arXiv subjects

Md. Mizanur Rahman

Publications and source records attributed to Md. Mizanur Rahman.

8 recordsLinked to original sources

arXiv2026

Numerical exploration on unveiling the photovoltaic potential of MgXS3(X = Ti, Zr, Hf) chalcogenide perovskites

Lead-free chalcogenide perovskites offer a nontoxic and thermally robust path beyond Pb-based perovskite solar cells (PSCs), but their device-level behavior in realistic three-dimensional geometries remains insufficiently characterized. In this work, we investigate ZnSe/MgXS3(X = Ti, Zr, Hf)/Sb2S3 solar cell architecture where MgXS3 absorbers from the II-IV-VI chalcogenide perovskite family is employed as the absorber layer. The device is analyzed using 3D finite-element simulations in COMSOL Multiphysics that self-consistently couple optical generation, drift-diffusion carrier transport, and heat transfer under AM 1.5G 1-sun illumination, following a fully coupled opto-electro-thermal framework. For each absorber composition, the impacts of absorber thickness, doping, and defect density are systematically investigated, and the contribution of an Sb2S3 back-surface-field (BSF) layer to carrier collection and spectral response is quantified. Under optimized conditions, MgZrS3, MgTiS3, MgHfS3-based devices achieves a simulated power conversion efficiency (PCE) of 28.18%, 26.72%, and 28.16%, respectively. The corresponding open-circuit voltage (VOC) values are 0.94 V, 0.74 V, and, 1.07 V while the short-circuit current density (JSC) values are 34.46 mA/cm2, 42.69 mA/cm2, and 29.89 mA/cm2, with fill factor (FF) values of 86.99%, 84.58%, and 88.02%, respectively. Coupled electro-thermal simulations further reveal a small spatially non-uniform steady-state temperature rise across the ultrathin cell stack, mainly governed by non-radiative recombination and Joule dissipation within the active layers. Overall, these results confirm MgXS3(X = Ti, Zr, Hf) chalcogenide perovskites as promising lead-free absorber materials and offer practical design guidance for achieving high-efficiency, thermally stable three-dimensional device architectures.

cond-mat.mtrl-sci↗

arXiv2025

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique

Context: Mpox is a zoonotic disease caused by the Mpox virus, which shares similarities with other skin conditions, making accurate early diagnosis challenging. Artificial intelligence (AI), especially Deep Learning (DL), has a strong tool for medical image analysis; however, pre-trained models like CNNs and XAI techniques for mpox detection is underexplored. Objective: This study aims to evaluate the effectiveness of pre-trained CNN models (VGG16, VGG19, InceptionV3, MobileNetV2) for the early detection of monkeypox using binary and multi-class datasets. It also seeks to enhance model interpretability using Grad-CAM an XAI technique. Method: Two datasets, MSLD and MSLD v2.0, were used for training and validation. Transfer learning techniques were applied to fine-tune pre-trained CNN models by freezing initial layers and adding custom layers for adapting the final features for mpox detection task and avoid overfitting. Models performance were evaluated using metrics such as accuracy, precision, recall, F1-score and ROC. Grad-CAM was utilized for visualizing critical features. Results: InceptionV3 demonstrated the best performance on the binary dataset with an accuracy of 95%, while MobileNetV2 outperformed on the multi-class dataset with an accuracy of 93%. Grad-CAM successfully highlighted key image regions. Despite high accuracy, some models showed overfitting tendencies, as videnced by discrepancies between training and validation losses. Conclusion: This study underscores the potential of pre-trained CNN models in monkeypox detection and the value of XAI techniques. Future work should address dataset limitations, incorporate multimodal data, and explore additional interpretability techniques to improve diagnostic reliability and model transparency

cs.CV↗

arXiv2022

MangoLeafBD: A Comprehensive Image Dataset to Classify Diseased and Healthy Mango Leaves

Agriculture is of one of the few remaining sectors that is yet to receive proper attention from the machine learning community. The importance of datasets in the machine learning discipline cannot be overemphasized. The lack of standard and publicly available datasets related to agriculture impedes practitioners of this discipline to harness the full benefit of these powerful computational predictive tools and techniques. To improve this scenario, we develop, to the best of our knowledge, the first-ever standard, ready-to-use, and publicly available dataset of mango leaves. The images are collected from four mango orchards of Bangladesh, one of the top mango-growing countries of the world. The dataset contains 4000 images of about 1800 distinct leaves covering seven diseases. Although the dataset is developed using mango leaves of Bangladesh only, since we deal with diseases that are common across many countries, this dataset is likely to be applicable to identify mango diseases in other countries as well, thereby boosting mango yield. This dataset is expected to draw wide attention from machine learning researchers and practitioners in the field of automated agriculture.

cs.CV↗

arXiv2022

Effects of MAC Parameters on the Performance of IEEE 802.11 DCF in NS-3

This paper presents the design procedure of the NS-3 script for WLAN that is organized according to the hierarchical manner of TCP/IP model. We configure all layers by using NS-3 model objects and set and modify the values used by objects to investigate the effects of MAC parameters (access mechanism, CWmin, CWmax and retry limit) on the performance metrics viz. packet delivery ratio, packet lost ratio, aggregated throughput, and average delay. The simulation results show that RTS/CTS access mechanism outperforms basic access mechanism in saturated state, whereas the MAC parameters have no significant impact on network performance in non-saturated state. A higher value of CWmin improves the aggregated throughput in expense of average delay. The tradeoff relationships among the performance metrics are also observed in results for the optimal values of MAC parameters. Our design procedure represents a good guideline for new NS-3 users to design and modify script and results greatly benefit the network design and management.

cs.NI↗

arXiv2021

Vaccine efficacy and SARS CoV 2 control in California and USA during the session 2020 2026: A modeling study

Besides maintaining health precautions, vaccination has been the only prevention from SARS-CoV-2, though no clinically proved 100% effective vaccine has been developed till date. At this stage, to withhold the debris of this pandemic, experts need to know the impact of the vaccine efficacy rate's threshold and how long this pandemic may extent with vaccines that have different efficacy rates. In this article, a mathematical model study has been done on the importance of vaccination and vaccine efficiency rate during an ongoing pandemic. We simulated a five compartment mathematical model to analyze the pandemic scenario in both California, and whole U.S. We considered four vaccines, Pfizer, Moderna, AstraZeneca, and Johnson and Johnson, which are being used rigorously to control the COVID-19 pandemic, in addition with two special cases: a vaccine with 100% efficacy rate and no vaccine under use. Both the infection and death rates are very high in California. Our model suggests that the pandemic situation in California will be under control in the last quartile of the year 2023 if frequent vaccination is continued with the Pfizer vaccine. During this time, six waves will happen from the beginning of the immunization where the case fatality and recovery rates will be 1.697% and 98.30%, respectively. However, according to the considered model, this period might be extended to the mid of 2024 when vaccines with lower efficacy rates are used. The more effective a vaccine, the less people suffer from this malign infection. Although specific groups of people get prioritized initially, mass vaccination is needed to control the spread of the disease.

q-bio.PE↗

arXiv2020

Ramifications and Diminution of Image Noise in Iris Recognition System

Human Identity verification has always been an eye-catching goal in digital based security system. Authentication or identification systems developed using human characteristics such as face, finger print, hand geometry, iris, and voice are denoted as biometric systems. Among the various characteristics, Iris recognition trusts on the idiosyncratic human iris patterns to find out and corroborate the identity of a person. The image is normally contemplated as a gathering of information. Existence of noises in the input or processed image effects degradation in the image superiority. It should be paramount to restore original image from noises for attaining maximum amount of information from corrupted images. Noisy images in biometric identification system cannot give accurate identity. So Image related data or information tends to loss or damage. Images are affected by various sorts of noises. This paper mainly focuses on Salt and Pepper noise, Gaussian noise, Uniform noise, Speckle noise. Different filtering techniques can be adapted for noise diminution to develop the visual quality as well as understandability of images. In this paper, four types of noises have been undertaken and applied on some images. The filtering of these noises uses different types of filters like Mean, Median, Weiner, Gaussian filter etc. A relative interpretation is performed using four different categories of filter with finding the value of quality determined parameters like mean square error (MSE), peak signal to noise ratio (PSNR), average difference value (AD) and maximum difference value (MD).

eess.IV↗

arXiv2013

An Improved GEF Fast Addition Algorithm

In this paper, an improved GEF fast addition algorithm is proposed. The proposed algorithm reduces time and memory space. In this algorithm, carry is calculated on the basis of arrival timing of the operand's bits without overhead of sorting. Intermediate terms are generated from the most significant bit and the carry is generated from the least significant bit using the functions of efficient operators. This algorithm shows better performance for use in the fastest computational devices of the near future.

cs.DS↗

arXiv2012

Effect of AWGN & Fading (Raleigh & Rician) channels on BER performance of a WiMAX communication System

The emergence of WIMAX has attracted significant interests from all fields of wireless communications including students, researchers, system engineers and operators. The WIMAX can also be considered to be the main technology in the implementation of other networks like wireless sensor networks. Developing an understanding of the WIMAX system can be achieved by looking at the model of the WIMAX system. This paper discusses the model building of the WIMAX physical layer using computer MATLAB 7.5 versions. This model is a useful tool for BER (Bit error rate) performance evaluation for the real data communication by the WIMAX physical layer under different communication channels AWGN and fading channel (Rayleigh and Rician), different channel encoding rates and digital modulation schemes which is described in this paper. This paper investigates the effect of communication channels of IEEE 802.16 OFDM based WIMAX Physical Layer. The performance measures we presented in this paper are: the bit error rate (BER) versus the ratio of bit energy to noise power spectral density (Eb/No). The system parameters used in this paper are based on IEEE 802.16 standards. The simulation model built for this research work, demonstrates that AWGN channel has better performance than Rayleigh and Rician fading channels. Synthetic data is used to simulate this research work.

cs.NI↗