Ktrend - International Journal of Data Science and Machine Learning (IJDSML)

The Ktrend – International Journal of Data Science and Machine Learning (IJDSML) aims to advance knowledge and innovation in data science, machine learning, artificial intelligence, and intelligent computing through the publication of high-quality original research articles, review papers, technical reports, and case studies. The journal serves as an international forum for the dissemination of cutting-edge methodologies, computational models, analytical techniques, and real-world applications that contribute to scientific discovery, technological advancement, and data-driven decision-making.

The scope of the journal includes, but is not limited to, Data Science, Machine Learning, Artificial Intelligence, Deep Learning, Neural Networks, Natural Language Processing, Computer Vision, Data Analytics, Predictive Analytics, Big Data Analytics, Data Mining, Business Intelligence, Statistical Learning, Computational Intelligence, Reinforcement Learning, Explainable Artificial Intelligence, Knowledge Discovery, Pattern Recognition, Intelligent Systems, Information Retrieval, Recommender Systems, Data Visualization, Time Series Analysis, Cloud Computing, Edge Computing, Internet of Things Analytics, Bioinformatics, Computational Biology, Financial Analytics, Healthcare Analytics, Cybersecurity Analytics, Robotics, Intelligent Automation, Quantum Machine Learning, and other emerging areas of data science and machine learning research.

The journal welcomes theoretical, experimental, computational, industrial, and application-oriented studies that contribute to the advancement of intelligent technologies, data-driven innovation, digital transformation, and the responsible development of artificial intelligence systems.

To publish high-quality and original research in data science and machine learning, to advance knowledge in artificial intelligence and intelligent computing, to provide an international platform for researchers, scientists, and industry professionals, to promote innovative data-driven solutions to complex real-world problems, to encourage interdisciplinary research in computational sciences and analytics, to support the development of advanced algorithms and predictive models, to foster innovation in big data technologies and intelligent systems, to facilitate the dissemination of scientific discoveries and technological advancements, to maintain high standards of academic integrity and peer review, to promote open access to scientific and technological knowledge, to encourage collaboration among researchers worldwide, and to contribute to the advancement of digital transformation and intelligent technologies.

Data Science, Machine Learning, Artificial Intelligence, Deep Learning, Neural Networks, Natural Language Processing, Computer Vision, Data Analytics, Predictive Analytics, Big Data, Data Mining, Business Intelligence, Statistical Learning, Computational Intelligence, Reinforcement Learning, Supervised Learning, Unsupervised Learning, Generative Artificial Intelligence, Explainable AI, AI Ethics, Knowledge Discovery, Pattern Recognition, Intelligent Systems, Decision Support Systems, Information Retrieval, Recommender Systems, Time Series Analysis, Data Visualization, Cloud Computing, Edge Computing, Internet of Things Analytics, Bioinformatics, Computational Biology, Financial Analytics, Healthcare Analytics, Cybersecurity Analytics, Computational Statistics, Operations Research Analytics, Smart Systems, Robotics and Intelligent Automation, Quantum Machine Learning, and Interdisciplinary Data Science Applications.