Radial basis neural network optimization using fruit fly

Master's Thesis from the year 2014 in the subject Computer Sciences - Artificial Intelligence, grade: A, , course: Master Of Technology Computer Science and Engineering, language: English, abstract: This research presents the optimization of radial basis function (RBF) neural network by means of aFOA and establishment of network model, adopting it with the combination of the evaluation of the mean impact value (MIV) to select variables. The form of amended fruit fly optimization algorithm (aFOA) is easy to learn and has the characteristics of quick convergence and not readily dropping into local optimum. The validity of model is tested by two actual examples, furthermore, it is simpler to learn, more stable and practical. Our aim is to find a variable function based on such a large number of experimental data in many scientific experiments such as Near Infrared Spectral data and Atlas data. But this kind of function is often highly uncertain, nonlinear dynamic model. When we perform on the data regression analysis, this requires choosing appropriate independent variables to establish the independent variables on the dependent variables regression model. Generally, experiments often get more variables, some variables affecting the results may be smaller or no influence at all, even some variable acquisition need to pay a large cost. If drawing unimportant variables into model, we can reduce the precision of the model, but cannot reach the ideal result. At the same time, a large number of variables may also exist in multicollinearity. Therefore, the independent variable screening before modeling is very necessary. Because the fruit fly optimization algorithm has concise form, is easy to learn, and have fault tolerant ability, besides algorithm realizes time shorter, and the iterative optimization is difficult to fall into the local extreme value. And radiate basis function (RBF) neural network's structure is simple, training concise and fasting speed of convergence by learning, can approximate any nonlinear function, having a 'local perception field' reputation. For this reason, this paper puts forward a method of making use of the amended fruit flies optimization algorithm to optimize RBF neural network (aFOA-RBF algorithm) using for variable selection.

Post Doc and Ph.D. in Artificial Intelligence. I have a profound understanding of cutting-edge technologies such as AI/ML, Data Science, NLP, Generative and Responsive AI, AGI, Neural Networks, Neuro-Fuzzy Expert System, and Quantum Computing. I have had the privilege of guiding and supervising of 75+ postgraduate students in various programs. My research endeavours have yielded over 56+ published research papers, all of which have been recognized in international and national journals with indexing in prestigious databases like Scopus, SCI, Web of Sciences, UGC CARE, and others. In addition, my contributions to the field of innovation include securing three Indian patents. I am a seasoned computer programmer with extensive experience in handling AI projects. Over the years, I have honed my skills in designing, developing, and implementing various AI solutions. My journey in the realm of programming began with a passion for solving complex problems through code.I delved into traditional programming languages, mastering the fundamentals of logic and algorithm design. As technology evolved, so did my expertise. The advent of AI captured my interest, prompting me to explore the possibilities it offered for transforming how we approach problem-solving in the digital age. I have hands-on experience with a range of AI technologies, including machine learning, natural language processing, and computer vision. My proficiency extends to frameworks like TensorFlow and PyTorch, enabling me to build robust and efficient models. Collaboration has been a cornerstone of my career. I've had the privilege of working with diverse teams, contributing my programming expertise to interdisciplinary projects. This collaborative approach has not only enhanced my technical skills but also enriched my understanding of how AI intersects with various domains, from pharmaceutical science to cognitive radio network. In the fast-paced world of technology, staying updated is crucial. Continuous learning is not just a professional obligation but a personal commitment to ensuring that my skills remain cutting-edge. As a forward-thinking programmer, I recognize the ethical considerations surrounding AI. Striking a balance between innovation and responsibility is paramount. I advocate for ethical AI practices, ensuring that the solutions I contribute to are not only technically sound but also align with ethical standards.