scholarly journals Classification of Rice Leaf Spot Disease using Local Binary Patterns

2020 ◽  
Vol 9 (6) ◽  
pp. 510-512
Keyword(s):  
Feature Vector ◽  
Research Work ◽  
Local Binary Patterns ◽  
Extraction Process ◽  
Healthy Plant ◽  
Leaf Spot Disease ◽  
Salient Point ◽  
Point Selection ◽  
Single Feature

The fundamental objective of this work is to develop an image processing framework that can perceive a proper methodology for ContentBasedImageRetrieval(CBIR) in Leaf Inadequacy. The salient point selection concept is utilized by selecting the Salient points from the edgy image and the concept of inter-plane relationship method is imposed, LocalBinaryPatterns (LBPs) are computed with respect to the center pixel of the salient point. The research work consists primarily of three sections, namely representation of the leaf image, extraction of features and classifying. During the extraction process of the application the most important and special features of the image are retrieved. The image is contrasted with the data base images in the classification phase. The surface of the plant leaf is divided into smaller regions using which the LBP is obtained and the combination of them produces a single feature vector. An accurate model is constructed by this feature vector which is used to measure differences between flawed and healthy plant images.

scholarly journals Classification of Hematoxylin and Eosin Images Using Local Binary Patterns and 1-D SIFT Algorithm

Proceedings ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. 94 ◽  
Author(s):  
Oğuzhan Oğuz ◽  
A. Çetin ◽  
Rengul Atalay
Keyword(s):  
Feature Vector ◽  
Local Binary Patterns ◽  
Extraction Process ◽  
Sift Algorithm ◽  
One Dimensional ◽  
Color Histograms ◽  
Hematoxylin And Eosin ◽  
New Feature

In this paper, Hematoxylin and Eosin (H&E) stained liver images are classified by using both Local Binary Patterns (LBP) and one dimensional SIFT (1-D SIFT) algorithm. In order to obtain more meaningful features from the LBP histogram, a new feature vector extraction process is implemented for 1-D SIFT algorithm. LBP histograms are extracted with different approaches and concatenated with color histograms of the images. It is experimentally shown that,with the proposed approach, it possible to classify the H&E stained liver images with the accuracy of 88 % .

A Caption Text Detection Method from Images/Videos for Efficient Indexing and Retrieval of Multimedia Data

2015 ◽  
Vol 29 (01) ◽  
pp. 1555003 ◽  
Author(s):  
Samabia Tehsin ◽  
Asif Masood ◽  
Sumaira Kausar ◽  
Yunous Javed
Keyword(s):  
Feature Vector ◽  
Extraction Process ◽  
Text Detection ◽  
Multimedia Data ◽  
Support Vector ◽  
Feature Vectors ◽  
Text Extraction ◽  
Indexing And Retrieval ◽  
Detection And Localization

Textual information embedded in multimedia can provide a vital tool for indexing and retrieval. Text extraction process has many inherent problems due to the variation in font sizes, color, backgrounds and resolution. Text detection and localization are the most challenging phases of text extraction process whereas text extraction results are highly dependent upon these phases. This paper focuses on the text localization because of its very fundamental importance. Two effective feature vectors are introduced for the classification of the text and nontext objects. First feature vector is represented by the Radon transform of text candidate objects. Second feature vector is derived from the detailed geometrical analysis of text contents. Union of two feature vectors is used for the classification of text and nontext objects using support vector machine (SVM). Text detection and localization results are evaluated on two publicly available datasets namely ICDAR 2013 and IPC-Artificial text. Moreover, results are compared with state-of-the-art techniques and the Comparison demonstrates the superiority of the presented research.

scholarly journals An Efficient Machine Learning Method for Facial Expression Recognition

2019 ◽  
Vol 8 (9S) ◽  
pp. 539-546
Keyword(s):  
Feature Vector ◽  
Rejection Rate ◽  
Extraction Process ◽  
Expression Recognition ◽  
Accuracy Rate ◽  
Efficient Machine ◽  
Feature Optimization ◽  
Error Probabilities ◽  
Intelligence Approach

Emotions play important role in human sentiments so broad studies are carried out to explore the relation between human sentiments and machine interactions. This paper deals with an automatic system which spontaneously identifies the facial emotion. Gradient filtering and component analysis is used to extract feature vector and feature optimization is taken place using swarm intelligence approach. Thus emotion recognition with optimized feature extraction process is carried out with high accuracy rate and less error probabilities. Finally the testing process is obtained for the classification of emotions and then performance is measured in terms of false acceptance rate, false rejection rate, and accuracy.

PROBLEMS OF CLASSIFICATION OF CULTURAL HERITAGE OBJECTS

2020 ◽  
Vol 10 (2) ◽  
pp. 158-168
Author(s):  
SVETLANA IVANOVA ◽  
Keyword(s):  
Cultural Heritage ◽  
Cultural Values ◽  
Research Work ◽  
Cultural Property ◽  
Cultural Inheritance ◽  
Federal Laws ◽  
Cultural Heritage Objects ◽  
Cultural Monuments

The purpose of the research work is to analyze the norms of Federal laws, as well as the laws of the Russian Federation's constituent entities, devoted to the definitions and classification of the concepts “cultural heritage”, “historical and cultural monuments”, “cultural values”. Conclusions obtained in the course of the research: based on the study of current legislation, it is concluded that the definitions of “cultural values”, “cultural property”, “objects of cultural inheritance” contained in various normative legal acts differ in content. Based on the research, the author proposes the concept of “cultural values”.

scholarly journals Cotton Wastes Functionalized Biomaterials from Micro to Nano: A Cleaner Approach for a Sustainable Environmental Application

Polymers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1006
Author(s):  
Samsul Rizal ◽  
Abdul Khalil H. P. H. P. S. ◽  
A. A. Oyekanmi ◽  
Niyi G. Olaiya ◽  
C. K. Abdullah ◽  
...  
Keyword(s):  
Building Materials ◽  
Expansive Soils ◽  
Research Work ◽  
Functional Materials ◽  
Extraction Process ◽  
Process Conditions ◽  
Environmental Application ◽  
Isolation Technique ◽  
Metals Removal ◽  
Combined Pretreatment

The exponential increase in textile cotton wastes generation and the ineffective processing mechanism to mitigate its environmental impact by developing functional materials with unique properties for geotechnical applications, wastewater, packaging, and biomedical engineering have become emerging global concerns among researchers. A comprehensive study of a processed cotton fibres isolation technique and their applications are highlighted in this review. Surface modification of cotton wastes fibre increases the adsorption of dyes and heavy metals removal from wastewater. Cotton wastes fibres have demonstrated high adsorption capacity for the removal of recalcitrant pollutants in wastewater. Cotton wastes fibres have found remarkable application in slope amendments, reinforcement of expansive soils and building materials, and a proven source for isolation of cellulose nanocrystals (CNCs). Several research work on the use of cotton waste for functional application rather than disposal has been done. However, no review study has discussed the potentials of cotton wastes from source (Micro-Nano) to application. This review critically analyses novel isolation techniques of CNC from cotton wastes with an in-depth study of a parameter variation effect on their yield. Different pretreatment techniques and efficiency were discussed. From the analysis, chemical pretreatment is considered the most efficient extraction of CNCs from cotton wastes. The pretreatment strategies can suffer variation in process conditions, resulting in distortion in the extracted cellulose’s crystallinity. Acid hydrolysis using sulfuric acid is the most used extraction process for cotton wastes-based CNC. A combined pretreatment process, such as sonication and hydrolysis, increases the crystallinity of cotton-based CNCs. The improvement of the reinforced matrix interface of textile fibres is required for improved packaging and biomedical applications for the sustainability of cotton-based CNCs.

NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 06 (01) ◽  
pp. 61-75 ◽  
Author(s):  
R. PANCHAL ◽  
B. VERMA
Keyword(s):  
Breast Cancer ◽  
Research Work ◽  
Breast Cancer Diagnosis ◽  
Classification Rate ◽  
Test Dataset ◽  
Benchmark Database ◽  
Different Types ◽  
Breast Tissues ◽  
Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

Wavelet Transform Based Feature Extraction for EEG Signal Classification

2021 ◽  
Vol 20 ◽  
pp. 199-206
Author(s):  
Seda Postalcioglu
Keyword(s):  
Wavelet Transform ◽  
High Performance ◽  
Feature Vector ◽  
Fast Response ◽  
Eeg Signal ◽  
Real Time Control ◽  
Time Control ◽  
Band Power ◽  
Performance Rate

This study focused on the classification of EEG signal. The study aims to make a classification with fast response and high-performance rate. Thus, it could be possible for real-time control applications as Brain-Computer Interface (BCI) systems. The feature vector is created by Wavelet transform and statistical calculations. It is trained and tested with a neural network. The db4 wavelet is used in the study. Pwelch, skewness, kurtosis, band power, median, standard deviation, min, max, energy, entropy are used to make the wavelet coefficients meaningful. The performance is achieved as 99.414% with the running time of 0.0209 seconds

scholarly journals Modified Decision Tree Technique for Ransomware Detection at Runtime through API Calls

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Faizan Ullah ◽  
Qaisar Javaid ◽  
Abdu Salam ◽  
Masood Ahmad ◽  
Nadeem Sarwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Feature Vector ◽  
Machine Learning Algorithms ◽  
The Novel ◽  
Proposed Model ◽  
Testing Accuracy ◽  
Financial Losses

Ransomware (RW) is a distinctive variety of malware that encrypts the files or locks the user’s system by keeping and taking their files hostage, which leads to huge financial losses to users. In this article, we propose a new model that extracts the novel features from the RW dataset and performs classification of the RW and benign files. The proposed model can detect a large number of RW from various families at runtime and scan the network, registry activities, and file system throughout the execution. API-call series was reutilized to represent the behavior-based features of RW. The technique extracts fourteen-feature vector at runtime and analyzes it by applying online machine learning algorithms to predict the RW. To validate the effectiveness and scalability, we test 78550 recent malign and benign RW and compare with the random forest and AdaBoost, and the testing accuracy is extended at 99.56%.

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

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