About Me Dr. Harsha B K, an experienced educator with an unyielding commitment to excellence in the field of engineering education has 15 years of dedicated teaching experience. He has guiding and mentoring countless aspiring engineers on their path to success.

His journey as an educator is marked by a relentless pursuit of knowledge, innovation, and a deep passion for nurturing young talents. Over the years, he has the privilege of witnessing his students' transformation from curious learners to accomplished professionals, and it's a journey that continues to inspire him every day. His teaching philosophy is rooted in the belief that education is not just about imparting knowledge but also about fostering critical thinking, problem-solving skills, and a deep sense of curiosity. He is dedicated to empowering students with the skills they need to excel in the ever-evolving world of engineering. With 15 years of teaching experience, he brings a wealth of knowledge and expertise to the classroom. His work is driven by a genuine love for teaching and a desire to see his students thrive. He continuously explores new and effective teaching methods to keep his students engaged and inspired. Legacy: Many of his former students have gone on to achieve remarkable success in their careers, a testament to the quality of education they received.

Get in Touch He is always eager to connect with students, fellow educators, and anyone who shares a passion for engineering and education. Feel free to contact him at harshabk@rvu.edu.in.v

पुस्तकस्था तु या विद्या परहस्तगतं धनम् । कार्यकाले समुत्पन्ने नसा विद्या न तद्धनम्।

• The research work, "Performance Analysis of Supervised Classification Algorithms for Non- Parametric Skin Detection," has delved into the realm of computer vision and image processing. The primary focus is to assess and analyze the effectiveness of various supervised classification algorithms when applied to the specific task of non-parametric skin detection within images.

  Non-parametric skin detection refers to the identification and segmentation of human skin regions in images without relying on strict mathematical models or assumptions about the skin's characteristics. This is particularly important in applications such as face recognition, gesture-based interfaces, and medical image analysis.

  Through the research, the aim to provide a comprehensive evaluation of the performance of these supervised classification algorithms, which may include but are not limited to methods like Support Vector Machines (SVM), Decision Trees, Random Forests, Neural Networks, and others. The work involves conducting experiments, collecting and analyzing data, and comparing the outcomes of these algorithms in terms of accuracy, efficiency, and robustness.

  The significance of the research lies in its potential to enhance the accuracy and reliability of skin detection within images, which, in turn, can have far-reaching implications in various fields, including computer vision, biometrics, and medical imaging. Moreover, the findings may guide the selection of the most suitable algorithm for specific applications, thereby aiding researchers, engineers, and developers in making informed choices when it comes to image processing and analysis.

  In summary, the research work represents a valuable contribution to the field of computer vision by shedding light on the performance and applicability of supervised classification algorithms in the context of non-parametric skin detection, ultimately advancing the state-of- the-art in image processing techniques.

• Research Interests: Image Processing, AI-ML, Design-For-Testability

• Best faculty award for predominant and consistent board exam results and feedback

• Best Paper award in 2013 for his research work.