Journal of Data Science and Intelligent Systems

🚀 Exciting update from the Journal of Data Science and Intelligent Systems! A new article, "GDPR Compliance of Hospital Management Systems in the UAE" by Inas Al Khatib, Norhan Ahmed, and Malick Ndiaye Ndyiaye, all from American University of Sharjah, has just been published.   Check it out here: https://lnkd.in/gfBU_2J9 Abstract: While the UAE is making strides in healthcare digitalization and adopting global best practices, the absence of a unified data protection framework equivalent to the GDPR poses significant challenges for hospital management systems (HMS) in the region. This gap creates uncertainties in compliance, especially regarding cross-border data transfers, third-party vendor management, and the protection of patients' privacy rights. The lack of clear regulations tailored to the UAE’s unique healthcare landscape hinders the implementation of robust data protection measures, raising concerns about potential data breaches, legal liabilities, and the overall trustworthiness of healthcare institutions. Addressing these challenges is crucial for aligning the UAE’s healthcare sector with international standards while ensuring the security and privacy of patient data in a rapidly evolving digital environment. The General Data Protection Regulation (GDPR) has significantly impacted hospital management systems (HMS) by setting strict data protection requirements. This study provides a systematic literature review of GDPR compliance in HMS, focusing on key challenges such as regulatory complexity, permission management, data subject rights, data breaches, third-party vendor management, and cross-border data transfers. Suggested mitigation measures include privacy by design, data protection impact assessments, improved consent management, robust breach detection, and efficient vendor management. Legislative reforms are needed to clarify GDPR’ s application to healthcare. The study also highlights increased investments in privacy technologies, improved patient trust, and the demand for advanced solutions. Future research should explore the effectiveness of these mitigations, GDPR’s impact on patient satisfaction, ethical data processing, and standardized data protection frameworks in healthcare. Achieving GDPR compliance is crucial for protecting patient data, building trust, and ensuring secure and ethical use of healthcare information. This study aims to guide healthcare organizations, particularly hospitals, along with regulators and researchers, in navigating these challenges and implementing effective solutions. #HealthcareInnovation #DataProtection #hospitalmanagement

🚀 We’re thrilled to share a new online-first article exploring the intersection of machine learning, reproducibility, and energy efficiency. Title: Random Numbers for Machine Learning: A Comparative Study of Reproducibility and Energy Consumption Contributors: Benjamin Antunes：Polytechnic Institute of Clermont-Auvergne, Université Clermont Auvergne, France David R. C. Hill：Polytechnic Institute of Clermont-Auvergne, Université Clermont Auvergne, France Abstract: Pseudo-Random Number Generators (PRNGs) have become ubiquitous in machine learning (ML) technologies because they are interesting for numerous methods. In the context of ML, multiple stochastic streams, produced in black boxes for methods such as stochastic gradient descent or dropout, can produce a lack of repeatability, impacting the ability to debug and explain results. The field of machine learning holds the potential for substantial advancements across various domains. However, despite the growing interest, persistent concerns include issues related to reproducibility and energy consumption. Reproducibility is crucial for robust scientific inquiry and explainability, while energy efficiency underscores the imperative to conserve finite global resources. This study delves into the investigation of whether the leading Pseudo-Random Number Generators (PRNGs) employed in machine learning languages, libraries, and frameworks uphold statistical quality and numerical reproducibility when compared to the original C implementation of the respective PRNG algorithms. Additionally, we aim to evaluate the time efficiency and energy consumption of various implementations. Our experiments encompass Python, NumPy, TensorFlow, and PyTorch, utilizing the Mersenne Twister, Permuted Congruential Generator (PCG), and Philox algorithms. Remarkably, we verified that the temporal performance of machine learning technologies closely aligns with that of C-based implementations, with instances of achieving even superior performances. On the other hand, it is noteworthy that ML technologies consumed only 10% more energy than their C-implementation counterparts. However, while statistical quality was found to be comparable, achieving numerical reproducibility across different platforms for identical seeds and algorithms was not achieved. Read the full article here: https://lnkd.in/geV-XyeR

New Research: Tackling Phase Errors in NMR with Innovative Modeling Approaches published! Nuclear Magnetic Resonance (NMR) is a powerful tool in various fields, but phase errors can significantly impact data accuracy. A new paper tackles this challenge head-on with novel modeling approaches and optimization functions. The paper, titled “NMR Phase Error Correction with New Modelling Approaches,” explores two innovative methods: Nonlinear Intensity Shrinkage: This method leverages the inherent phase-free information in NMR spectra to derive accurate absorption spectra without the need for complex optimization processes. Multiple Linear Phase Models: The researchers introduce a strategy of applying multiple linear models to different signal ranges within the spectrum, offering a more robust approach to phase error correction. The authors also propose a new optimization function, Delta Absolute Net Minimization (DANM), which improves the accuracy of phase correction by simultaneously considering positive and negative areas under the curve. This research offers a valuable contribution to the field of NMR by addressing a critical challenge in data analysis. We applaud Aixiang Jiang, Andrée E. Gravel, Ethan Tse, Sanjoy Kumar Das, James Hanley and Robert Nadon and McGill University, The University of British Columbia, British Columbia Cancer Research Centre for Lymphoid Cancer, Research Institute McGill University Health Centre for their innovative work! Learn more about this groundbreaking research: https://lnkd.in/gKB8FgKZ #NMR #phasecorrection #datascience #machinelearning #research #innovation

New Research: Improving Fall Detection for the Elderly with Machine Learning and Feature Engineering Falls are a serious concern for elderly individuals, especially those living alone. A new research paper by Dr. Boris Assanovich at Yanka Kupala State University of Grodno and Dr. Katsiaryna Kosarava at Uniwersytet Kardynała Stefana Wyszyńskiego w Warszawie presents an innovative vision-based fall detection system that leverages machine learning and feature engineering to improve accuracy and efficiency. The paper, titled “Vision-Based Fall Detector for Elderly Based on Sliding Window Approach and Feature Engineering,” explores a novel approach that utilizes the tsfresh tool to generate features from object motion parameters, and applies a sliding window approach to perform classification. This method achieves a remarkable 96% accuracy in detecting falls, outperforming traditional methods. The authors demonstrate that the proposed system offers several key advantages: Improved Accuracy: The model achieves high accuracy in detecting falls, with a minimal false-positive rate. Faster Processing: The system utilizes a sliding window approach and efficient feature engineering, resulting in a significantly faster detection time. Automated Model Selection: The researchers leverage the auto-sklearn library to automatically identify and optimize the best machine learning model for fall detection. This research represents a significant step towards developing more reliable and efficient fall detection systems, which can help ensure the safety and well-being of vulnerable individuals. A big thank you to Dr. Boris Assanovich at Yanka Kupala State University of Grodno and Dr. Katsiaryna Kosarava at Uniwersytet Kardynała Stefana Wyszyńskiego w Warszawie for their valuable contributions to this critical field! Learn more about this innovative research: https://lnkd.in/gFeNhFAr #falldetection #elderlycare #machinelearning #featureengineering #healthtech #research #innovation

Exciting update from the Journal of Data Science and Intelligent Systems! A new article, "Financial Inclusion and Climate Resilience: The Role for an AI-Enhanced Digital Wallet in Caribbean SIDS," by Dr. Don Charles, an independent research consultant, from Republic of Trinidad and Tobago, has just been published. The article is also available at https://lnkd.in/gi97jMyP Abstract: Caribbean Small Island Developing States (SIDS) are highly vulnerable to extreme weather events and climate change. Caribbean SIDS climate vulnerability is worsened by their high level of financial exclusion. Many people do not have bank accounts and access to electronic fund transfer (EFT). As such, they cannot electronically receive funds before or after a natural disaster to cope with the effects. The financial exclusion problem can be addressed through a digital wallet. A digital wallet is a financial transaction application that securely stores a user’s banking and payment information on a cloud interface and allows the user to perform a transaction while hiding their banking information from a vendor. The biggest concern of users with regards to the use of digital wallets are its convenience and security. While digital wallets offer outstanding convenience of purchasing goods and services, data privacy and fraud risks deter people from adopting mobile payment. Potential fraud risk to digital wallets can be identified with anomaly detection techniques. The research problem investigated in this study is how the implementation of an artificial intelligence (AI) enhanced digital wallet can facilitate financial inclusion in the Caribbean, particularly in the context of disaster preparedness and recovery. Since security is an important aspect of a digital wallet, a sub-objective of this study is to derive an appropriate model for anomaly detection in financial transactions in a digital wallet. This study modifies Liu et al. [1] Gated Transformer Networks (GTN) architecture to allow for univariate time series classification. The corresponding new model is referred to as the Univariate Gated Transformer Network (UGTN). The UGTN is used for anomaly detection in financial data from a digital wallet. This study also provides policy recommendations for the implementation of a digital wallet to facilitate financial inclusion and climate resilience in Caribbean SIDS. #ClimateResilience #FinancialInclusion #DigitalWallet #DataScience #CaribbeanIslands

A new article titled "Enhancing Data Analytics and Visualization Support in CSViewer for Analysts - Version 1.1: Access to an Integrative Database and Knowledge Model of Cayo Santiago Rhesus Macaques" was published in JDSIS Online First! The article was authored by Martin Q. Zhao, Cooper Novak, Ethan R. Widener, Raj A. Patel, Rui Gong from Mercer University, and George Francis, Qian Wang from Texas A&M University College of Dentistry. The article is also available at: https://lnkd.in/g8RQnDPD Abstract: The CSViewer for Analysts app is designated to provide a myriad of graphical user interfaces and analytical tools for researchers to study the valuable datasets collated from the Cayo Santiago Rhesus macaque colony, which has been raised on the "monkey island" off Puerto Rico since 1938. It is a part of the effort to integrate decades of accumulated genealogy and demographic information with newly collected osteology data using the CS derived skeletal sets. When Version 1.0 was first shared among the collaborating teams in early 2023, CSViewer enabled its users to browse the CS population in the forms of matrilineal and patrilineal trees, as well as to visualize skull dimension data and photos when they are available. After choosing a Java-based dataframe library, a planned redesign has been implemented into Version 1.1, which supports data selection for animal subjects, bone measures to include, as well as facilitates more streamlined data management within the system. Various data analytics and visualization features have been added in v1.1, and a workshop was held during AABA 2024 to introduce CSViewer to interested users. Project-related materials have also been used to provide teaching materials in related undergraduate classes at the Computer Science Department of Mercer University to engage students in this NSF-funded project. #datamanagement #biomedicalinformatics #problembasedlearning #datascience #journal

We are delighted to announce that the Journal of Data Science and Intelligent Systems (JDSIS) is now indexed by EBSCO Information Services! 🥳 You can now access JDSIS on EBSCO’s platform directly from our website: https://lnkd.in/gZZ2uWYb. You can also search for “Journal of Data Science and Intelligent Systems” on EBSCO to find our published articles! #JDSIS #EBSCO #DataScience #AI #MachineLearning #Research #Publication #Indexing

📢 Exciting news! We’re thrilled to share that the Journal of Data Science and Intelligent Systems (JDSIS) has officially surpassed 500 citations! 🚀 This milestone reflects the hard work and dedication of our editorial team, as well as the invaluable contributions of our authors. Your commitment to excellence and innovation is what propels our journal forward. Here’s to many more achievements in the future! 🥂✨ #Milestone #JDSIS #AcademicPublishing #DataScience #BonViewPress #Citations

Exciting update from the Journal of Data Science and Intelligent Systems! A new article, "Automated Defect Detection Using Image Recognition in Manufacturing," by Sercan Dikici and Dr Rachel J. from IU International University of Applied Sciences in Germany, has just been published. This study leverages convolutional neural networks (CNNs) to automate defect detection in manufacturing, offering a powerful solution for quality assurance by identifying defects with high accuracy, minimizing manual inspection requirements, and reducing production costs. By analyzing grayscale images of impeller castings, the CNN-based model achieves an impressive accuracy rate, underscoring the potential of machine learning to transform quality control processes. Explore how this research could redefine manufacturing efficiency and accuracy by providing automated, real-time defect detection solutions for industry applications! https://lnkd.in/gcY3V7Jv #convolutionalneuralnetworks, #imagerecognition, #EarlyStoppingstrategy, #defectidentification

Delighted to share that the Journal of Data Science and Intelligent Systems has just published online a new article titled "Enhancing Smartphone-based Pedestrian Positioning: Using Factor Graph Optimization with Indoor/Outdoor Detection for 3DMA GNSS/Visual-Inertial State Estimation." This study, authored by Hiu-Yi Ho, Hoi-Fung Ng, Weisong Wen, and Li-Ta Hsu from The Hong Kong Polytechnic University, and Yanlei Gu from Hiroshima University, introduces an innovative approach to pedestrian positioning. Leveraging Factor Graph Optimization, the study tackles challenges in GNSS-denied environments (like indoors) by integrating advanced machine learning and visual-inertial odometry techniques for superior accuracy and reliability. Their work provides a robust framework that has undergone extensive testing, showing remarkable improvements in positioning accuracy for urban environments. Check out the full article to explore how this breakthrough could redefine smartphone-based navigation in dense cityscapes! https://lnkd.in/gEJvwZuR #FGO, #pedestrianpositioning, #smartphone, #sensorintegration, #IO, #VINS, #3DMAGNSS