An Efficient Wavelet Based Feature Reduction and Classification Technique for the Diagnosis of Dementia

The effectiveness of a learning task depends on data complexity (class overlap, class imbalance, irrelevant features, etc.). When more than one complexity factor appears, two or more preprocessing techniques should be applied. Nevertheless, no much effort has been devoted to investigate the importance of the order in which they can be used. This paper focuses on the joint use of feature reduction and balancing techniques, and studies which could be the application order that leads to the best classification results. This analysis was made on a specific problem whose aim was to identify the melodic track given a MIDI file. Several experiments were performed from different imbalanced 38- dimensional training sets with many more accompaniment tracks than melodic tracks, and where features were aggregated without any correlation study. Results showed that the most effective combination was the ordered use of resampling and feature reduction techniques.

The early diagnosis of chronic diseases plays a vital role in the field of healthcare communities and biomedical, where it is necessary for detecting the disease at an initial phase to reduce the death rate. This paper investigates the use of feature selection, dimensionality reduction and classification techniques to predict and diagnose chronic disease. The appropriate selection of attributes plays a crucial role in improving the classification accuracy of the diagnosis systems. Additionally, dimensionality reduction techniques effectively improve the overall performance of the machine learning algorithms. On chronic disease databases, the classification techniques deliver efficient predictive results by developing intelligent, adaptive and automated system. Parallel and adaptive classification techniques are also analyzed in chronic disease diagnosis which is used to stimulate the classification procedure and to improve the computational cost and time. This survey article represents the overview of feature selection, dimensionality reduction and classification techniques and their inherent benefits and drawbacks.

Feature reduction and selection of the best features for classification is a crucial stage for data analysis to reduce training time and overfitting. Commonly, feature reduction techniques rely on selecting features with high relevance to the outcome and minimum mutual information among one another. However, satisfying these criteria does not guarantee the selected features have a high classification power. In this study, we provide an algorithm to build predictive models for the desired outcome, while selecting the most useful features for high classification outcomes. The results of the proposed algorithm are compared with the outcomes of five popular feature reduction and selection techniques. A dataset from an obstructive sleep apnea study (data of 113 and 86 participants as training and blind testing datasets, respectively) was used to illustrate the algorithm's performance. The extracted features used in the algorithm were modeled using three-, four-, and five-feature combinations. The models with a high correlation to the outcome and low overlap percentages among specific subgroups of the data were selected and examined. Then, a set of the best selected models were averaged to provide better classification accuracy. The accuracy of the proposed algorithm has been 25% higher than that when using five popular feature reduction/selection techniques. Furthermore, the proposed algorithm is about 20 times faster than the five popular techniques.

BackgroundLarge number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated classification tools on stand-alone computing systems inconvenient due to the required time to complete the classification tasks. Combinations of image feature sets, feature reduction and classification techniques for crystallization images benefiting from trace fluorescence labeling are investigated.ResultsFeatures are categorized into intensity, graph, histogram, texture, shape adaptive, and region features (using binarized images generated by Otsu’s, green percentile, and morphological thresholding). The effects of normalization, feature reduction with principle components analysis (PCA), and feature selection using random forest classifier are also analyzed. The time required to extract feature categories is computed and an estimated time of extraction is provided for feature category combinations. We have conducted around 8624 experiments (different combinations of feature categories, binarization methods, feature reduction/selection, normalization, and crystal categories). The best experimental results are obtained using combinations of intensity features, region features using Otsu’s thresholding, region features using green percentile G90 thresholding, region features using green percentile G99 thresholding, graph features, and histogram features. Using this feature set combination, 96% accuracy (without misclassifying crystals as non-crystals) was achieved for the first level of classification to determine presence of crystals. Since missing a crystal is not desired, our algorithm is adjusted to achieve a high sensitivity rate. In the second level classification, 74.2% accuracy for (5-class) crystal sub-category classification. Best classification rates were achieved using random forest classifier.ContributionsThe feature extraction and classification could be completed in about 2 s per image on a stand-alone computing system, which is suitable for real time analysis. These results enable research groups to select features according to their hardware setups for real-time analysis.

In this study presents an improved classification and feature reduction techniques are newly introduced in our proposed search area of image retrieval. Especially we are including a shape feature that has been so active and successful in the past few years. In our proposed approach the texture feature is extracted by using improved multi texton technique and GLCM technique and the textual features are keywords, annotations. The Visual features are extracted with the aid of a bag of visual words (BOW) also it can be approved by employ Scaling Invariant Feature Transform (SIFT) and Fuzzy C-Means Clustering technique the features are edge detection, corner detection. Improving a performance and accuracy newly we are including a shape based features the features are Active Appearance Model (AAM) and shape surface plot (SURF). In the second phase, we are including feature reduction method is applied to reduce the features’ space without losing the accuracy of classification in this reduction phases we are including an OLPP technique to perform a feature reduction phases. After that, the images are classified with the aid of modified neural network algorithm. This will classify the images in the different class in order to improve the precision and recall rate. After classification, we are including a squared Euclidian distance method to retrieve minimum distance image the similar images are retrieved from the relevant class as per the given query image.

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Introduction: The early and accurate detection of COVID-19 remains a life-threatening challenge in medical analysis. Machine learning is used for predicting disease outcomes based on clinical parameters. This analysis proposes a comparative analysis of feature selection method and classification techniques to enhance COVID-19 detection accuracy using blood biomarkers. We used a pensourse dataset of 1,724 cases, including 35 features. To improve the model performance data preprocessing process included outlier handling, normalization, and transformation techniques to improve model performance. To identify the relevant features, we employed the three-feature selection methods Chi-Square test, Pearson correlation coefficient, and Random Forest. The model prediction accuracy was enhanced using a stacking ensemble classication techniques. The machine learning based classification models effectively predicted COVID-19 infectious disease using blood biomarkers with optimized feature selection techniques. Objectives: To enhance the accuracy of COVID-19 prediction using machine learning techniques by applying feature selection and classification techniques on blood biomarkers. Methods: The comparative analysis utilized a publicly available dataset containing 1,724 cases with 35 attributes. Data preprocessing involved outlier handling, normalization, and transformation techniques. Employed Chi-Square test, Pearson correlation coefficient, and Random Forest feature selection techniques. Stacking ensemble classification algorithm was utilized for the better performance of a model. Results: The classification models demonstrated efficiency in predicting COVID-19 using blood biomarkers. Optimized feature selection significantly improved predictive accuracy, highlighting the importance of selecting relevant features for model performance enhancement. Conclusions: This study showcases the potential of ML-driven approaches for COVID-19 detection, emphasizing the role of feature selection in improving classification accuracy. The findings contribute to the advancement of diagnostic tools, offering a data-driven solution for rapid and reliable COVID-19 screening.

The Internet of Things (IoT) with advanced machine learning techniques presents significant potential for agricultural applications, particularly in the domain of crop disease detection. Paddy, a staple food for millions, is highly susceptible to various diseases that can drastically affect yield and quality. Early and accurate disease detection is crucial for effective management and mitigation. Traditional methods are often labor-intensive and less reliable, underscoring the need for automated, accurate, and scalable solutions. The primary challenge lies in developing a robust system capable of accurately identifying diseases in paddy leaves using IoT-collected data. This task is complicated by the variability in disease manifestation and environmental conditions, which can affect the quality and consistency of the collected data. Efficient feature extraction and classification techniques are essential to address these issues and ensure high accuracy. This study proposes a novel approach combining Particle Swarm Optimization (PSO) for feature extraction with Deep Belief Networks (DBNs) for classification. IoT devices capture high-resolution images of paddy leaves, which are then processed in the cloud. PSO is employed to optimize the feature extraction process by selecting the most relevant features from the image data. These optimized features are fed into a DBN, which is trained to classify the images into healthy or diseased categories. The use of cloud computing ensures the scalability and computational efficiency of the system. The proposed method demonstrates significant improvements in accuracy and processing speed. The PSO-based feature extraction enhances the relevance of features, reducing the dimensionality and improving the DBN''s performance. Experimental results show an accuracy rate of 96.3%, with a reduction in processing time by 35% compared to traditional methods. The system''s precision and recall rates are 95.8% and 94.7%, respectively, highlighting its effectiveness in real-world applications.

<div class="section abstract"><div class="htmlview paragraph">Lane change events can be a source of traffic accidents; drivers can make improper lane changes for many reasons. In this paper we present a comprehensive study of a passive method of predicting lane changes based on three physiological signals: electrocardiogram (ECG), respiration signals, and galvanic skin response (GSR). Specifically, we discuss methods for feature selection, feature reduction, classification, and post processing techniques for reliable lane change prediction. Data were recorded for on-road driving for several drivers. Results show that the average accuracy of a single driver test was approx. 70%. It was greater than the accuracy for each cross-driver test. Also, prediction for younger drivers was better.</div></div>

The enhancement of Intrusion Detection Systems (IDS) is required to ensure protection of network resources and services. This is a hot research topic, especially in the presence of advanced intrusions and attacks. This paper provides a comparison between Distributed Cumulative Histogram (DCH) as a Feature Selection (FS) technique, Information Gain Ratio (IGR) FS and wrapper-based FS in terms of accuracy and Root Mean Square Error (RMSE). The utilization of DCH of the traffic instances in normal and attack cases allows us to compare the traffic charts. We can observe the difference between effective features and less effective ones. We verify the feasibility of using DCH as an FS technique in the field of anomaly detection with just six selected features giving more accurate results with most classifiers compared to the IGR and wrapper-based FS. We applied our experiments on the modern UNSW dataset with the WEKA simulation platform that contains a group of classification, feature reduction and selection techniques.

The development of computer based systems expands the usage of computer based application in human life. It can be observed that illegal activities such as unauthorized data access, data theft, data modification and various other intrusion activities are rapidly growing during last decade. Hence, deployment and continuous improvement of Intrusion Detection Systems (IDS) are of paramount importance. Training, testing and evaluation of IDS with real network traffic is significant challenge, so most of IDS evaluation is based on intrusion datasets. Therefore, analysis of intrusion datasets are of paramount importance. In this paper, we evaluated Aegean Wi-Fi Intrusion Dataset (AWID) with different machine learning techniques. Feature reduction techniques such as Information Gain (IG) and Chi-Squared statistics (CH) were applied to evaluate dataset performance with feature reduction. Results of experiments show that feature reduction can lead to better analysis in terms of accuracy, processing time and complexity. It was observed that, the maximum increment of classification accuracy with feature reduction from 110 to 41 is 2.4%.

Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies

Due to the exponential growth of digital platforms and the Internet era around us, particularly: cyberattacks facilitated by malware have become increasingly diversified and complex. The increase in malware strains has created an increased demand for advanced detection methods that can handle a large volume of data quickly. In this study, we use machine learning classifiers along with optimum feature vector extraction using principal component analysis (PCA) and Particle swarm optimization (PSO) to get better malware detection and classification. The key classifiers of this method are logistic regression (LR), Naive Bayes (NB), nearest neighbor (KNN), and decision trees (ID3 and C4. 5). PCA and PSO are successful methods for reducing the feature set without hindering or even improving the classification results; therefore, experimental results show that using feature reduction techniques with these classifiers provides (i) very high detection accuracy and (ii) lower computational complexity. These can be seen as evidence for the usefulness of hybrid approaches in enhancing malware detection performance on large-scale data sets.

In recent scenario speaker recognition is used in many places for security purpose. Speaker recognition is technique which can verify or identify the person who is speaking. It is different than speech recognition system. In this paper we have discussed the feature extraction techniques like Mel Frequency Cepstral Co-efficient (MFCC), Linear Predictive Coding (LPC) etc. and classification techniques like Gaussian Mixture Model (GMM), Artificial Neural Networks (ANN) etc. that is available for speaker recognition. Also the survey is about how to get better efficiency in terms of speaker recognition rate by simply modifying the existing feature extraction and classification techniques.

Machine learning is the worldwide recent research technique for various systems as they are intelligent enough to find the solution for classification and prediction problems. The proposed work is about a hybrid genetic fuzzy algorithm that performs an optimal search as well as classification upon uncertain data. The data which is uncertain is suitable for fuzzy classifiers to predict the disease. The hybrid genetic fuzzy system applied on the attributes selects relevant attributes. The selected attributes are fed into the fuzzy classifier. The fuzzy rules are again generated using genetic algorithms. This algorithm is applied on three of the important and bench marking data sets taken from the UCI machine learning repository. The heart disease, Wisconsin breast cancer and Pima Indian diabetes datasets produce classification accuracy as 89.65%, 99.5% and 88.93% respectively. In this article there is a comparative study on few of the feature selection and feature reduction techniques.