scholarly journals Comprehensive Review of achine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniques

2021 ◽  
Author(s):  
Zainab Hussein Arif ◽  
Moamin A. Mahmoud ◽  
Karrar Hameed Abdulkareem ◽  
Mazin Abed Mohammed ◽  
Mohammed Nasser Al‐Mhiqani ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Comprehensive Review ◽  
Classification Techniques ◽  
Image Defogging

scholarly journals Human Behavior Prediction and Analysis Using Machine Learning-A Review

2021 ◽  
Vol 12 (5) ◽  
pp. 870-876
Author(s):  
Monali Gulhane, T.Sajana
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Social Behavior ◽  
Human Behavior ◽  
Technical Difficulty ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Classification Techniques

Nowadays many trends are being in the area of medicine to predict the human behaviour and analysis of patient behaviour is being studied but the technical difficulty of cost efficient method to predict the behaviour of user is overcome in the proposed researched methodology .The mental health of the used can lead to good immunity system to be healthy in this pandemic of COVID-19. Hence After a detailed study on different human health disease classification techniques it is found that machine learning techniques are reliable for the feature extraction and analysis of the different human parameters. CNN is the most optimum choice of classification of diseases. Feature extraction and feature selection is automatically managed by the CNN layers, which reduces the training speed. Techniques like sensor-based feature extraction like EEG, ECG, etc. will be further explored using machine learning algorithms for detection of early detections of diseases from human behavior on different platforms in this research. Social behavior and eating habits play a vital role in disease detection. A system that combines such a wide variety of features with effective classification techniques at each stage is needed. The research in this paper contributes the review of the human behavior analysis through different body parameters, food habits and social media influences with social behavior of the person. The main objective of research is to analysis theses different area parameters to predict the early signs of the diseases.

EVALUATION OF FEATURE EXTRACTION AND CLASSIFICATION TECHNIQUES FOR EEG-BASED SUBJECT IDENTIFICATION

2016 ◽  
Vol 78 (9-3) ◽  
Author(s):  
Dini Handayani ◽  
Abdul Wahab ◽  
Hamwira Yaacob
Keyword(s):  
Feature Extraction ◽  
Affective Computing ◽  
User Profiling ◽  
Support Vector ◽  
Emotional States ◽  
Mel Frequency Cepstral Coefficients ◽  
Classification Techniques ◽  
Power Spectral ◽  
Wide Range ◽  
Subject Identification

The ability to identify a subject is indispensable in affective computing research due to its wide range of applications. User profiling was created based on the strength of emotional patterns of the subject, which can be used for subject identification. Such system is made based on the emotional states of happiness and sadness, indicated by the electroencephalogram (EEG) data. In this paper, we examine several techniques used for subject profiling or identification purposed. Those techniques include feature extraction and classification techniques. In the experimental study, we compare three techniques for feature extraction namely, Power Spectral Density (PSD), Kernel Density Estimation (KDE), and Mel Frequency Cepstral Coefficients (MFCC). As for classification we compare three classification techniques, they are; Multilayer Perceptron (MLP), Naive Bayesian (NB), and Support Vector Machine (SVM). The best result achieved was 59.66%, using the MFCC and MLP-based techniques using 5-fold cross validation. The experiment results indicated that these profiles could be more accurate in identifying subject compared to NB and SVM. The comparisons demonstrated that profile-based methods for subject identification provide a viable and simple alternative to this problem.

scholarly journals Spam Review Detection Techniques: A Systematic Literature Review

2019 ◽  
Vol 9 (5) ◽  
pp. 987 ◽  
Author(s):  
Naveed Hussain ◽  
Hamid Turab Mirza ◽  
Ghulam Rasool ◽  
Ibrar Hussain ◽  
Mohammad Kaleem
Keyword(s):  
Feature Extraction ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Online Reviews ◽  
Detection Methods ◽  
Spam Detection ◽  
Successful Implementation ◽  
Comprehensive Review ◽  
Detection Techniques ◽  
Review Spam

Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.

scholarly journals SIGNATURE IDENTIFICATION AND VERIFICATION SYSTEMS: A COMPARATIVE STUDY ON THE ONLINE AND OFFLINE TECHNIQUES

2020 ◽  
Vol 5 (1) ◽  
pp. 28-45
Author(s):  
Nehal Hamdy Al-banhawy ◽  
◽  
Heba Mohsen ◽  
Neveen Ghali ◽  
◽  
...  
Keyword(s):  
Feature Extraction ◽  
Comparative Study ◽  
Active Area ◽  
Signature Verification ◽  
Classification Techniques ◽  
Verification Systems ◽  
Identification Techniques ◽  
Handwritten Signature ◽  
Verification Techniques

Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and offline systems with taking into consideration the datasets used and results for each system

scholarly journals Investigation of EEG Signal Classification Techniques for Brain Computer Interface

2020 ◽  
Vol 9 (3) ◽  
pp. 3266-3274
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Signal Classification ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signals ◽  
Classification Techniques ◽  
Feature Extraction And Selection ◽  
Bci System

Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.

scholarly journals A Study on Various Techniques Involved in Gender Prediction System: A Comprehensive Review

2019 ◽  
Vol 19 (2) ◽  
pp. 51-73
Author(s):  
Payal Maken ◽  
Abhishek Gupta ◽  
Manoj Kumar Gupta
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Research Area ◽  
Prediction System ◽  
Handwriting Analysis ◽  
Comprehensive Review ◽  
System A

Abstract Predicting gender on the foundation of handwriting investigation is a very invoking research area. Handwriting analysis also has numerous applications. It is useful for forensic experts to investigate classes of writers. This prediction is executed fundamentally by two steps: feature extraction and classification. As a whole, prediction solely depends upon the feature extraction. Distinct and most varied features make the classification accurate. This paper describes the problem comprehensively along with its foundation. It presents a survey of the available methods to solve the discussed problem. A comparative analysis of the discussed methods of feature extraction and classification along with the available databases is also presented in this paper.

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