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16 articles for “PCA”
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Evaluating the Performance of Test Cricket Players Using Principal Component Analysis and the Weighted Average Method
Abstract: This study evaluates cricket player performance using Principal Component Analysis (PCA) and a weighted average approach. In order to achieve this, we analyzed detailed batting and bowling datasets from the International Cricket Council (ICC) to calculate player performance based on various performance indicators. The datasets included comprehensive statistics from multiple matches and tournaments, allowing for an in-depth evaluation of players’ skills and contributions. PCA ranked players according to their participation …
Published in Recent Trends in Sports · Vol. 2, Issue 2, 2025 · pp. 20–30 Read article
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Comparative Analysis of Fermented Milk Prepared Using Natural Fermentation Starters
Abstract: Background: Fermented milk products are a staple of traditional diets and offer considerable nutritional and health benefits due to their probiotic and bioactive compounds. While conventional starters, like old buttermilk are widely used, alternative plant-based starters, like clove and green chili stalk, have potential advantages, including enhanced antioxidant properties and cultural relevance. Objectives: This study aimed to compare the fermentation dynamics of milk inoculated with clove, green chili stalk, and …
Published in Research and Reviews : Journal of Dairy Science and Technology · Vol. 14, Issue 3, 2025 · pp. 11–28 Read article
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AI-Powered Face Detection and Recognition Using Machine Learning
Abstract: These days, one of the biggest computer vision technologies is facial recognition. Face identification in computer vision, lighting position, and facial expression is always an extremely challenging issue. In real-time video pictures captured by a video camera, face recognition tracks specific objects. Put simply, it is a system tool that uses a still picture or video frame to automatically identify a person. In this research paper we use different different …
Published in Trends in Machine design · Vol. 11, Issue 2, 2024 · pp. 1–12 Read article
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Fusion of deep learning autoencoders with random forest for wetland classification using Sentinel-2A data: A case study on Sirpur wetland
Abstract: Present study analyses the performance of deep leaning algorithm-autoencoder to reduce data dimension as compared to conventional models. Classification accuracies of Sirpur wetland using Sentinel 2A dataset with different inputs have also been studied. These inputs sets comprise the reconstructed data through compression of original 13 bands into 4 bands using decoder algorithm, first four Principal Components, all spectral bands, and spectral indices. Random Forest classifier (RF) is used to …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 1, 2026 · pp. 25–35 Read article
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Matching Minutiae Fingerprint Q-Learning Approach for Detail Coordination: Identifiable Mark Point
Abstract: The use of fingerprints for high-precision recognition and identification of people is one of the most reliable biometric symbols because it is non-invasive. In this paper, we propose an innovative approach to detect details on low contrast resolution image quality of fingerprint images. Existing algorithms are not very susceptible to sound and image excellence due to the lack of level of intensity. We recommend a reliable route to find fingerprints …
Published in International Journal of Information Security Engineering · Vol. 1, Issue 1, 2023 · pp. 1–15 Read article
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Comparative Study of Facial Spoofing Detection using CNN Architecture
Abstract: Facial recognition systems face a high risk of security breach due to various facial spoofing attacks. This challenge was addressed by the study of several deep learning models. This study proposes an idea to detect facial spoofing using deep learning architecture to differentiate live faces form various types of spoofed images/videos using different CNN models. In addition, the study seeks to strengthen security measured in facial recognition system demonstrating that …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 3, 2024 · pp. 9–17 Read article
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GreenDiagnosis: Intelligent Crop Disease Detection Using Deep Learning Algorithm
Abstract: Agriculture in parts of India relies on labour-intensive traditions, maintaining disease-free crops is crucial. Manual methods can be inaccurate, driving farmers towards AI-based solutions. AI offers a proactive approach to address real-time farming challenges. Among these is the invasion of pests, which diminishes crop quality. Combating pest-related diseases poses a challenge, prompting innovation. Effective surveillance and early detection of crop diseases play a pivotal role in ensuring global food security …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 8–18 Read article
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Dimensionality Reduction Techniques and their Applications in Cancer Classification: A Comprehensive Review
Abstract: Dimensionality reduction techniques have become a vital tool in the investigation of high-dimensional data like gene expression profiles in cancer research. Here is a review, we deliver a comprehensive overview of dimensionality reduction techniques and their applications in cancer classification. Firstly, we introduce the concepts and approaches of dimensionality reduction, and after that, we explore several methods for decreasing dimensionality. These techniques include Linear Discriminant Analysis (LDA), Principal Component Analysis …
Published in International Journal of Genetic Modifications and Recombinations · Vol. 1, Issue 2, 2023 · pp. 35–45 Read article
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Genetic Variability and Statistical Methods: Key Insights for Computational Genetics Research
Abstract: Genetic variability, defined as the differences in DNA sequences among individuals, serves as the foundation of evolutionary biology and plays a pivotal role in species’ adaptability, resilience, and overall survival. Advances in genomic technologies, particularly high-throughput sequencing, have enabled unprecedented exploration of genetic diversity, fostering the growth of computational genetics. This interdisciplinary field combines statistical methods and computational tools to analyze genetic data, identify patterns, and link phenotypes to genotypes. …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 19–23 Read article
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Data Integration and Visualization in Bioinformatics: Techniques and Challenges
Abstract: Data integration and visualization play essential roles in bioinformatics, facilitating the thorough analysis, and interpretation of intricate biological datasets. In the field of bioinformatics, vast amounts of data are generated from various experimental platforms, such as genomic sequencing, proteomics, transcriptomics, and metabolomics. However, the heterogeneity of these datasets, coupled with their large scale and complexity, presents significant challenges in terms of integration, analysis, and visualization. Data integration techniques aim to …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 1–8 Read article
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Signal Feature Extraction and Machine Learning Techniques for Human Activity Recognition
Abstract: Human Activity Recognition (HAR) has emerged as a critical field of study with diverse applications in healthcare, fitness tracking, smart homes, and human-computer interaction. The aim of this research is to create an efficient HAR system through advanced techniques characterized by signal feature extraction and machine learning algorithms. The MEMS sensors are used appropriately during data mining to extract time-domain, frequency-domain, and statistical features, which are subsequently passed to the …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 1, 2025 · pp. 24–41 Read article
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Responses of Phytoplankton Community Structure to Polycyclic Aromatic Hydrocarbon Constituents in Imo River, Oyigbo, Niger Delta, Nigeria
Abstract: Phytoplankton perform a pivotal role in the food chain and serve as indicator species in environmental studies. Polycyclic aromatic hydrocarbons (PAHs) are toxic group of divergent chemicals with two or more aromatic rings coalesce side by side representing serious persistent organic contaminants posing environmental risk to aquatic life and human. Imo River receive contaminants from illegal oil refinery. The study was conducted to assess the responses of phytoplankton community structure …
Published in Journal of Water Pollution & Purification Research · Vol. 12, Issue 1, 2025 · pp. 45–53 Read article
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DFT/Data Guided Predictive Modelling of Absorption Maxima in the OLED Rubrene Derivatives
Abstract: This study investigates the optical properties of rubrene derivatives to develop an accurate predictive model for absorption maxima using computational chemistry and chemoinformatic techniques. We benchmarked various quantum chemical methods, identifying that the M06-2X/aug-cc-pVDZ method in dichloromethane (DCM) provided the strongest correlation with experimental data. Key molecular descriptors such as band gap, ionization potential, and electrophilicity index were calculated and analyzed using principal component analysis (PCA) to identify significant factors …
Published in International Journal of Cheminformatics · Vol. 4, Issue 1, 2026 · pp. 41–56 Read article
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Differential Gene Expression Analysis of Human Atrial Fibroblasts Reveals Dysregulation of RNA Metabolism and Translational Machinery in Atrial Fibrillation
Abstract: Atrial fibrillation (AF) is a complex cardiac arrhythmia characterized by extensive structural remodeling and the activation of atrial fibroblasts, which drive the progression of fibrosis. To identify the underlying transcriptomic alterations, we analyzed six human atrial fibroblast RNA-Seq datasets (three control and three AF) retrieved from the Sequence Read Archive. After performing rigorous quality control and adapter trimming, we aligned the reads to the GRCh38 human reference genome using a …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 · pp. 15–25 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
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Multi-Scale Analysis of Polymer Based Energy Storage Systems for High Performance Battery Applications
Abstract: The energy storage systems based on polymers are becoming promising materials for the next generation of high performance batteries because of their excellent mechanical flexibility, improved safety, and favorable electrochemical properties. Even with computational tools in Python, polymer-based energy storage systems remain plagued by poor ionic conductivity, complicated electrochemical reactions and potential thermal runaway. Therefore, a multi-scale model is proposed to improve battery performance, thermal stability, reliability, and large-scale deployment …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1035–1048 Read article