http://192.168.40.123:8080/handle/123456789/15215 2026-09-25T23:56:12Z http://192.168.40.123:8080/handle/123456789/18127 A Comparative Study of AI-Generated 3D Models and Conventional Software-Based 3D Modeling Techniques: Accuracy, Efficiency, and Creative Potential Hasan, Kazi Jahid; Abhi, Abhijit Roy; Uddin, Md. Salah This comparative exploration differences between AI-generated 3D models and manually made models, with a focus on accuracy, efficiency, and production readiness. Two approaches were tested: text- and image-based AI tools (Edify-3D, Tripo AI) and conventional polygonal modeling in Autodesk Maya. A stylized low-poly war robot was modeled using both methods under the same design constraints. Evaluation considered build time, mesh topology, UV layout, editability, and suitability for animation workflows. AI tools generated results within minutes and that made them attractive for rapid prototyping. But the generated meshes showed structural weaknesses such as irregular topology, disconnected surfaces, and overlapping UVs, limiting their use in animation and real-time environments. Manual modeling required several hours but shaped clean geometry, consistent edge flow that are game and animation ready assets. From the results it became clear that there is a trade-off. The AI tools were useful for getting ideas out quickly, but they did not give the consistency required in a production setting. The manual process, although slower, gave models that were cleaner and easier to adapt. The study proposes that hybrid workflows, where AI provides initial drafts and human CG artists refine topology and details could balance efficiency with quality. Conference paper 2026-02-09T00:00:00Z http://192.168.40.123:8080/handle/123456789/16564 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 2024-01-29T00:00:00Z http://192.168.40.123:8080/handle/123456789/16563 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 2023-09-15T00:00:00Z http://192.168.40.123:8080/handle/123456789/16562 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 2024-12-20T00:00:00Z