Neural Network Applications | Artificial Neural Network

The content collectively explores various applications and methodologies of artificial neural networks (ANNs) and other machine learning techniques across multiple domains. Topics include medical diagnostics, cybersecurity, remote sensing, and predictive modeling, highlighting innovative approaches and comparative analyses of different algorithms. Research findings demonstrate the effectiveness of ANNs in classification tasks, anomaly detection, and control systems, revealing their potential to improve performance, efficacy, and decision-making in various fields.

Introduction to Deep learning and ANN .pptx
multi layer perceptron and convolutional neural network
Assessing the Quality of Lye-Peeled Garlic: A ConcentrationDependent Approach with Artificial Neural Networks and Multivariate Analysis
6th International Conference of Education (CONEDU 2026)
5th International Conference on Education and Technology (EDUTEC 2026)
Blockchain and ML in land registries a transformative alliance
11th International Conference on Computer Science, Engineering and Applications (CSEA 2025)
Artificial neural network (ANN) construction.pptx
11th International Conference on Signal and Image Processing (SIGPRO 2025)
11th International Conference on Data Mining (DTMN 2025)
NEURAL_Network_introduction_12345678.pdf
Broiler meats tenderness prediction using near infrared spectroscopy against non-linear predictive modelling
Semi-automatic voice comparison approach using spiking neural network for forensics
Applications of artificial intelligence in indoor fire prevention and fighting
Deep neural network for maximizing output power estimation of dual-axis solar tracker
MATHEMATICAL EXPLANATION TO SOLUTION FOR EX-NOR PROBLEM USING MLFFN
Classification of Bharatanatyam postures using tailored features and artificial neural network
Levenberg-Marquardt-optimized neural network for rainfall forecasting
Predicting water resistance and pitching angle during take-off: an artificial neural network approach
Unveiling DNA sequences: a comparison of machine learning and deep learning techniques for prediction