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Historically, the analysis of stimulus-dependent time–frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders. [ABSTRACT FROM AUTHOR]

Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar levels or impaired glucose tolerance. This can result in significant harm to various body systems, including the nerves and blood vessels. In this paper, we propose a multiclass diabetes mellitus detection and classification approach using an extremely imbalanced Laboratory of Medical City Hospital data dynamics. We also formulate a new dataset that is moderately imbalanced based on the Laboratory of Medical City Hospital data dynamics. To correctly identify the multiclass diabetes mellitus, we employ three machine learning classifiers namely support vector machine, logistic regression, and k-nearest neighbor. We also focus on dimensionality reduction (feature selection—filter, wrapper, and embedded method) to prune the unnecessary features and to scale up the classification performance. To optimize the classification performance of classifiers, we tune the model by hyperparameter optimization with 10-fold grid search cross-validation. In the case of the original extremely imbalanced dataset with 70:30 partition and support vector machine classifier, we achieved maximum accuracy of 0.964, precision of 0.968, recall of 0.964, F1-score of 0.962, Cohen kappa of 0.835, and AUC of 0.99 by using top 4 feature according to filter method. By using the top 9 features according to wrapper-based sequential feature selection, the k-nearest neighbor provides an accuracy of 0.935 and 1.0 for the other performance metrics. For our created moderately imbalanced dataset with an 80:20 partition, the SVM classifier achieves a maximum accuracy of 0.938, and 1.0 for other performance metrics. For the multiclass diabetes mellitus detection and classification, our experiments outperformed conducted research based on the Laboratory of Medical City Hospital data dynamics. [ABSTRACT FROM AUTHOR]

Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy problem by considering five available heart disease datasets and their combined form. All potential training and testing mode combinations are systematically executed to assess discrepancies before and after applying the proposed methods. Imbalance data handling using SMOTE-Tomek, feature selection using random forest (RF), and feature extraction using principle component analysis (PCA) with a long preprocessing pipeline are used to mitigate the inter-dataset discrepancy problem. The preprocessing pipeline builds on missing value handling using RF regression, log transformation, outlier removal, normalization, and data balancing that convert the datasets to more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic regression, and multilayer perceptron are used as classifiers. Experimental results show that feature selection and classification using RF produce better results than other combination strategies in both single- and inter-dataset setups. In certain configurations of individual datasets, RF demonstrates 100% accuracy and 96% accuracy during the feature selection phase in an inter-dataset setup, exhibiting commendable precision, recall, F1 score, specificity, and AUC score. The results indicate that an effective preprocessing technique has the potential to improve the performance of the ML model without necessitating the development of intricate prediction models. Addressing inter-dataset discrepancies introduces a novel research avenue, enabling the amalgamation of identical features from various datasets to construct a comprehensive global dataset within a specific domain. [ABSTRACT FROM AUTHOR]

Educational Data Mining (EDM) has become a promising research field for improving the quality of students and the education system. Although EDM dates back to several years, there is still lack of works for measuring and enhancing the computer programming skills of tertiary students. As such, we, in this paper, propose an EDM system for evaluating and improving tertiary students' programming skills. The proposed EDM system comprises two key modules for (i) classification process and (ii) learning process,. The classification module predicts the current status of a student and the learning process module helps generate respective suggestions and feedback to enhance the student's quality. In particular, for the classification module, we prepare a real dataset related to this task and evaluate the dataset to investigate six key Machine Learning (ML) algorithms, Support Vector Machine (SVM), decision tree, artificial neural network, Random Forest (RF), k -nearest neighbor and naive Bayes classifier, using accuracy-related performance measure metrics and goodness of the fit. The experimental results manifest that RF and SVM can predict the students more accurately than the other models. In addition, critical factors analysis is accomplished to identify the critical features toward achieving high classification accuracy. At last, we design an improvement mechanism in the learning process module that helps the students enhance their programming skills. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) are one of the most successfully used tools for precisely and potentially detecting key ground surfaces, vegetation, and minerals. HSIs contain a large amount of information about the ground scene; therefore, object classification becomes the most difficult task for such a high-dimensional HSI data cube. Additionally, the HSI's spectral bands exhibit a high correlation, and a large amount of spectral data creates high dimensionality issues as well. Dimensionality reduction is, therefore, a crucial step in the HSI classification pipeline. In order to identify a pertinent subset of features for effective HSI classification, this study proposes a dimension reduction method that combines feature extraction and feature selection. In particular, we exploited the widely used denoising method minimum noise fraction (MNF) for feature extraction and an information theoretic-based strategy, cross-cumulative residual entropy (CCRE), for feature selection. Using the normalized CCRE, minimum redundancy maximum relevance (mRMR)-driven feature selection criteria were used to enhance the quality of the selected feature. To assess the effectiveness of the extracted features' subsets, the kernel support vector machine (KSVM) classifier was applied to three publicly available HSIs. The experimental findings manifest a discernible improvement in classification accuracy and the qualities of the selected features. Specifically, the proposed method outperforms the traditional methods investigated, with overall classification accuracies on Indian Pines, Washington DC Mall, and Pavia University HSIs of 97.44%, 99.71%, and 98.35%, respectively. [ABSTRACT FROM AUTHOR]

A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the "curse of dimensionality" problem because (i) the image bands are highly correlated both spectrally and spatially, (ii) not every band can carry equal information, (iii) there is a lack of enough training samples for some classes, and (iv) the overall computational cost is high. Therefore, effective feature (band) reduction is necessary through feature extraction (FE) and/or feature selection (FS) for improving the classification in a cost-effective manner. Principal component analysis (PCA) is a frequently adopted unsupervised FE method in HSI classification. Nevertheless, its performance worsens when the dataset is noisy, and the computational cost becomes high. Consequently, this study first proposed an efficient FE approach using a normalized mutual information (NMI)-based band grouping strategy, where the classical PCA was applied to each band subgroup for intrinsic FE. Finally, the subspace of the most effective features was generated by the NMI-based minimum redundancy and maximum relevance (mRMR) FS criteria. The subspace of features was then classified using the kernel support vector machine. Two real HSIs collected by the AVIRIS and HYDICE sensors were used in an experiment. The experimental results demonstrated that the proposed feature reduction approach significantly improved the classification performance. It achieved the highest overall classification accuracy of 94.93% for the AVIRIS dataset and 99.026% for the HYDICE dataset. Moreover, the proposed approach reduced the computational cost compared with the studied methods. [ABSTRACT FROM AUTHOR]

The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands. The classification accuracy is not often satisfactory in a cost-effective way using the entire original HSI for practical applications. To enhance the classification result of HSIs the band reduction strategies are applied which can be divided into feature extraction and feature selection methods. PCA (Principal Component Analysis), a linear unsupervised statistical transformation, is frequently adopted for the extraction of features from HSIs. In this paper, PCA and SPCA (Segmented-PCA), SSPCA (Spectrally Segmented-PCA), FPCA (Folded-PCA) and MNF (Minimum Noise Fraction) as linear variants of PCA together with KPCA (Kernel-PCA) and KECA (kernel Entropy Component Analysis) as nonlinear variants of PCA have been investigated. The top transformed features were picked out using accumulation of variance for all other feature extraction methods except for MNF and KECA. MNF uses SNR (Signal-to-Noise Ratio) values and KECA employs Renyi quadratic entropy measurement for this purpose. The studied approaches are equated and analyzed for Indian Pine agricultural and urban Washington DC Mall HSI classification using SVM (Support Vector Machine) classifier. The experiment illustrates that the costly effective and improved classification performance of the feature extraction approaches over the performance using the entire original dataset. MNF offers the highest classification accuracy and FPCA offers the least space and time complexity with satisfactory classification result. [ABSTRACT FROM AUTHOR]

Subspace detection from high dimensional hyperspectral image (HSI) data cube has become an important area of research for efficient identification of ground objects. Standard feature extraction method such as Principal Component Analysis (PCA) has some drawbacks as it depends solely on global variance of the dataset generated. Folded-PCA (FPCA), an improvement of PCA, offers more benefits over PCA as it envisages both local and global structures of image contents and requires less computation and memory. These superior properties make FPCA more effective for feature extraction in high dimensional remote sensing images e.g. HSIs. Therefore, the proposed feature reduction method combines FPCA feature extraction with Normalized Cross Cumulative Residual Entropy (NCCRE) feature selection, termed as FPCA-NCCRE, for efficient features' subspace detection. NCCRE is utilised as a means of feature selection over the new features generated from FPCA to obtain a more informative subspace. It is experimented on a real mixed agricultural and an urban hyperspectral dataset. Finally, Kernel Support Vector Machine (KSVM) is implemented to calculate the classification accuracy using the detected subspace. From the experiments, it is observed that the proposed method outperforms the baseline approaches and obtains the highest accuracy of 97.67 and 98.57% on the two real hyperspectral images. [ABSTRACT FROM AUTHOR]

Remote sensing hyperspectral images (HSIs) are rich sources of information about land cover captured across hundreds of narrow, contiguous spectral wavelength bands. However, using the entire original HSI for practical applications can lead to suboptimal classification accuracy. To address this, band reduction techniques, categorized as feature extraction and feature selection methods, are employed to enhance classification results. One commonly used feature extraction approach for HSIs is Principal Component Analysis (PCA). However, PCA may fall short of capturing the local and specific characteristics present in the HSI data. In this paper, we introduce two novel feature extraction methods: Segmented Truncated Singular Value Decomposition (STSVD) and Spectrally Segmented Truncated Singular Value Decomposition (SSTSVD) to improve classification performance. Segmentation is carried out based on highly correlated bands’ segments and spectral bands’ segments within the HSI data. Our study evaluates and compares these newly proposed methods against classical feature extraction methods, including PCA, Incremental PCA, Sparse-PCA, Kernel PCA, Segmented-PCA (SPCA), and Truncated Singular Value Decomposition (TSVD). We perform this analysis on three distinct HSI datasets, namely the Indian Pines HSI, the Pavia University HSI, and the Kennedy Space Center HSI, using per-pixel Support Vector Machine (SVM) and Random Forest (RF) classification. The experimental results demonstrate the superiority of our proposed methods for all three datasets. The best-performing feature extraction methods when classification is performed using an SVM classifier are STSVD3 (89.03%), SSTSVD2 (95.55%), and STSVD3 (97.74%) for the Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively. Similarly, for the RF classifier, the best-performing feature extraction methods are SSTSVD4 (88.98%), SSTSVD3 (96.04%), and SSTSVD4 (96.09%) for Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively. [ABSTRACT FROM AUTHOR]