Browsing by Author "Jahan, Hosney"

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A Neural Network Based Software Defect Prediction Approach Using SMOTE and Noise Filtering-CLNI
(Research and Development Wing, MIST, 2025-12-30) Ashfaque, Ahmmed Bin; Sattar, Abdus; Jahan, Hosney; Akhtaruzzaman, M.; Nur, Fernaz Narin
Software defects can cause significant loss and system failures in software development life cycle. Software Defect Prediction (SDP) is a vital step for ensuring the quality of software. Till now, a number of machine learning models have been proposed to predict potential defects and make the software more reliable. However, SDP models suffer from the problem of imbalanced dataset, resulting in poor prediction accuracy. To mitigate this, issue several data balancing techniques, i.e., over sampling, under sampling etc. have been proposed to balance the dataset. In some cases, the data balancing methods may further introduce noisy and mislabeled samples in the dataset. To deal with these issues, in this paper, we propose a neural network based approach that combines the oversampling technique Synthetic Minority Oversampling Technique (SMOTE) with the noise filtering technique Class Level Noise Identification (CLNI). Here, we applied three different CLNI methods which are Edited Nearest Neighbor (ENN), Repeated ENN (RENN) and All-KNN. Our aim is to make the dataset clean, balanced and efficient by combining SMOTE with CLNI. In addition, we applied a number of feature selection methods to identify the most important features, further contributing towards achieving better prediction accuracy. To evaluate the effectiveness of the proposed model, we conduct experiments on several benchmark datasets (MC1, PC1, PC2, PC3 and PC4) obtained from NASA MDP and (ML, LC and JDT) AEEEM repository. The experimental results have been evaluated and compared in terms of accuracy, precision, recall and AUC-ROC curve. The experimental results demonstrated that our proposed approach has achieved up to 98% accuracy and outperformed state-of- the-art approaches.
A New Method to Handle Facebook Users in the Distributed Database System
(Scopus, 2020) Rana, Md. Shohel; Hossin, Md Altab; Mahmud, S M Hasan; Jahan, Hosney; Hossen, Md. Anwar
The hasty growth of technology and social media has carried momentous changes to humanoid communication. Facebook, the largest online social media in the last few years has more than 200 million active users where more than 3.5 billion minutes are spent on Facebook daily. Since the competence of Facebook is subject to mostly on the processing of the massive volume of data. The volume of data is increasing day to day as well as the number of inactive and fake users. In this paper, we propose a new model using distributed database concept for management of users and their activities. This proposed mod-el helps to keep the system scalable, reliable, and faster and let the Facebook accessible from anywhere with high accessibility.
A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron
(MDPI, 2023-06-18) Aziz, Md. Tarek; Mahmud, S. M. Hasan; Elahe, Md. Fazla; Jahan, Hosney; Rahman, Md Habibur; Nandi, Dip; Smirani, Lassaad K.; Ahmed, Kawsar; Bui, Francis M.; Moni, Mohammad Ali
Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.
CSV-ANNOTATE: Generate annotated tables from CSV file
(IEEE, 2018-06-28) Mahmud, S M Hasan; Hossin, Md Altab; Jahan, Hosney; Noori, Sheak Rashed Haider; Bhuiyan, Touhid
The Semantic Web is a part of the current World Wide Web (WWW), which can facilitate a common mechanism to publish, share, and reuse data beyond the boundaries of web applications. It is widely believed that the majority of the datasets stored on the current web are in tabular data format (CSV, spreadsheets, SQL dumps, HTML tables etc), commonly in the comma-separated values (CSV) format. In order to prepare the CSV data semantically structured, interoperable, accessible and reusable for various web applications, they need to be extracted from the CSV files and converted into annotated table. Therefore, we propose an effective approach to generate annotated tables from CSV file. However, annotated table for CSV provides possibilities for data publishers to refer data validating, converting, displaying and inputting by following the Semantic Web standard. This research presents the conversion strategies of CSV file into annotated tables. Here, we design a parsing algorithm and development techniques to demonstrate the annotated tabular data model (column, row, and cell). An experiment is carried out to observe and compare the time efficiency of the annotation process. This method and findings provide a valuable reference for potential implementers to further operate the Semantic data.
Decentralized LRM System Architecture with Biometric Authentication and Digital Certificate Verification through Blockchain Technology
(Research and Development Wing, MIST, 2025-12-30) Mosharrof, Shakil; Nizami, Farhan Nasif; Mohtasim, Mahdi; Adib, Mahdi; Akhtaruzzaman, M.; Islam, Md Shofiqul; Rahman, Muhammad Towfiqur; Jahan, Hosney
Managing land records is a fundamental duty of a government, ensuring the accuracy, consistency, integrity of ownership, and reliable transaction of data. Conventional paper-based or centralized digital technology-based land record systems are unable to hold the system trust, efficiency, and consistency, thus mostly demonstrate errors, fraud, and corruptions. On the other hand, blockchain technology presents a transformative solution. It offers transparent, tamper-proof, secure, and reliable approach for Land Record Management (LRM) system. In this study a blockchaindriven LRM system architecture with distributed ledger technology is presented. The proposed strategy enhances security and trust by ensuring transparency, acceptance, and accountability. The study also designs the smart-contracts algorithm in detail that facilitates land registration, ownership transfers, verification, and streamlining processes. The proposed architecture ensures automated functionalities with little human intervention, uplifting the system security. Moreover, the proposed blockchain architecture integrates finger-print biometric authentication that boosts the system strength in terms of security through identity verification. This mitigates the risks of errors, fraud, illegal modification, and unauthorized access. This article outlines a blockchain-based LRM framework and verified through implementation and testing, reflecting the potentiality of viable adoption of this advanced technology.
IDTi-CSsmoteB
(IEEE Access, 2019-04-11) Mahmud, S. M. Hasan; Chen, Wenyu; Jahan, Hosney; Liu, Yongsheng; Sujan, Nasir Islam; Ahmed, Saeed
Identifying interaction between drug and protein is a crucial challenge in drug discovery, which can lead the researchers to develop novel drug compounds or new target proteins for the existing drugs. The determination of drug-target interactions (DTIs) is an extremely time-consuming, costly, and tedious task with wet-lab experiments. To date, multiple computational techniques have been presented to simplify the drug discovery process, but a huge number of interactions are still undiscovered. Furthermore, a class imbalance is a critical challenge regarding this experiment which can significantly degrade the classification accuracy that has not been effectively addressed yet. In this paper, we proposed a novel high-throughput computational model, called iDTi-CSsmoteB, for identification of DTIs based on drug chemical structures and protein sequences. More specifically, the protein sequence is extracted through position-specific scoring matrix (PSSM)-Bigram, amphiphilic pseudo amino acid composition (AM-PseAAC) and dipeptide PseAAC descriptors which represents evolutionary and sequence information. The drug chemical structure is represented as a molecular substructure fingerprint (MSF) which describes the existence of the functional fragments or groups. Finally, we used the over-sampling SMOTE technique to overcome the imbalance issue of the datasets and applied XGBoost algorithm as a classifier to predict DTIs. To evaluate the performance of iDTi-CSsmoteB, several experiments have been conducted on four benchmark datasets, namely, enzyme, ion channel, GPCR, and nuclear receptor based on fivefold cross validation. The experimental analysis exhibits that our model outperforms similar methods in terms of area under the ROC (auROC) curve. In addition, our achieved results indicate the effectiveness of the feature extraction techniques, balancing methods, and classifier for predicting the DTIs which can provide substance for new drug development. iDTi-CSsmoteB webserver is available online at http://idticssmoteb-uestc.me/
PRMT: Predicting Risk Factor of Obesity among Middle-Aged People Using Data Mining Techniques
(Elsevier B.V., 2018-06-08) Hossain, Rifat; Mahmud, S.M. Hasan; Hossin, Md Altab; Noori, Sheak Rashed Haider; Jahan, Hosney
Obesity is an anatomical condition characterized by an extreme growth of body fat. The obesity rate is increasing gradually; from prior research, obesity is the serious health disease in the globe. This study collected 259 data from specified urban and rural areas regarding different risk factor of our daily activities. The purpose of the study is to simulate the risk factor by using statistical tools (SPSS), which helpsto predict the major risk factor of obesity by testing the class level attribute according to cross-sectional study with other attributes. By analyzing the P-value (p<0.05), the outcome of this process Age (0.002), Height (0.002), Weight 0.000), Healthy lifestyle (0.000), Marital status (0.001), BMI (0.000), Economic (0.028), Sleep per day (0.011) has a significant relationship with our obesity class. This study proposed a risk mining technique (PRMT)that foretells a model to analyze the risk factor of obesity class using different data mining classifiers, using WEKA to estimate the accuracy and error measurement. The outcome of this process Naïve Bayes is the best classifier for the 10-fold cross-validation study. The proposed model collaborates to predict human factor who want to control and mitigate this major cardiovascular disease.
Publishing CSV Data as Linked Data on the Web
(Scopus, 2020) Mahmud, S. M. Hasan; Hossin, Md. Altab; Hasan, Md. Rezwan; Jahan, Hosney; Noori, Sheak Rashed Haider; Ahmed, Md. Razu
The majority of datasets on Open Government Data (OGD) portals are stored in comma-separated values (CSV) file. Publishing CSV data as a Linked Open Data (LOD) on the Web is an active field of research. However, there are very few effective applications have been developed with this purpose. Linked Data refer many ways for connecting and publishing structured data to data consumers, but available datasets are in CSV format. Therefore, publishing the CSV model on the webpage, it is needed to change CSV in RDF file format. Many methods and tools have been proposed for data mapping and publishing, however, most of them are not followed by the W3C recommendations rules. The contribution and goal of this paper are to develop a Semantic approach that can effectively convert CSV data into RDF data with rich semantics and release RDF data on the web using LOD principles. We utilize Semantic Web resources and W3C recommendation rules in automatic data publishing method, which enables distributed system for scalability. We apply the proposed method to existing CSVW Implementation Report-W3C and U.S Government’s application (data.gov). Our experimental results indicate that the proposed approach successfully converts CSV to RDF data and publish those RDF as LOD on the Web, with adequate performance on any sized datasets.