ANN with NSGA II for Optimization

@targaryen007

Hello, I can help you with your project on Artificial Neural Networks (ANN) and multi-objective optimization using NSGA-II. I have a strong background in machine learning and optimization and can: Build an ANN model using your real-world manufacturing dataset Preprocess and analyze the experimental data properly Implement NSGA-II to optimize multiple objectives (e.g., quality, cost, efficiency, etc.) Generate and explain Pareto fronts and optimization results Provide clear explanations suitable for a student project or report What you’ll receive Well-structured and commented code (Python) Explanation of ANN architecture and NSGA-II workflow Result analysis and plots (loss curves, Pareto front) Support in understanding results for viva/report writing Once you share the dataset and objectives, I can suggest the best ANN structure and optimization setup. Looking forward to working with you.

@MaithriW

I can help with this. I have experience building ANN models using real-world datasets and applying multi-objective optimization with NSGA-II. I’m comfortable cleaning and preprocessing experimental manufacturing data, training reliable models, and defining objective functions that make practical sense. I can implement NSGA-II to optimize the trained model, analyze trade-offs between objectives, and present the Pareto-optimal solutions clearly. I’ll also document the approach so the results are easy to understand and reproduce. Once you share the dataset and objectives, I can review them and confirm the modeling and optimization strategy before starting.

@huonghy89

Hi, I hope you are doing well. Very happy to bid your on project because my skills are fitted in your project. I have strong experience building ANN models on real-world datasets and applying NSGA-II multi-objective optimization for manufacturing and process improvement problems. I will clean and structure your experimental manufacturing data, then develop and validate an ANN surrogate model that accurately predicts the target outputs. Next, I will implement NSGA-II to optimize your objectives (e.g., quality, cost, time, energy), generate the Pareto front, and deliver clear plots, code, and a step-by-step explanation so you can replicate everything for your thesis/project. If you send the message , we can discuss the project more. Thanks.

@Juancohen117

I can support you end-to-end on your ANN and NSGA-II project with a clear, academic-ready implementation and full explanation of the methodology. I have a strong background in machine learning, neural networks, and optimization, supported by IBM professional certifications in Data Science and AI, where I worked extensively with real-world datasets, model development, and optimization-driven decision systems. I am comfortable translating theory into clean, working code and explaining each step clearly for student projects. Proposal 1. Data Preparation (Real Manufacturing Data) * Clean, preprocess, and normalize experimental manufacturing data * Handle noise, missing values, and scaling (critical for ANN performance) 2. ANN Model Development * Design and train an ANN suitable for manufacturing prediction tasks * Validate performance using appropriate metrics * Clearly explain architecture, activation functions, and training logic 3. NSGA-II Multi-Objective Optimization * Implement NSGA-II to optimize multiple conflicting objectives * Define objective functions and constraints clearly * Generate and interpret Pareto fronts 4. Explanation & Academic Clarity * Step-by-step explanation of ANN + NSGA-II integration * Clean, reusable Python code * Guidance so you can confidently present or defend the project

@tanvibhayani

Hello, I can help you complete your project on Artificial Neural Networks (ANN) and multi-objective optimization using NSGA-II with real-world manufacturing data. I have hands-on experience in building ANN models for prediction, performing data preprocessing, and applying NSGA-II to obtain Pareto-optimal solutions. I will first clean and analyze the experimental manufacturing dataset, then develop and validate an ANN-based predictive model. After that, I will implement NSGA-II for multi-objective optimization and clearly explain the Pareto front and trade-offs between objectives. You will receive well-documented Python/MATLAB code, optimization results, graphs, and a clear explanation suitable for academic submission. I am committed to timely delivery and quality work.