extract the image features that
effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of
retrieval performance of image features. This paper presents a review of fundamental aspects of content
based image retrieval including feature extraction of color and texture features. Commonly used color
features including color moments, color histogram and color correlogram and Gabor texture are
compared. The paper reviews the increase in efficiency of image retrieval when the color and texture
features are combined. The similarity measures based on which matches are made and images are
retrieved are also discussed. For effective indexing and fast searching of images based on visual features,
neural network based pattern learning can be used to achieve effective classification.
Prediction of Interpolants in Subsampled Radargram Slicesijtsrd
This paper provides an algorithmic procedure to predict interpolants of subsampled images. Given a digital image, one can subsample it by forcing pixel values in the alternate columns and rows to zero. Thus, the size of the subsampled image is reduced to half of the size of the original image. This means 75 of the information in the original image is lost in the subsampled image. The question that arises here is whether it is possible to predict these lost pixel values, which are called interpolants so that the reconstructed image is in accordance with the original image. In this paper, two novel interpolant prediction techniques, which are reliable and computationally efficient, are discussed. They are i interpolant prediction using neighborhood pixel value averaging and ii interpolant prediction using extended morphological filtering. T. Kishan Rao | E. G. Rajan | Dr. M Shankar Lingam "Prediction of Interpolants in Subsampled Radargram Slices" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38207.pdf Paper URL : https://www.ijtsrd.com/computer-science/artificial-intelligence/38207/prediction-of-interpolants-in-subsampled-radargram-slices/t-kishan-rao
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
Image Retrieval using Equalized Histogram Image Bins MomentsIDES Editor
CBIR operates on a totally different principle
from keyword indexing. Primitive features characterizing
image content, such as color, texture, and shape are computed
for both stored and query images, and used to identify the
images most closely matching the query. There have been
many approaches to decide and extract the features of images
in the database. Towards this goal we propose a technique by
which the color content of images is automatically extracted to
form a class of meta-data that is easily indexed. The color
indexing algorithm uses the back-projection of binary color
sets to extract color regions from images. This technique use
without histogram of image histogram bins of red, green and
blue color. The feature vector is composed of mean, standard
deviation and variance of 16 histogram bins of each color
space. The new proposed methods are tested on the database
of 600 images and the results are in the form of precision and
recall.
Retrieval of Monuments Images Through ACO Optimization ApproachIRJET Journal
This document presents a content-based image retrieval system for retrieving monument images using ant colony optimization for feature selection. It extracts low-level features including shape, texture, and color from images. Shape feature is extracted using morphological gradients. Texture feature uses an improved local binary pattern method. Color feature uses color moments. Ant colony optimization is then used to select the most relevant features. This approach aims to make it easier to identify and retrieve similar monument images from a database.
Authenticate Aadhar Card Picture with Current Image using Content Based Image...ijtsrd
This paper proposes to give review on algorithms which helps to match human face on Aadhar card with their current image using Content based Image Retrieval CBIR .The concert of Content Based Image Retrieval CBIR system is depends on competent feature extraction and accurate repossession of similar images. Content based image retrieval is the work for retrieving the images from the large collection of database on the basis of their own visual content. This paper express the method to obtain better retrieval efficiency from current picture of person which will give maximum matching score with same person Aadhar card photo. Nehali M. Ghosalkar ""Authenticate Aadhar Card Picture with Current Image using Content-Based Image Processing"" 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/ijtsrd25070.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/25070/authenticate-aadhar-card-picture-with-current-image-using-content-based-image-processing/nehali-m-ghosalkar
MMFO: modified moth flame optimization algorithm for region based RGB color i...IJECEIAES
Region-based color image segmentation is elementary steps in image processing and computer vision. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced, an improved algorithm modified moth flame optimization (MMFO) algorithm for RGB color image segmentation which is based on bio-inspired techniques. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different number of clusters is used during the segmentation process.
The document summarizes a research paper that proposes a methodology to extract text from printed image documents and identify the Devanagari script (Hindi language). It first uses morphological operations to extract text from images. The extracted text is then analyzed to identify Hindi text using various techniques. These include segmentation using projection profiles, feature extraction with visual discrimination, and classification with heuristic search. The proposed methodology is compared to other existing techniques and is found to perform better in identifying Hindi text from images based on precision and recall rates.
Content Based Image Retrieval : Classification Using Neural Networksijma
In a content-based image retrieval system (CBIR), the main issue is to extract the image features that
effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of
retrieval performance of image features. This paper presents a review of fundamental aspects of content
based image retrieval including feature extraction of color and texture features. Commonly used color
features including color moments, color histogram and color correlogram and Gabor texture are
compared. The paper reviews the increase in efficiency of image retrieval when the color and texture
features are combined. The similarity measures based on which matches are made and images are
retrieved are also discussed. For effective indexing and fast searching of images based on visual features,
neural network based pattern learning can be used to achieve effective classification.
Prediction of Interpolants in Subsampled Radargram Slicesijtsrd
This paper provides an algorithmic procedure to predict interpolants of subsampled images. Given a digital image, one can subsample it by forcing pixel values in the alternate columns and rows to zero. Thus, the size of the subsampled image is reduced to half of the size of the original image. This means 75 of the information in the original image is lost in the subsampled image. The question that arises here is whether it is possible to predict these lost pixel values, which are called interpolants so that the reconstructed image is in accordance with the original image. In this paper, two novel interpolant prediction techniques, which are reliable and computationally efficient, are discussed. They are i interpolant prediction using neighborhood pixel value averaging and ii interpolant prediction using extended morphological filtering. T. Kishan Rao | E. G. Rajan | Dr. M Shankar Lingam "Prediction of Interpolants in Subsampled Radargram Slices" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38207.pdf Paper URL : https://www.ijtsrd.com/computer-science/artificial-intelligence/38207/prediction-of-interpolants-in-subsampled-radargram-slices/t-kishan-rao
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
Image Retrieval using Equalized Histogram Image Bins MomentsIDES Editor
CBIR operates on a totally different principle
from keyword indexing. Primitive features characterizing
image content, such as color, texture, and shape are computed
for both stored and query images, and used to identify the
images most closely matching the query. There have been
many approaches to decide and extract the features of images
in the database. Towards this goal we propose a technique by
which the color content of images is automatically extracted to
form a class of meta-data that is easily indexed. The color
indexing algorithm uses the back-projection of binary color
sets to extract color regions from images. This technique use
without histogram of image histogram bins of red, green and
blue color. The feature vector is composed of mean, standard
deviation and variance of 16 histogram bins of each color
space. The new proposed methods are tested on the database
of 600 images and the results are in the form of precision and
recall.
Skin Color Detection Using Region-Based ApproachCSCJournals
Skin color provides a powerful cue for complex computer vision applications. Although skin color detection has been an active research area for decades, the mainstream technology is based on the individual pixels. This paper, which extended our previous work [1], presented a new region- based technique for skin color detection which outperformed the current state-of-the-art pixel- based skin color detection technique on the popular Compaq dataset [2]. Color and spatial distance based clustering technique is used to extract the regions from the images, also known as superpixels followed by a state-of-the-art non-parametric pixel-based skin color classifier called the basic skin color classifier. The pixel-based skin color evidence is then aggregated to classify the superpixels. Finally, the Conditional Random Field (CRF) is applied to further improve the results. As CRF operates over superpixels, the computational overhead is minimal. Our technique achieved 91.17% true positive rate with 13.12% false negative rate on the Compaq dataset tested over approximately 14,000 web images.
A Survey of Modern Character Recognition Techniquesijsrd.com
This document summarizes several modern techniques for handwritten character recognition. It discusses common feature extraction methods like statistical, structural and global transformation features. It then summarizes several papers that have proposed different techniques for handwritten character recognition, including using associative memory nets, moment invariants with support vector machines, neural networks, hidden markov models, gradient features, and multi-scale neural networks. The document concludes that neural networks are commonly used for training, and that feature extraction methods continue to be improved, but handwritten character recognition remains an active area of research.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
The document reviews various feature extraction techniques that have been used for content-based image retrieval (CBIR) systems. It discusses several approaches for extracting color, texture, shape and spatial features from images. It also examines different similarity measures and evaluation methods for CBIR systems, including precision, recall and distance metrics. Feature extraction is a key factor for CBIR, and the paper provides an overview of some of the major techniques that have been explored for this task.
PREDICTING GROWTH OF URBAN AGGLOMERATIONS THROUGH FRACTAL ANALYSIS OF GEO-SPATIAL DATA
Location Analytics is one of the fastest emerging fields in the broad area of Business Intelligence/Data Science. By
some industry estimates, almost 80% of all data has a location dimension to it. Consequently, identification of
trends and patterns in spatially distributed information has far reaching applications ranging from urban planning, to
logistics and supply chain management, location based marketing, sales territory planning and retail store location.
In view of this, we present an approach based on Fractal Analysis (FA) of highly granular geo-spatial data.
Specifically, we use proprietary data available at approximately1 square km level for New Delhi, India provided by Indicus Analytics (India’s leading economic data analytics firm based in New Delhi). We compare and contrast the patterns and insights generated using the FA approach with other more traditional approaches such as spatial to correlation and structural similarity indices. Preliminary results indicate that there are indeed “selfsimilar” local patterns that are completely missed by spatial correlation that are accurately captured by the more sophisticated FA approach. These patterns provide deep insights into the underlying socio-economic and demographic processes and can be used to predict the spatial distribution of these variables in the future. For example, questions such as what are the pockets of population growth in a city and how will businesses and government respond to that growth can be answered using the proposed approach.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
An effective RGB color selection for complex 3D object structure in scene gra...IJECEIAES
Our goal of the project is to develop a complete, fully detailed 3D interactive model of the human body and systems in the human body, and allow the user to interacts in 3D with all the elements of that system, to teach students about human anatomy. Some organs, which contain a lot of details about a particular anatomy, need to be accurately and fully described in minute detail, such as the brain, lungs, liver and heart. These organs are need have all the detailed descriptions of the medical information needed to learn how to do surgery on them, and should allow the user to add careful and precise marking to indicate the operative landmarks on the surgery location. Adding so many different items of information is challenging when the area to which the information needs to be attached is very detailed and overlaps with all kinds of other medical information related to the area. Existing methods to tag areas was not allowing us sufficient locations to attach the information to. Our solution combines a variety of tagging methods, which use the marking method by selecting the RGB color area that is drawn in the texture, on the complex 3D object structure. Then, it relies on those RGB color codes to tag IDs and create relational tables that store the related information about the specific areas of the anatomy. With this method of marking, it is possible to use the entire set of color values (R, G, B) to identify a set of anatomic regions, and this also makes it possible to define multiple overlapping regions.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. The detected keywords are then used to index and retrieve similar textual images from two benchmark datasets. Experimental results show the approach effectively retrieves similar textual images by exploiting the dominant text information in the images.
CBIR Processing Approach on Colored and Texture Images using KNN Classifier a...IRJET Journal
This document presents a content-based image retrieval system that uses color and texture features. It uses a K-nearest neighbor classifier to classify images based on color features and extract texture features using log-Gabor filters. Images are then ranked based on their similarity to the query image using Spearman's rank correlation coefficient. The system is tested on a dataset of flag images to retrieve the most similar flags to a given query image based on color and texture features. Experimental results show that the combined approach of using classification, similarity measures and log-Gabor filtering for color and texture features provides better retrieval performance than methods using only wavelets or Gabor filters.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
Colour Object Recognition using Biologically Inspired Modelijsrd.com
This document presents a biologically inspired model for color object recognition. The model extracts features in the YCbCr color space and classifies objects using a support vector machine classifier. The model consists of four computational layers (S1, C1, S2, C2) that mimic the visual cortex. Features are extracted by convolving images with log-Gabor filters and pooling responses. Prototype image patches are also used. The model was tested on image datasets and achieved a classification accuracy of 91.3% for objects in the Cb color plane.
The document discusses several papers on counterfactual explanations for convolutional neural networks (CNNs). It summarizes four papers:
1. "Explaining Image Classifiers by Counterfactual Generation" which finds important image regions affecting a classifier's decision using masked and GAN-generated images.
2. "Counterfactual Visual Explanations" which generates counterfactual images to change a model's prediction by replacing image regions.
3. "Global Explanations of Convolutional Neural Networks With Concept Attribution" which measures concept importance to model predictions.
4. "Generative Counterfactual Introspection for Explainable Deep Learning" which aims to minimally change an input to alter the model's prediction.
October 202:top read articles in signal & image processingsipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
EFFECTIVE SEARCH OF COLOR-SPATIAL IMAGE USING SEMANTIC INDEXINGIJCSEA Journal
Most of the data stored in libraries are in digital form will contain either pictures or video, which is tough to search or browse. Methods which are automatic for searching picture collections made large use of color histograms, because they are very strong to wide changes in viewpoint, and can be calculated trivially. However, color histograms unable to present spatial data, and therefore tend to give lesser results. By using combination of color information with spatial layout we have developed several methods, while retrieving the advantages of histograms. A method computes a given color as a function of the distance between two pixels, which we call a color correlogram. We propose a color-based image descriptor that can be used for image indexing based on high-level semantic concepts. The descriptor is
based on Kobayashi’s Color Image Scale, which is a system that includes 130 basic colors combined in 1180 three-color combinations. The words are represented in a two dimensional semantic space into groups based on perceived similarity. The modified approach for statistical analysis of pictures involves transformations of ordinary RGB histograms. Then a semantic image descriptor is derived, containing semantic data about both color combinations and single colors in the image.
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...csandit
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing the distance within classes. PCA
finds the axes with maximum variance for the whole
data set where LDA tries to find the axes
for best class seperability. The proposed method is
experimented over a general image database
using Matlab. The performance of these systems has
been evaluated by Precision and Recall
measures. Experimental results show that PCA based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
Computer Vision Based 3D Reconstruction : A ReviewIJECEIAES
3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.
Multimedia Big Data Management Processing And AnalysisLindsey Campbell
The document discusses various techniques used for extracting useful information from images, including image classification, feature extraction, face detection and recognition, and image retrieval. Image classification involves applying machine learning algorithms to assign images to predefined categories or classes. Feature extraction identifies distinguishing aspects of images like color, shape and texture. Face detection locates human faces within images while face recognition identifies specific faces by comparing features to face databases. Image retrieval finds similar images in databases based on visual features. These techniques extract meaningful information from images to enable enhanced image searching capabilities.
A Powerful Automated Image Indexing and Retrieval Tool for Social Media SampleIRJET Journal
The document proposes an automated image indexing and retrieval tool for social media images. It uses local binary patterns for face detection and matching global self-similarities for background detection. This allows identifying similar images to reduce sending redundant photos. The method segments faces from backgrounds, extracts features using local binary patterns, and indexes images based on faces and backgrounds. Experimental results show this compressed method significantly reduces the number and size of images sent by identifying and removing redundant images.
This document discusses a method for face matching in videos using low-level facial geometries. It aims to detect human faces in video frames and retrieve similar face images from a large database. The method uses Viola-Jones face detection to extract frames from videos and locate faces. It then extracts features like color maps, edge maps and 68 facial landmarks. Local binary pattern descriptors are extracted from facial grids as local features. These are quantized into codewords using attribute-enhanced sparse coding, which considers human attributes to improve face retrieval. The codewords are used to build an attribute embedded inverted index for efficient image ranking and retrieval. Experiments on public datasets show the method can effectively retrieve similar face images from videos.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
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.
Skin Color Detection Using Region-Based ApproachCSCJournals
Skin color provides a powerful cue for complex computer vision applications. Although skin color detection has been an active research area for decades, the mainstream technology is based on the individual pixels. This paper, which extended our previous work [1], presented a new region- based technique for skin color detection which outperformed the current state-of-the-art pixel- based skin color detection technique on the popular Compaq dataset [2]. Color and spatial distance based clustering technique is used to extract the regions from the images, also known as superpixels followed by a state-of-the-art non-parametric pixel-based skin color classifier called the basic skin color classifier. The pixel-based skin color evidence is then aggregated to classify the superpixels. Finally, the Conditional Random Field (CRF) is applied to further improve the results. As CRF operates over superpixels, the computational overhead is minimal. Our technique achieved 91.17% true positive rate with 13.12% false negative rate on the Compaq dataset tested over approximately 14,000 web images.
A Survey of Modern Character Recognition Techniquesijsrd.com
This document summarizes several modern techniques for handwritten character recognition. It discusses common feature extraction methods like statistical, structural and global transformation features. It then summarizes several papers that have proposed different techniques for handwritten character recognition, including using associative memory nets, moment invariants with support vector machines, neural networks, hidden markov models, gradient features, and multi-scale neural networks. The document concludes that neural networks are commonly used for training, and that feature extraction methods continue to be improved, but handwritten character recognition remains an active area of research.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
The document reviews various feature extraction techniques that have been used for content-based image retrieval (CBIR) systems. It discusses several approaches for extracting color, texture, shape and spatial features from images. It also examines different similarity measures and evaluation methods for CBIR systems, including precision, recall and distance metrics. Feature extraction is a key factor for CBIR, and the paper provides an overview of some of the major techniques that have been explored for this task.
PREDICTING GROWTH OF URBAN AGGLOMERATIONS THROUGH FRACTAL ANALYSIS OF GEO-SPATIAL DATA
Location Analytics is one of the fastest emerging fields in the broad area of Business Intelligence/Data Science. By
some industry estimates, almost 80% of all data has a location dimension to it. Consequently, identification of
trends and patterns in spatially distributed information has far reaching applications ranging from urban planning, to
logistics and supply chain management, location based marketing, sales territory planning and retail store location.
In view of this, we present an approach based on Fractal Analysis (FA) of highly granular geo-spatial data.
Specifically, we use proprietary data available at approximately1 square km level for New Delhi, India provided by Indicus Analytics (India’s leading economic data analytics firm based in New Delhi). We compare and contrast the patterns and insights generated using the FA approach with other more traditional approaches such as spatial to correlation and structural similarity indices. Preliminary results indicate that there are indeed “selfsimilar” local patterns that are completely missed by spatial correlation that are accurately captured by the more sophisticated FA approach. These patterns provide deep insights into the underlying socio-economic and demographic processes and can be used to predict the spatial distribution of these variables in the future. For example, questions such as what are the pockets of population growth in a city and how will businesses and government respond to that growth can be answered using the proposed approach.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
An effective RGB color selection for complex 3D object structure in scene gra...IJECEIAES
Our goal of the project is to develop a complete, fully detailed 3D interactive model of the human body and systems in the human body, and allow the user to interacts in 3D with all the elements of that system, to teach students about human anatomy. Some organs, which contain a lot of details about a particular anatomy, need to be accurately and fully described in minute detail, such as the brain, lungs, liver and heart. These organs are need have all the detailed descriptions of the medical information needed to learn how to do surgery on them, and should allow the user to add careful and precise marking to indicate the operative landmarks on the surgery location. Adding so many different items of information is challenging when the area to which the information needs to be attached is very detailed and overlaps with all kinds of other medical information related to the area. Existing methods to tag areas was not allowing us sufficient locations to attach the information to. Our solution combines a variety of tagging methods, which use the marking method by selecting the RGB color area that is drawn in the texture, on the complex 3D object structure. Then, it relies on those RGB color codes to tag IDs and create relational tables that store the related information about the specific areas of the anatomy. With this method of marking, it is possible to use the entire set of color values (R, G, B) to identify a set of anatomic regions, and this also makes it possible to define multiple overlapping regions.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. The detected keywords are then used to index and retrieve similar textual images from two benchmark datasets. Experimental results show the approach effectively retrieves similar textual images by exploiting the dominant text information in the images.
CBIR Processing Approach on Colored and Texture Images using KNN Classifier a...IRJET Journal
This document presents a content-based image retrieval system that uses color and texture features. It uses a K-nearest neighbor classifier to classify images based on color features and extract texture features using log-Gabor filters. Images are then ranked based on their similarity to the query image using Spearman's rank correlation coefficient. The system is tested on a dataset of flag images to retrieve the most similar flags to a given query image based on color and texture features. Experimental results show that the combined approach of using classification, similarity measures and log-Gabor filtering for color and texture features provides better retrieval performance than methods using only wavelets or Gabor filters.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
Colour Object Recognition using Biologically Inspired Modelijsrd.com
This document presents a biologically inspired model for color object recognition. The model extracts features in the YCbCr color space and classifies objects using a support vector machine classifier. The model consists of four computational layers (S1, C1, S2, C2) that mimic the visual cortex. Features are extracted by convolving images with log-Gabor filters and pooling responses. Prototype image patches are also used. The model was tested on image datasets and achieved a classification accuracy of 91.3% for objects in the Cb color plane.
The document discusses several papers on counterfactual explanations for convolutional neural networks (CNNs). It summarizes four papers:
1. "Explaining Image Classifiers by Counterfactual Generation" which finds important image regions affecting a classifier's decision using masked and GAN-generated images.
2. "Counterfactual Visual Explanations" which generates counterfactual images to change a model's prediction by replacing image regions.
3. "Global Explanations of Convolutional Neural Networks With Concept Attribution" which measures concept importance to model predictions.
4. "Generative Counterfactual Introspection for Explainable Deep Learning" which aims to minimally change an input to alter the model's prediction.
October 202:top read articles in signal & image processingsipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
EFFECTIVE SEARCH OF COLOR-SPATIAL IMAGE USING SEMANTIC INDEXINGIJCSEA Journal
Most of the data stored in libraries are in digital form will contain either pictures or video, which is tough to search or browse. Methods which are automatic for searching picture collections made large use of color histograms, because they are very strong to wide changes in viewpoint, and can be calculated trivially. However, color histograms unable to present spatial data, and therefore tend to give lesser results. By using combination of color information with spatial layout we have developed several methods, while retrieving the advantages of histograms. A method computes a given color as a function of the distance between two pixels, which we call a color correlogram. We propose a color-based image descriptor that can be used for image indexing based on high-level semantic concepts. The descriptor is
based on Kobayashi’s Color Image Scale, which is a system that includes 130 basic colors combined in 1180 three-color combinations. The words are represented in a two dimensional semantic space into groups based on perceived similarity. The modified approach for statistical analysis of pictures involves transformations of ordinary RGB histograms. Then a semantic image descriptor is derived, containing semantic data about both color combinations and single colors in the image.
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...csandit
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing the distance within classes. PCA
finds the axes with maximum variance for the whole
data set where LDA tries to find the axes
for best class seperability. The proposed method is
experimented over a general image database
using Matlab. The performance of these systems has
been evaluated by Precision and Recall
measures. Experimental results show that PCA based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
Computer Vision Based 3D Reconstruction : A ReviewIJECEIAES
3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.
Multimedia Big Data Management Processing And AnalysisLindsey Campbell
The document discusses various techniques used for extracting useful information from images, including image classification, feature extraction, face detection and recognition, and image retrieval. Image classification involves applying machine learning algorithms to assign images to predefined categories or classes. Feature extraction identifies distinguishing aspects of images like color, shape and texture. Face detection locates human faces within images while face recognition identifies specific faces by comparing features to face databases. Image retrieval finds similar images in databases based on visual features. These techniques extract meaningful information from images to enable enhanced image searching capabilities.
A Powerful Automated Image Indexing and Retrieval Tool for Social Media SampleIRJET Journal
The document proposes an automated image indexing and retrieval tool for social media images. It uses local binary patterns for face detection and matching global self-similarities for background detection. This allows identifying similar images to reduce sending redundant photos. The method segments faces from backgrounds, extracts features using local binary patterns, and indexes images based on faces and backgrounds. Experimental results show this compressed method significantly reduces the number and size of images sent by identifying and removing redundant images.
This document discusses a method for face matching in videos using low-level facial geometries. It aims to detect human faces in video frames and retrieve similar face images from a large database. The method uses Viola-Jones face detection to extract frames from videos and locate faces. It then extracts features like color maps, edge maps and 68 facial landmarks. Local binary pattern descriptors are extracted from facial grids as local features. These are quantized into codewords using attribute-enhanced sparse coding, which considers human attributes to improve face retrieval. The codewords are used to build an attribute embedded inverted index for efficient image ranking and retrieval. Experiments on public datasets show the method can effectively retrieve similar face images from videos.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
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.
Content-Based Image Retrieval by Multi-Featrus Extraction and K-Means ClusteringEECJOURNAL
Nowadays, Content-Based Image Retrieval has received a massive attention in the literature of image information retrieval, and accordingly a broad range of techniques have been proposed. However, these techniques are not free of defects in terms of recognition. In this paper, content based image retrieval has been proposed with a new method of building feature vector to represente an image for the clustertnig, which consiss of 140 elements taken from several feature types as following color historgram, color moments, Gabor filters, GLCM matrix, wavelet transformation, tamura feature, and moment invaraints. Aftering preparing the feature vector, clustering operation named K-Mean is exploited here to give the centroid of each image features. Finally Minkowski-Form Distance and Euclidean distance as a similarity measurement are applied for clustering groups of images having the same charactersitcs, shape and colors. The experiment is run on IMPLIcity database which has 1000 colored images. The evaluation of this proposed algorithm was by selecting random five images as query images, a fruitful result has been gotten as clustering set of images as illustared in the result section of this paper.
A hybrid approach for categorizing images based on complex networks and neur...IJECEIAES
There are several methods for categorizing images, the most of which are statistical, geometric, model-based and structural methods. In this paper, a new method for describing images based on complex network models is presented. Each image contains a number of key points that can be identified through standard edge detection algorithms. To understand each image better, we can use these points to create a graph of the image. In order to facilitate the use of graphs, generated graphs are created in the form of a complex network of small-worlds. Complex grid features such as topological and dynamic features can be used to display image-related features. After generating this information, it normalizes them and uses them as suitable features for categorizing images. For this purpose, the generated information is given to the neural network. Based on these features and the use of neural networks, comparisons between new images are performed. The results of the article show that this method has a good performance in identifying similarities and finally categorizing them.
Performance Comparison of Face Recognition Using DCT Against Face Recognition...CSCJournals
In this paper, a face recognition system using simple Vector quantization (VQ) technique is proposed. Four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to generate codebooks of desired size. Euclidean distance is used as similarity measure to compare the feature vector of test image with that of trainee images. Proposed algorithms are tested on two different databases. One is Georgia Tech Face Database which contains color JPEG images, all are of different size. Another database used for experimental purpose is Indian Face Database. It contains color bitmap images. Using above VQ techniques, codebooks of different size are generated and recognition rate is calculated for each codebook size. This recognition rate is compared with the one obtained by applying DCT on image and LBG-VQ algorithm which is used as benchmark in vector quantization. Results show that KFCG outperforms other three VQ techniques and gives better recognition rate up to 85.4% for Georgia Tech Face Database and 90.66% for Indian Face Database. As no Euclidean distance computations are involved in KMCG and KFCG, they require less time to generate the codebook as compared to LBG and KPE
This paper presents to building identification from satellite images. Because of monitoring illegal land usage. Nowadays rapid urbanization leads to
increase the land usage, in this case of monitoring illegal land usage is very important. This project implemented to building identification from
satellite images, images are provided from Bing maps. Adaptive Neuro Fuzzy Inference System used to check data base information. In this proposed
system, I can identify only building images from the satellite images, To improving the image details effectively.
Ppt for paper id 696 a review of hybrid data mining algorithm for big data mi...Prasanta Paul
This document presents a review of hybrid data mining algorithms for big data mining. It defines hybrid data mining as combining different classifiers to potentially increase classification accuracy. The literature review covers several proposed hybrid algorithms, including combinations of K-means and BIRCH, decision trees and genetic algorithms, Extreme K-means and ELM. The document compares different image classification hybrid approaches and evaluates their performance and drawbacks. It concludes that hybrid evolution clustering with an empty clustering solution provides better precision than other hybrid methods.
Query Image Searching With Integrated Textual and Visual Relevance Feedback f...IJERA Editor
There are many researchers who have studied the relevance feedback in the literature of content based image
retrieval (CBIR) community, but none of CBIR search engines support it because of scalability, effectiveness
and efficiency issues. In this, we had implemented an integrated relevance feedback for retrieving of web
images. Here, we had concentrated on integration of both textual features (TF) and visual features (VF) based
relevance feedback (RF), simultaneously we also tested them individually. The TFRF employs and effective
search result clustering (SRC) algorithm to get salient phrases. Then a new user interface (UI) is proposed to
support RF. Experimental results show that the proposed algorithm is scalable, effective and accurated
October 2023-Top Cited Articles in IJU.pdfijujournal
International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Color is a widely used visual feature for content-based video retrieval. There are two main methods discussed in the document: block-based and global color feature extraction. The block-based method extracts color histograms from divided blocks of each video frame, while the global method extracts a single color histogram from the entire frame. These color features are then used to measure similarity between videos for retrieval. The document also discusses challenges with high-dimensional color histograms and methods to reduce dimensions like transforms and selecting significant colors. Overall the paper presents color-based video retrieval techniques and evaluates performance of the block-based and global methods.
The document discusses color-based video retrieval using block and global methods. It summarizes that color features are widely used in video retrieval and content analysis. It describes extracting color histograms globally and from divided blocks. Two methods are discussed: global color extracts overall color frequency while block color quantizes each divided region, maintaining some spatial data. Videos are retrieved by comparing feature vectors of queries to those in a database using distance metrics like Euclidean distance. MATLAB is used to implement the color feature extraction and retrieval methods.
September 2022: Top 10 Read Articles in Signal & Image Processingsipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
October 2022: Top 10 Read Articles in Signal & Image Processingsipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
IMAGE GENERATION WITH GANS-BASED TECHNIQUES: A SURVEYijcsit
In recent years, frameworks that employ Generative Adversarial Networks (GANs) have achieved immense results for various applications in many fields especially those related to image generation both due to their ability to create highly realistic and sharp images as well as train on huge data sets. However, successfully training GANs are notoriously difficult task in case ifhigh resolution images are required. In this article, we discuss five applicable and fascinating areas for image synthesis based on the state-of-theart GANs techniques including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Manipulation, 3D Image Synthesis and DeepMasterPrints. We provide a detailed review of current GANs-based image generation models with their advantages and disadvantages.The results of the publications in each section show the GANs based algorithmsAREgrowing fast and their constant improvement, whether in the same field or in others, will solve complicated image generation tasks in the future.
In recent years, frameworks that employ Generative Adversarial Networks (GANs) have achieved immense results for various applications in many fields especially those related to image generation both due to their ability to create highly realistic and sharp images as well as train on huge data sets. However, successfully training GANs are notoriously difficult task in case ifhigh resolution images are required. In this article, we discuss five applicable and fascinating areas for image synthesis based on the state-of-theart GANs techniques including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Manipulation, 3D Image Synthesis and DeepMasterPrints. We provide a detailed review of current GANs-based image generation models with their advantages and disadvantages.The results of the publications in each section show the GANs based algorithmsAREgrowing fast and their constant improvement, whether in the same field or in others, will solve complicated image generation tasks in the future.
Similar to Indian Monuments Classification using Support Vector Machine (20)
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.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
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.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
2. IJECE ISSN: 2088-8708
Indian Monuments Classification using Support Vector Machine (Malay S. Bhatt)
1953
Bollywood and Television industry are also considered as major sources of multimedia data (Movie
stills, Posters etc.) As they release more than 2000 movies, a lot of music videos, produce serials and also
organize events for 100 years [9]. It can be observed that pilot scenes and song sequences consist of the
monuments in the background and thus movies promote tourism and increase the revenue of the country as a
whole. Figure 1 depicts the presence of the monuments in scenes of popular movies.
Travel and tourism industry incorporate heritage, medical, business and sports tourism. The main
objective of this sector is to develop and promote tourism, to maintain competitiveness of India as a tourist
destination and to improve and to expand existing tourism products to ensure employment generation and
economic growth [10]. The government is also promoting tourism through advertisements, campaigns and
takes special interest to preserve the beauty of these monuments. Every year, lots of foreign delegates and
tourists visit India too. As per India Tourism Statistics 2013, 6.97 million foreign tourists arrived with an
annual growth rate of 5.9% and approximately 1145 million domestic tourists with an annual growth rate of
9.6% were observed [11].
Table 1. Bollywood Movies and Monuments
Movie Name Release
Year
Monument Movie Name Release
Year
Monument
Ki & Ka 2016 India Gate Fanaa 2006 Qutub Minar
Ki & Ka 2016 Gate Way of India Jannat 2 2012 Qutub Minar
FAN 2016 Red Fort Rang De Basanti 2006 India Gate
Tevar 2015 Taj Mahal Rang De Bassanti 2006 Golden Temple
Jeans 1998 Taj Mahal Rab Ne banadi Jodi 2008 Golden Temple
Jhoom Barabar Jhoom 2007 Taj Mahal Ki & Ka 2016 India Gate
Leader 1964 Taj Mahal Ki & Ka 2016 Gate Way of India
Mere Brother ki
Dulhan
2011 Taj Mahal FAN 2016 Red Fort
Namastey London 2007 Taj Mahal Tevar 2015 Taj Mahal
Youngistaan 2014 Taj Mahal Jeans 1998 Taj Mahal
Figure 1. (a) 'Fanaa' Movie (b) 'Mere Brother Ki Dulhan' Movie (c) 'Leader' Movie (d) 'Tevar' Movie
(e) 'Rab Ne Bana Di Jodi' Movie (f) 'Rab Ne Bana Di Jodi' Movie (g) 'Jannat 2' Movie and (h) 'Rang De
Basanti' Movie.[12]
2. LITERATURE SURVEY
Bhatt M.S. and Patalia T. P. [12] used Generalized Co-occurrence Matrix (GCM) obtained from
HSV color space having 64 gray-levels with various distance values (3, 6, 9, 12 and 15) as input to Genetic
Programming System. Genetic Programming evolved spatial descriptor using 15 Generalized Co-Occurrence
matrices as terminals have been implemented with 7 functions and 2 operators. Each GCMs having size of
64x64 is considered as input. Obtained Genetic programming evolved spatial descriptor is tested on manually
created Indian monuments database having four classes, namely 'Taj Mahal', 'Qutub Minar', 'Golden Temple'
and 'India Gate'. Fitness function used in GP system is linear SVM with 10 fold cross validation. They
obtained accuracy of 92 %.
Murala et al. [13] proposed Local Tetra Pattern (LTrP) as a new feature descriptor for Content
Based Image Retrieval. LTrP uses four distinct values for encoding of information and also uses direction
information. They highlighted advantages of LTrP over Local Binary Pattern (LBP), Local Ternary Pattern
(LTP) and Local Derivative Pattern (LDP). Benchmark databases viz., Corel 1000 database, Brodatz texture
database and MIT VisTex database were used for performance comparison.
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Youness et al. [14] proposed content based image retrieval based on 2-D ESPRIT (Estimation of
Signal Parameters via Rotational Invariance Techniques) and Gabor Filter. They used Brodatz gray scale
image dataset, having 13 texture classes with 16 samples for each class, for the purpose of evaluation. They
achieved an average precision of 80.19%.
Nazarloo et al. [15] applied content based image retrieval for gender classification. The face is one
of the most important biometric of human and contains lots of useful information. For gender classification,
they merged the Gabor Filters and Local Binary Pattern Features of the face. Self-Organized Map was used
for the classification and achieved an accuracy of 92.5%.
Guo et al. [16] explained Completed Local Binary Pattern (CLBP) which is a modification to LBP.
CLBP composed of Local Difference Sign Magnitude Transform (LDSMT) and center gray level value.
They tested the proposed approach on CUReT and Outex texture databases. It is observed that sign
component preserves much better local difference as compared to magnitude component and thus
outperforms conventional LBP.
Das et al. [17], [18] highlighted the importance of classified query for content based image
recognition based on niblack's thresholding. They applied binarization on Red, Blue and Green planes
separately based on niblack's thresholding. On the binarized images, upper mean, lower mean, upper standard
deviation and lower standard deviation are calculated. For each plane 4 features are extracted and thus a
feature vector of size 12 is generated. They tested the system on benchmark databases 'Wang Database' and
'OT-Scene'. Highest precision of 0.838 and recall of 0.838 was achieved using Artificial Neural Network
with a Multi Layer Perceptron for 'Wang Database' while highest precision of 0.753 and recall of 0.754 are
obtained using Support Vector Machine for 'OT-Scene' database.
Streater, J [19] explains the design and implementation of genetic programming system for
construction of feature descriptor for skin lesion images. 6 Generalized Co-occurrence Matrices (GCMs) of
size 64x64 are constructed using RGB color space with 5 inter-pixel distances for 100 images. Highest
accuracy achieved was 72%. Fisher's Discriminant Ratio, naïve Bayes classifier is also used to compare the
results obtained by the SVM.
Extraction of information from movies has been focused since last 10 years. Hollywood Movie
Database 51 [20], YouTube [21] and the Hollywood datasets [22] are challenging datasets used widely for
action recognition. Action recognition from movies is a subset of general human recognition activity.
Laptev et al. [23] highlighted limitations of the human action dataset in a controlled environment
and also described the difficulties faced during the recognition of real movie actions. They identified
similarities and dissimilarities between action recognition from movies with object recognition in still
images. The first task accomplished with the accuracy of 60% is the automatic annotation of the human
action using the movie scripts. Inaccuracy is due to script video misalignment. Classification of human-action
is the main goal, achieved in the paper with 91.8% accuracy. Experiments are carried out for 8 different
actions. HoG, HoF, Spatio-Temporal Bag of Features (BoF) and a combination of the above are used with the
non-linear support vector machine to achieve the desired result.
Lei Chen et al. [24] depict a top-down approach based on rules for video editing and audio cues to
extract dialogue and action scenes. A finite state machine with an audio-based support vector machine
(SVM) classifier is applied for detection of skin type. Classifier uses three features, namely: variance of zero
crossing rate, silence ratio, and harmonic ratio. The precision and recall rates achieved are 76.56% and 81.6%
respectively.
Doudpota et al. [25] focused on the impact and popularity of Bollywood movies in South Asia,
Middle East, UK, USA and other parts of the world. They mined song sequences from the Bollywood
movies. They used in the first part, Zero Crossing Rate, Spectrum Flux and Short Time, Energy as features in
Support Vector machine for binary classification of extracting segment into music and non-music. In the
second phase, extracted music segments are further classified into song and non-song sequences using
Probabilistic Timed Automata (Song Grammar). An experiment was carried out on 10 Bollywood movies
having 74 songs and out of which 69 were successfully extracted. Recall achieved is 93.24%, while the
Precision is 87.34%.
Vaudeville et al. [26] have proposed integrated color and intensity co-occurrence matrix (ICICM)
for content based image retrieval. The ICICM composed of four other matrices namely, ICICMCC, ICICMCI,
ICICMIC and ICICMII. ICICMcc captures color perception of pixel p and color perception of the nighborhood
of P while ICICMCI captures color perception of pixel p and intensity perception of the neighbourhood of P.
The other two can be described similarly. ICICM is updated based on the weight which is a function of
Saturation and Intensity. The development of ICICM is based on the properties of the HSV color space. They
have tested the system with a combination of various color (C) and gray level perception (I) levels on two
different datasets. The first database is constructed from general purpose images obtained from International
Microcomputer Software Inc. (IMSI) while the other is constructed using a crawler. A web-based application
4. IJECE ISSN: 2088-8708
Indian Monuments Classification using Support Vector Machine (Malay S. Bhatt)
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for CBIR based on ICICM is available in the public domain for performing the experiments with images in
our database as well as with externally uploaded images.
Deselaers et al. [27] performed a quantitative comparison of image features like color histograms,
invariant feature histograms, Gabor feature histograms, Tamura texture feature, local feature and region
based features. Correlation among these features is analyzed. They focused on two image retrieval tasks:
color photographs (WANG Dataset) and medical radiographs (IRMA Dataset). Low correlation exists among
region features, image features, invariant feature histogram and Gabor histograms. The combination of these
will produce better image retrieval for color photographs. Invariant Feature histogram gives 15.9% error on
WANG Dataset while error rate of 29.2% is identified in IRMA Dataset. Similar kind of comparison
describes all remaining features. It is clear that selection of feature depends on the task at hand and the
combination of positively correlated features does not improve the classification result.
Desai et al. [28] highlighted the importance of monuments classification to archaeologists in an
assessment of their findings and in classification. Art galleries and museums focus on visual aspects of
objects. A CBIR system based on visual shape based feature and texture feature was developed.
Morphological operations were carried out for shape extraction and Gray level Co-occurrence Matrix was
used for texture feature extraction. Five different classes with a total of 500 images are collected and
performance was compared with Canny and Sobel edge detection approaches.
Information about Indian movies can be obtained via Indian Movie Database [29].
3. RESEARCH METHOD
Figure 2 provides a flow chart of a content based image retrieval system. An image query is the
image file that is given as an input to the system. The features of the input are calculated. A query of the
extracted features is then generated and is compared with all the other features of the image files present in
the database. Based on similarity measures, the system retrieves the required image files from the database
and presents it in the form of the result.
Figure.2 Content Based Image Retrieval System
3.1. Preprocessing
A set of 500 input images is re-sized into 256x384 Resulted images are converted from RGB color
space into Hue, Saturation and Value (HSV) color space [30]. The layers of human retina sense the light
through rod cells and cone cells [31]. The gray-levels are perceived by rod cells at low-levels of illumination
while at higher levels of illumination cone cells are also excited. The human perceives the color same as the
HSV color space. RGB color representation is different and not as per human perception. Hue indicates the
pure color, S indicates the percentage of white added in the pure color while V represents intensity. The HSV
color space can be represented as a hexacone [32]. When saturation is zero, we get only shades of gray from
black to white by increasing the intensity. Incident light composed of many spectral components, but causes
loss of color information when saturation is low even though illumination is very high. By changing the
saturation from 0 to 1, perceived color changes from shades of gray to pure color under the given hue and
intensity. It is known that HSV color space has more discriminating power as compared to RGB color space.
Figure 3(a) Displays RGB image while corresponding HSV image is shown in Figure 3(b).
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Figure 3. (a) RGB Image and (b) HSV Image
Generalized co-occurrence matrix properties with different distance and direction are also computed
and it results into 20 additional features. H, S and V planes are also extracted from the input image. The
Computation of Centre Symmetric Local Binary Pattern with 16 bins and Histogram with 16 bins are carried
out on each plane. Generalized co-occurrence matrix properties are also extracted from each plane. Figure 4
shows the pre-processing & Feature Vector GenerationFigure 5 covers several techniques which were
merged together to generate the feature vector.
Figure 4. Pre-processing & Feature Vector Generation
LBP CS- LBP GCM Properties Histogram Edge Property
Histogram
Figure 5. Feature Vector
3.2. Generalized Co-Occurrence Matrix
Generalized C0-Occurrence Matrix is useful to extract the texture of the image. It is represented as
4-tuple (i, j, d, ϴ)[33]. Here, í' and 'j' represent gray levels, d is the distance between pixels p1 and p2.
Graylevels of p1 and p2 are i and j respectively. ϴ is the angle between pixels p1 and p2.
6. IJECE ISSN: 2088-8708
Indian Monuments Classification using Support Vector Machine (Malay S. Bhatt)
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Figure 6. The Generalized Co-occurrence Matrices (GCM) of size 128x128 are calculated for inter-
pixel distances 3,9, 15 and 64 in horizontal direction for H, S and V planes. (3 planes x 4 inter- pixel
distance)
Figure 7(a) describes the working of GCM for a sample image of size 5x5 with 4 gray levels while
Figure 7(b) contains calculated GCM in the horizontal direction with inter-pixel distance '1'.
(a) (b)
Figure 7. (a) A Sample Image (b) The GCM in horizontal Direction with inter- pixel distance '1'
Table 2 shows the generalized Co-Occurrence Matrix Properties [34].
Table 2. Generalized Co-Occurrence Matrix Properties [34]
Property Description
Contrast It measures the intensity contrast between a pixel and its neighbour over the whole image.
Correlation It indicates how correlated a pixel is to its neighbour over the whole image.
Energy It represents the sum of squared elements in the GLCM. It is also known as uniformity.
Homogeneity It measures the closeness of the distribution of elements in the GLCM to the GLCM diagonal.
Contrast:
),(||
,
2
jipji
ji
(1)
Correlation:
ji
ji jipji
ji
),())((
,
(2)
Energy:
ji
jip
,
),( 2
(3)
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Homogeneity:
ji ji
jip
, ||1
),(
(4)
p (i, j) represents count at position (i, j) in GLCM, µ denotes mean and σ indicates the standard
deviation in the above equations. Here, small, medium and large distance values are considered to capture the
span of the monument in the horizontal and vertical direction. For example, span of the 'Red fort' is an almost
whole image in the horizontal direction (left-to-right) with Hue, close to Red whereas 'Hawa Mahal' is
spanned in both horizontal and vertical direction with Hue close to Red. Thus homogeneity and correlation
properties are high for 'Red Fort' in the horizontal direction while the same properties are high in the
horizontal as well as in the vertical direction in 'Hawa Mahal'.
Table 3 shows the generalized Co-Occurrence Matrix Used as Features
Table 3. Generalized Co-Occurrence Matrix Used as Features
Number of Graylevels Distance Direction
128 3 Horizontal
128 9 Horizontal
128 15 Horizontal
128 64 Horizontal
128 64 Vertical
3.3. Local Binary Pattern and Centre-Symmetric Local Biinary Pattern
The Local Binary Pattern effectively captures texture information from the local neighbourhood.
Figure 8 explains working of the Local Binary Pattern and the Centre-Symmetric Local Binary Pattern.
2)(),(
1
0
n
i
ic nnsyxLBP I
(5)
s(x) = 1 if x >=0
0 otherwise
Here, nc indicates the graylevel of the centre pixel of 8-neighbourhood, ni indicates ith pixel of the
neighbourhood. The signs of the differences in a neighbourhood are interpreted as N-bit binary number
resulting in 2N distinct values in the binary pattern. The LBP features are robust against illumination
changes, they are very fast to compute, do not require many parameters to be set, and have high
discriminative power [36]. In CS-LBP, center symmetric pairs of pixels are compared. LBP produces 256
distinct binary patterns, whereas CS-LBP generates 16 distinct binary patterns. The robustness on flat image
regions is obtained by thresholding the gray level differences with a small value T. In our proposed system,
histogram of CS-LBP is generated for all 3 planes of HSV image resulting in 48 (16*3) while the histogram
of LBP is obtained for all 3 planes of RGB image resulting into 768 (256*3) features.
Figure 8. Local Binary Pattern and Centre Symmetric Local Binary Pattern [35]
3.4. Edge Histogram
Color information is obtained through histograms, an area information is added into feature vector
using generalized co-occurrence matrix using different distance and direction, texture information is achieved
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using LBP and CS-LBP histogram. To add the structural (behavior at the edge points) information in the
feature descriptor, canny edge detector is used with threshold 0.2 so that most prominent edges are preseved.
Canny edge detector consists of smoothing, finding gradients, non-maxima supression, double thresholding
and edge tracking by hysteresis [37]. For each detected edge point, 5x5 neighbourhood is considered and the
mean and the standard deviation are calculated. The unique values obtained from these statistical properties
vary for every image because the detected edge points are not fixed. It is observed that unique values are in
the range of 2000-10000. Two Histograms with bin size 100 are generated for mean and standard deviation.
3.5. Fitness Function
Here, we have adopted the classification accuracy calculated by a linear SVM classifier on the
training set as well as the testing set. We adopted tenfold cross-validation for which total dataset set is
divided randomly into 10 equal-sized parts and perform ten repetitions of training the SVM on 9/10 of the
set and testing on the remaining 1/10 [38]. The overall fitness „Er‟ is the average of the tenfold cross-
validation accuracy. In our case, the value of n is 10. Accuracy (i) represents the accuracy of fold I by the
SVM. The fitness function is defined as follows:
Er = (1- (∑ (SVM[accuracy(i)])/n)))*100 % (6)
4. RESULTS AND ANALYSIS
4.1. Implementation
Our goal is to classify above mentioned monuments from large repositories of photographs
uploaded on the social networking websites. We have evaluated our proposed method using 64-bit MATLAB
2013a, 8GB of RAM running on the Windows 8.1 OS with i7 5th generation processor.
4.2. Datasets
The WANG database [39] is a subset of 1,000 images of the Corel stock photo database which have
been manually selected and it forms 10 classes of 100 images each. It is shown in Figure 9. The WANG
database can be considered similar to common stock photo retrieval tasks with several images from each
category and a potential user having an image from a particular category and looking for similar images. The
10 classes are used for relevance estimation: given a query image, it is assumed that the user is searching for
images from the same class, and therefore the remaining 99 images from the same class are considered
relevant and the images from all other classes are considered irrelevant. Figure 10 shows Oliva and Torralba
(OT-Scene) dataset (8 categories, 2688 images). The OT-Scene Database was also considered for the
evaluation purpose [41]. Figure 11 displays the example images of Monuments dataset. The Most important
task is the collection of data as no direct dataset is available for the task at hand. In our data set 10 different
classes are considered, namely: 'Taj Mahal', 'Gate way of India, 'Golden Temple',' Lotus Temple', 'India
Gate', 'Qutub Minar‟, 'Red Fort', 'Chatra Pati Shivaji Railway Station', 'Hawa Mahal' and 'Victoria Memorial'.
For each mentioned class, 50 images are collected from websites and a total of 500 images are collected. An
individual tourist, family and tourist groups in various poses are considered for both training and testing. To
achieve diversity, Front view (day), Near View (night), Far View, Left-View (45 degree), right-side view (45
degree), people posing (sitting or standing etc.) in front of monuments are considered.
Figure 9. The Wang Sample Database [18]
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Figure 10. The OT-Scene Database [18]
Figure 11. The Sample Images from the Monuments Database
4.3. Experimental Results
The performance of the system is evaluated based on Error Rate, Precision, Recall, Accuracy and F-
Score [40]. Table 5 shows confusion matrix status for Wang and Monuments Dataset while, Table 6 focuses
on OT-Scene dataset. Confusion Matrix for the Indian Monuments dataset is shown in Table 4.
Precision= tp / (tp + fp) (7)
Recall= tp / (tp + fn) (8)
Accuracy= (tp + tn) / (tp + tn + fp + fn) (9)
F-score= 2 * ((Precision * Recall) / (precision +recall)) (10)
Here, tp indicates true positive, fp represents false positive, fn and tn are false negative and true
negative respectively. The F-score is also known as harmonic mean of precision and recall.
Table 4. The Confusion Matrix (Monuments Database)
Monuments
Taj Mahal 47 0 0 0 0 0 0 0 0 3
Gateway of India 0 41 0 0 3 0 0 0 0 6
Golden Temple 0 0 43 0 0 0 2 0 0 5
Lotus Temple 0 1 0 46 0 0 0 0 0 3
India Gate 0 1 0 0 36 1 0 0 0 12
Qutub Minaar 1 1 0 1 4 39 0 0 0 4
Red Fort 0 1 1 0 0 0 42 0 0 6
CST 0 0 0 0 0 0 1 49 0 0
Hawa Mahal 0 0 1 0 0 0 0 0 48 1
Victoria Memorial 0 0 0 0 0 0 0 0 0 50
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Table 5. The Confusion Matrix Status
Monuments Dataset Wang Dataset
Monuments Precision Recall Accuracy F-Score Class Label Precision Recall Accuracy F-
Score
Taj Mahal 0.97 0.94 0.99 0.95 Tribals 0.94 0.86 0.98 0.90
Gateway of
India
0.91 0.82 0.97 0.86 Sea Beach 0.88 0.82 0.97 0.84
Golden
Temple
0.95 0.86 0.98 0.90 Gothic
Structure
0.97 0.83 0.98 0.89
Lotus
Temple
0.97 0.92 0.99 0.94 Bus 1 0.99 0.99 0.99
India Gate 0.83 0.72 0.95 0.77 Dinosaur 1 1 1 1
Qutub
Minaar
0.97 0.78 0.97 0.86 Elephant 0.95 0.91 0.98 0.93
Red Fort 0.93 0.84 0.97 0.88 Roses 1 0.99 0.99 0.99
CST 1 0.98 0.99 0.98 Horses 0.98 0.98 0.99 0.98
Hawa
Mahal
1 0.96 0.99 0.97 Mountains 0.98 0.78 0.97 0.87
Victoria
Memorial
0.55 1 0.92 0.71 Food 0.61 0.98 0.93 0.75
Average 0.91 0.88 0.97 0.88 Average 0.93 0.91 0.98 0.91
Table 6. The Confusion Matrix Status (OT-Scene Database)
OTScene Dataset Precision Recall Accuracy F-Score
Coast & Beach 1 1 1 1
Open Country 0.94 0.93 0.98 0.93
Forest 0.96 0.95 0.99 0.95
Mountain 0.97 0.91 0.99 0.94
Highway 0.97 0.93 0.99 0.95
Street 0.96 0.87 0.97 0.91
City Center 0.90 0.87 0.98 0.89
Average 0.93 0.92 0.98 0.92
Figure 12 shows the Receiver Operating Characteristic curve for Monuments Dataset. Similar
curves can be easily plotted for the other benchmark databases.
Figure 12. ROC Curve of the Monuments Dataset
5. CONCLUSION
Recently, Content Based Image Classification has generated successful applications in industries
like agriculture, pharmaceutical, surveillance and many more. The tourism industry of any country plays a
vital role in the economic growth of the nation. The presence of the monuments in the Bollywood movies and
its impact on the tourism industry is highlighted as tourists prefer such places to visit. Feature vector is
generated using Histograms, Local Binary Pattern, Generalized Co-Occurrence Matrix and Canny-Edge
Detector. Ten popular Indian Monuments were considered and image database has been constructed. The
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system achieved an average accuracy of 97% with high precision and recall for the Indian monument
database. The proposed system also works well on the other benchmark databases.
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BIOGRAPHIES OF AUTHORS
M. S. Bhatt was born at Bhuj in Gujarat, India on 20th February, 1981. Prof. Bhatt completed
Bachelor of Engineering with specialization in Computer Engineering from C.U. Shah College of
Engineering & Technology, Surendranagar, Gujarat, India in the year 2002. Prof. Bhatt completed
Masters in Engineering with computer engineering as specialization from Dharmsinh Desai University,
Nadiad, Gujarat, India in the year 2004. Prof. Bhatt is Pursuing Ph.D. in Computer Engineering from
Rai University, Ahmedabad, Gujarat. The author‟s major field of study covers Image Processing and
Multimedia Information Retrieval. He has been working as Associate Professor in Computer
Engineering Department at Dharmsinh Desai University, Nadiad, Gujarat, India since 2008. He
delivered expert talks and acted as reviewer in international conferences. He has also presented papers
in international journals, IEEE conferences, National Journals and National Conferences.
Tejas P. Patalia was born at Rajkot in Gujarat, India on 14th July, 1978. Dr. Patalia completed
Bachelor of Engineering with specialization in Computer Engineering from Sarvajanik College of
Engineering & Technology, Surat, Gujarat, India in the year 2002. Dr. Patalia completed Masters in
Engineering with computer engineering as specialization from BVM Engineering College, V. V.
Nagar, Gujarat, India in the year 2009. Dr. Patalia achieved Ph.D. in Computer science and
Engineering from Singhania University in the year 2012. The author‟s major field of study covers
Wireless Sensor Network and Information Retrieval. He has been working as Head of Computer
Engineering Department in V.V.P. Engineering college, Rajkot, Gujarat, India since 2003. He has
received research grants from funding agencies like GUJCOST and Gujarat Technological University.
He has presented many papers in reputed international journals, IEEE conferences, National Journals
and National Conferences.