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639 articles for “data sciences”
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Applications of Machine Learning Algorithms in Health Data Science (HDS) for Next Research Directions: A Survey Report
Abstract: At present time, data science is the big trend in computer science. The functioning of this technology is purely based on other advanced technology known as machine learning (ML). Data science and ML are subsets of artificial intelligence (AI). When a process of data science is used in healthcare systems, the new system is known as health data science (HDS). HDS is a branch of data science used to handle …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 16–21 Read article
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Data Handling Algorithms for the Healthcare System for the Prediction of Diabetes in Health Data Science (HDS): A Review Report
Abstract: In recent years, diabetes has become the biggest disease in different countries around the world. This disease is caused by adulteration in food ingredients, unhealthy food habits, a lack of physical exercise, and changing the lifestyle every time without a routine chart. The main objective of this review paper is to provide a proper understanding of the machine learning algorithm used in the healthcare system to handle diabetic patients' data. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
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From Differential Equations to Data Science: A Survey on Analytical Methods in Contemporary Problems
Abstract: The integration of differential equations and data science methods represents a dynamic and evolving approach to solving contemporary challenges across a wide range of disciplines, including engineering, physics, biology, economics, and finance. Differential equations have long served as fundamental tools for modeling continuous systems and processes, offering powerful insights into the behavior of natural and man-made phenomena. For example, they describe how heat diffuses through materials, how populations grow in …
Published in Recent Trends in Mathematics · Vol. 2, Issue 2, 2025 · pp. 1–6 Read article
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Leveraging Full Stack Data Science for Healthcare Transformation: An Exploration of the Microsoft Intelligent Data Platform
Abstract: The rapid progress of the Fourth Industrial Revolution has been largely driven by the evolution of artificial intelligence (AI), with notable contributions from technologies such as Generative Pre-trained Transformers (GPT). This revolution has seen the convergence of physical, digital, and biological technologies, leading to transformative impacts across various sectors. Data science, serving as a crucial enabler, has enabled the development of intelligent value chains. However, the application of data science …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 3, 2024 · pp. 1–8 Read article
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Stock Market Analysis Using Data Science
Abstract: Stock market prediction using data science has become a popular area of research and application in recent years. This is because the stock market is a complex system with many variables and factors that affect its behavior, making it difficult to predict with certainty. The stock market has always been the aggression of buyers and sellers of stocks, therefore in the global finance market, stock trading is one of the …
Published in E-Commerce for Future & Trends · Vol. 11, Issue 1, 2024 · pp. 1–4 Read article
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Python: Empowering Data Science Applications and Research
Abstract: It was in 1991 that the first mention of Python came to light. Since it is one of the best programming languages, it is widely used in the data analytics industry. It is speedy, easy to use, and can smoothly alter data with no hiccups. It aids in the processes of data analytics generally, such as data collection, analysis, modelling, and visualization. Python stands out from other languages because it …
Published in Journal of Operating Systems Development & Trends · Vol. 10, Issue 1, 2023 · pp. 27–33 Read article
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Stock Price Prediction Using Data Science Techniques
Abstract: This study centers around persuasively presenting the potential to forecast the stability of future market stocks. Previous research has delved into predicting the trajectory of future market trends, leading to fluctuations in stock data, which opens avenues for refinement. The proposed model employs data science methodologies to predict the stock price index's value. This is achieved by contrasting supervised classification data science learning algorithms that predict either stock price increases …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 2, 2023 · pp. 33–41 Read article
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Data Science: Domains, Process, Roles and Responsibilities
Abstract: The study of information retrieval and analysis, or data science, tries to identify information and correspondence hidden in raw, defined information. To extract useful information from data, data science therefore combines programming expertise with understanding of mathematics and statistics. In the field of data science, machine learning algorithms are employed to handle various types of input, including numerical, textual, visual, and auditory data. Therefore, algorithms perform certain tasks related to …
Published in Journal of Advanced Database Management & Systems · Vol. 10, Issue 2, 2023 · pp. 29–35 Read article
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Role of Data Science in Enhancing Privacy in E-Healthcare Systems
Abstract: The test is that while learning and data science are basic empowering agents of productivity and viability in present day associations, exploiting them often implies entrusting delicate data to outer sellers. The overall privacy model will in general be binary: either the data science as a specialist resource has the association's full trust and gets immediate admittance to the data, or they do not have the trust and they get …
Published in Recent Trends in Parallel Computing · Vol. 10, Issue 1, 2023 · pp. 14–21 Read article
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Study of Data Science in Online Behavioral Advertising
Abstract: The proliferation of the COVID-19 pandemic has led to a growing significance of social media in people's lives. Online activity in the digital domain has expanded considerably, showing a higher reliance on social media platforms for communication in both personal and professional situations. Coronavirus has a global impact on e-commerce and hence transformed the nature of business. Despite the COVID-19 issue and economic slowdown, the Indian e-commerce industry exhibited an …
Published in Journal of Open Source Developments · Vol. 10, Issue 3, 2023 · pp. 1–6 Read article
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Interest Level Prediction in Rental Properties Using Data Science
Abstract: A key component of forecasting home prices and rental patterns is real estate market analysis. Data science, data mining methodologies, and statistical models are some of the strategies that have been created in recent years to solve this problem. A few problems are still required to be resolved, such as the obstacles caused by the availability and quality of the data; the presence of outliers, missing values, and inconsistent formats …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 1, 2024 · pp. 28–34 Read article
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Redefining Efficiency: Big Data Science in Supply Chain Design and Management
Abstract: The combination of resources, tools, and applications in the field of supply chain management (SCM) is rapidly expanding, creating both opportunities and challenges. The term "big data" is commonly used to refer to the large and complex sets of data that are now available. These data are believed to have the potential to improve decision making and increase profitability. To effectively analyze and utilize these data, new methods of data …
Published in Journal of Production Research & Management · Vol. 13, Issue 1, 2023 · pp. 6–10 Read article
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Human Retention using Data Science
Abstract: Employees are the backbone of any successful organization and hence employee retention is of utmost importance. Using Data Science, this research work seeks to assist HR and Project Managers in improving the retention rate of valued workers in an organization, thus lowering the company's employee turnover expense. Following extensive research into how to choose the most desirable employee and the application of methodological assumptions, performance was created using conditional logic …
Published in Journal of Advances in Shell Programming · Vol. 8, Issue 1, 2021 · pp. 18–24 Read article
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A Study on Data Science Analytics: Addressing Challenges, Exploring Open Research Issues: A Literature Centric Approach
Abstract: The rapid growth of data science and analytics has revolutionized industries across the globe. This paper presents a comprehensive examination of the challenges encountered in the field of data science analytics and investigates unresolved research issues through a literature-centric approach. By analyzing recent research papers, articles, and industry reports, this study offers insights into the evolving landscape of data science and the critical challenges that data scientists face. Additionally, it …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 3, 2023 · pp. 70–82 Read article
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Medical Image Processing Using Data Science: A Review
Abstract: The purpose of this work is to introduce data science in medical imaging. Both theoretical advancements and real-world applications are covered in this research work. The healthcare industry is distinct from all other industries and is a special sector. It is a top-priority industry that consumes a sizable percentage of the federal budget. In general, only doctors can analyses images, but thanks to technology, we are now able to use …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 9, Issue 3, 2022 · pp. 7–15 Read article
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Topology and Geometry in Data Science: Persistent Homology and Beyond
Abstract: In recent years, the interplay between topology, geometry, and data science has gained substantial momentum, offering powerful frameworks to analyze and interpret complex datasets. Traditional statistical and machine learning methods often rely on linear or metric- based assumptions, which may fail to capture the intrinsic structure of high-dimensional or nonlinear data. In contrast, topological and geometric methods provide shape-oriented, scale- invariant tools that focus on the continuity, connectivity, and global …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 21–27 Read article
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Python's Applications in the Profession of Data Science
Abstract: Because of its ease of use, adaptability, and huge ecosystem of libraries, Python has become one of the most influential programming languages in the field of data science. Python is highly valued for its straightforward and versatile nature. This study delves into its various uses in data science, including tasks like data preprocessing, exploratory data analysis (EDA), statistical modeling, machine learning, and creating visualizations. Libraries like Pandas and NumPy make …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 23–30 Read article
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Revolutionizing Low Carbon Concrete: Innovations at the Nexus of Computational Science, Data Analytics, and Sustainable Technologies
Abstract: Concrete production accounts for 7% of global CO2 emissions necessitating low carbon innovations to curb exponential demand threatening climate commitments. This research reviews sustainable construction literature integrating computational simulations, big data infrastructure monitoring and alternative process redesign. Analysis reveals 30-50% reductions achievable through combined use of industrial ecologies, smarter sensing coordinated with ML optimization and novel binders like alkali-activated geopolymers. Rigorous LCA quantification verifies environmental superiority over conventional formulations. Case …
Published in Journal of Construction Engineering, Technology & Management · Vol. 13, Issue 3, 2023 · pp. 16–23 Read article
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Develop a Data Science Approach for Optimizing Energy Consumption
Abstract: Optimizing energy consumption has become a critical challenge in the era of sustainability and increasing energy demand. Efficient energy management is essential to address environmental concerns, reduce costs, and ensure resource availability for future generations. This project leverages data science techniques to evaluate and improve energy consumption across diverse sectors, including residential, industrial, and commercial domains. By integrating advanced analytics, machine learning models, and real-time data processing, the project aims …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 31–44 Read article
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Radial oscillation modes of delta Scuti stars KIC 2439660, KIC 3219256 and KIC 6965789 investigated using Kepler asteroseismic science data
Abstract: With the rise of Asteroseismology, studying the interior properties of unreachable stars ispossible from Earth and Earth centered orbits. Delta Scuti stars KIC 2439660, KIC 3215692 andKIC 6965789 from the Kepler science data were used for our studies. Each star has around38750 data points within Kepler quarters of 17.1 and 17.2. After removing the null points in data,MATLAB software package was utilized to identify the best-fit curve of the frequency-amplitude …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 12, Issue 1, 2023 · pp. 9–20 Read article