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Journal : Journal of Electronics, Electromedical Engineering, and Medical Informatics

Advancements within Molecular Engineering for Regenerative Medicine and Biomedical Applications an Investigation Analysis towards A Computing Retrospective Akhtar, Zarif Bin; Gupta, Anik Das
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.351

Abstract

The field of molecular engineering in medicine has witnessed remarkable progress in recent years, revolutionizing healthcare, diagnostics, and therapy development. However, the pandemic showcased there is still more requirement for progress along with further detailed investigation which is paramount and also a necessity moving forward. This research investigation delves into the interdisciplinary realm of molecular engineering, exploring its impact on regenerative medicine, biomaterials, tissue engineering, and the innovation from various advanced biotechnologies which has accelerated health science. The main objective for this research aims at providing an in depth investigative exploration of biomaterial applications with their respective roles within regenerative medicine and its associated advancements along with, tissue engineering, organ-on-a-chip device peripheral mechanics functionality and how bioprinting is paving the way for the creation of functional tissues and organs with a case study analysis on drug discovery, immune engineering, to the field of precision medicine, gene editing with the insight towards drug discovery processing, design and screening pipelined for biologics and the how therapeutics and drugs will play out in future healthcare. This exploration also provides many meaningful and remarkable conclusions on the advanced technologies which are explored and investigated throughout the step-by-step systematic technical computing methods approached for the research.
A Comparative Analysis of Polynomial-fit-SMOTE Variations with Tree-Based Classifiers on Software Defect Prediction Nur Hidayatullah, Wildan; Herteno, Rudy; Reza Faisal, Mohammad; Adi Nugroho, Radityo; Wahyu Saputro, Setyo; Akhtar, Zarif Bin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.455

Abstract

Software defects present a significant challenge to the reliability of software systems, often resulting in substantial economic losses. This study examines the efficacy of polynomial-fit SMOTE (pf-SMOTE) variants in combination with tree-based classifiers for software defect prediction, utilising the NASA Metrics Data Program (MDP) dataset. The research methodology involves partitioning the dataset into training and test subsets, applying pf-SMOTE oversampling, and evaluating classification performance using Decision Trees, Random Forests, and Extra Trees. Findings indicate that the combination of pf-SMOTE-star oversampling with Extra Tree classification achieves the highest average accuracy (90.91%) and AUC (95.67%) across 12 NASA MDP datasets. This demonstrates the potential of pf-SMOTE variants to enhance classification effectiveness. However, it is important to note that caution is warranted regarding potential biases introduced by synthetic data. These findings represent a significant advancement over previous research endeavors, underscoring the critical role of meticulous algorithm selection and dataset characteristics in optimizing classification outcomes. Noteworthy implications include advancements in software reliability and decision support for software project management. Future research may delve into synergies between pf-SMOTE variants and alternative classification methods, as well as explore the integration of hyperparameter tuning to further refine classification performance.