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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.
Enhancing Cybersecurity through AI-Powered Security Mechanisms Akhtar, Zarif Bin; Tajbiul Rawol, Ahmed
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.16852

Abstract

In the rapidly evolving landscape of digital technology, the proliferation of interconnected systems has brought unprecedented opportunities and challenges. Among these challenges, the escalating frequency and sophistication of cyberattacks pose significant threats to individuals, organizations, and nations. In response, the fusion of Cybersecurity and Artificial Intelligence (AI) has emerged as a pivotal paradigm, offering proactive, intelligent, and adaptable defense mechanisms. This research explores the transformative impacts of AI-powered security on cybersecurity, demonstrating how AI techniques, including machine learning, natural language processing, and anomaly detection, fortify digital infrastructures. By analyzing vast volumes of data at speeds beyond human capacity, AI-driven cybersecurity systems can identify subtle patterns indicative of potential threats, allowing for early detection and prevention. The exploration consolidates existing studies, highlighting the trends and gaps that this research addresses. Expanded results and discussions provide a detailed analysis of the practical benefits and challenges of AI applications in cybersecurity, including case studies that offer concrete evidence of AI's impact. Novel contributions are emphasized through comparisons with other studies, showcasing improvements in accuracy, precision, recall, and F-score metrics, which demonstrate the effectiveness of AI in enhancing cybersecurity measures. The synergy between AI and human expertise is explored, highlighting how AI-driven tools augment human analysts' capabilities. Ethical considerations and the "black box" nature of AI algorithms are addressed, advocating for transparent and interpretable AI models to foster trust and collaboration between man and machine. The challenges posed by adversarial AI, where threat actors exploit AI system vulnerabilities, are examined. Strategies for building robust AI security mechanisms, including adversarial training, model diversification, and advanced threat modeling, are discussed. The research also emphasizes a holistic approach that combines AI-driven automation with human intuition and domain knowledge. As AI continues to rapidly evolve, a proactive and dynamic cybersecurity posture can be established, bolstering defenses, mitigating risks, and ensuring the integrity of our increasingly interconnected digital world.
Artificial Intelligence (AI) and Extended Reality (XR): A Biomedical Engineering Perspective Investigation Analysis Akhtar, Zarif Bin; Rawol, Ahmed Tajbiul
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 3 (2024): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i3.5

Abstract

The convergence of Artificial Intelligence (AI) and Extended Reality (XR) has heralded a new era in the field of Biomedical Engineering, offering unprecedented avenues for innovation, diagnostics, treatment, and education. This research delves into the symbiotic relationship between AI and XR, unraveling their collective potential to revolutionize healthcare practices. AI, characterized by its ability to learn and adapt, has transcended its role within data analysis to become an indispensable tool in healthcare. Through advanced algorithms, AI can predict disease patterns, enhance medical imaging, and optimize treatment protocols. On the other hand, XR technologies, encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), immerse users in virtual environments, facilitating interactive and experiential learning and treatment methods. This research focuses on the study that examines with the integration of AI and XR in biomedical applications, elucidating their role in diagnosis, treatment, and training. AI-driven image analysis augments medical imaging, expediting disease identification and tracking treatment progress. XR, through its immersive nature, empowers surgeons with detailed anatomical insights during procedures and aids in rehabilitation through engaging simulations. The synergistic marriage of AI and XR also redefines medical education by offering immersive training experiences to healthcare practitioners and bridging the gap between theory and practice. Furthermore, ethical considerations and challenges emerge as these technologies evolve. Privacy concerns, data security, and the need for regulatory frameworks are paramount in this dynamic landscape. Striking the right balance between innovation and patient safety remains an imperative task. In the context of this research, the fusion of AI and XR from a biomedical engineering perspective holds the potential to revolutionize healthcare. As AI refines diagnostics and treatment strategies, XR provides a tangible platform for immersive experiences that enhance training and therapeutic interventions. This research navigates the landscape of this transformative convergence, shedding light on its profound implications for Biomedical Engineering and the well-being of patients worldwide.