This document presents the design, development and evaluation of a grading system for peeled pistachios using machine vision and support vector machines. The system captures images of pistachio kernels and shells using a camera. Images are preprocessed and features are extracted. A support vector machine classifier with a cubic polynomial kernel achieves 99.17% accuracy in classifying kernels and shells. The system is able to sort pistachios at a rate of 22.74 kg/hour with 94.33% accuracy.
Analysis And Detection of Infected Fruit Part Using Improved k-means Clusteri...IJSRD
Drastic increase in the overseas commerce has increased nowadays .Modern food industries work on the quality and safety of the products. Fruits such as oranges and apple are imported and exported on large scale. Identifying the defect manually become time consuming process. The combined study of image processing and clustering technique gave a turning point to the defect defection in fruits. This paper gives a solution for defect detection and classification of fruits using improved K-means clustering algorithm. Based on their color pixels are clustered. Then the merging takes place to a specific no of regions. Although defect segmentation is not depend on the color, it causes to produce different power to different regions of image. We have taken some of the fruits for the experimental results to clarify the proposed approach to improve the analysis and detection of fruit quality to minimize the precious and computational time. The proposed system is effective due to result obtained.
Survey on semi supervised classification methods and feature selectioneSAT Journals
Abstract Data mining also called knowledge discovery is a process of analyzing data from several perspective and summarize it into useful information. It has tremendous application in the area of classification like pattern recognition, discovering several disease type, analysis of medical image, recognizing speech, for identifying biometric, drug discovery etc. This is a survey based on several semisupervised classification method used by classifiers , in this both labeled and unlabeled data can be used for classification purpose.It is less expensive than other classification methods . Different techniques surveyed in this paper are low density separation approach, transductive SVM, semi-supervised based logistic discriminate procedure, self training nearest neighbour rule using cut edges, self training nearest neighbour rule using cut edges. Along with classification methods a review about various feature selection methods is also mentioned in this paper. Feature selection is performed to reduce the dimension of large dataset. After reducing attribute the data is given for classification hence the accuracy and performance of classification system can be improved .Several feature selection method include consistency based feature selection, fuzzy entropy measure feature selection with similarity classifier, Signal to noise ratio,Positive approximation. So each method has several benefits. Index Terms: Semisupervised classification, Transductive support vector machine, Feature selection, unlabeled samples
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Feature selection for multiple water quality status: integrated bootstrapping...IJECEIAES
STORET is one method to determine the river water quality, and to classify them into four classes (very good, good, medium and bad) based on the data of water for each attribute or feature. The success of the formation of pattern recognition model much depends on the quality of data. There are two issues as the concern of this research as follows, the data having disproportionate amount among the classes (imbalance class) and the finding of noise on its attribute. Therefore, this research integrates the SMOTE Technique and bootstrapping to handle the problem of imbalance class. While an experiment is conducted to eliminate the noise on the attribute by using some feature selection algorithms with filter approach (information gain, rule, derivation, correlation and chi square). This research has some stages as follows: data understanding, pre-processing, imbalance class, feature selection, classification and performance evaluation. Based on the result of testing using 10-fold cross validation, it shows that the use of the SMOTE-bootstrapping technique is able to increase the accuracy from 83.3% to be 98.8%. While the process of noise elimination onthe data attribute is also able to increase the accuracy to be 99.5% (the use of feature subset produced by the information gain algorithm and the decision tree classification algorithm).
Agriculture is the backbone of human sustenance on this world. Now a days with growing population we need the productivity of the agriculture to be increased a lot to meet the demands. In olden days they used natural methods to increase the productivity, such as using the cow dung as a fertilizer in the fields. That resulted increase in the productivity enough to meet the requirements of the population. But later people started thinking of earning more profits by getting more outcome. So, there came a revolution called “Green Revolution”. In this paper we implemented image processing using MATLAB to detect the weed areas in an image we took from the fields.
Analysis And Detection of Infected Fruit Part Using Improved k-means Clusteri...IJSRD
Drastic increase in the overseas commerce has increased nowadays .Modern food industries work on the quality and safety of the products. Fruits such as oranges and apple are imported and exported on large scale. Identifying the defect manually become time consuming process. The combined study of image processing and clustering technique gave a turning point to the defect defection in fruits. This paper gives a solution for defect detection and classification of fruits using improved K-means clustering algorithm. Based on their color pixels are clustered. Then the merging takes place to a specific no of regions. Although defect segmentation is not depend on the color, it causes to produce different power to different regions of image. We have taken some of the fruits for the experimental results to clarify the proposed approach to improve the analysis and detection of fruit quality to minimize the precious and computational time. The proposed system is effective due to result obtained.
Survey on semi supervised classification methods and feature selectioneSAT Journals
Abstract Data mining also called knowledge discovery is a process of analyzing data from several perspective and summarize it into useful information. It has tremendous application in the area of classification like pattern recognition, discovering several disease type, analysis of medical image, recognizing speech, for identifying biometric, drug discovery etc. This is a survey based on several semisupervised classification method used by classifiers , in this both labeled and unlabeled data can be used for classification purpose.It is less expensive than other classification methods . Different techniques surveyed in this paper are low density separation approach, transductive SVM, semi-supervised based logistic discriminate procedure, self training nearest neighbour rule using cut edges, self training nearest neighbour rule using cut edges. Along with classification methods a review about various feature selection methods is also mentioned in this paper. Feature selection is performed to reduce the dimension of large dataset. After reducing attribute the data is given for classification hence the accuracy and performance of classification system can be improved .Several feature selection method include consistency based feature selection, fuzzy entropy measure feature selection with similarity classifier, Signal to noise ratio,Positive approximation. So each method has several benefits. Index Terms: Semisupervised classification, Transductive support vector machine, Feature selection, unlabeled samples
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Feature selection for multiple water quality status: integrated bootstrapping...IJECEIAES
STORET is one method to determine the river water quality, and to classify them into four classes (very good, good, medium and bad) based on the data of water for each attribute or feature. The success of the formation of pattern recognition model much depends on the quality of data. There are two issues as the concern of this research as follows, the data having disproportionate amount among the classes (imbalance class) and the finding of noise on its attribute. Therefore, this research integrates the SMOTE Technique and bootstrapping to handle the problem of imbalance class. While an experiment is conducted to eliminate the noise on the attribute by using some feature selection algorithms with filter approach (information gain, rule, derivation, correlation and chi square). This research has some stages as follows: data understanding, pre-processing, imbalance class, feature selection, classification and performance evaluation. Based on the result of testing using 10-fold cross validation, it shows that the use of the SMOTE-bootstrapping technique is able to increase the accuracy from 83.3% to be 98.8%. While the process of noise elimination onthe data attribute is also able to increase the accuracy to be 99.5% (the use of feature subset produced by the information gain algorithm and the decision tree classification algorithm).
Agriculture is the backbone of human sustenance on this world. Now a days with growing population we need the productivity of the agriculture to be increased a lot to meet the demands. In olden days they used natural methods to increase the productivity, such as using the cow dung as a fertilizer in the fields. That resulted increase in the productivity enough to meet the requirements of the population. But later people started thinking of earning more profits by getting more outcome. So, there came a revolution called “Green Revolution”. In this paper we implemented image processing using MATLAB to detect the weed areas in an image we took from the fields.
An automatic fruit quality inspection system for sorting and grading of tomato fruit and defected tomato detection discussed here.The main aim of this system is to replace the manual inspection system.
This helps in speed up the process improve accuracy and efficiency and reduce time. This system collect image from camera which is placed on conveyor belt.
Then image processing is done to get required features of fruits such as texture, color and size.
Defected fruit is detected based on blob detection, color detection is done based on thresholding.
Size detection is based on binary image of tomato. Sorting is done based on color and grading is done based on size.
A large number of techniques has been developed so far to tell the diversity of machine learning. Machine learning is categorized into supervised, unsupervised and reinforcement learning .Every instance in given data-set used by Machine learning algorithms is represented same set of features .On basis of label of instances it is divided into category. In this review paper our main focus is on Supervised, unsupervised learning techniques and its performance parameters.
IDENTIFICATION AND CLASSIFICATION OF POWDER MICROSCOPIC IMAGES OF INDIAN HERB...IAEME Publication
This paper proposes an automated algorithm for plant identification using microscopic images of powder of herbal plants. In current scenario, the task of identifying plant from its powder form is done by pharmaceutical companies, who performs this task manually. This process takes lots of effort and time. Microscopic images of powder contains varieties of information, which are important evidence for identification of the plant. With every image, different type of noise are present, which makes the segmentation as a critical job. In this paper, we are proposing an algorithm which performs this task automatically by a computer.
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn’t require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
Palm print recognition based on harmony search algorithm IJECEIAES
Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%.
Smart Fruit Classification using Neural Networksijtsrd
The objective of this project is to develop a system that helps the food industry to classify fruits based on specific quality features. Our system will give best performance when used to sort some brand of fruits. The fruit industry plays a vital role in a countrys economic growth. They account for a fraction of the agricultural output produced by a country. It forms a part of the food processing industry. Fruits are a major source of energy, vitamins, minerals, fiber and other nutrients. They contribute to an essential part of our diet. Fruits come in varying shapes, color and sizes. Some of them are exported, thereby yielding profit to the industry. K. Sandhiya | M. Vidhya | M. Shivaranjani | S. Saranya"Smart Fruit Classification using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd6986.pdf http://www.ijtsrd.com/engineering/computer-engineering/6986/smart-fruit-classification-using-neural-networks/k-sandhiya
The quality identification of fruits in image processing using matlabeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An unsupervised feature selection algorithm with feature ranking for maximizi...Asir Singh
Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,
efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,
finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The
predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and
redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be
removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm
namely unsupervised learning with ranking based feature selection (FSULR). It removes redundant features by clustering and eliminates
irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this
proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes (NB),
instance based (IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for
classifiers.
An automatic fruit quality inspection system for sorting and grading of tomato fruit and defected tomato detection discussed here.The main aim of this system is to replace the manual inspection system.
This helps in speed up the process improve accuracy and efficiency and reduce time. This system collect image from camera which is placed on conveyor belt.
Then image processing is done to get required features of fruits such as texture, color and size.
Defected fruit is detected based on blob detection, color detection is done based on thresholding.
Size detection is based on binary image of tomato. Sorting is done based on color and grading is done based on size.
A large number of techniques has been developed so far to tell the diversity of machine learning. Machine learning is categorized into supervised, unsupervised and reinforcement learning .Every instance in given data-set used by Machine learning algorithms is represented same set of features .On basis of label of instances it is divided into category. In this review paper our main focus is on Supervised, unsupervised learning techniques and its performance parameters.
IDENTIFICATION AND CLASSIFICATION OF POWDER MICROSCOPIC IMAGES OF INDIAN HERB...IAEME Publication
This paper proposes an automated algorithm for plant identification using microscopic images of powder of herbal plants. In current scenario, the task of identifying plant from its powder form is done by pharmaceutical companies, who performs this task manually. This process takes lots of effort and time. Microscopic images of powder contains varieties of information, which are important evidence for identification of the plant. With every image, different type of noise are present, which makes the segmentation as a critical job. In this paper, we are proposing an algorithm which performs this task automatically by a computer.
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn’t require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
Palm print recognition based on harmony search algorithm IJECEIAES
Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%.
Smart Fruit Classification using Neural Networksijtsrd
The objective of this project is to develop a system that helps the food industry to classify fruits based on specific quality features. Our system will give best performance when used to sort some brand of fruits. The fruit industry plays a vital role in a countrys economic growth. They account for a fraction of the agricultural output produced by a country. It forms a part of the food processing industry. Fruits are a major source of energy, vitamins, minerals, fiber and other nutrients. They contribute to an essential part of our diet. Fruits come in varying shapes, color and sizes. Some of them are exported, thereby yielding profit to the industry. K. Sandhiya | M. Vidhya | M. Shivaranjani | S. Saranya"Smart Fruit Classification using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd6986.pdf http://www.ijtsrd.com/engineering/computer-engineering/6986/smart-fruit-classification-using-neural-networks/k-sandhiya
The quality identification of fruits in image processing using matlabeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An unsupervised feature selection algorithm with feature ranking for maximizi...Asir Singh
Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,
efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,
finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The
predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and
redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be
removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm
namely unsupervised learning with ranking based feature selection (FSULR). It removes redundant features by clustering and eliminates
irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this
proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes (NB),
instance based (IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for
classifiers.
IRJET-Analysis of Face Recognition System for Different ClassifierIRJET Journal
M.Manimozhi, A. John Dhanaseely "Analysis of Face Recognition System for Different Classifier ", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net .published by Fast Track Publications
Abstract
Face recognition plays vital role for authenticating system. Human Face recognition is a challenging task in computer vision and pattern recognition. Face recognition has attracted much attention due to its potential value in security and law enforcement applications and its theoretical challenges. Different methods are used for feature extraction and classification. Kernel fisher analysis is used for feature extraction. The performance analysis for Euclidean, support vector machine is evaluated. The whole process is done using MATLAB software. A set of 10 person real time images is taken for our work. The classifier recognizes the similar posture as an output.
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.
Abstract
Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.
Keywords: chemometric analysis; transgenic agricultural products and foods; near-infrared spectroscopy; hyperspectral imaging
1. Introduction
Currently, genetics is widely used in various science fields. Transgenic technology is used to transfer the genes with known functional traits, such as high yield, resistance to disease and insects, and improvement of nutritional quality, into the target organism through modern scientific and technological means, so that new varieties and products are produced by adding new functional characteristics to the recipient organism. Many countries regard transgenic technology as a strategic choice to support development. Transgenics has become a strategic focus for countries to seize the commanding heights of science and technology, and to enhance the international competitiveness of agriculture.
At present, applications of transgenic technology in various fields, including to improve crops, produce vaccines, food, etc., are experiencing a very high growth rate. As a result, genetically modified (GM) production is increasing on the global market. Genetically modified crops are cultivated in 29 countries, with an area under cultivation of 190.4 million hectares [1]. Although GM crops have advantages such as insect resistance, weed resistance, disease resistance, improved nutritional value, and increased yield [2], the use of GM technology may have unintended negative effects on food and environmental safety, and therefore, GM foods have been severely restricted in most parts of the world due to legal pressure from regulatory agencies to control the production of GM products. Thus, it is a very necessary and important task to identify GM products.
Today, several me
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...CSCJournals
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
Automatic Seed Classification by Shape and Color Features using Machine Visio...Editor IJCATR
In this paper the proposed system uses content based image retrieval (CBIR) technique for identification of seed e.g.
wheat, rice, gram etc. on the basis of their features. CBIR is a technique to identify or recognize the image on the basis of features
present in image. Basically features are classified in to four categories 1.color 2.Shape 3. texture 4. size .In this system we are
extracting color, shape feature extraction. After that classifying images in to categories using neural network according to the
weights and image displayed from the category for which neural network shows maximum weight. category1 belongs to wheat and
category2 belongs to gram. Experiment was conducted on 200 images of wheat and gram by using Euclidean distance(ED) and
artificial neural network techniques. From 200 images 150 are used for training purpose and 50 images are used for testing
purpose. The precision rate of the system by using ED is 84.4 percent By using Artificial neural network precision rate is 95
percent.
Similar to IRJET- Design, Development and Evaluation of a Grading System for Peeled Pistachios (20)
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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