Shahadat Uddin
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View article: AI-Driven Cyber Threat Intelligence Systems: A National Framework for Proactive Defense Against Evolving Digital Warfare
AI-Driven Cyber Threat Intelligence Systems: A National Framework for Proactive Defense Against Evolving Digital Warfare Open
The current study suggests the national framework based on the concept of AI-enabled cyber threat intelligence systems. The increased risk of digital warfare and the shortcomings of the traditional patterns of cybersecurity. The use of art…
View article: Toward fair medical advice: Addressing and mitigating bias in large language model-based healthcare applications
Toward fair medical advice: Addressing and mitigating bias in large language model-based healthcare applications Open
Large Language Models (LLMs) are increasingly deployed in web-based medical advice applications, offering scalable and accessible healthcare solutions. However, their outputs often reflect demographic biases, raising concerns about fairnes…
View article: Creating a Knowledge Hub: AI-Powered Learning Management Systems for BA-QA Training
Creating a Knowledge Hub: AI-Powered Learning Management Systems for BA-QA Training Open
Brief Summary of the Research: This investigation studies AI-driven Learning Management Systems which function as educational centers to support employee training at Business Analyst and Quality Assurance levels on projects based in the Un…
View article: Fairness-Preserving Implementation of Machine Learning Models
Fairness-Preserving Implementation of Machine Learning Models Open
Fairness in machine learning systems is essential for building trustworthy, ethical, and socially responsible AI, particularly in high-stakes domains such as healthcare and human services. This study proposes a comprehensive fairness-prese…
View article: A novel approach for assessing fairness in deployed machine learning algorithms
A novel approach for assessing fairness in deployed machine learning algorithms Open
Fairness in machine learning (ML) emerges as a critical concern as AI systems increasingly influence diverse aspects of society, from healthcare decisions to legal judgments. Many studies show evidence of unfair ML outcomes. However, the c…
View article: A parameterised model for link prediction using node centrality and similarity measure based on graph embedding
A parameterised model for link prediction using node centrality and similarity measure based on graph embedding Open
Link prediction is a crucial aspect of graph machine learning, with applications as diverse as disease prediction, social network recommendations, and drug discovery. It involves the prediction of potential new links between nodes within a…
View article: Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data
Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data Open
Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by e…
View article: A comparative evaluation of machine learning ensemble approaches for disease prediction using multiple datasets
A comparative evaluation of machine learning ensemble approaches for disease prediction using multiple datasets Open
Purpose Machine learning models are used to develop and improve various disease prediction systems. Ensemble learning is a machine learning technique that combines many classifiers to increase performance by making more accurate prediction…
View article: An NLP-based novel approach for assessing national influence in clause dissemination across bilateral investment treaties
An NLP-based novel approach for assessing national influence in clause dissemination across bilateral investment treaties Open
International investment agreements (IIAs) promote foreign investment. However, they can undermine crucial health programs, creating a dilemma for governments between corporate and public health interests. For this reason, including clause…
View article: Machine learning and deep learning in project analytics: methods, applications and research trends
Machine learning and deep learning in project analytics: methods, applications and research trends Open
Project analytics refers to applying analytical techniques and methods to past and present data to gain insights into how the underlying project is performing. Machine learning (ML) and Deep learning (DL) have acquired extensive usage in v…
View article: Fusion of Graph and Natural Language Processing in Predictive Analytics for Adverse Drug Reactions
Fusion of Graph and Natural Language Processing in Predictive Analytics for Adverse Drug Reactions Open
Adverse Drug Reactions (ADRs) pose a critical challenge to patient safety and healthcare economics worldwide. This study presents a novel graph-assisted machine learning algorithm applied to a comprehensive dataset provided by the Commonwe…
View article: Unsupervised machine learning for disease prediction: a comparative performance analysis using multiple datasets
Unsupervised machine learning for disease prediction: a comparative performance analysis using multiple datasets Open
Purpose Disease risk prediction poses a significant and growing challenge in the medical field. While researchers have increasingly utilised machine learning (ML) algorithms to tackle this issue, supervised ML methods remain dominant. Howe…
View article: Impact of COVID-19 on Journal Impact Factor
Impact of COVID-19 on Journal Impact Factor Open
Research related to COVID-19 has grown significantly in recent years and dominated health-related publications. Data-driven explorations, such as analysing the quality of COVID-19 research across journals, how the journals prioritised emer…
View article: A parameterised model for link prediction using node centrality and similarity measure based on graph embedding
A parameterised model for link prediction using node centrality and similarity measure based on graph embedding Open
Link prediction is a key aspect of graph machine learning, with applications as diverse as disease prediction, social network recommendations, and drug discovery. It involves predicting new links that may form between network nodes. Despit…
View article: Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model
Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model Open
Hospital readmission and length-of-stay predictions provide information on how to manage hospital bed capacity and the number of required staff, especially during pandemics. We present a hybrid deep model called the Genetic Algorithm-Optim…
View article: Hospital Readmission and Length of Stay Prediction Using an Optimized Hybrid Deep Model
Hospital Readmission and Length of Stay Prediction Using an Optimized Hybrid Deep Model Open
Hospital readmission and length of stay prediction provide info to manage hospitals’ bed capacity and the number of required staff, especially during pandemics. We present a hybrid deep model called Genetic Algorithm-Optimized Convolutiona…
View article: Ensemble Learning for Disease Prediction: A Review
Ensemble Learning for Disease Prediction: A Review Open
Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions t…
View article: Integrating machine learning and network analytics to model project cost, time and quality performance
Integrating machine learning and network analytics to model project cost, time and quality performance Open
This study aims to connect project management, network science and machine learning in an accessible overview applied to a real original dataset. Based on an initial literature review of applicable project performance measures and attribut…
View article: Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends
Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends Open
Graph machine-learning (ML) methods have recently attracted great attention and have made significant progress in graph applications. To date, most graph ML approaches have been evaluated on social networks, but they have not been comprehe…
View article: HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN Open
In this paper have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolu…
View article: Evolutionary Features for Dynamic Link Prediction in Social Networks
Evolutionary Features for Dynamic Link Prediction in Social Networks Open
One of the inherent characteristics of dynamic networks is the evolutionary nature of their constituents (i.e., actors and links). As a time-evolving model, the link prediction mechanism in dynamic networks can successfully capture the und…
View article: Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions
Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions Open
Recent years have witnessed booming data on drugs and their associated adverse drug reactions (ADRs). It was reported that these ADRs have resulted in a high hospitalisation rate worldwide. Therefore, a tremendous amount of research has be…