Hybrid Feature Extraction Technique Research Articles

Gender classification based on handwriting using Gurmukhi characters and hybrid feature extraction techniques

Gender classification based on handwriting using Gurmukhi characters and hybrid feature extraction techniques

A Hybrid Technique to Predict Brain Tumour using MRI Image

Currently, the radiologist can more accurately identify brain tumours through the development of Computer-Assisted Diagnosis (CAD), Machine Learning and Deep Learning. Recently, Deep Learning (DL) strategies have gained traction as a means to rapidly and accurately construct automated systems for diagnosing and segmenting the image. The standard approach to this issue is to create a custom feature for classification. Most neurological diseases originate from abnormal growth of brain cells, which can compromise brain architecture and even lead to malignant brain tumours. Brain tumour detection and classification algorithms that are both quick and accurate have been the subject of extensive study. This facilitates the straight forward diagnosis of brain tumours using Magnetic Resonance Image (MRI) images. Through Deep Learning (DL) model the diagnosis of brain malignancies in MRI images using Convolutional Neural Network (CNN) is possible by training the data. So, in this paper the brain tumouris predicted byproposing a Hybridfeature extraction technique i.e., tuned CNN model with ResNet150 and U-net.

Automatic Identification of Emergency Sounds for Self-Driving Cars

As the development of Self-Driving Cars (SDCs) advances, one important feature that requires attention is their ability to sense and respond to emergencies on the road. Drivers of emergency vehicles prioritize speed over safety during emergencies to save lives, potentially leading to accidents. However, manual cars sometimes may not understand the urgency of the situation, further increasing the risk of collisions. In such cases, self-driving cars offer a better solution to replace manual cars and minimize road accidents. These cars with emergency sound detections offer a level of responsiveness, accuracy, and consistency that surpasses manual cars, contributing to improved road safety and efficiency in emergencies. Therefore, the key objective of the research is to improve the listening capability of self-driving cars to detect emergency vehicles with the help of a hybrid feature extraction technique. The suggested technique leverages a combination of Complex Morlet Wavelet and Co-occurrence Matrix to obtain statistical features from the emergency sounds. The proposed technique can work with the input length of 1.2 seconds of raw waveforms. This research work investigates that self-driving cars can accurately identify emergency vehicles by examining the distinctive emergency sound patterns emitted by the emergency vehicle with the highest accuracy of 94%. At the same time, the proposed technique reduces the computational cost by 20 – 40 milliseconds when compared with other techniques. The result of this work not only provides better accuracy but also reduces detection time, which is a crucial requirement for real-time applications such as self-driving cars.

Plant leaf disease detection using hybrid feature extraction techniques

Excessive yield losses are due to delayed identification of leaf diseases, resulting in lower farmer revenue. To address this, early and precise detection methods are crucial. Sunflower, a vital commercial crop in India for edible oil production, faces issues like leaf blight, downy mildew, powdery mildew, and leaf spot. Our research aims to create an algorithm that effectively identifies these diseases using hybrid feature extraction techniques. We compiled sunflower leaf images from village databases and farm visits. Initially, color images were converted to grayscale, and the noise was reduced using Gaussian filtering during image pre-processing. Disease-affected areas were segmented using edge-based approaches. Hybrid feature extraction was then employed to encompass color and texture-related attributes. The K-Nearest Neighbors (KNN) algorithm facilitated disease classification. The proposed algorithm’s performance was benchmarked against SVM, RF and DT classifiers. Remarkably, our algorithm achieved exceptional accuracy: 98% for leaf blight, 97.3% for downy mildew, 95% for powdery mildew, and 96.5% for leaf spot detection. These results underscore the algorithm’s effectiveness in combating plant diseases and enhancing agricultural productivity.

Content-Based Image and Video Retrieval Based on Hybrid Feature Extraction Techniques

Content-Based Image and Video Retrieval Based on Hybrid Feature Extraction Techniques

Improving sentiment analysis of financial news headlines using hybrid Word2Vec-TFIDF feature extraction technique

With the evolution of big data and information technology, sentiment analysis has become a research hotspot in the financial market. Researchers are increasingly focused on improving the efficiency of sentiment analysis using different machine learning and deep learning architectures. Feature extraction is a fundamental process in sentiment analysis that enhances text representation and classification. Though various feature extraction techniques are present in the field, limited attention has been given to hybrid feature extraction techniques. The primary objective of this study is to improve the sentiment analysis of financial news headlines using a hybrid Word2Vec-TFIDF feature extraction technique. The study evaluates the performance of Word2Vec, Doc2Vec, TFIDF, Word2Vec-TFIDF, and Doc2Vec-TFIDF with six machine learning classifiers. The results find that the hybrid feature extraction technique, Word2vec-TFIDF with SVM classifier, outperforms all other sentiment analysis models with an accuracy of 82 percent.

CirMNet: A Shape-Based Hybrid Feature Extraction Technique Using CNN and CMSMD for Alzheimer’s MRI Classification

CirMNet: A Shape-Based Hybrid Feature Extraction Technique Using CNN and CMSMD for Alzheimer’s MRI Classification

Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques

ABSTRACT The remarkable development in technology has led to the increase of massive big data. Machine learning processes provide a way for investigators to examine and particularly classify big data. Besides, several machine learning models rely on powerful feature extraction and feature selection techniques for their success. In this paper, a big data classification approach is developed using an optimized deep learning classifier integrated with hybrid feature extraction and feature selection approaches. The proposed technique uses local linear embedding-based kernel principal component analysis and perturbation theory, respectively, to extract more representative data and select the appropriate features from the big data environment. In addition, the feature selection task is fine-tuned by using perturbation theory through heuristic search based on their output accuracy. This feature selection heuristic search method is analysed with five recent heuristic optimization algorithms for deciding the final feature subset. Finally, the data are categorized through an attention-based bidirectional long short-term memory classifier that is optimized with a golden eagle-inspired algorithm. The performance of the proposed model is experimentally verified on publicly accessible datasets. From the experimental outcomes, it is demonstrated that the proposed framework is capable of classifying large datasets with more than 90% accuracy.

A A Novel BCI - based Silent Speech Recognition using Hybrid Feature Extraction Techniques and Integrated Stacking Classifier

A A Novel BCI - based Silent Speech Recognition using Hybrid Feature Extraction Techniques and Integrated Stacking Classifier

Hybrid feature extraction technique for automatic classification of COVID-19 chest CT images

ABSTRACT COVID-19 has seriously affected normal life as well as public safety. It is extremely transmissible and has now infected millions of people worldwide. To obtain more image features of the lungs, Computed Tomography (CT) scans are widely used. However, manual examination of CT images for abnormal areas of COVID-19 disease can be time-consuming, and it is highly subjective to determine whether they are infected. To rapidly screen patients, Machine Learning (ML) can be used to determine whether patients have the disease. In this paper, a hybrid extraction technique is used to extract feature vectors from CT images, which is a mixture of a histogram of orientation gradients (HOG) extraction technique and a local binary pattern (LBP) extraction technique. In this experiment, 960 NON-COVID-19 and 960 COVID-19 were adopted to train the model, and 240 NON-COVID-19 and 240 COVID-19 were used to test the model. And the CT images were scaled to a uniform size. After obtaining the feature vectors using HOG and LBP feature extraction methods, The CT images were classified using a Support Vector Machine (SVM) classifier optimised by Particle Swarm Optimisation (PSO). In the performance evaluation of the presented classification model, the combination of the HOG feature extraction technique and the LBP feature extraction technique resulted in a substantial improvement in the classification effectiveness of the SVM. HOG_LBP PSO SVM improved Accuracy to 97.5%, Precision to 97.75%, Recall to 97.27%, Specificity to 97.25%, F1_score to 97.50%, and Mcc to 95.01%.

A Novel Hybrid Feature Extraction Technique and Spam Review Detection using Ensemble Machine Learning Algorithm by Web Scrapping

A Novel Hybrid Feature Extraction Technique and Spam Review Detection using Ensemble Machine Learning Algorithm by Web Scrapping

Multi-Classification of Electroencephalogram Epileptic Seizures Based on Robust Hybrid Feature Extraction Technique and Optimized Support Vector Machine Classifier

Multi-Classification of Electroencephalogram Epileptic Seizures Based on Robust Hybrid Feature Extraction Technique and Optimized Support Vector Machine Classifier

PCSVD: A hybrid feature extraction technique based on principal component analysis and singular value decomposition

<p>Feature extraction plays an important role in accurate preprocessing and real-world applications. High-dimensional features in the data have a significant impact on the machine learning classification system. Relevant feature extraction is a fundamental step not only to reduce the dimensionality but also to improve the performance of the classifier. In this paper, the author proposes a hybrid dimensionality reduction technique using principal component analysis (PCA) and singular value decomposition (SVD) in a machine classification system with a support vector classifier (SVC). To evaluate the performance of PCSVD, the results are compared without using feature extraction techniques or with existing methods of independent component analysis (ICA), PCA, linear discriminant analysis (LDA), and SVD. In addition, the efficiency of the PCSVD method is measured on an increased scale of 1.54% accuracy, 2.70% sensitivity, 3.71% specificity, and 3.58% precision. In addition, reduce the 15% dimensionality and 40.60% RMSE, which are better than existing techniques found in the literature.</p>

Hybrid Feature Extraction and Selection Techniques for Drugs Classifier System Based on Machine Learning

Nowadays, there are thousands of approved drugs that can be used for treating people who have medical problems. Therefore, drug warnings and precautions are denoted to recognize a discrete set of adverse effects and other implied protection uncertainties that are useful for patient control. Methods/analysis/findings: In this study, the intended framework is divided into two principal stages: data retrieval and data processing. Firstly, in the data collection stage, drug reports, drug interactions, malfunctions, number of deaths, and other factors had been obtained from various references, including RxNorm and Drug Bank using web service. Secondly, in the data processing phase, different data mining algorithms used to classify drugs into suitable drugs and non-suitable drugs. Application/improvements: According to the experimental results, we found that the decision tree has more accuracy (97.9%) than other state-of-art methods.

HBA optimized Efficient CNN in Human Activity Recognition

ABSTRACT A HAR can be a significant test in different applications, counting human–computer connectivity. Hence, in this paper, developing an efficient background subtraction and a deep learning model aimed at classifying human actions from the videos. The main motive of the research work can be developed efficient technique for identifying human activities in videos using a Convolutional Neural Network (CNN) and hybrid feature extraction techniques. The projected CNN classifier can be developed and combined with the Honey Badger Algorithm (HBA) and CNN in the process of feature extraction. The projected classifier is utilized to identify human actions such as bending, walking and so on. HBA can be consumed to optimize the weighting parameters which enhance the performance of CNN. The projected approach is validated considering Weizmann and KTH datasets. Additionally, the performance metrics are considered to justify the performance of the projected technique such as specificity, sensitivity and accuracy.

Experimental evaluation of unsupervised image retrieval application using hybrid feature extraction by integrating deep learning and handcrafted techniques

Abstract Content Based Image Retrieval is ever growing technology for many applications such as medical, remote sensing, social media search engines and surveillance monitoring, etc,. Representing image content with appropriate features is tedious task using traditional low level feature extraction methods. Deep learning models achieved high precision in classification and object detection algorithms by extracting automated high level feature extraction process. This paper proposed a hybrid feature extraction technique by combining the high level features and low level features to improve the robustness of the feature vector. The proposed model used pre-trained Googlenet model as feature extractor and combined with Gabor multiscale texture features. The final feature vector will be used for retrieving the relevant image data from the large scale image dataset. It has achieved the precision of 91 percent which shows better than state of art methods.

Multi-resolution analysis and deep neural network architecture based hybrid feature extraction technique for plant disease identification and severity estimation

Multi-resolution analysis and deep neural network architecture based hybrid feature extraction technique for plant disease identification and severity estimation

Detection and classification of brain tumor using hybrid feature extraction technique

Detection and classification of brain tumor using hybrid feature extraction technique

An effective automatic segmentation of abdominal adipose tissue using a convolution neural network

Background and aimsComputer-aided diagnosis and prognosis rely heavily on fully automatic segmentation of abdominal fat tissue using Emission Tomography images. The identification of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in abdomen fat faces two main challenges: (1) the great difficulties in comparison to multi-stage semantic segmentation (VAT and SAT), and (2) the subtle differences due to the high similarity of the two classes in abdomen fat and complicated VAT distribution. MethodsIn this research, we built an automated convolutional neural network (A-CNN) for segmenting Abdominal adipose tissue (AAT) from radiology images. ResultsWe developed a point-to-point design for the A-CNN learning process, wherein the representing features might be learned together with a hybrid feature extraction technique. We tested the proposed model on a CT dataset and evaluated it to existing CNN models. Furthermore, our suggested approach, A-CNN, outperformed existing deep learning methods regarding segmentation outcomes, notably in the AAT segment. ConclusionsProposed method is extremely fast with remarkable performance on limited-scale low dose CT-scanning and demonstrates the strength in providing an efficient computer-aimed tool for segmentation of AAT in the clinic.

DeepRHD: An efficient hybrid feature extraction technique for protein remote homology detection using deep learning strategies

In computational biology, the Protein Remote homology Detection technique (PRHD) has got undeniable significance. It is mostly important for structure and function identification of a protein sequence. The previous years have seen a challenge that lacks postulating a correlation among the sequences. However, the sequences are of variable length. Thereby, it inhibits the proper derivation of evolutionary information among the sequences. The challenges are the usage of physico-chemical properties as a source to get the evolutionary information and the number of sequences generated every day. This however facilitates a new technique to integrate huge amount of data with a massive feature set. In this article, a new and efficient technique is proposed to predict homology for distantly located sequences of proteins. Deep neural network(CNN-GRU model) is used for the classification of the protein sequences. This is based on different protein families and methods of feature extraction.The efficiency of the proposed model DeepRHD is tested on average 8000 sequences per superfamily taken from SCOP benchmark dataset and the results shows that the proposed model is better than other state of art methods. This model is useful in detecting diseases like sickle cell anemia and influenza and developing a drug thereafter.