http://ar.iub.edu.bd:80 The IUBAR digital repository system captures, stores, indexes, preserves, and distributes digital research material. Wed, 13 May 2026 07:31:46 GMT 2026-05-13T07:31:46Z https://ar.iub.edu.bd/handle/11348/1199 SHUCHOK: Early Identification of ADHD in Bangladeshi Children Using Machine Learning Models and a Questionnaire-Based App Interface Panday, Sharbany; Bairagi, Sourish; Rahman, Sinthia Sayma The study focuses on attention deficit hyperactivity disorder that is underdiagnosed in Bangladeshi children, a critical public health concern. Due to the lack of awareness, high levels of social stigma and a severe shortage of available and culturally-appropriate screening methods, many children with signs of ADHD are undiagnosed, thus impeding their behavioral and academic progress. This research aims to bridge this gap by developing a culturally sensitive, economical and data-driven machine learning algorithm to detect signs of ADHD in the early stages, and to develop an app interface of ADHD screening (SHUCHOK) in the Bangladeshi setting. Our method included a collection of a survey-based data set based on 110 participants between the ages of 4 and 17 with diagnosis of ADHD (n = 44) and without (n = 66). A DSM-5-based questionnaire translated into Bengali was used to capture behavioral symptoms, demographics, developmental history, and environmental factors. The four machine learning models were Random Forest, Support Vector Machine, Logistic Regression and Decision Tree which were trained and evaluated after subjecting the data to extensive data preprocessing and feature selection approaches, including Recursive Feature Elimination and Select Percentile. The Random Forest model had the highest accuracy (91%), precision (0.90), recall (0.88), F1-Score (0.90) and Area Under the Curve (0.95) when compared to the others. In this sample, the most important predictive characteristics of ADHD are inattention, fidgeting, study focus and level of irritation of the child. The main implication of this research is that it has great potential to enhance public awareness and early diagnosis of ADHD in low-resource countries such as Bangladesh. This study will significantly improve the accessibility of diagnoses and timeliness of interventions by offering a machine learning-based screening tool, which is proven to enhance the developmental outcome of the children affected. Wed, 01 Apr 2026 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1199 2026-04-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/1198 Anomaly Detection in Petroleum Production Wells Using Machine Learning: A Comparative Study on the Petrobras 3W Dataset Rahman, Sayed Rakinur; Suraiya, Amanta; Rafsan, Md. Turan Anomaly detection in offshore oil production systems is a safety-critical task, with un-detected faults contributing directly to equipment failures, environmental incidents, and significant production losses. Machine Learning (ML) has shown significant potential for fault classification in industrial time-series data. However, few comparative studies sys-thematically evaluate models across multiple learning paradigms, including classical, super-vised deep learning, and unsupervised learning on real-world operational data. Existing studies tend to evaluate individual model architectures, offering limited analysis across fault types, class-imbalance conditions, and temporal data characteristics. Multi-class fault detection in oil wells presents multiple challenges, such as severe class imbalance, overlapping faults, and the risk of temporal data leakage as a result of splitting strategies applied to sensor time series. This report presents an extensive comparison study of machine learning approaches for multi-class anomaly detection using the Petrobras 3W dataset across eight fault types, a publicly available benchmark containing real-world sensor data from offshore oil wells. Five distinct models are evaluated: Random Forest (RF) as a classical machine-learning baseline, while Long Short-Term Memory (LSTM) and Transformer classifiers as supervised deep-learning approaches. An LSTM Autoen- coder for unsupervised anomaly detection. This study proposes an Advanced Stacking Ensemble that combines all three supervised paradigms using cross-validated out-of-fold (OOF) prediction generation, meta-feature engineering including prediction entropy, inter- model agreement, confidence statistics and an eXtreme Gradient Boosting (XGBoost) meta-learner. A thorough preprocessing pipeline is implemented, including file-level data splitting to prevent temporal leakage, sliding-window segmentation, statistical feature engineering, and Synthetic Minority Over-sampling Technique (SMOTE) for class balancing. On the held-out test set, the Stacking Ensemble achieves the highest accuracy of 96.25%, while the RF has an accuracy of 95.98%. The RF also attains weighted F1-score of 0.9558. The Transformer (F1: 0.9295) and LSTM (F1: 0.9237) demonstrate competitive performance, with the Transformer offering the advantage of temporal modeling. The LSTM Autoencoder achieves an F1-score of 0.8588 for binary anomaly detection without requiring labeled anomaly data. Model interpretability is addressed through Shap-ley AdditiveexPlanations (SHAP) based feature-level importance analysis and Transformer attention-weight visualization for revealing fault-specific temporal interpretability attended to by the model. Wed, 01 Apr 2026 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1198 2026-04-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/1197 Valuation of startup firms: a case study on Pathao Ullah, G. M. Wali; Mahtab, Farhan; Ahmed, Samiul Parvez; Ahmed, Sarwar Uddin The article discusses the concept of startups, their sources of financing, and the stages of funding such as seed capital, Series A, and Series B financing. It further explains different startup valuation methods including the Cost-to-Duplicate Approach, Market Approach, Discounted Cash Flow (DCF) Approach, and Development Stage Approach. Using Pathao as a case example, the study demonstrates how startup valuation differs from traditional company valuation due to uncertainty, limited historical data, and future growth potential. Additionally, the paper analyzes how Pathao successfully raised investments from local and foreign investors, including funding from Go-Jek. The study also explores Pathao’s future expansion plans, digital payment initiatives, and challenges such as competition, regulatory pressure, customer expectations, and safety concerns. Overall, the case study provides valuable insights into startup financing, valuation techniques, and the growth potential of technology-based businesses in emerging economies like Bangladesh. Tue, 01 May 2018 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1197 2018-05-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/1196 Simple costing analysis at FARR Ceramics Limited Mahtab, Naheem; Safiuddin, Md.; Mandal, Susmita; Alam, Md. Shohidul; Razzaque, Rushdi In this case study we first start by determining variable & fixed costs of FARR Ceramics and then figure out breakeven point and output level needed to achieve a target operating income for FARR Ceramics. Then we further analyze how the managers at FARR Ceramics use CVP analysis to make their decisions. Tue, 01 May 2018 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1196 2018-05-01T00:00:00Z