arXiv subjects

Md. Mostafizur Rahman

Publications and source records attributed to Md. Mostafizur Rahman.

6 recordsLinked to original sources

arXiv2026

Protected QR Code-based Anti-counterfeit System for Pharmaceutical Manufacturing

The pharmaceutical manufacturing faces critical challenges due to the global threat of counterfeit drugs. This paper proposes a new approach of protected QR codes to secure unique product information for safeguarding the pharmaceutical supply chain. The proposed solution integrates secure QR code generation and encrypted data transmission to establish a comprehensive anti-counterfeit ecosystem. The protected QR codes encapsulate product information that cannot be identified using traditional QR code scanners which protect the information against replication and tampering. The system is developed with scalability in mind, which can be easily implemented without introducing any additional modification in the traditional supply chain.

cs.CR↗

arXiv2026

IoT-Driven Cloud-based Energy and Environment Monitoring System for Manufacturing Industry

This research focused on the development of a cost-effective IoT solution for energy and environment monitoring geared towards manufacturing industries. The proposed system is developed using open-source software that can be easily deployed in any manufacturing environment. The system collects real-time temperature, humidity, and energy data from different devices running on different communication such as TCP/IP, Modbus, etc., and the data is transferred wirelessly using an MQTT client to a database working as a cloud storage solution. The collected data is then visualized and analyzed using a website running on a host machine working as a web client.

eess.SY↗

arXiv2026

IoT-enabled Stability Chamber for the Pharmaceutical Industry

A stability chamber is essential for pharmaceutical facilities to test the stability and quality of products over time by exposing them to different environmental conditions. This paper introduces an IoT-enabled stability chamber designed for the pharmaceutical industry. We constructed four stability chambers by leveraging the existing infrastructure within a manufacturing facility. Each chamber is controlled using a state-of-the-art Proportional Integral Derivative (PID) system based on the Siemens S7-1200 PLC. The Siemens WinCC Runtime Advanced platform, compliant with FDA 21 CFR Part 11, was used for visualizing chamber data. Additionally, an Internet of Things (IoT) application was developed to remotely monitor sensor data through any client application. This research aims to enhance the performance of traditional stability chambers by integrating IoT functionalities, making them more cost-effective and user-friendly.

eess.SY↗

arXiv2025

Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network

Generative Adversarial Networks (GAN) have shown potential in expanding limited medical imaging datasets. This study explores how different ratios of GAN-generated and real brain tumor MRI images impact the performance of a CNN in classifying healthy vs. tumorous scans. A DCGAN was used to create synthetic images which were mixed with real ones at various ratios to train a custom CNN. The CNN was then evaluated on a separate real-world test set. Our results indicate that the model maintains high sensitivity and precision in tumor classification, even when trained predominantly on synthetic data. When only a small portion of GAN data was added, such as 900 real images and 100 GAN images, the model achieved excellent performance, with test accuracy reaching 95.2%, and precision, recall, and F1-score all exceeding 95%. However, as the proportion of GAN images increased further, performance gradually declined. This study suggests that while GANs are useful for augmenting limited datasets especially when real data is scarce, too much synthetic data can introduce artifacts that affect the model's ability to generalize to real world cases.

eess.IV↗

arXiv2025

A Model-Mediated Stacked Ensemble Approach for Depression Prediction Among Professionals

Depression is a significant mental health concern, particularly in professional environments where work-related stress, financial pressure, and lifestyle imbalances contribute to deteriorating well-being. Despite increasing awareness, researchers and practitioners face critical challenges in developing accurate and generalizable predictive models for mental health disorders. Traditional classification approaches often struggle with the complexity of depression, as it is influenced by multifaceted, interdependent factors, including occupational stress, sleep patterns, and job satisfaction. This study addresses these challenges by proposing a stacking-based ensemble learning approach to improve the predictive accuracy of depression classification among professionals. The Depression Professional Dataset has been collected from Kaggle. The dataset comprises demographic, occupational, and lifestyle attributes that influence mental well-being. Our stacking model integrates multiple base learners with a logistic regression-mediated model, effectively capturing diverse learning patterns. The experimental results demonstrate that the proposed model achieves high predictive performance, with an accuracy of 99.64% on training data and 98.75% on testing data, with precision, recall, and F1-score all exceeding 98%. These findings highlight the effectiveness of ensemble learning in mental health analytics and underscore its potential for early detection and intervention strategies.

cs.LG↗

arXiv2013

Local Feature or Mel Frequency Cepstral Coefficients - Which One is Better for MLN-Based Bangla Speech Recognition?

This paper discusses the dominancy of local features (LFs), as input to the multilayer neural network (MLN), extracted from a Bangla input speech over mel frequency cepstral coefficients (MFCCs). Here, LF-based method comprises three stages: (i) LF extraction from input speech, (ii) phoneme probabilities extraction using MLN from LF and (iii) the hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. In the experiments on Bangla speech corpus prepared by us, it is observed that the LFbased automatic speech recognition (ASR) system provides higher phoneme correct rate than the MFCC-based system. Moreover, the proposed system requires fewer mixture components in the HMMs.

cs.CL↗