BRIDGING TECHNICAL EDUCATION GAPS: LEVERAGING AI-DRIVEN NEURAL NETWORKS FOR CROSS-DISCIPLINARY APPLICATIONS IN IMAGE PROCESSING AND VLSI

Abstract

The rapid advancement of artificial intelligence (AI) and neural network architectures has created new opportunities to address long-standing gaps in technical education, particularly in disciplines that require a convergence of theoretical knowledge and applied problem-solving. This research explores how AI-driven neural networks can serve as an educational and technological bridge between two traditionally distinct but increasingly interconnected domains: image processing and Very Large-Scale Integration (VLSI) design. Both fields demand advanced analytical skills, computational proficiency, and a strong grasp of mathematical modeling, yet educational programs often compartmentalize them, limiting interdisciplinary learning and innovation. The study investigates the pedagogical and practical potential of deploying neural network models to enhance teaching, learning, and application within these areas. For image processing, AI-based systems are capable of simplifying complex tasks such as noise reduction, edge detection, and object recognition, making them more accessible to students and researchers. In VLSI design, neural networks are increasingly employed for tasks such as fault detection, circuit optimization, and power efficiency modeling, providing learners with exposure to cutting-edge design methodologies. By embedding AI-driven tools into academic curricula and laboratory practices, this research demonstrates how students can gain holistic insights into both theoretical constructs and real-world applications, thereby narrowing the gap between academic learning and industry expectations. The paper presents a comparative analysis of case studies and experimental modules where neural networks were applied to problem-solving in both domains. The results suggest that AI-driven platforms significantly enhance comprehension, facilitate interactive learning, and encourage cross-disciplinary innovation. Moreover, the integration of neural networks provides learners with hands-on exposure to transferable skills such as algorithm design, model training, and performance evaluation, which are essential for addressing the evolving demands of industries reliant on computational intelligence. Ultimately, this research emphasizes that leveraging neural networks not only strengthens the academic foundation of students in image processing and VLSI but also fosters a multidisciplinary mindset that is vital for the future of technical education. By bridging isolated domains through AI-enabled methodologies, the study highlights a sustainable framework for developing adaptive, industry-ready professionals capable of driving innovation at the intersection of computer science, electronics, and engineering design.