Browsing by Author "Rahman, Md Habibur"

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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.
Addressing Agricultural Challenges: An Identification of Best Feature Selection Technique for Dragon Fruit Disease Recognition
(Elsevier, 2023-11-02) Shakil, Rashiduzzaman; Islam, Shawn; Shohan, Yeasir Arafat; Mia, Anonto; Rajbongshi, Aditya; Rahman, Md Habibur; Akter, Bonna
Dragon fruit is a prominent substance in global agriculture. Despite this, it is gaining popularity and is a viable solution in resource-poor, environmentally degraded areas because of its many health benefits. Nevertheless, many dragon fruit plantations have been impacted by the disease, reducing their yield, and the detection system is still conventional. Farmers’ lack of disease identification and management expertise diminished crop quality and products. As a result, little research was carried out to assist those specific farmers requiring adequate agricultural support. This research has proposed an autonomous agro-based system to recognize dragon diseases using in-depth analysis of feature selection techniques. After the collection of real-time images of the dragon, the images are preprocessed using various image-processing techniques. The two important features are retrieved after segmentation. The analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) are used as feature selection techniques to assess the feature rank based on the mutual score. To analyze the effectiveness of the machine learning algorithms that were used, six distinct machine learning classifiers were applied to the top-ranked feature sets, and their performance was measured using seven distinct performance evaluation metrics. AdaBoost and Random Forest classifiers for the LASSO feature ranking approach got the maximum accuracy, which is 96.29%, based on a comparison of classifiers based on the ANOVA and LASSO feature set. Despite this, we have optimized the computational resources of each classifier for the LASSO feature set.
Antioxidant, analgesic and toxic potentiality of stephania japonica (Thunb.) miers. leaf
(© 2011 Asian Network for Scientific Information., 2011) Rahman, Md Habibur; Alam, Md. Badrul; Chowdhury, N.S.; Kumar Jha, Mithilesh; M, Hasan; Khan, Md. Maruf; Rahman, Md. Saifur; Haque, M. Ekramul
In the present study crude methanolic extract of Stephania japonica leaf was investigated for possible antioxidant, analgesic and toxic activity. The extract showed antioxidant activity in DPPH radical scavenging activity, nitric oxide scavenging activity and reducing power assays. In both DPPH radical and NO scavenging assay, the extract exhibited moderate antioxidant activity and the IC50 values in DPPH radical scavenging and NO scavenging assays were found to be 105.55±1.06 and 129.12±0.15 ug mL-1, respectively while the IC50 values of ascorbic acid were 12.30±0.11 and 18.64±0.22 ug mL-1, respectively. Reducing power activity of the extract increased in a dose dependent manner. Analgesic activity of the crude extract was evaluated using acetic acid-induced writhing model of pain in mice. The crude extract at 200 and 400 mg kg-1 b. wt. doses displayed significant (p<0.001) reduction in acetic acid induced writhing in mice with a maximum effect of 75.89% reduction at 400 mg kg-1 b.wt. which is comparable to the standard, diclofenac sodium (86.52%). The extract was also investigated for toxic potentiality using Brine Shrimp lethality bioassay. In this bioassay the extract showed significant toxicity to Brine Shrimp nauplii with the LC50 value of 25.19±0.98 ug mL-1. The study clearly indicates that the extract possesses good analgesic and cytotoxic activity along with moderate antioxidant potential.
Antioxidant, analgesic and toxic potentiality of stephania japonica (Thunb.) miers. leaf
(© 2011 Asian Network for Scientific Information., 2011) Rahman, Md Habibur; Alam, Md. Badrul; Chowdhury, N.S.; Kumar Jha, Mithilesh; M, Hasan; Khan, Md. Maruf; Rahman, Md. Saifur; Haque, M. Ekramul
In the present study crude methanolic extract of Stephania japonica leaf was investigated for possible antioxidant, analgesic and toxic activity. The extract showed antioxidant activity in DPPH radical scavenging activity, nitric oxide scavenging activity and reducing power assays. In both DPPH radical and NO scavenging assay, the extract exhibited moderate antioxidant activity and the IC50 values in DPPH radical scavenging and NO scavenging assays were found to be 105.55±1.06 and 129.12±0.15 ug mL-1, respectively while the IC50 values of ascorbic acid were 12.30±0.11 and 18.64±0.22 ug mL-1, respectively. Reducing power activity of the extract increased in a dose dependent manner. Analgesic activity of the crude extract was evaluated using acetic acid-induced writhing model of pain in mice. The crude extract at 200 and 400 mg kg-1 b. wt. doses displayed significant (p<0.001) reduction in acetic acid induced writhing in mice with a maximum effect of 75.89% reduction at 400 mg kg-1 b.wt. which is comparable to the standard, diclofenac sodium (86.52%). The extract was also investigated for toxic potentiality using Brine Shrimp lethality bioassay. In this bioassay the extract showed significant toxicity to Brine Shrimp nauplii with the LC50 value of 25.19±0.98 ug mL-1. The study clearly indicates that the extract possesses good analgesic and cytotoxic activity along with moderate antioxidant potential.
Bioinformatics and System Biology Approach to Identify the Influences of Sars-cov-2 Infections to Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease Patients
(Briefings in bioinformatics, 2021) Mahmud, S M Hasan; Al-Mustanjid, Md; Akter, Farzana; Rahman, Md Shazzadur; Ahmed, Kawsar; Rahman, Md Habibur; Chen, Wenyu; Moni, Mohammad Ali
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.
Financial Performance Analysis of Social Islami Bank Limited
(Daffodil International University, 2021-01-10) Rahman, Md Habibur
I attempted to examination the financial performances of Social Islami Bank Limited applying proportion investigation method. The primary information gathered from the yearly monetary report of SIBL in 2015-2019. Proportion examination has been finished zeroing in on five basics measurements of banking activities like: liquidity, profitability, efficiency, solvency and market aspect. Productivity proportions assign a banks in general proficiency and execution. By investigating we said that return on equity ratio is useful for the SIBL. Management is compelling in creating benefit by utilizing investors value. Earnings per ratio shows that market trust SIBL will improve later on. So I found that general productivity of SIBL is acceptable. Investigation from dissolvability shows that SIBL utilizing more influence. We realize that from the obligation to value proportion that SIBL is utilizing an excess of obligation to back is comparative with investors value store. Dissolvability of SIBL isn't exactly acceptable. From the price earnings ratio, I found that SIBL cost profit proportion expanded throughout the long term. So we see that market believes SIBL will do better in the future. After analyzing of total asset turnover ratio and inventory turnover ratio found that SIBL is very efficiently managing its internal asset and liabilities. Liquidity ratio shows how quick SIBL is able to convert its asset into cash. So we clear that liquidity state of SIBL is excellent. We accept that SIBL will have the option to accomplish enough current resources for take care of current liabilities later on time too.
Identification and Determination of Caffeine Contents in Soft Drinks Commercially Available in Bangladesh Market By Using Reserve Phase HPLC Method
(Daffodil International University, 2019-09-07) Rahman, Md Habibur
Caffeine is a central nervous system (CNS) stimulant. It is also called psychoactive drugs but it is legal and unregulated in nearly all parts of the world. Caffeine is a bitter, white crystalline purine and a methylxanthine alkaloid. The purpose of this study is to identify and determine the amount of caffeine in soft drinks by reverse phase HPLC method. Six branded soft drinks were taken which include two of the top brand samples. Quantitative analysis was done by reverse phase HPLC method with methanol: water (40:60v/v) as mobile phase and C18 a stationary phase with a flow rate of 1 ml/min and maximum UV 254 nm as the detector. The minimum caffeine level of soft drinks was observed in Mojo (23 mg/per 250 ml bottle), while the sample Current showed the highest caffeine content (199 mg/per 250 ml bottle). The levels of caffeine in all energy drinks samples are well below the maximum allowable limits set by the food regulatory bodies because regulatory bodies suggest that the daily intake of caffeine for healthy adults with no medical issues is 300 mg-400 mg can be safe without any adverse effects but they are not recommended to take more than 200 mg caffeine for pregnant or lactating women and children.
Machine Learning Approach on Multiclass Classification of Internet Firewall Log Files
(IEEE, 2023-01-15) Rahman, Md Habibur; Islam, Taminul; Rana, Md Masum; Tasnim, Rehnuma; Mona, Tanzina Rahman; Sakib, Md. Mamun
"Firewalls are critical components in securing communication networks by screening all incoming (and occasionally exiting) data packets. Filtering is carried out by comparing incoming data packets to a set of rules designed to prevent malicious code from entering the network. To regulate the flow of data packets entering and leaving a network, an Internet firewall keeps a track of all activity. While the primary function of log files is to aid in troubleshooting and diagnostics, the information they contain is also very relevant to system audits and forensics. Firewall’s primary function is to prevent malicious data packets from being sent. In order to better defend against cyberattacks and understand when and how malicious actions are influencing the internet, it is necessary to examine log files. As a result, the firewall decides whether to 'allow,' 'deny,' 'drop,' or 'reset-both' the incoming and outgoing packets. In this research, we apply various categorization algorithms to make sense of data logged by a firewall device. Harmonic mean F1 score, recall, and sensitivity measurement data with a 99% accuracy score in the random forest technique are used to compare the classifier's performance. To be sure, the proposed characteristics did significantly contribute to enhancing the firewall classification rate, as seen by the high accuracy rates generated by the other methods.
Systems Biology Models To Identify the Influence of SARS-CoV-2 Infections to the Progression of Human Autoimmune Diseases
(Daffodil International University, 2022-08-02) Al-Mustanjid, Md.; Mahmud, S. M. Hasan; Akter, Farzana; Rahman, Md Shazzadur; Hossen, Md Sajid; Rahman, Md Habibur; Moni, Mohammad Ali
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been circulating since 2019, and its global dominance is rising. Evidences suggest the respiratory illness SARS-CoV-2 has a sensitive affect on causing organ damage and other complications to the patients with autoimmune diseases (AD), posing a significant risk factor. The genetic interrelationships and molecular appearances between SARS-CoV-2 and AD are yet unknown. We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on AD progression. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and AD affected tissues. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene disease (diseasesome) association network and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and AD. We found a total of 77, 21, 9, 54 common DEGs for SARS-CoV-2 and inflammatory bowel disorder (IBD), SARS-CoV-2 and rheumatoid arthritis (RA), SARS-CoV-2 and systemic lupus erythematosus (SLE) and SARS-CoV-2 and type 1 diabetes (T1D). The enclosure of these common DEGs with bimolecular networks revealed 10 hub proteins (FYN, VEGFA, CTNNB1, KDR, STAT1, B2M, CD3G, ITGAV, TGFB3). Drugs such as amlodipine besylate, vorinostat, methylprednisolone, and disulfiram have been identified as a common ground between SARS-CoV-2 and AD from drug repurposing investigation which will stimulate the optimal selection of medications in the battle against this ongoing pandemic triggered by COVID-19.
Unraveling the potential effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on the Protein structure and function of the human SLC30A8 gene on type 2 diabetes and colorectal cancer: An In silico approach
(Scopus, 2024-08-31) Uddin, Md Moin; Hossain, Md Tanvir; Hossain, Md Arju; Ahsan, Asif; Shamim, Kamrul Hasan; Hossen, Md Arif; Rahman, Md Shahinur; Rahman, Md Habibur; Ahmed, Kawsar; Bui, Francis M; Zahran, Fahad Ahmed Al-
Background and aims: The single nucleotide polymorphisms (SNPs) in SLC30A8 gene have been recognized as contributing to type 2 diabetes (T2D) susceptibility and colorectal cancer. This study aims to predict the structural stability, and functional impacts on variations in non-synonymous SNPs (nsSNPs) in the human SLC30A8 gene using various computational techniques. Materials and methods: Several in silico tools, including SIFT, Predict-SNP, SNPs&GO, MAPP, SNAP2, PhD-SNP, PANTHER, PolyPhen-1,PolyPhen-2, I-Mutant 2.0, and MUpro, have been used in our study. Results: After data analysis, out of 336 missenses, the eight nsSNPs, namely R138Q, I141N, W136G, I349N, L303R, E140A, W306C, and L308Q, were discovered by ConSurf to be in highly conserved regions, which could affect the stability of their proteins. Project HOPE determines any significant molecular effects on the structure and function of eight mutated proteins and the three-dimensional (3D) structures of these proteins. The two pharmacologically significant compounds, Luzonoid B and Roseoside demonstrate strong binding affinity to the mutant proteins, and they are more efficient in inhibiting them than the typical SLC30A8 protein using Autodock Vina and Chimera. Increased binding affinity to mutant SLC30A8 proteins has been determined not to influence drug resistance. Ultimately, the Kaplan-Meier plotter study revealed that alterations in SLC30A8 gene expression notably affect the survival rates of patients with various cancer types. Conclusion: Finally, the study found eight highly deleterious missense nsSNPs in the SLC30A8 gene that can be helpful for further proteomic and genomic studies for T2D and colorectal cancer diagnosis. These findings also pave the way for personalized treatments using biomarkers and more effective healthcare strategies.