arXiv subjects

Md Al Amin

Publications and source records attributed to Md Al Amin.

9 recordsLinked to original sources

arXiv2026

Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning

Chronic Kidney Disease (CKD), characterized by the gradual loss of kidney function, remains a significant public health challenge. Early detection is crucial for preventing severe complications and enhancing patient outcomes. In this study, Federated Learning (FL) with a VotingClassifier was used to predict CKD using a clinical dataset, where Random Forest, AdaBoost, and XGBoost were utilized to compare and identify the best-fitting model for the global server. Additionally, GridSearchCV was applied to optimize the models' performance on the client's side. To enhance model transparency and trustworthiness, explainable AI (XAI) techniques were incorporated to interpret the prediction mechanisms. The global model's average accuracy was 99%, highlighting the potential of interpretable FL models in supporting early CKD diagnosis and advancing data-driven healthcare solutions.

cs.LG↗

arXiv2026

Spatial Prediction of Soil Microplastics and Organic Matter Using Graph Attention Networks

Accurate estimation of soil microplastics and organic matter is essential to assess ecosystem health and support sustainable land use. This study presents a graph-based deep learning approach using Graph Attention Networks (GATs) to model spatial dependencies among 91 georeferenced soil samples. By incorporating spatial coordinates, soil properties, and land use data, a two-layer GAT architecture was developed to capture local interactions. The final model showed strong performance, achieving RMSEs of 625.06 ($R^2 = 0.87$) for microplastics and 0.43 ($R^2 = 0.91$) for organic matter. However, cross-validation results revealed limited generalization, probably due to the small sample size and sparse graph structure. These findings demonstrate the potential of GATs for spatial soil prediction and underscore the need for dense datasets and improved graph connectivity.

cs.LG↗

arXiv2025

Colon Polyps Detection from Colonoscopy Images Using Deep Learning

Colon polyps are precursors to colorectal cancer, a leading cause of cancer-related mortality worldwide. Early detection is critical for improving patient outcomes. This study investigates the application of deep learning-based object detection for early polyp identification using colonoscopy images. We utilize the Kvasir-SEG dataset, applying extensive data augmentation and splitting the data into training (80\%), validation (20\% of training), and testing (20\%) sets. Three variants of the YOLOv5 architecture (YOLOv5s, YOLOv5m, YOLOv5l) are evaluated. Experimental results show that YOLOv5l outperforms the other variants, achieving a mean average precision (mAP) of 85.1\%, with the highest average Intersection over Union (IoU) of 0.86. These findings demonstrate that YOLOv5l provides superior detection performance for colon polyp localization, offering a promising tool for enhancing colorectal cancer screening accuracy.

eess.IV↗

arXiv2025

MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model

Accurate demand forecasting is crucial for optimizing supply chain management. Traditional methods often fail to capture complex patterns from seasonal variability and special events. Despite advancements in deep learning, interpretable forecasting models remain a challenge. To address this, we introduce the Multi-Channel Data Fusion Network (MCDFN), a hybrid architecture that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU) to enhance predictive performance by extracting spatial and temporal features from time series data. Our comparative benchmarking demonstrates that MCDFN outperforms seven other deep-learning models, achieving superior metrics: MSE (23.5738), RMSE (4.8553), MAE (3.9991), and MAPE (20.1575%). Theil's U statistic of 0.1181 (U<1) of MCDFN indicates its superiority over the naive forecasting approach, and a 10-fold cross-validated statistical paired t-test with a p-value of 5% indicated no significant difference between MCDFN's predictions and actual values. We apply explainable AI techniques like ShapTime and Permutation Feature Importance to enhance interpretability. This research advances demand forecasting methodologies and offers practical guidelines for integrating MCDFN into supply chain systems, highlighting future research directions for scalability and user-friendly deployment.

cs.LG↗

arXiv2024

Empowering Patients for Disease Diagnosis and Clinical Treatment: A Smart Contract-Enabled Informed Consent Strategy

Digital healthcare systems have revolutionized medical services, facilitating provider collaboration, enhancing diagnosis, and optimizing and improving treatments. They deliver superior quality, faster, reliable, and cost-effective services. Researchers are addressing pressing health challenges by integrating information technology, computing resources, and digital health records. However, digitizing healthcare introduces significant risks to patient data privacy and security, with the potential for unauthorized access to protected health information. Although patients can authorize data access through consent, there is a pressing need for mechanisms to ensure such given consent is informed and executed properly and timely. Patients deserve transparency and accountability regarding the access to their data: who access it, when, and under what circumstances. Current healthcare systems, often centralized, leave much to be desired in managing these concerns, leading to numerous security incidents. To address these issues, we propose a system based on blockchain and smart contracts for managing informed consent for accessing health records by the treatment team members, incorporating safeguards to verify that consent processes are correctly executed. Blockchain's inherent immutability ensures the integrity of consent. Smart contracts automatically execute agreements, enhancing accountability. They provide a robust framework for protecting patient privacy in the digital age. Experimental evaluations show that the proposed approach can be integrated easily with the existing healthcare systems without incurring financial and technological challenges.

cs.CR↗

arXiv2024

Utilizing Blockchain and Smart Contracts for Enhanced Fraud Prevention and Minimization in Health Insurance through Multi-Signature Claim Processing

Healthcare insurance provides financial support to access medical services for patients while ensuring timely and guaranteed payment for providers. Insurance fraud poses a significant challenge to insurance companies and policyholders, leading to increased costs and compromised healthcare treatment and service delivery. Most frauds, like phantom billing, upcoding, and unbundling, happen due to the lack of required entity participation. Also, claim activities are not transparent and accountable. Fraud can be prevented and minimized by involving every entity and making actions transparent and accountable. This paper proposes a blockchain-powered smart contract-based insurance claim processing mechanism to prevent and minimize fraud in response to this prevailing issue. All entities patients, providers, and insurance companies actively participate in the claim submission, approval, and acknowledgment process through a multi-signature technique. Also, every activity is captured and recorded in the blockchain using smart contracts to make every action transparent and accountable so that no entity can deny its actions and responsibilities. Blockchains' immutable storage property and strong integrity guarantee that recorded activities are not modified. As healthcare systems and insurance companies continue to deal with fraud challenges, this proposed approach holds the potential to significantly reduce fraudulent activities, ultimately benefiting both insurers and policyholders.

cs.CR↗

arXiv2024

Balancing Patient Privacy and Health Data Security: The Role of Compliance in Protected Health Information (PHI) Sharing

Protected Health Information (PHI) sharing significantly enhances patient care quality and coordination, contributing to more accurate diagnoses, efficient treatment plans, and a comprehensive understanding of patient history. Compliance with strict privacy and security policies, such as those required by laws like HIPAA, is critical to protect PHI. Blockchain technology, which offers a decentralized and tamper-evident ledger system, hold promise in policy compliance. This system ensures the authenticity and integrity of PHI while facilitating patient consent management. In this work, we propose a blockchain technology that integrates smart contracts to partially automate consent-related processes and ensuring that PHI access and sharing follow patient preferences and legal requirements.

cs.CR↗

arXiv2023

Healthcare Policy Compliance: A Blockchain Smart Contract-Based Approach

This paper addresses the critical challenge of ensuring healthcare policy compliance in the context of Electronic Health Records (EHRs). Despite stringent regulations like HIPAA, significant gaps in policy compliance often remain undetected until a data breach occurs. To bridge this gap, we propose a novel blockchain-powered, smart contract-based access control model. This model is specifically designed to enforce patient-provider agreements (PPAs) and other relevant policies, thereby ensuring both policy compliance and provenance. Our approach integrates components of informed consent into PPAs, employing blockchain smart contracts to automate and secure policy enforcement. The authorization module utilizes these contracts to make informed access decisions, recording all actions in a transparent, immutable blockchain ledger. This system not only ensures that policies are rigorously applied but also maintains a verifiable record of all actions taken, thus facilitating an easy audit and proving compliance. We implement this model in a private Ethereum blockchain setup, focusing on maintaining the integrity and lineage of policies and ensuring that audit trails are accurately and securely recorded. The Proof of Compliance (PoC) consensus mechanism enables decentralized, independent auditor nodes to verify compliance status based on the audit trails recorded. Experimental evaluation demonstrates the effectiveness of the proposed model in a simulated healthcare environment. The results show that our approach not only strengthens policy compliance and provenance but also enhances the transparency and accountability of the entire process. In summary, this paper presents a comprehensive, blockchain-based solution to a longstanding problem in healthcare data management, offering a robust framework for ensuring policy compliance and provenance through smart contracts and blockchain technology.

cs.CR↗

arXiv2021

Sentiment Analysis of Microblogging dataset on Coronavirus Pandemic

Sentiment analysis can largely influence the people to get the update of the current situation. Coronavirus (COVID-19) is a contagious illness caused by the coronavirus 2 that causes severe respiratory symptoms. The lives of millions have continued to be affected by this pandemic, several countries have resorted to a full lockdown. During this lockdown, people have taken social networks to express their emotions to find a way to calm themselves down. People are spreading their sentiments through microblogging websites as one of the most preventive steps of this disease is the socialization to gain people's awareness to stay home and keep their distance when they are outside home. Twitter is a popular online social media platform for exchanging ideas. People can post their different sentiments, which can be used to aware people. But, some people want to spread fake news to frighten the people. So, it is necessary to identify the positive, negative, and neutral thoughts so that the positive opinions can be delivered to the mass people for spreading awareness to the people. Moreover, a huge volume of data is floating on Twitter. So, it is also important to identify the context of the dataset. In this paper, we have analyzed the Twitter dataset for evaluating the sentiment using several machine learning algorithms. Later, we have found out the context learning of the dataset based on the sentiments.

cs.SI↗