The current study explores the application of Machine Learning (ML) and Deep Learning (DL) techniques to predict stroke risk, addressing major challenges such as model accuracy, adaptability, feature importance, transparency, and external validation. A quantitative approach is used to evaluate various ML algorithms, and in particular, Random Forest has been highlighted because of its superior predictive accuracy, while stressing the adaptability of DL models across different demographic contexts. The study further explores the role of feature significance in enhancing context-specific predictions, the challenges of model explainability in clinical adoption, and the critical importance of external validation in ensuring generalizability. The results underline the transformative potential of ML and DL in advancing personalized healthcare strategies for stroke prediction while identifying existing gaps in transparency and validation practices. This synthesis lays a foundation for future research to standardize external validation protocols and improve model transparency for wider clinical adoption.
This has revolutionized exploration geophysics with the inclusion of machine learning, especially deep learning, with conventional geophysical methods. The paper discusses how machine learning influences the efficiency and accuracy in seismic imaging, gravity and magnetic data inversion, environmental monitoring, extraterrestrial resource exploration, and remote sensing applications. The study confirms that machine learning algorithms improve imaging accuracy in complex geological settings, optimize inversion processes for gravity and magnetic data, enhance real-time environmental monitoring, and advance extraterrestrial resource exploration through a comprehensive literature review and quantitative data analysis. Moreover, the integration of machine learning with remote sensing significantly boosts geophysical data analysis and interpretation. Despite these successes, significant challenges persist with variability of data, algorithm adaptation, and computational cost. Results illustrate the transformative nature of machine learning for geophysics, highlighting the need for future research that will bridge the existing limitations into its wider applicability in geological and extraterrestrial environments.
Released Glucose Equivalent in red sorghum malt mashing optimization study using the Response Surface Methodology (RSM) will be discussed here. The work concentrates on three crucial factors: β-amylase temperature, α-amylase temperature, and duration of mashing, so as to obtain maximal glucose extraction at the time of mashing. This study adopts a quantitative approach, with experiments conducted in the controlled laboratory between 2020 and 2023. Research observations indicate a relationship between α-amylase temperature and mashing duration; however, their effects on glucose release are both significant, with a quadratic nature associated with duration. The interaction effect between α-amylase temperature and mashing duration was also significant. In contrast to other malts, unique patterns of enzyme activity were found in sorghum malt. These results point out the need for optimization of multiple parameters to enhance brewing efficiency and provide new insights into enzymatic activity in sorghum malt. The study concludes by pointing out gaps in previous literature and suggesting future research directions for broader applications and conditions.
This research discusses the developments of Optical Character Recognition (OCR) methods for figure processing between 2014 and 2020. The study covered five sub-research questions on text detection, extraction, segmentation methods, state-of-the-art techniques, and their effectiveness in bridging existing research gaps. This is a quantitative method in which electronic data was analyzed systematically, based on independent variables like detection, extraction, and segmentation methods, along with dependent variables like accuracy, efficiency, and applicability. The key findings in this paper include the importance of deep learning to enhance the accuracy of text detection and segmentation, hybrid techniques that can improve text extraction efficiency, and integrated OCR frameworks for processing figures. The outcomes reveal recent trends, gaps in literature, and potential future directions that could further fuel innovation in OCR technology.
This study presents a comparative analysis of the botanical, physicochemical, and phytochemical properties of Zanthoxylum zanthoxyloides stem and root bark powders. The research underlines the possibility of their interchangeability in phytomedicine to promote sustainable resource utilization. Botanical characteristics, physicochemical parameters such as ash and moisture content, and phytochemical profiles were evaluated using methanolic and aqueous extracts. The similarities in botanical characteristics, physicochemical properties, and phytochemical profiles support the possibility of substitutability in traditional medicine. However, significant differences between the antioxidant activities of methanolic extracts from the stem bark and root bark suggest that methods of extraction significantly impact therapeutic optimization. The data obtained contribute to the standardization of phytomedicines based on Z. zanthoxyloides, supporting resource conservation and sustainable harvesting.