http://192.168.40.123:8080/handle/123456789/15215 2026-11-10T08:08:34Z http://192.168.40.123:8080/handle/123456789/18438 The Comparison of Machine Learning Algorithms to Find the Career Path by Bloom’s Taxonomy Evaluation Ahmed, Fizar; Bijoy, Md. Hasan Imam; Haider Noori, Sheak Rashed; Rebonya, Tasnova; Hemal, Habibur Rahman This research paper aims to compare various machine learning algorithms to identify the best career paths for individuals based on Bloom’s taxonomy evaluation. The study uses data from a sample of individuals to train and test the models and evaluates their efficiency based on accuracy rate, precision, recall, and F1-score metrics. The results of the study show that certain machine learning algorithms perform better than others in predicting career paths based on Bloom’s taxonomy evaluation. These findings have implications for career counseling and guidance and may help individuals make informed decisions about their career paths based on their skills, interests, and aptitudes. There are four machine learning models—logistic regression classifier, decision tree classifier, random forest classifier, and K-nearest neighbors’ classifier—which are used to make the comparison of the results. Among the four applied machine learning classifiers (MLCs), random forest beat the other classifiers with an accuracy of 87.77%. Conference Paper 2024-03-30T00:00:00Z http://192.168.40.123:8080/handle/123456789/18435 Explainable and physics-informed machine learning for seasonal water quality prediction in the monsoon-driven Padma River Basin, Bangladesh Islam, Abu Reza Md Towfiqul; Mamun, Md Abdullah-Al; Uddin, Md Nashir; Sowrav, Sheikh Fahim Faysal; Alam, M Nur E; Khan, Shahidur R; Chowdhury, Mohaiminul Haider; Choudhury, Tasrina Rabia River water quality in monsoon-driven subtropical basins exhibits strong seasonal variability driven by hydroclimatic forcing and increasing anthropogenic pressure, posing challenges for reliable assessment and management. Despite advances in water quality modeling, most Water Quality Index (WQI) prediction frameworks require extensive sampling and lack interpretability, limiting rapid baseline assessment during critical periods. This study develops the first integrated Explainable Artificial Intelligence (XAI) framework combining Machine Learning (ML), Deep Learning (DL), and Physics-Informed Neural Networks (PINNs) to predict, interpret, and spatially characterize seasonal water quality dynamics in the Padma River Basin, Bangladesh. Forty-four surface water samples collected during winter and monsoon seasons were evaluated using WQI assessment, explainable modeling, probabilistic uncertainty analysis, and spatial regionalization. Results show that seasonal variability dominates over spatial variability (p < 0.0001), with winter low-flow conditions promoting solute concentration and localized degradation, while monsoon discharge drives basin-wide dilution and recovery. Model performance is strongly region-dependent: Deep Neural Networks achieve the highest accuracy in winter (R2 = 0.98; RMSE = 1.16), whereas Ridge Regression and Voting Ensemble models perform more robustly during the monsoon (R2 ≈ 0.97; RMSE ≈ 1.01). Explainable AI analysis identifies NO3- emerged as the dominant contaminant (24.0 ± 36.3 mg/L winter, 47.5 ± 68.7 mg/L monsoon, with isolated samples exceeding WHO limits), whereas pH and DO exhibit dual seasonal influences. PINN-based data augmentation improves model generalization under limited sampling while preserving hydrochemical consistency. Monte Carlo simulations quantify prediction uncertainty and reveal seasonal shifts in WQI probability distributions, while spatial autocorrelation analysis identifies localized winter degradation hotspots and widespread monsoon improvement. The proposed physics-informed and explainable AI framework enhances predictive reliability, interpretability, and decision relevance, offering a transferable approach for uncertainty-aware water quality assessment and adaptive management in monsoon-affected, data-limited river basins. Conference paper 2026-02-01T00:00:00Z http://192.168.40.123:8080/handle/123456789/18434 Explainable Deep Neural Diagnostics: Vision Transformer-Based Retinal Disease Classification from OCT Images Using Gradient-Driven Visual Attribution Omi, Md. Sakibul Hassan; Islam, K. M. Arafat; Turzo, Shovan Samanta; Dhar, Pranab Kumar; Bijoy, Md. Hasan Imam The research introduces the Convolutional Neural Network (CNN) model that automatizes the process of classifying retinal diseases based on the Optical Coherence Tomography (OCT) images. This model will categorize four retinal disorders namely: Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen Macular Degeneration (DMD) and normal retinal conditions. In order to make the predictions of the CNN more interpretable, we will use Grad-CAM (Gradient-weighted Class Activation Mapping) Explainable AI (XAI) in the state of the art. Grad-CAM also provides a visual representation of what parts of the image influence the most the classification decision of the CNN this way clinicians can get some kind of insight as to why the model is producing its outputs. That transparency can be especially useful in medical settings, where the rationale behind a decision can be of the great importance in a clinical verification. Hyperparameters are also optimized by using random search to tune on important variables including batch size and dropout rate and the optimizer. In the suggested CNN model, the classification accuracy reaches 96.01. As compared to others, the proposed method is more accurate and more interpretable than the other methods that are available and validates its utility in large-scale screening and decision support in diagnosing retinal diseases. This model can be a valuable addition to the toolkit of the ophthalmologist because it conveys high diagnostic performance and interpretability of the main datasets used during its production. Conference paper 2026-02-20T00:00:00Z http://192.168.40.123:8080/handle/123456789/18417 Supercontinuum Generation in Dispersion Engineered Silicon Carbide Photonic Crystal Fiber Rafi, Rakayet; Nath, Runi; Sabuz, Md.; Zakir, Zahida; Karim, M R; Rahman, B This study presents the design of a hexagonal pattern photonic crystal fiber (PCF) using silicon carbide as the guiding material. This is achieved by modifying the PCF's geometrical characteristics, such as lattice pitch and air hole diameter. We observed that flatter dispersion curves and less confinement loss may be achieved by raising the pitch from 1.5µm to 1.9µm. We have further investigated the Supercontinuum Generation phenomenon using Generalized Non-linear Schrodinger Equation (GNLSE), of a 10mm length of PCF design having parameters as pitch 1.9µm, air filled fraction 0.9. It resulted in spectra spanning from 800 nm to 2800 nm at above −40 dB spectral power range which is in the Mid Infrared region. This is, to the authors knowledge, first exploration of SupercontinuumGeneration in a SiC based photonic crystal fiber (PCF). Conference Paper 2024-05-23T00:00:00Z