Journal of Data Science and Intelligent Systems

We are thrilled to announce that the Journal of Data Science and Intelligent Systems (JDSIS) has reached 700 citations! 📈🎉 This achievement is a testament to the hard work of our editorial team and the groundbreaking research of our incredible authors. Your dedication to advancing data science and intelligent systems continues to drive the success of our journal. 💡🔍 A huge thank you to our contributors, reviewers, and readers for your support—this milestone is just the beginning! Here’s to pushing the boundaries of innovation together. 🚀📖 #DataScience #IntelligentSystems #AcademicResearch #JDSIS #Milestone

🚀 Bon View Publishing Joins DORA (San Francisco Declaration on Research Assessment)! 📚 We are excited to announce that Bon View Publishing has officially signed the San Francisco Declaration on Research Assessment (DORA) on February 11th, making a significant step towards advancing research evaluation. This commitment marks a shift away from traditional reliance on quantitative metrics and emphasizes the importance of holistic evaluation frameworks. By joining DORA, Bon View Publishing is committed to fostering a more scientific, comprehensive, and equitable research evaluation ecosystem. Let’s transform the future of research! #DORA #ResearchAssessment #AcademicPublishing #OpenAccess #ResearchIntegrity #Innovation

🚀 I'm excited to announce the publication of our latest research article, "Digital Twin-Assisted Deep Learning with Model Fusion for Detecting Multiple Sclerosis in MRI Modalities," in the Journal of Data Science and Intelligent Systems. This work was coauthored by Ramya Palaniappan and Siva Rathinavelayutham from SRM Institute of Science and Technology (SRMIST) of Science and Technology, India. Abstract: The advancement of technologies such as IoT, Big Data, Data Science, Augmented Reality/Virtual Reality, and cloud computing has transformed the manufacturing sector through the Digital Twin (DT), serving as a potent instrument for simulating concepts into practice.Recently, artificial intelligence has been merged with digital technology to conduct research in the healthcare sector, facilitating judgments onthe planning of patients’ clinical courses and the allocation of available medical resources. DTs can be utilized in the medical industry to facilitate clinical decision-making, providing prognoses and personalized treatment for patients. Multiple sclerosis (MS) is an autoimmune neurological disorder that impacts the central nervous system, potentially resulting in neurological impairment and mortality if left untreated. [X1]. In this research study, magnetic resonance imaging (MRI) samples collected from the E-Health lab and IXI databases are used to construct the DT empowered with artificial intelligence for the diagnosis of MS in a robust manner. We propose a hybrid CNN-RNN model for detecting MS in two phases. In the first phase, the deep features of the MRI modalities are extracted by two transfer learning models, m-InceptionV3 and m-DenseNet121. In the second phase, the classification of extracted features to either healthy or MS is performed by the Long Short-Term Memory RNN model. Deep learning metrics like precision, recall, F1 score, and accuracy are used to validate the performance of the proposed model. The proposed model outperformed the other state-of-the-art models achieving a good performance of 99.67% in validation accuracy. This healthcare DT pipeline may assist in clinical decisionmaking for MS detection and planning post-MS rehabilitation. Read the full article here: https://lnkd.in/gcJTzw63 #DigitalTwin #AIinHealthcare #MultipleSclerosis #MedicalImaging #MachineLearning #HealthTech #precisiontreatment  

Excited to highlight new research published in JDSIS! This innovative study, “Enhancing Cardiac Health Diagnoses Through Machine Learning Analysis of Phonocardiograms (PCG),” explores how combining machine learning models like XGBoost and Random Forest with STFT and MFCC features can improve cardiac diagnostics. A huge thank you to Dr. Popal Khan Popalzai, Khurram Shehzad Khattak (University of Engineering and Technology, Lahore), Anwar Mehmood Sohail (University of Malaya), and zawar H. khan, Ph.D. P. Eng. (University of Victoria), for their valuable contribution to the field! Learn more about their findings: https://lnkd.in/gRZEQaaQ #MachineLearning #CardiacHealth #Diagnostics #PCG #SignalAnalysis

Welcome, Dr. Feng Shu! 👏

👏 We're delighted to welcome Dr. Feng Shu from Hainan University to the Editorial Board of the Journal of Data Science and Intelligent Systems! With his expertise, we look forward to his valuable contributions to advancing data science and AI research. #DataScience #ArtificialIntelligence #AIResearch #AcademicPublishing #EditorialBoard #MachineLearning #BigData #JDSIS

Thrilled to share our latest #JDSIS publication on improving predictive models through combined data analysis and machine learning! Huge thanks to authors Nikolaos Papafilippou (Aristotle University of Thessaloniki (AUTH)), Zacharenia Kyrana (Aristotle University of Thessaloniki (AUTH)), Emmanouil Pratsinakis (Aristotle University of Thessaloniki (AUTH)), Efstratios Kiranas (International Hellenic University), Alexandra-Maria Michailidou (Aristotle University of Thessaloniki (AUTH)), Angelos Markos (Democritus University of Thrace - (D.U.Th.)), and George Menexes (Aristotle University of Thessaloniki (AUTH)) for their valuable contribution! Their work shows how leveraging BMI as a qualitative variable can enhance prediction reliability. Check out the full article here: https://lnkd.in/eHiwCmEX #DataAnalysis #MachineLearning #PredictiveModeling #Research #BMI #MultivariateData

https://lnkd.in/gn9AHZgC The World of Artificial Intelligence: A Hierarchy of Intelligence

https://lnkd.in/ggJqYfpE The road to Data Science!

It is a pleasure to have a publication on Large Language Model (LLM). Our work proposed a framework to combine those two big concepts, Federated Learning and LLM. We did our experiments for client selection in Federated Learning. You may access the paper in the link below. https://lnkd.in/g9CrbpN3 Please feel free to drop any comments about our work!