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    Project-based Model in Physics Learning: The Influence on Computational Thinking Skills on the Eleventh-Grade Natural Science Major Students
    (Scopus, 2024-01-29) Subekti, Diah Aghni; Latifah, Sri; Anugrah, Adyt; Fitri, Megawati Ridwan; Makbuloh, Deden; Islam, Monirul
    The low level of computational thinking skills of students is a problem of 21st-century skills. One of the efforts to support 21st-century education is by applying a Project-based learning model. This study aims to determine the effect of the application of a project-based learning model on the computational thinking skills of students in class XI IPA. The research was conducted at MA Al-Hikmah Bandar Lampung. The population in this study was XI IPA class with samples of XI IPA 1 (experimental class) and XI IPA (control class). Using saturated sampling technique with Quasi-Experimental Research design. The results of this study indicate that the t-test value with a significant level of 5% there is an effect of the project-based learning model on the computational thinking skills of students in class XI IPA with a sig value <0.05 which is equal to 0.000 then H0 is rejected and H1 is accepted. Therefore, computational thinking skills can be used to solve problems in physics learning by applying indicators of decomposition, abstraction, algorithms, and generalization of patterns.
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    Internet of Sensing Things-Based Machine Learning Approach to Predict Parkinson
    (2023-09-15) Afroz, Sohana; Ullah Akhund, Tajim Md. Niamat; Khan, Tarikuzzaman; Hasan, Md. Umaid; Jesmin, Rashida; Sarker, M. Mesbahuddin
    With the help of the Internet of things, therapeutic science has progressed surprisingly. Lots of elderly individuals are affected by Parkinson’s disease. This work proposed an Internet of sensing things-based system to collect data from Parkinson’s affected people analyze the collected data in a cloud server with machine learning algorithms and predict the condition of the patient. Multiple types of sensors are used and tested. Micro-controllers are used to collect data from sensors and send them to a cloud server. Then, multiple machine learning algorithms are used to predict the patient’s condition. Results between several methods are also compared.
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    Integrated Bioinformatics and Machine Learning Analysis Uncovers Key Pathways and Therapeutic Targets for Hypertension and Chronic Kidney Disease
    (2024-12-20) Wasima, Jeba; Hosen, Md. Faruk; D Cruze, Francis Rudra; Shahin Uddin, Muhammad
    Hypertension is a serious cardiovascular disease that substantially raises morbidity and mortality rates worldwide. People who have high blood pressure have been found to have an increased risk of developing chronic kidney disease (CKD) in recent years. The goal of this research is to use modern bioinformatics approaches to find potential treatment candidates and clarify the underlying biological pathways linked to both hypertension and CKD. Sample from individuals with CKD and hypertension were taken from two publicly available microarray datasets, GSE33463 and GSE66494. Consistent differentially expressed genes (DEGs) were found following thorough pre- processing and Python analysis. A Venn diagram was used to show where these DEGs’ regulatory crossings were. The most functionally important genes were then identified via topological analysis after protein-protein interaction (PPI) networks were built. UBC, ARRIB1, FADD and EIF3D have been identified as important hub genes. These concordant DEGs are tightly linked to the Toll-like receptor signaling pathway, which is a crucial mechanism in the control of the immunological response, according to pathway enrichment analysis performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG).In order to better understand gene relationships, future research will examine modular network studies, transcription factor (TF), microRNA (miRNA) network regulation, and gene ontology (GO) analysis. Concordant DEGs have been used to select a number of possible medicinal molecules, providing a promising path forward for therapeutic research.
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    Integrated Bioinformatics and Machine Learning Analysis Reveals Shared Key Candidate Biomarkers and Therapeutic Targets in Ulcerative Colitis and Colorectal Cancer
    (2024-10-24) Sarker, Sakib; Hosen, Md. Faruk; Abul Basar, Md.; Ahammed, Emon
    The interplay between ulcerative colitis (UC) and colorectal cancer (CRC) has garnered significant research interest due to their potential shared molecular mechanisms. This study aims to identify common significant biomarkers and potential therapeutic targets for UC and CRC. We utilized two microarray datasets to perform differential expression analysis, identifying DEGs for both conditions. Subsequent ML-based gene selection was conducted using SHapley Additive exPlanations (SHAP) algorithm models on the respective datasets. Common ML-based DEGs were then identified and a protein-protein interaction (PPI) network was constructed using the STRING database. The PPI network was visualized and analyzed in Cytoscape, with the top ten hub genes identified using the Degree method in the cytoHubba plugin. The hub genes identified were CDC20, ANLN, HMMR, CCNB1, CDK1, KIF20A, ECT2, KIF11, NUF2, and CCNA2. These genes were further validated through survival analysis, establishing their significance in patient outcomes. Finally, we explored the drug-gene interaction network to identify potential therapeutic drugs targeting these hub genes. This comprehensive bioinformatics approach provides insights into the shared molecular pathways in UC and CRC and highlights poten- tial therapeutic targets for future research and drug development.
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    Prediction Hepatitis C Virus and Classifier Blood Donor and Disease using an Ensemble Approach in the Machine Learning Algorithm
    (Scopus, 2024-12-19) Hirok, Md Kamruzzaman; Parvin, Masuma; Sharmin, Shayla
    Hepatitis C, caused by the hepatitis C virus, is a liver condition that can lead to severe complications if left untreated. The disease progresses through different stages, and while it is more easily treatable in the early stages, reaching the final stage without proper treatment makes recovery much harder, often resulting in high costs and significant pain. The current research emphasizes the importance of early detection as a simple and effective way to manage the condition. This study focuses on accurately predicting hepatitis C status, categorizing individuals as either blood donors or affected by the disease, using an ensemble machine learning approach. The research utilizes thirteen attributes and classifies the target into five categories: Blood Donor (including Blood Donor and Suspect Blood Donor) and Disease (encompassing Hepatitis, Fibrosis, and Cirrhosis). Several machine learning algorithms are employed, includeincludeing Decision Tree, K-nearest neighbor, Random Forest, and a Stacking Classifier. Among these, the Stacking Classifier outperformed the others, achieving an accuracy of 99.4%, precision of 99.7%, recall of 97.7%, and an F1-score of 98.7%.
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    A Machine Learning Based Approach to Classify Tense from English Text
    (Scopus, 2024-12-19) Ayman, Umme; Islam, Md. Shafiqul; Rahat, Md. Azmain Mahtab; Raza, Dewan Mamun; Chakraborty, Narayan Ranjan; Bijoy, Md. Hasan Imam
    This paper investigates the classification of tense in English text using machine learning algorithms. Support Vector Machine (SVM), Random Forest (RF), Multinomial Naive Bayes (MNB), Decision Tree (DT), XGBoost, and K-Nearest Neighbors (KNN) are the six classifiers used in the study. The dataset was collected from diverse sources including novels, books, blogs, articles, social media platforms, newspapers, websites and some of them self-made. The data underwent preprocessing steps such as cleaning, normalization, and feature extraction using TfidfVectorizer. Among the other algorithms, SVM achieved the highest accuracy at 97.17%. Classifier performance was assessed with metrics such as F1-score, recall, accuracy, and precision. To evaluate performance, ROC curves, and confusion matrices were also examined. The study underlines the necessity for focused approaches and draws attention to the significant gaps in the field of natural language processing (NLP) regarding tense classification studies. By leveraging machine learning, this research aims to enhance the accuracy and contextual appropriateness of tense classification, thereby improving cross-cultural communication and understanding in machine translation systems. This research contributes to NLP by offering a robust approach to tense classification and demonstrates the potential of SVM in achieving high accuracy for this task. Future work will focus on addressing limitations such as short training data, overfitting and tense conversion.
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    Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI
    (Scopus, 2024) Islam, Taminul; Sheakh, Md. Alif; Tahosin, Mst. Sazia; Hena, Most. Hasna; Akash, Shopnil; Jardan, Yousef A. Bin; Wondmie, Gezahign Fentahun; Nafidi, Hiba-Allah; Bourhia, Mohammed
    Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model’s predictions and understand the impact of each feature on the model’s output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.
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    ALPR: ResNet50 powered Bangla License Plate Detectionand OCR by Root Mean Square Propagation Optimizer and Linear SVM Classifier
    (Scopus, 2024-06-10) Chowdhury, Abdulla Nasir; Summit, Samya Pal; Laskar, Md. Fuad Ahmed; Chowdhury, Gulam Mahfuz; Chowdhury, Ishmam Ahmed; Hasan, Mehedi
    This paper implements the MATLAB Image Processing Toolbox in detecting the license plate region using several user-defined functions in order to pre-process and process the image up until the point of extraction of characters. The extracted characters were then classified by utilizingResNet50 from the Deep Learning Toolbox of MATLAB, custom training it on above a thousand images of Bangla and English characters and numbers alongside possible categories of noise extracted from the ROI after processing the image which resulted in a datastore of 103 total categories. The output is converted to a string and saved in an excel sheet to be accessed later on. In this ALPR model, the model will scan through the images of vehiclesfrom a folder in a destination specified by the code to identify the license plates and characters and perform necessary actions on them. The aim of this paper is to properly implement the Image Processing Toolbox by MATLAB in order to identify the Region of Interest and study the performance of the Linear SVM(Support Vector Machine) classifier with ResNet50 when it comes to Bangla OCR. The training and validation accuracy achieved by using the Root Mean Square Optimizer was 97.57%. The final accuracies and precision achieved while testing the model on 50% of the image dataset was 99.2%. Moreover, theER (Error Rate) and FPR (False Positive Rate)were limited within 0.02%. The model scored 100% on F1 scores and Matthews Correlation Coefficient for every category of image classified
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    NewsNet: A Comprehensive Neural Network Hybrid Model for Efficient Bangla News Categorization
    (2024-11-04) Rana, Shakil; Haque, Md. Injamul; Sultana, Naznin; Amid, Abdul Fattah; Hosen, Md Jabed; Islam, Saiful
    Through the internet, Bangla news has grown enormously within the modern era of digital information. Every news outlet came up with its own categorizing system in order to handle such a huge quantity of content. The organization and categorization of online Bangla news articles, however, might not always correspond with the particular requirements of different users because of the heterogeneous nature of these platforms. Also, multiclass Bangla text classification has become increasingly important for Bangla newspaper platforms to enhance their recommendation system and reduce the manual labor required to classify their various article categories. To address the above limitation, we introduced NewsNet a text classification approach by combining the embedding layer, convolutional neural network(cnn), and recurrent neural network. In recurrent neural networks(rnn), we have employed two models including gated recurrent unit and bidirectional-LSTM (biLSTM) respectively. We have also used several preprocessing techniques such as Label encoder and tokenization correspondingly. We have experimented with our model on a Kaggle dataset called “Bangla Newspaper Dataset”.NewsNet achieved a good accuracy of 94.57%, 94.51% precision, 94.32% recall, and 94.43% f1 score respectively. NewsNet has demonstrated superior performance compared to other approaches on this Kaggle dataset.
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    The crucial role of the cerebellum in autism spectrum disorder: Neuroimaging, neurobiological, and anatomical insights
    (Scopus, 2024-07-03) Biswas, Mohammad Shahangir; Roy, Suronjit Kumar; Hasan, Rubait; Uddin, Md Moyen
    Background and Aims Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by a wide range of symptoms and challenges. While ASD is primarily associated with atypical social and communicative behaviors, increasing research has pointed towards the involvement of various brain regions, including the cerebellum. This review article aims to provide a comprehensive overview of the role of cerebellar lobules in ASD, highlighting recent findings and potential therapeutic implications. Methods Using published articles found in PubMed, Scopus, and Google Scholar, we extracted pertinent data to complete this review work. We have searched for terms including anatomical insights, neuroimaging, neurobiological, and autism spectrum disorder. Results The intricate relationship between the cerebellum and other brain regions linked to ASD has been highlighted by neurobiological research, which has shown abnormalities in neurotransmitter systems and cerebellar circuitry. The relevance of the cerebellum in the pathophysiology of ASD has been further highlighted by anatomical studies that have revealed evidence of cerebellar abnormalities, including changes in volume, morphology, and connectivity. Conclusion Thorough knowledge of the cerebellum's function in ASD may lead to new understandings of the underlying mechanisms of the condition and make it easier to create interventions and treatments that are more specifically targeted at treating cerebellar dysfunction in ASD patients.