Plant leaf identification using image can be constructed by ensemble classifier. Ensemble
classifier executes classification of various features independently. This experiment utilized texture feature
and geometry feature of plant leaf to find out which features are more powerful. Each classifier trained by
specific feature produced different accuracy rate. To integrate ensemble classifier the results of the
classification were weighted, so as the score obtained from better features contributed greater to the final
results. Weighted classification results were combined to get the final result. The proposed method was
evaluated using dataset comprises of 156 variety of plants with 4559 images. Weighting and combining
classifier used in this study were Weighted Majority Vote (WMV) and Naïve Bayes Combination. Both of
those method result showed better accuracy than using single classifier. The average accuracy of single
classifier was 61.2% for geometry classifier and 70.3% for texture classifier, while WMV method was
77.8% and Naïve Bayes Comb ination was 94.6%. The calculation of classifier’s weight b y using WMV
method produces a weight value of 0.54 for texture feature classifier and 0.46 for geometry feature
classifier.
Image Mining for Flower Classification by Genetic Association Rule Mining Usi...IJAEMSJORNAL
Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.
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
Filter Based Approach for Genomic Feature Set Selection (FBA-GFS)IJCSEA Journal
Feature selection is an effective method used in text categorization for sorting a set of documents into certain number of predefined categories. It is an important method for improving the efficiency and accuracy of text categorization algorithms by removing irredundant terms from the corpus. Genome contains the total amount of genetic information in the chromosomes of an organism, including its genes and DNA sequences. In this paper a Clustering technique called Hierarchical Techniques is used tocategories the Features from the Genome documents. A framework is proposed for Genomic Feature set Selection. A Filter based Feature Selection Method like
2 statistics, CHIR statistics are used to select the Feature set. The Selected Feature set is verified by using F-measure and it is biologically validated for Biological relevance using the BLAST tool.
In this paper we have focused on an efficient feature selection method in classification of audio files.
The main objective is feature selection and extraction. We have selected a set of features for further
analysis, which represents the elements in feature vector. By extraction method we can compute a
numerical representation that can be used to characterize the audio using the existing toolbox. In this
study Gain Ratio (GR) is used as a feature selection measure. GR is used to select splitting attribute
which will separate the tuples into different classes. The pulse clarity is considered as a subjective
measure and it is used to calculate the gain of features of audio files. The splitting criterion is
employed in the application to identify the class or the music genre of a specific audio file from
testing database. Experimental results indicate that by using GR the application can produce a
satisfactory result for music genre classification. After dimensionality reduction best three features
have been selected out of various features of audio file and in this technique we will get more than
90% successful classification result.
Header Based Classification of Journals Using Document Image Segmentation and...CSCJournals
Document image segmentation plays an important role in classification of journals, magazines, newspaper, etc., It is a process of splitting the document into distinct regions. Document layout analysis is a key process of identifying and categorizing the regions of interest in the scanned image of a text document. A reading system requires the segmentation of text zones from non- textual ones and the arrangement in their correct reading order. Detection and labelling of text zones play different logical roles inside the document such as titles, captions, footnotes, etc. This research work proposes a new approach to segment the document and classify the journals based on the header block. Documents are collected from different journals and used as input image. The image is segmented into blocks like heading, header, author name and footer using Particle Swarm optimization algorithm and features are extracted from header block using Gray Level Co-occurrences Matrix. Extreme Learning Machine has been used for classification based on the header blocks and obtained 82.3% accuracy.
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
Flower Grain Image Classification Using Supervised Classification AlgorithmIJERD Editor
This document presents a method for classifying flower pollen grain images using supervised machine learning algorithms. Color and texture features are extracted from the images using HSV color space and gray level co-occurrence matrix (GLCM) respectively. An artificial neural network with a feed forward backpropagation architecture is trained on a dataset of 122 pollen grain images from 7 classes. The model achieves an average accuracy of 77.14% when using both color and texture features for classification, outperforming models using only color or texture features individually. The proposed approach aims to automatically identify and classify pollen grains to help study plant reproduction and biodiversity.
Identification and Classification of Leaf Diseases in Turmeric PlantsIJERA Editor
Plant disease identification is the most important sector in agriculture. Turmeric is one of the important
rhizomatous crops grown in India. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot,
and leaf blotch. The identification of plant diseases requires close monitoring and hence this paper adopts
technologies to manage turmeric plant diseases caused by fungi to enable production of high quality crop yields.
Various image processing and machine learning techniques are used to identify and classify the diseases in
turmeric leaf. The dataset with 800 leaf images of different categories were pre-processed and segmented to
promote efficient feature extraction. Machine learning algorithms like support vector machine, decision tree and
naïve bayes were applied to train the model. The performance of the model was evaluated using 10 fold cross
validation and the results are reported.
Image Mining for Flower Classification by Genetic Association Rule Mining Usi...IJAEMSJORNAL
Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.
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
Filter Based Approach for Genomic Feature Set Selection (FBA-GFS)IJCSEA Journal
Feature selection is an effective method used in text categorization for sorting a set of documents into certain number of predefined categories. It is an important method for improving the efficiency and accuracy of text categorization algorithms by removing irredundant terms from the corpus. Genome contains the total amount of genetic information in the chromosomes of an organism, including its genes and DNA sequences. In this paper a Clustering technique called Hierarchical Techniques is used tocategories the Features from the Genome documents. A framework is proposed for Genomic Feature set Selection. A Filter based Feature Selection Method like
2 statistics, CHIR statistics are used to select the Feature set. The Selected Feature set is verified by using F-measure and it is biologically validated for Biological relevance using the BLAST tool.
In this paper we have focused on an efficient feature selection method in classification of audio files.
The main objective is feature selection and extraction. We have selected a set of features for further
analysis, which represents the elements in feature vector. By extraction method we can compute a
numerical representation that can be used to characterize the audio using the existing toolbox. In this
study Gain Ratio (GR) is used as a feature selection measure. GR is used to select splitting attribute
which will separate the tuples into different classes. The pulse clarity is considered as a subjective
measure and it is used to calculate the gain of features of audio files. The splitting criterion is
employed in the application to identify the class or the music genre of a specific audio file from
testing database. Experimental results indicate that by using GR the application can produce a
satisfactory result for music genre classification. After dimensionality reduction best three features
have been selected out of various features of audio file and in this technique we will get more than
90% successful classification result.
Header Based Classification of Journals Using Document Image Segmentation and...CSCJournals
Document image segmentation plays an important role in classification of journals, magazines, newspaper, etc., It is a process of splitting the document into distinct regions. Document layout analysis is a key process of identifying and categorizing the regions of interest in the scanned image of a text document. A reading system requires the segmentation of text zones from non- textual ones and the arrangement in their correct reading order. Detection and labelling of text zones play different logical roles inside the document such as titles, captions, footnotes, etc. This research work proposes a new approach to segment the document and classify the journals based on the header block. Documents are collected from different journals and used as input image. The image is segmented into blocks like heading, header, author name and footer using Particle Swarm optimization algorithm and features are extracted from header block using Gray Level Co-occurrences Matrix. Extreme Learning Machine has been used for classification based on the header blocks and obtained 82.3% accuracy.
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
Flower Grain Image Classification Using Supervised Classification AlgorithmIJERD Editor
This document presents a method for classifying flower pollen grain images using supervised machine learning algorithms. Color and texture features are extracted from the images using HSV color space and gray level co-occurrence matrix (GLCM) respectively. An artificial neural network with a feed forward backpropagation architecture is trained on a dataset of 122 pollen grain images from 7 classes. The model achieves an average accuracy of 77.14% when using both color and texture features for classification, outperforming models using only color or texture features individually. The proposed approach aims to automatically identify and classify pollen grains to help study plant reproduction and biodiversity.
Identification and Classification of Leaf Diseases in Turmeric PlantsIJERA Editor
Plant disease identification is the most important sector in agriculture. Turmeric is one of the important
rhizomatous crops grown in India. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot,
and leaf blotch. The identification of plant diseases requires close monitoring and hence this paper adopts
technologies to manage turmeric plant diseases caused by fungi to enable production of high quality crop yields.
Various image processing and machine learning techniques are used to identify and classify the diseases in
turmeric leaf. The dataset with 800 leaf images of different categories were pre-processed and segmented to
promote efficient feature extraction. Machine learning algorithms like support vector machine, decision tree and
naïve bayes were applied to train the model. The performance of the model was evaluated using 10 fold cross
validation and the results are reported.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
Ant System and Weighted Voting Method for Multiple Classifier Systems IJECEIAES
Combining multiple classifiers is considered as a general solution for classification tasks. However, there are two problems in combining multiple classifiers: constructing a diverse classifier ensemble; and, constructing an appropriate combiner. In this study, an improved multiple classifier combination scheme is propose. A diverse classifier ensemble is constructed by training them with different feature set partitions. The ant system-based algorithm is used to form the optimal feature set partitions. Weighted voting is used to combine the classifiers’ outputs by considering the strength of the classifiers prior to voting. Experiments were carried out using k-NN ensembles on benchmark datasets from the University of California, Irvine, to evaluate the credibility of the proposed method. Experimental results showed that the proposed method has successfully constructed better k-NN ensembles. Further more the proposed method can be used to develop other multiple classifier systems.
Defect Fruit Image Analysis using Advanced Bacterial Foraging Optimizing Algo...IOSR Journals
This document presents a method for segmenting defect areas on fruit images using an improved bacterial foraging optimization algorithm (ABFOA). The algorithm first decomposes the input fruit image into its red, green, and blue color components. It then applies the ABFOA to each color component separately to calculate individual thresholds. The final threshold is calculated as the average of the individual thresholds. This threshold is then applied to the original image to segment the defected areas. The method is tested on images of apples with defects like scab, rot, and blotch disease. Results show the ABFOA approach more accurately segments the defect areas compared to Otsu thresholding in terms of entropy, standard deviation, and peak signal-to
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.
Hybrid Method HVS-MRMR for Variable Selection in Multilayer Artificial Neural...IJECEIAES
The variable selection is an important technique the reducing dimensionality of data frequently used in data preprocessing for performing data mining. This paper presents a new variable selection algorithm uses the heuristic variable selection (HVS) and Minimum Redundancy Maximum Relevance (MRMR). We enhance the HVS method for variab le selection by incorporating (MRMR) filter. Our algorithm is based on wrapper approach using multi-layer perceptron. We called this algorithm a HVS-MRMR Wrapper for variables selection. The relevance of a set of variables is measured by a convex combination of the relevance given by HVS criterion and the MRMR criterion. This approach selects new relevant variables; we evaluate the performance of HVS-MRMR on eight benchmark classification problems. The experimental results show that HVS-MRMR selected a less number of variables with high classification accuracy compared to MRMR and HVS and without variables selection on most datasets. HVS-MRMR can be applied to various classification problems that require high classification accuracy.
Identification of Plant Types by Leaf Textures Based on the Backpropagation N...IJECEIAES
The number of species of plants or flora in Indonesia is abundant. The wealth of Indonesia's flora species is not to be doubted. Almost every region in Indonesia has one or some distinctive plant(s) which may not exist in other countries. In enhancing the potential diversity of tropical plant resources, good management and utilization of biodiversity is required. Based on such diversity, plant classification becomes a challenge to do. The most common way to recognize between one plant and another is to identify the leaf of each plant. Leaf-based classification is an alternative and the most effective way to do because leaves will exist all the time, while fruits and flowers may only exist at any given time. In this study, the researchers will identify plants based on the textures of the leaves. Leaf feature extraction is done by calculating the area value, perimeter, and additional features of leaf images such as shape roundness and slenderness. The results of the extraction will then be selected for training by using the backpropagation neural network. The result of the training (the formation of the training set) will be calculated to produce the value of recognition accuracy with which the feature value of the dataset of the leaf images is then to be matched. The result of the identification of plant species based on leaf texture characteristics is expected to accelerate the process of plant classification based on the characteristics of the leaves.
Performance Evaluation Of Ontology And Fuzzybase Cbiracijjournal
In This Paper, We Have Done Performance Evaluation Of Ontology Using Low-Level Features Like
Color, Texture And Shape Based Cbir, With Topic Specific Cbir.The Resulting Ontology Can Be Used
To Extract The Appropriate Images From The Image Database. Retrieving Appropriate Images From An
Image Database Is One Of The Difficult Tasks In Multimedia Technology. Our Results Show That The
Values Of Recall And Precision Can Be Enhanced And This Also Shows That Semantic Gap Can Also Be
Reduced. The Proposed Algorithm Also Extracts The Texture Values From The Images Automatically
With Also Its Category (Like Smooth, Course Etc) As Well As Its Technical Interpretation
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has suppli
ed a large volume of data to many fields. The gene
microarray analysis and classification have demonst
rated an effective way for the effective diagnosis
of
diseases and cancers. In as much as the data achiev
ing from microarray technology is very noisy and al
so
has thousands of features, feature selection plays
an important role in removing irrelevant and redund
ant
features and also reducing computational complexity
. There are two important approaches for gene
selection in microarray data analysis, the filters
and the wrappers. To select a concise subset of inf
ormative
genes, we introduce a hybrid feature selection whic
h combines two approaches. The fact of the matter i
s
that candidate’s features are first selected from t
he original set via several effective filters. The
candidate
feature set is further refined by more accurate wra
ppers. Thus, we can take advantage of both the filt
ers
and wrappers. Experimental results based on 11 micr
oarray datasets show that our mechanism can be
effected with a smaller feature set. Moreover, thes
e feature subsets can be obtained in a reasonable t
ime
An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Ra...IJMER
A leaf is an organ of a vascular plant, as identified in botanical terms, and in particular in plant morphology. Naturally a leaf is a thin, flattened organ bear above ground and it is mainly used for photosynthesis. Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. Most of the leaves cannot be recognized easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons).In this paper, we mainly focused on tea leaves to identify the leaf type for improving tea leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction and classification to process the loaded image. The tea leaf images can be identified accurately in the preprocessing phase by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT) in order to remove the noisy features and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) are derived to improve the classification accuracy. Radial Basis Function (RBF) is used for efficient classification. The RBF is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method classifies the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in retrieving the leaf type.
Fuzzy Max-Min Composition in Quality Specifications of Multi-Agent Systemijcoa
In this paper a new technique named Fuzzy Max- Min Composition is used to obtain prioritization of quality specifications that assist quality engineer in achieving the desired level of quality for Multi-agent systems. Intuitionistic fuzzy Set has been used to capture the uncertainties associated with stakeholder’s recommendation.
A Combined Method with automatic parameter optimization for Multi-class Image...AM Publications
Multi-class image semantic segmentation deals with many applications in consumer electronics
fields such as image editing and image retrieval. Segmentation is done by combining the top down and bottomup
segmentation. Top-Down Process can be done by Semantic Texton Forest and bottom up- process using
JSEG. These two segmentation process can be executed in a combined manner. But this cannot choose the
optimal value of JSEG parameter for each interested semantic category. Hence an automatic parameter selection
algorithm has been proposed. An automatic parameter selection technique called an automatic multilevel
thresholding algorithm using stratified sampling and PSO is used to remedy the limitations.
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
SVM-PSO based Feature Selection for Improving Medical Diagnosis Reliability u...cscpconf
Improving accuracy of supervised classification algorithms in biomedical applications,
especially CADx, is one of active area of research. This paper proposes construction of rotation
forest (RF) ensemble using 20 learners over two clinical datasets namely lymphography and
backache. We propose a new feature selection strategy based on support vector machines
optimized by particle swarm optimization for relevant and minimum feature subset for obtaining
higher accuracy of ensembles. We have quantitatively analyzed 20 base learners over two
datasets and carried out the experiments with 10 fold cross validation leave-one-out strategy
and the performance of 20 classifiers are evaluated using performance metrics namely accuracy
(acc), kappa value (K), root mean square error (RMSE) and area under receiver operating
characteristics curve (ROC). Base classifiers succeeded 79.96% & 81.71% average accuracies
for lymphography & backache datasets respectively. As for RF ensembles, they produced
average accuracies of 83.72% & 85.77% for respective diseases. The paper presents promising
results using RF ensembles and provides a new direction towards construction of reliable and robust medical diagnosis systems.
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
Texture Images Classification using Secant Lines Segments Histogramijtsrd
Texture classification is the process to classify different textures from the given images. The aim of texture classification is to classify the category of a texture image. To design an effective algorithm for texture classification, it is essential to find a set of texture features with good discriminating power. This paper presents a texture classification system using secant lines segments histogram and Euclidean Distance. Secant lines segments histogram is used to generate the features from texture images as a histogram. These features offer a better discriminating strategy for texture classification. These features are first used for training and later on for classifying the texture images. Euclidean Distance is used for distinguishing each of the known categories for classification. Ei Phyu Win | Mie Mie Tin | Pyae Phyo Thu "Texture Images Classification using Secant Lines Segments Histogram" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27984.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/27984/texture-images-classification-using-secant-lines-segments-histogram/ei-phyu-win
IRJET- Surveillance for Leaf Detection using HexacopterIRJET Journal
The document describes a method for detecting plant leaves using image processing techniques on images captured by a hexacopter. The method involves pre-processing images using median filtering, extracting features like texture using gray-level co-occurrence matrix, segmenting leaves, and classifying leaves using support vector machine. This detection system is aimed to identify various plant species and protect endangered species by utilizing computer vision algorithms to analyze leaf images captured by a hexacopter surveillance system.
1) The document presents an object-oriented approach for extracting information from high-resolution satellite imagery using fuzzy rule sets.
2) It involves segmenting the image, establishing a class hierarchy based on features like NDVI and NIR ratios, and classifying image objects using fuzzy membership functions.
3) The methodology is tested on a Landsat-8 satellite image, achieving an overall classification accuracy of 99.79% according to an error matrix assessment.
Object-Oriented Approach of Information Extraction from High Resolution Satel...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A NOVEL DATA DICTIONARY LEARNING FOR LEAF RECOGNITIONsipij
Automatic leaf recognition via image processing has been greatly important for a number of professionals, such as botanical taxonomic, environmental protectors, and foresters. Learn an over-complete leaf dictionary is an essential step for leaf image recognition. Big leaf images dimensions and training images number is facing of fast and complete data leaves dictionary. In this work an efficient approach applies to construct over-complete data leaves dictionary to set of big images diminutions based on sparse representation. In the proposed method a new cropped-contour method has used to crop the training image. The experiments are testing using correlation between the sparse representation and data dictionary and with focus on the computing time.
Identification and Classification of Leaf Diseases in Turmeric PlantsIJERA Editor
Plant disease identification is the most important sector in agriculture. Turmeric is one of the important
rhizomatous crops grown in India. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot,
and leaf blotch. The identification of plant diseases requires close monitoring and hence this paper adopts
technologies to manage turmeric plant diseases caused by fungi to enable production of high quality crop yields.
Various image processing and machine learning techniques are used to identify and classify the diseases in
turmeric leaf. The dataset with 800 leaf images of different categories were pre-processed and segmented to
promote efficient feature extraction. Machine learning algorithms like support vector machine, decision tree and
naïve bayes were applied to train the model. The performance of the model was evaluated using 10 fold cross
validation and the results are reported.
Automatic Recognition of Medicinal Plants using Machine Learning TechniquesIRJET Journal
The document presents a method for automatic recognition of medicinal plants using machine learning techniques. Leaves from 24 medicinal plant species were collected and their features were extracted, including length, width, color, and area. A random forest classifier achieved 90.1% accuracy in identifying the plants using 10-fold cross-validation, outperforming other classifiers like k-nearest neighbor and support vector machines. The proposed method uses feature extraction and a support vector machine classifier to identify medicinal plant leaves with high accuracy.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
Ant System and Weighted Voting Method for Multiple Classifier Systems IJECEIAES
Combining multiple classifiers is considered as a general solution for classification tasks. However, there are two problems in combining multiple classifiers: constructing a diverse classifier ensemble; and, constructing an appropriate combiner. In this study, an improved multiple classifier combination scheme is propose. A diverse classifier ensemble is constructed by training them with different feature set partitions. The ant system-based algorithm is used to form the optimal feature set partitions. Weighted voting is used to combine the classifiers’ outputs by considering the strength of the classifiers prior to voting. Experiments were carried out using k-NN ensembles on benchmark datasets from the University of California, Irvine, to evaluate the credibility of the proposed method. Experimental results showed that the proposed method has successfully constructed better k-NN ensembles. Further more the proposed method can be used to develop other multiple classifier systems.
Defect Fruit Image Analysis using Advanced Bacterial Foraging Optimizing Algo...IOSR Journals
This document presents a method for segmenting defect areas on fruit images using an improved bacterial foraging optimization algorithm (ABFOA). The algorithm first decomposes the input fruit image into its red, green, and blue color components. It then applies the ABFOA to each color component separately to calculate individual thresholds. The final threshold is calculated as the average of the individual thresholds. This threshold is then applied to the original image to segment the defected areas. The method is tested on images of apples with defects like scab, rot, and blotch disease. Results show the ABFOA approach more accurately segments the defect areas compared to Otsu thresholding in terms of entropy, standard deviation, and peak signal-to
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.
Hybrid Method HVS-MRMR for Variable Selection in Multilayer Artificial Neural...IJECEIAES
The variable selection is an important technique the reducing dimensionality of data frequently used in data preprocessing for performing data mining. This paper presents a new variable selection algorithm uses the heuristic variable selection (HVS) and Minimum Redundancy Maximum Relevance (MRMR). We enhance the HVS method for variab le selection by incorporating (MRMR) filter. Our algorithm is based on wrapper approach using multi-layer perceptron. We called this algorithm a HVS-MRMR Wrapper for variables selection. The relevance of a set of variables is measured by a convex combination of the relevance given by HVS criterion and the MRMR criterion. This approach selects new relevant variables; we evaluate the performance of HVS-MRMR on eight benchmark classification problems. The experimental results show that HVS-MRMR selected a less number of variables with high classification accuracy compared to MRMR and HVS and without variables selection on most datasets. HVS-MRMR can be applied to various classification problems that require high classification accuracy.
Identification of Plant Types by Leaf Textures Based on the Backpropagation N...IJECEIAES
The number of species of plants or flora in Indonesia is abundant. The wealth of Indonesia's flora species is not to be doubted. Almost every region in Indonesia has one or some distinctive plant(s) which may not exist in other countries. In enhancing the potential diversity of tropical plant resources, good management and utilization of biodiversity is required. Based on such diversity, plant classification becomes a challenge to do. The most common way to recognize between one plant and another is to identify the leaf of each plant. Leaf-based classification is an alternative and the most effective way to do because leaves will exist all the time, while fruits and flowers may only exist at any given time. In this study, the researchers will identify plants based on the textures of the leaves. Leaf feature extraction is done by calculating the area value, perimeter, and additional features of leaf images such as shape roundness and slenderness. The results of the extraction will then be selected for training by using the backpropagation neural network. The result of the training (the formation of the training set) will be calculated to produce the value of recognition accuracy with which the feature value of the dataset of the leaf images is then to be matched. The result of the identification of plant species based on leaf texture characteristics is expected to accelerate the process of plant classification based on the characteristics of the leaves.
Performance Evaluation Of Ontology And Fuzzybase Cbiracijjournal
In This Paper, We Have Done Performance Evaluation Of Ontology Using Low-Level Features Like
Color, Texture And Shape Based Cbir, With Topic Specific Cbir.The Resulting Ontology Can Be Used
To Extract The Appropriate Images From The Image Database. Retrieving Appropriate Images From An
Image Database Is One Of The Difficult Tasks In Multimedia Technology. Our Results Show That The
Values Of Recall And Precision Can Be Enhanced And This Also Shows That Semantic Gap Can Also Be
Reduced. The Proposed Algorithm Also Extracts The Texture Values From The Images Automatically
With Also Its Category (Like Smooth, Course Etc) As Well As Its Technical Interpretation
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has suppli
ed a large volume of data to many fields. The gene
microarray analysis and classification have demonst
rated an effective way for the effective diagnosis
of
diseases and cancers. In as much as the data achiev
ing from microarray technology is very noisy and al
so
has thousands of features, feature selection plays
an important role in removing irrelevant and redund
ant
features and also reducing computational complexity
. There are two important approaches for gene
selection in microarray data analysis, the filters
and the wrappers. To select a concise subset of inf
ormative
genes, we introduce a hybrid feature selection whic
h combines two approaches. The fact of the matter i
s
that candidate’s features are first selected from t
he original set via several effective filters. The
candidate
feature set is further refined by more accurate wra
ppers. Thus, we can take advantage of both the filt
ers
and wrappers. Experimental results based on 11 micr
oarray datasets show that our mechanism can be
effected with a smaller feature set. Moreover, thes
e feature subsets can be obtained in a reasonable t
ime
An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Ra...IJMER
A leaf is an organ of a vascular plant, as identified in botanical terms, and in particular in plant morphology. Naturally a leaf is a thin, flattened organ bear above ground and it is mainly used for photosynthesis. Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. Most of the leaves cannot be recognized easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons).In this paper, we mainly focused on tea leaves to identify the leaf type for improving tea leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction and classification to process the loaded image. The tea leaf images can be identified accurately in the preprocessing phase by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT) in order to remove the noisy features and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) are derived to improve the classification accuracy. Radial Basis Function (RBF) is used for efficient classification. The RBF is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method classifies the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in retrieving the leaf type.
Fuzzy Max-Min Composition in Quality Specifications of Multi-Agent Systemijcoa
In this paper a new technique named Fuzzy Max- Min Composition is used to obtain prioritization of quality specifications that assist quality engineer in achieving the desired level of quality for Multi-agent systems. Intuitionistic fuzzy Set has been used to capture the uncertainties associated with stakeholder’s recommendation.
A Combined Method with automatic parameter optimization for Multi-class Image...AM Publications
Multi-class image semantic segmentation deals with many applications in consumer electronics
fields such as image editing and image retrieval. Segmentation is done by combining the top down and bottomup
segmentation. Top-Down Process can be done by Semantic Texton Forest and bottom up- process using
JSEG. These two segmentation process can be executed in a combined manner. But this cannot choose the
optimal value of JSEG parameter for each interested semantic category. Hence an automatic parameter selection
algorithm has been proposed. An automatic parameter selection technique called an automatic multilevel
thresholding algorithm using stratified sampling and PSO is used to remedy the limitations.
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
SVM-PSO based Feature Selection for Improving Medical Diagnosis Reliability u...cscpconf
Improving accuracy of supervised classification algorithms in biomedical applications,
especially CADx, is one of active area of research. This paper proposes construction of rotation
forest (RF) ensemble using 20 learners over two clinical datasets namely lymphography and
backache. We propose a new feature selection strategy based on support vector machines
optimized by particle swarm optimization for relevant and minimum feature subset for obtaining
higher accuracy of ensembles. We have quantitatively analyzed 20 base learners over two
datasets and carried out the experiments with 10 fold cross validation leave-one-out strategy
and the performance of 20 classifiers are evaluated using performance metrics namely accuracy
(acc), kappa value (K), root mean square error (RMSE) and area under receiver operating
characteristics curve (ROC). Base classifiers succeeded 79.96% & 81.71% average accuracies
for lymphography & backache datasets respectively. As for RF ensembles, they produced
average accuracies of 83.72% & 85.77% for respective diseases. The paper presents promising
results using RF ensembles and provides a new direction towards construction of reliable and robust medical diagnosis systems.
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
Texture Images Classification using Secant Lines Segments Histogramijtsrd
Texture classification is the process to classify different textures from the given images. The aim of texture classification is to classify the category of a texture image. To design an effective algorithm for texture classification, it is essential to find a set of texture features with good discriminating power. This paper presents a texture classification system using secant lines segments histogram and Euclidean Distance. Secant lines segments histogram is used to generate the features from texture images as a histogram. These features offer a better discriminating strategy for texture classification. These features are first used for training and later on for classifying the texture images. Euclidean Distance is used for distinguishing each of the known categories for classification. Ei Phyu Win | Mie Mie Tin | Pyae Phyo Thu "Texture Images Classification using Secant Lines Segments Histogram" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27984.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/27984/texture-images-classification-using-secant-lines-segments-histogram/ei-phyu-win
IRJET- Surveillance for Leaf Detection using HexacopterIRJET Journal
The document describes a method for detecting plant leaves using image processing techniques on images captured by a hexacopter. The method involves pre-processing images using median filtering, extracting features like texture using gray-level co-occurrence matrix, segmenting leaves, and classifying leaves using support vector machine. This detection system is aimed to identify various plant species and protect endangered species by utilizing computer vision algorithms to analyze leaf images captured by a hexacopter surveillance system.
1) The document presents an object-oriented approach for extracting information from high-resolution satellite imagery using fuzzy rule sets.
2) It involves segmenting the image, establishing a class hierarchy based on features like NDVI and NIR ratios, and classifying image objects using fuzzy membership functions.
3) The methodology is tested on a Landsat-8 satellite image, achieving an overall classification accuracy of 99.79% according to an error matrix assessment.
Object-Oriented Approach of Information Extraction from High Resolution Satel...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A NOVEL DATA DICTIONARY LEARNING FOR LEAF RECOGNITIONsipij
Automatic leaf recognition via image processing has been greatly important for a number of professionals, such as botanical taxonomic, environmental protectors, and foresters. Learn an over-complete leaf dictionary is an essential step for leaf image recognition. Big leaf images dimensions and training images number is facing of fast and complete data leaves dictionary. In this work an efficient approach applies to construct over-complete data leaves dictionary to set of big images diminutions based on sparse representation. In the proposed method a new cropped-contour method has used to crop the training image. The experiments are testing using correlation between the sparse representation and data dictionary and with focus on the computing time.
Identification and Classification of Leaf Diseases in Turmeric PlantsIJERA Editor
Plant disease identification is the most important sector in agriculture. Turmeric is one of the important
rhizomatous crops grown in India. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot,
and leaf blotch. The identification of plant diseases requires close monitoring and hence this paper adopts
technologies to manage turmeric plant diseases caused by fungi to enable production of high quality crop yields.
Various image processing and machine learning techniques are used to identify and classify the diseases in
turmeric leaf. The dataset with 800 leaf images of different categories were pre-processed and segmented to
promote efficient feature extraction. Machine learning algorithms like support vector machine, decision tree and
naïve bayes were applied to train the model. The performance of the model was evaluated using 10 fold cross
validation and the results are reported.
Automatic Recognition of Medicinal Plants using Machine Learning TechniquesIRJET Journal
The document presents a method for automatic recognition of medicinal plants using machine learning techniques. Leaves from 24 medicinal plant species were collected and their features were extracted, including length, width, color, and area. A random forest classifier achieved 90.1% accuracy in identifying the plants using 10-fold cross-validation, outperforming other classifiers like k-nearest neighbor and support vector machines. The proposed method uses feature extraction and a support vector machine classifier to identify medicinal plant leaves with high accuracy.
This document presents a method for leaf identification using feature extraction and an artificial neural network. Leaf images are preprocessed, segmented, and features like eccentricity, aspect ratio, area, and perimeter are extracted. These features are used as inputs to train an artificial neural network classifier. The neural network is tested on leaf images and achieves 98.8% accuracy at identifying leaves using a minimum of seven input features. This approach provides an effective and computationally efficient way to identify plant leaves based on images.
AN EFFICIENT FEATURE SELECTION IN CLASSIFICATION OF AUDIO FILEScscpconf
In this paper we have focused on an efficient feature selection method in classification of audio files.
The main objective is feature selection and extraction. We have selected a set of features for further
analysis, which represents the elements in feature vector. By extraction method we can compute a
numerical representation that can be used to characterize the audio using the existing toolbox. In this
study Gain Ratio (GR) is used as a feature selection measure. GR is used to select splitting attribute
which will separate the tuples into different classes. The pulse clarity is considered as a subjective
measure and it is used to calculate the gain of features of audio files. The splitting criterion is
employed in the application to identify the class or the music genre of a specific audio file from
testing database. Experimental results indicate that by using GR the application can produce a
satisfactory result for music genre classification. After dimensionality reduction best three features
have been selected out of various features of audio file and in this technique we will get more than
90% successful classification result.
Filter Based Approach for Genomic Feature Set Selection (FBA-GFS)IJCSEA Journal
Feature selection is an effective method used in text categorization for sorting a set of documents into certain number of predefined categories. It is an important method for improving the efficiency and accuracy of text categorization algorithms by removing irredundant terms from the corpus. Genome contains the total amount of genetic information in the chromosomes of an organism, including its genes and DNA sequences. In this paper a Clustering technique called Hierarchical Techniques is used to categories the Features from the Genome documents. A framework is proposed for Genomic Feature set Selection. A Filter based Feature Selection Method like statistics, CHIR statistics are used to select the Feature set. The Selected Feature set is verified by using F-measure and it is biologically validated for Biological relevance using the BLAST tool.
Orientation Spectral Resolution Coding for Pattern RecognitionIOSRjournaljce
In the approach of pattern recognition, feature descriptions are of greater importance. Features are represented in spatial domain and transformed domain. Wherein, spatial domain features are of lower representation, transformed domains are finer and more informative. In the transformed domain representation, features are represented using spectral coding using advanced transformation technique such as wavelet transformation. However, the feature extraction approach considers the band coefficients; the orientation variation is not considered. In this paper towards inherent orientation variation among each spectral band is derived, and the approach of orientation filtration is made for effective feature representation. The obtained result illustrates an improvement in the recognition accuracy, in comparison to conventional retrieval system.
Flower Classification Using Neural Network Based Image ProcessingIOSR Journals
Abstract: In this paper, it is proposed to have a method for classification of flowers using Artificial Neural Network (ANN) classifier. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT). A flower image is segmented using a threshold based method. The data set has different flower images with similar appearance .The database of flower images is a mixture of images taken from World Wide Web and the images taken by us. The ANN has been trained by 50 samples to classify 5 classes of flowers and achieved classification accuracy more than 85% using GLCM features only. Keywords: Artificial Neural Network, DWT, GLCM, Segmentation.
Multilinear Kernel Mapping for Feature Dimension Reduction in Content Based M...ijma
In the process of content-based multimedia retrieval, multimedia information is processed in order to
obtain descriptive features. Descriptive representation of features, results in a huge feature count, which
results in processing overhead. To reduce this descriptive feature overhead, various approaches have been
used to dimensional reduction, among which PCA and LDA are the most used methods. However, these
methods do not reflect the significance of feature content in terms of inter-relation among all dataset
features. To achieve a dimension reduction based on histogram transformation, features with low
significance can be eliminated. In this paper, we propose a feature dimensional reduction approaches to
achieve the dimension reduction approach based on a multi-linear kernel (MLK) modeling. A benchmark
dataset for the experimental work is taken and the proposed work is observed to be improved in analysis in
comparison to the conventional system.
IRJET-Multimodal Image Classification through Band and K-Means ClusteringIRJET Journal
This document proposes a bilayer graph-based learning framework for multimodal image classification using limited labeled pixels. It constructs a simple graph in the first layer where each vertex is a pixel and edge weights encode pixel similarity. Unsupervised learning estimates grouping relations among pixels. These relations form a hypergraph in the second layer, on which semisupervised learning classifies pixels to address challenges of complex relationships and limited labels in multimodal images. The framework effectively exploits the underlying data structure.
This document summarizes a research paper that compares different classification techniques for remotely sensed Landsat images. It discusses extracting textural features using GLCM, spatial features using PSI and SFS, and applying feature selection using S-Index. Supervised neural network classifiers like k-NN, BPNN and PCNN are tested on a Landsat image of Brazil and compared to unsupervised techniques. Results show BPNN achieved the highest classification accuracy.
This document summarizes a research paper that compares different classification techniques for remotely sensed LANDSAT images using neural network approaches. It first preprocesses a LANDSAT image from Brazil to reduce its bands and extracts textural, spatial and spectral features. It then uses these features as inputs for both unsupervised (k-means) and supervised (k-NN, BPNN, PCNN) classifiers. The results show that supervised classifiers like BPNN perform better, achieving up to 87.12% accuracy when using spatial and spectral features. Overall, feature-based classification is found to overcome the limitations of pixel-based classification for analyzing multispectral images.
The Effects of Segmentation Techniques in Digital Image Based Identification ...TELKOMNIKA JOURNAL
This paper presents the effects of segmentation techniques in the identification of Ethiopian
coffee variety. In Ethiopia, coffee varieties are classified based on their growing region. The most widely
coffee growing regions in Ethiopia are Bale, Harar, Jimma, Limu, Sidamo and Welega. Coffee beans of
these regions very in color shape and texture. We investigated various segmentation techniques for
efficient coffee beans variety identification system. Images of six different coffee beans varieties in Oromia
and Southern Ethiopia were acquired and analyzed. For this study Otsu, Fuzzy-C-Means (FCM) and Kmeans
segmentation techniques are considered. For classification of the varieties of Ethiopian coffee
beans back propagation neural network (BPNN) is used. From the experiment 94.54% accuracy is
achieved when BPNN is used on FCM segmentation technique.
International Journal of Computational Engineering Research(IJCER) ijceronline
This document presents a hybrid methodology for classifying segmented images using both unsupervised and supervised classification techniques. The proposed methodology involves first segmenting the image into spectrally homogeneous regions using region growing segmentation. Then, a clustering algorithm is applied to the segmented regions for initial classification. Selected regions are used as training data for a supervised classification algorithm to further categorize the image. The hybrid approach combines the benefits of unsupervised clustering and supervised classification. The methodology is evaluated on natural and aerial images to compare its performance to existing seeded region growing and texture extraction segmentation methods.
K-Means Segmentation Method for Automatic Leaf Disease DetectionIJERA Editor
Automatic detection of leaf disease is an essential research topic in agricultural research. It may prove benefits
in monitoring fields and early detection of leaf diseases by the symptoms that appear on the leaves. Defect
segmentation is carried out in two steps. This paper illustrates K-means clustering method for segmentation of
diseased portion of leaf. At first, the pixels are clustered based on their color and spatial features, where the
clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. This
approach provides a feasible robust solution for defect segmentation of leaves
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has supplied a large volume of data to many fields. The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. In as much as the data achieving from microarray technology is very noisy and also has thousands of features, feature selection plays an important role in removing irrelevant and redundant features and also reducing computational complexity. There are two important approaches for gene selection in microarray data analysis, the filters and the wrappers. To select a concise subset of informative genes, we introduce a hybrid feature selection which combines two approaches. The fact of the matter is that candidate’s features are first selected from the original set via several effective filters. The candidate feature set is further refined by more accurate wrappers. Thus, we can take advantage of both the filters and wrappers. Experimental results based on 11 microarray datasets show that our mechanism can be effected with a smaller feature set. Moreover, these feature subsets can be obtained in a reasonable time.
Texture Segmentation Based on Multifractal Dimensionijsc
Texture segmentation can be considered the most important problem, since human can distinguish different
textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for
research. In this paper focus on implement novel supervised algorithm for multitexture segmentation and
this algorithm based on blocking procedure where each image divide into block (16×16 pixels) and extract
vector feature for each block to classification these block based on these feature. These feature extract
using Box Counting Method (BCM). BCM generate single feature for each block and this feature not
enough to characterize each block ,therefore, must be implement algorithm provide more than one slide for
the image based on new method produce multithresolding, after this use BCM to generate single feature for
each slide.
Similar to Weighted Ensemble Classifier for Plant Leaf Identification (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
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.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
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.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...PIMR BHOPAL
Variable frequency drive .A Variable Frequency Drive (VFD) is an electronic device used to control the speed and torque of an electric motor by varying the frequency and voltage of its power supply. VFDs are widely used in industrial applications for motor control, providing significant energy savings and precise motor operation.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
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.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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.
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then is treated separately in terms of its classification process using SVM. The last decision is
taken after the best-performance classifier is known. This kind of decision making will ignore
other classifiers with lower performance. Thus, a method that can be used to combine every
classification result on ensemble classifier is needed.
There is method to combine ensemble classifier such as PDR (Product Decision
Rule) [9]. PDR method uses prior and posterior probability value of each classifier in
determining the final decision. Other than that, there are several ensemble classifier techniques
used to combine the result of classifications that can be applied in ensemble classifier, one of
which is simple vote as used in a research [10] and Majority Vote [11]. However, both of those
techniques applied the same weight to all classifiers used, therefore it takes a method of
combining that is influenced by the weight of each classifier, so it will show which features are
more powerful depending on the weight of classifier. Naïve Bayes combination method also
uses the value of each classifier prior to the calculation of the combined probability. Naïve
Bayes Combination method performed by calculating combined probability input data by using
two or many classifier are mutually independent. The calculation of these probability is obtained
using prior probability and the amount of data in the class predictions of each classifier.
Basically, each feature is different from each other in terms of its accuracy result. Hence, a
certain classifier weighting technique is required to determine the influence of each classifier on
its classification results. Combination method with the weighting on the so-called weighted
ensemble classifier classifier. [12] used weighted ensemble classifier methods namely Weighted
Majority Vote to determine the final outcome of the ensemble classifier. WMV uses the weight
values obtained from the accuracy values during training phase. These weights will later be
used in the calculation of the probability of data entered into each set of existing classes
The objective of this paper are and to show wich feature is stronger to classify plant leaf
using image and to combine the ensemble classifier using WMV and compare it result with
Naive Bayes Combination in plant image retrieval system which are based on texture and
geometry features found in the leaf image. This paper consists of: Section 2, about the feature
extraction; Chapter 3, introduction of ensemble classifier and combination of ensemble
classifier; Section 4, where the result of the experiment is shown, and Section 5, conclusion and
summary.
2. Proposed Method
2.1 Feature extraction
Feature extraction is performed to obtain significant information that can be used to
identify and distinguish an object with other objects. In this research, we identify plants species
using texture and leaves’ geometry features. The texture feature extraction was done by using
FLBP (Fuzzy Local Binary Pattern), while geometry feature extraction was retrieved from the
characteristics of the area, circularity, eccentricity, and centroid-radii.
2.1.1 Fuzzy local binary pattern (FLBP)
FLBP is used to get a better representation of the texture contained in the image. FLBP
is a method that applies the concept of fuzzy on local binary pattern method. Fuzzification of
LBP .approach include the transformation of input variables into fuzzy variables according to a
set of fuzzy rules. Two fuzzy rules were choosen to obtain a binary value and fuzzy values
based on the relationship between the value of circular sampling ( ) and center pixel (
similar with [13] and [14] research.
After we use two fuzzy rule, then two membership function will be formed. In FLBP,
each LBP value will have different level of contribution, it depends on the value of the
membership function of and . Then after obtaining LBP code, LBP code will be
represented in the histogram.
2.1.2 Geometrical feature
There are many geometrical feature from leaf image that can be used to classify [15]
using perimeter, area, roundness, and others to classify the object. Geometrical feature used in
this study were the area, circularity, eccentricity and centroid radii. Geometry feature extraction
process performed on the image of the leaves that had been converted into a binary image.
Area feature is a feature that describes value of the leaf surface area. Circularity is a feature
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that measures how an object is closely resembled as a perfect circle. Eccentricity explain how
the regions points are scattered around the centre of the region. While the centroid-radii is a
measurement of the distance between the center point of the leaf with the edges of the leaves.
2.2. Ensemble classifier combination
Ensemble classifier is a decision making method towards incoming data taken from
some separate classification processes to enhance the stability of accuracy from a single
classifier [16]. Ensemble is often called Multiple Classifier System (MCS). MCS is used because
every existing feature has different information and characteristics, leading to the conducting of
training processes with different classifiers. MSC has four approaches [17], which are MCS with
different combination schemes, MSC with different models, MSC with different feature subsets,
and MSC with different training sets. These four MSC approaches can be seen in Figure 1.
Combiner
Classifier LClassifier iClassifier 1
X
...
Combiner
Classifier LClassifier iClassifier 1
X
...
Combiner
Classifier LClassifier iClassifier 1
X
...
D1 Di
...
Dn
Approach 1 : Different combination schemes Approach 2 : Different calssifeir models
Approach 3 : Different feature subsets
Approach 4 : Different training sets
Figure 1. Ensemble classifier approaches
This research employs the third approach, which is MCS or ensemble classifier with
different feature subsets. This approach is chosen since the two different features from texture
and geometry characteristics are extracted from the leaf to be used in this research. Later, both
features will be adopted in conducting classification processes, which will be done separately by
using SVM.
In order to get the final results from several classifiers, the process of combining
classifiers with ensemble classifier needs to be done. There are three ensemble classifier
combination categories that can be used. Those are simple vote strategy, weighted classifier
ensemble, and selective or pruning classifier. Ensemble classifier combination method using
simple vote strategy combines the results of several classification processes with same
coefficient or weight classifiers for entire classifiers that are used. Second combination method
is weighted classifier ensemble. Weighted classifier ensemble method combines several
classification result by weighting each classifier first. The last combination method is selective or
pruning method. This method combines the result of several classifications with a weight vector
for every classifier, including classifier with zero weight value, to indicate classifier not having a
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significant influence or even negative influence on the classification. This research will use the
ensemble classifier combination with weighted ensemble classifier methods weighted majority
vote and compare the result obtained by naive bayes combining method.
Weighted ensemble classifier combines ensemble classifiers by weighting each
classifier first. This research will use ensemble classifier combination with weighted classifier
methods, namely weighted majority vote and compare the result with that obtained by naive
bayes combination method.
2.2.1. Weighted majority vote
Weighted Majority Vote (WMV) is a decision-making method retrieved from several
classifiers that are separated and independent by giving each classifier more abilities [18]. The
scheme from this classifier incorporation can be seen in Figure 2.
Figure 2. The scheme of classifier and weighting combination
To determine the final decision, each classifier will be given weight that is suitable with its
accuracy value, retrieved during training phase. These weights are a measure of each
classifier’s influence on the result of the final decision. Classifier’s weight is a value that will later
be multiplied by the probabilities of a certain object entering each class. The class with the
highest multiplying result will eventually become the class from that new object, as seen in
equation (1).
( ∑ (1)
Where, is incoming data whose probability towards entire class will be calculated. (
is data ‘s probability that is included in class . conveys the number of classifiers. C states
the total number of classes, while i is coeficient weight from classifier to , and ij is ‘s
probability to enter class j by classifier to i. Before combining the result of classifiers from both
features, each probability is multiplied by the weight value of each classifier retrieved from
equation (2) [15].
i = (2)
Where, represent classifier’s accuracy value to i during training phase. This number is used
as a multiplying number for the probability of data’s entering each existing class. The higher the
classifier’s accuracy during the training phase, the higher the weight will be, and vice versa; the
lower the accuracy, the lower the weight will be. This is conducted in order to distinguish the
constribution of each classifier to the final result.
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2.2.2. Naïve Bayes Combination
In naïve bayes combination, it is assumed that each classifier does not depend on
each other when it comes to determining class’ lable. In determining the lable prediction of an
object, the calculation is done using confusion matrix. Confusion matrix is a matrix which was
created based on real class’ lable from training data and on prediction lable produced by
classifier. According to confusion matrix, prediction lable ( r) from the sample is determined by
using equation (3) [16].
( r)= ( ∏ ) (3)
Where, is number of actual samples with class label j in the training data set, L is number
of classifier, and is number of samples that belong to j but goes to k by classifier-i.
3. Results and Discussion
3.1. Image classification
To test the proposed ensemble classifier combination method, an experiment using
IPBiotics’ leaf image data which consists of 4559 images of 156 species was conducted.
Separate classification was used on the data retrieved from the feature extraction results of the
leaf images. There are two feature extractions that were conducted. Those are texture feature
extraction and geometry feature extraction. The method used to extract these features was
similar to the one used in [17]. After each feature was submitted to separated classification
process by using SVM classification method, the classification results from both classifiers were
combined using two proposed methods, which are wighted majority vote and naïve bayes
combination. To see the accuracy of each ensemble classifier combination methods, an
evaluation using k-fold cross validation method with k=5 was conducted. The accuracy of both
combination methods are can be seen in Figure 3.
Figure 3. The correct classification rate using WMV’s and Naïve Bayes combination methods
55.6%
64.8% 65.2% 62.6%
57.9%
61.2%64.3%
78.0% 76.6%
73.6%
59.4%
70.4%72.1%
83.3% 82.3% 80.0%
71.5%
77.8%
92.6%
96.2% 96.0% 94.7% 93.6% 94.6%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 Average
Accuracy
Fold
Single feature geometry
Single feature texture
WMV
Bayes
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After measuring the accuracy of each method, the result showed that ensemble
classifier method resulted in higher accuracy point compared to single feature and classifier.
The average accuracy of Naïve Bayes Combination method was 94.6%, while WMV was
77.8%. It was stated in a research [13] that Naive Bayes Combination method is actually reliable
when using small number of classifiers and large classes, but its accuracy decreases when
many classifiers are used. In this research, the number of classes used is in accordance to the
number of species from the data, which is 156. Therefore, the accuracy of Naive Bayes
Combination method’s result is higher than Weighted Majority Vote method. In addition,
dissimilar to WMV and Naïve Bayes Combination methods that use the same classifier weight
values for the combined probability of data going into each species, the calculations using the
Naïve Bayes Combination method use different weights for each species. This weight is taken
from the amount of data that is in the predicted class of both classifier, so each species will be
given different treatments according to which feature is better for classifying the species.
Therefore, by using a combination of Naïve Bayes, species that can not be classified well by the
geometry classifier can still be classified with texture classifier, and vice versa.
The calculation of classifier’s weight by using WMV method produces a weight value of
0.54 for texture feature classifier and 0.46 for geometry feature classifier. Based on those
weight values, it can be concluded that texture feature has greater contribution in determining
the classification’s final result.
3.2 Result analysis
After obtaining accuracy rate of the two methods of ensemble classifier combination, it
showed that classifier combination using Naïve Bayes Combination method scored higher
accuracy. From the results of these experiments there were 131 species that successfully
predicted correctly with an 100% of accuracy. It means that none of the images leaves
incorrectly predicted in these species. Figure 4 shows a few leaves images of the species that
have been successfully predicted correctly by 100%, the species include Codiaeum variegatum,
Centella asiatica, Melastoma malabatrikum and Coleus scutellarioides.
Figure 4. Codiaeum variegatum, Centella asiatica, Melastoma malabatrikum, Coleus
scutellarioides
From the experimental results there were also some species of the leaves are not
successfully predicted correctly and got low accuracy. Such species include Acanthus ilicifolius,
Alyxia reindwardtu, Coleus scutellarioides, Kalanchoe pinnata, Melastoma Malabatrikum.
Examples of failed species-class images and examples of images in predicted species can be
seen in Figure 5.
In Figure 5 shows that the leaf species have in common texture and geometrically with
leaf species predicted results. From the data that were tested, several species of leaf that have
0% accuracy have a few of data training, while the other species have a much larger data
training. Lack of image data in some species was one cause of low accuracy.
7. ISSN: 1693-6930
TELKOMNIKA Vol. 16, No. 3, June 2018: 1386-1393
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Figure 5. Picture of species leaves compared with leaf predictions results image
4. Conclusion
In this study, the combination of the ensemble classifier for texture classifier and
geometry classifier were built using one of the methods weighted ensemble classifier, it is
weighted majority vote and Naïve Bayes Combination. The calculation of classifier’s weight by
using WMV method produces a weight value of 0.54 for texture feature classifier and 0.46 for
geometry feature classifier, in other words the classifier texture has a greater weight in the
determination of the final classification result than the geometry classifier. The average
accuracy point of WMV method was 77.8%, while naïve bayes combination’s was 94.6%. This
Excoecaria bicolor Allamanda cathartica
Clidemia hirta Kipeeut
Ananas comosus
Cordyline rubra hueg
Pimeleodendron macrocarpum Blumeodendron tokbrai
8. TELKOMNIKA ISSN: 1693-6930
Weighted Ensemble Classifier for Plant Leaf Identification... (R. Putri Ayu Pramesti)
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result showed that ensemble classifier method performs better in this study. It showed that
Naive Bayes Combination method is actually reliable when using small number of classifiers
and large classes.
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