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This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition. [ABSTRACT FROM AUTHOR]

Medical image classification plays a pivotal role in diagnosing various diseases. However, selecting informative features from these images remains a challenging task due to the high dimensionality and complexity of the data. Genetic algorithms (GAs) offer a promising approach for feature selection in medical image classification tasks by mimicking the process of natural selection to evolve optimal solutions. This study proposes a genetic algorithm optimization framework for feature selection in medical image classification. The GA iteratively searches the feature space to find the subset of features that maximizes the classification performance. Fitness evaluation is based on a classifier’s performance using selected features, and genetic operators such as crossover and mutation are applied to produce new generations of feature subsets. The proposed framework contributes to enhancing the efficiency and effectiveness of medical image classification by identifying relevant features. By employing GAs, it overcomes the limitations of traditional feature selection methods and adapts to the complexity of medical image data. Experimental results on benchmark medical image datasets demonstrate the effectiveness of the proposed approach. Significant improvements in classification accuracy and computational efficiency are observed compared to baseline methods. Moreover, the selected features exhibit robustness across different classifiers, highlighting the generalizability of the proposed framework. [ABSTRACT FROM AUTHOR]

Biogeographical ancestry (BGA) inference from ancestry-informative markers (AIMs) has strong potential to support forensic investigations. Over the past two decades, several forensic panels composed of AIMs have been developed to predict ancestry at a continental scale. These panels typically comprise fewer than 200 AIMs and have been designed and tested with a limited set of populations. How well these panels recover patterns of genetic diversity relative to larger sets of markers, and how accurately they infer ancestry of individuals and populations not included in their design remains poorly understood. The lack of comparative studies addressing these aspects makes the selection of appropriate panels for forensic laboratories difficult. In this study, the model-based genetic clustering tool STRUCTURE was used to compare three popular forensic BGA panels: MAPlex, Precision ID Ancestry Panel (PIDAP), and VISAGE Basic Tool (VISAGE BT) relative to a genome-wide reference set of 10k SNPs. The genotypes for all these markers were obtained for a comprehensive set of 3957 individuals from 228 worldwide human populations. Our results indicate that at the broad continental scale (K = 6) typically examined in forensic studies, all forensic panels produced similar genetic structure patterns compared to the reference set (G′ ≈ 90%) and had high classification performance across all regions (average AUC-PR > 97%). However, at K = 7 and K = 8, the forensic panels displayed some region-specific clustering deviations from the reference set, particularly in Europe and the region of East and South-East Asia, which may be attributed to differences in the design of the respective panels. Overall, the panel with the most consistent performance in all regions was VISAGE BT with an average weighted AUC ̅ W score of 96.26% across the three scales of geographical resolution investigated. • New global reference dataset generated by collating genotypes from 3,957 individuals and 228 populations. • Evaluation leverages an extensive reference marker set comprising 10k Human Origins Array-based SNPs. • Analyses were used to examine patterns of genetic diversity and differentiation, and classification performance with STRUCTURE. • Presented workflow paves the way for further comparative evaluations of forensic BGA panels. • We make our extensive global reference dataset available for the scientific community. [ABSTRACT FROM AUTHOR]

Abstract: Objective comparison of classification performance of earth observation images, acquired at different spatial resolutions (e.g. NOAA-AVHRR, IRS-MOS, IRS-WiFS, Landsat-TM, IRS-LISS), is complicated because both class definition and training site selection are hampered by the inherent scale differences. This paper presents a new, generic method to compare the information content of such a set of images, the “Stained Glass Procedure”. It overcomes the stated problems by computing the scale-dependent, internal spectral variation in an image and by using this as an indicator for land cover information. The Stained Glass Procedure creates segments in the images and calculates the internal spectral variation in a high-spatial-resolution image for each segment. For each image from the set the average variance, weighted to area, is calculated. The Stained Glass Procedure can be used to predict the performance of sensors that are not available, yet, or to roughly determine the optimal spatial resolution for the classification of a specific area. The procedure was applied to images with pixel sizes ranging from 23 to 1100m. Classification detail of Envisat-MERIS (300m pixel size), not included in the image set, could be predicted accurately using the Stained Glass Procedure. The Stained Glass Procedure applies one procedure to all images, without any subjective decision during the analysis, thus offering a method to compare images with different pixel sizes in terms of classification detail that is truly objective. [Copyright &y& Elsevier]

Ensembles of classifiers offer higher classification accuracy than single classifiers. One method for constructing an ensemble is to have the base classifiers work on different feature sets. In this paper, we present a method for selecting the feature sets of the base classifiers by means of a multi-objective genetic algorithm, aimed at maximising the classification performance and the diversity among the classifiers and at minimising the number of features in the subsets. A static voting technique is used to effectively combine the outputs of the base classifiers to construct the ensemble output. The proposed approach is applied to the classification of (simulated) transients in the feedwater system of a boiling water reactor, and the results are compared with those obtained using an optimal single classifier. [ABSTRACT FROM AUTHOR]