DIU Faculty Publication

Project-based Model in Physics Learning: The Influence on Computational Thinking Skills on the Eleventh-Grade Natural Science Major Students

Mon, 29 Jan 2024 00:00:00 GMT

Project-based Model in Physics Learning: The Influence on Computational Thinking Skills on the Eleventh-Grade Natural Science Major Students 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. Conference paper

Internet of Sensing Things-Based Machine Learning Approach to Predict Parkinson

Fri, 15 Sep 2023 00:00:00 GMT

Internet of Sensing Things-Based Machine Learning Approach to Predict Parkinson 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. Conference Paper

Integrated Bioinformatics and Machine Learning Analysis Uncovers Key Pathways and Therapeutic Targets for Hypertension and Chronic Kidney Disease

Fri, 20 Dec 2024 00:00:00 GMT

Integrated Bioinformatics and Machine Learning Analysis Uncovers Key Pathways and Therapeutic Targets for Hypertension and Chronic Kidney Disease 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. Conference paper

Integrated Bioinformatics and Machine Learning Analysis Reveals Shared Key Candidate Biomarkers and Therapeutic Targets in Ulcerative Colitis and Colorectal Cancer

Thu, 24 Oct 2024 00:00:00 GMT

Integrated Bioinformatics and Machine Learning Analysis Reveals Shared Key Candidate Biomarkers and Therapeutic Targets in Ulcerative Colitis and Colorectal Cancer 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. Conference paper