Artificial Intelligence And Intelligent Systems By Np Padhy Pdf Free __HOT__ Download

The book discusses current trends in AI and its application to various fields. Intelligent systems such as expert systems, fuzzy systems, artificial neural networks, genetic algorithms, and swarm intelligent systems are discussed in detail with examples to facilitate in-depth understanding of AI. The text emphasizes the solution of real-world problems using the latest AI techniques. Since the ultimate goal of AI is the construction of programs to solve problems, an entire chapter has been devoted to the programming languages used in AI problem solving. Written in a clear and lucid style, this student-friendly book has been specially designed for undergraduate engineering students. With its application oriented approach and inclusion of recent topics, the book would also be useful to postgraduate students and researchers in th is field.

artificial intelligence and intelligent systems by np padhy pdf free download

Abstract:The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.Keywords: artificial intelligence; deep learning; internet of things; industrial internet of things; smart industry

This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.

Prediction of thermophysiological properties of ZnO NPs coated samples is a challenging task due to the complex relationship between applied chemicals and obtained results. ANN has achieved important milestones in the field of artificial intelligence (AI)35. ANN models are inspired by biological neural networks that allow them to capture linear or nonlinear complex relationships between dependent and independent variables36. It includes large groups of neurons connected by axons. The artificial neuron has multiple inputs that are weighted, summed up and followed by an activation function or transfer function. Every neuron receives input from various sources and applies activation function to provide the desired results. The advantages of ANN are exploration, creation and derivation of new data through training process37.