Data Science is a multidisciplinary field that involves extracting knowledge and insights from data using scientific methods, processes, algorithms, and tools. It combines aspects of statistics, mathematics, computer science, and domain expertise to analyze and interpret data, uncover patterns, make predictions, and solve complex problems. Data scientists utilize techniques such as data mining, machine learning, statistical modeling, and data visualization to extract valuable information and support data-driven decision-making in various industries and domains.
This study proposes the creation of a publicly accessible repository housing a diverse collection of 103 three-dimensional (3D) datasets representing clinically scanned surgical instruments. The dataset, meticulously curated through a four-stage process, aims to accelerate advancements in medical machine learning (MML) and the integration of medical mixed realities (MMR)
This study delves into the transformative potential of data science in African healthcare and research, emphasizing the critical role of ethical governance. It highlights ongoing initiatives, investments, and challenges while stressing the need for collaboration and investment in ethical oversight to drive impactful research in the continent.
Researchers introduce SynthAML, the first publicly available synthetic dataset for studying critical challenges in anti-money laundering (AML). This dataset, created using innovative synthesis techniques, addresses issues like efficiency, effectiveness, class imbalance, concept drift, and interpretability, offering a platform for standardized assessment and academic research in the AML domain.
ZairaChem, a groundbreaking AI and machine learning tool, is transforming drug discovery in resource-limited settings. This fully automated framework for quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modeling accelerates the identification of lead compounds and offers a promising solution for efficient drug discovery.
This article explores a recent research paper that introduces an innovative approach to urban noise monitoring by combining binaural sensing and cloud-based data processing. The proposed system utilizes a 3D-printed artificial head equipped with microphones to capture acoustic data, enabling more accurate and comprehensive noise analysis. The cloud-based architecture further processes the data, offering valuable spatial indicators for urban soundscape evaluations, thereby contributing to enhanced urban planning strategies and overall quality of life.
Researchers present a distributed, scalable machine learning-based threat-hunting system tailored to the unique demands of critical infrastructure. By harnessing artificial intelligence and machine learning techniques, this system empowers cyber-security experts to analyze vast amounts of data in real-time, distinguishing between benign and malicious activities, and paving the way for enhanced threat detection and protection.
Researchers employ a combination of artificial intelligence (AI) methods, logical-mathematical models, and physicochemical parameters to predict water quality (WQ) in the challenging context of the Loa River basin in the Atacama Desert. By integrating AI-driven techniques, such as Random Forest (RF), with expert insights, the study introduces a novel method for generating WQ labels and classifications.
Researchers at the U.S. Department of Energy's Argonne National Laboratory and the University of Chicago are collaborating on a project using reinforcement learning to optimize the charging of electric vehicles (EVs) at charging stations. The algorithm learns from positive and negative outcomes to make intelligent decisions, aiming to efficiently manage the charging process, reduce peak demands on the grid, and provide clean and low-cost charging solutions, paving the way for effective integration of EVs into the power grid.
Machine learning models identify miRNA biomarkers with potential clinical significance, shedding light on the complex landscape of cancer. The study reveals the relevance of specific miRNAs in cancer classification and highlights their potential as diagnostic and classification biomarkers.
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