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Technology

Data Science at IgMin Research | Technology Group

Our mission is to foster interdisciplinary dialogue and accelerate the advancement of knowledge across a wide spectrum of scientific domains.

About

Data Science is a dynamic field at the intersection of computer science, statistics, and domain-specific knowledge, aimed at extracting valuable insights and knowledge from vast and complex datasets. In the digital age, the abundance of data has transformed the way industries and disciplines operate, making data-driven decision-making essential. At IgMin Research: STEM, we delve into the multifaceted realm of Data Science, exploring its techniques, methodologies, and applications across various sectors.

With an emphasis on interdisciplinary collaboration, IgMin Research: STEM welcomes researchers, practitioners, and enthusiasts from diverse backgrounds to contribute to the ongoing discourse on Data Science. Our journal provides a platform to share cutting-edge research, practical implementations, and novel advancements in data analysis, machine learning, artificial intelligence, and more. As we embrace the ever-evolving landscape of technology and data, we aim to foster knowledge exchange that fuels innovation.

  • Exploratory Data Analysis
  • Machine Learning Algorithms
  • Predictive Modeling
  • Natural Language Processing
  • Data Visualization Techniques
  • Big Data Analytics
  • Deep Learning Architectures
  • Pattern Recognition
  • Data Mining Methods
  • Statistical Inference
  • Feature Engineering
  • Time Series Analysis
  • Sentiment Analysis
  • Anomaly Detection
  • Image Processing and Analysis
  • Dimensionality Reduction
  • Recommender Systems
  • Ethical Considerations in Data Science
  • Business Intelligence
  • Healthcare and Medical Data Analytics
  • Environmental Data Analysis
  • Social Network Analysis
  • Fraud Detection
  • IoT Data Analytics
  • Data Privacy and Security

Technology Group (2)

Research Article Article ID: igmin172
Cite

Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.

Improved Energy Valley Optimizer with Levy Flight for Optimization Problems
by Nabila H Shikoun and Islam S Fathi

Energy Valley Optimizer (EVO) is one of the recent metaheuristic algorithms. It draws inspiration from advanced principles in physics related to particle stability and decay modes. This paper presents a new Energy Valley Optimizer (EVO) and levy flights that are hybrid to improve the EVO in solving optimization problems. Levy flight is one of the most important randomization techniques. Fifteen mathematical test functions (five unimodal functions, four multimodal functions, and six composite functions) are solved with the proposed algorithm. We... also compare our results with previous results of metaheuristic algorithms. The statistical results show that the results of the Levy Energy Valley Optimizer (LEVO) outperform other algorithms in almost all mathematical test functions.

Data Science Machine LearningData Mining
Research Article Article ID: igmin112
Cite

Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.

Federated Learning- Hope and Scope
by Lhamu Sherpa and Nandan Banerji

People are suffering from” data obesity” as a result of the expansion and quick development of various Artificial Intelligence (AI) technologies and machine learning fields. The management of the current techniques is becoming more challenging due to the data created in the Smart-Health and Fintech service sectors. To provide stable and reliable methods for processing the data, several Machine Learning (ML) techniques were applied. Due to privacy-related issues with the aforementioned two providers, ML cannot fully use the data, whi...ch becomes difficult since it might not give the results that were expected. When the misuse and exploitation of personal data were gaining attention on a global scale and traditional machine learning (CML) was facing difficulties, Google introduced the concept of Federated Learning (FL). In order to enable the cooperative training of machine learning models among several organizations under privacy requirements, federated learning has been a popular research area. The expectation and potential of federated learning in terms of smart-health and fintech services are the main topics of this research.

Data Science Machine LearningData Security