Название: Applied Intelligence for Industry 4.0Автор: Nazmul Siddique, Mohammad Shamsul Arefin, M. Shamim Kaiser
Издательство: CRC Press
Год: 2023
Страниц: 279
Язык: английский
Формат: pdf (true)
Размер: 24.9 MB
We are all aware that Artificial Intelligence (AI) has brought a change in our lives, driven by a new form of interaction between man and machine. We are in the era of the fourth Industrial Revolution (IR) where AI plays vital roles in human development by enabling extraordinary technological advances making fundamental changes to the way we live, work and relate to one another. It is an opportunity to help everyone, including leaders, policymakers and people from all income groups and nations, to harness converging technologies in order to create an inclusive, human-centered future.
We need to prepare our graduates as well as researchers to conduct their research with 4.0 IR-related technologies. We need to develop policies and implement those policies to focus on the components of 4.0 IR for sustainable developments. Applied Intelligence for Industry 4.0 will cover cutting edge topics in the fields of AI and industry 4.0. The text will appeal to beginners and advanced researchers in computer science, information sciences, engineering and robotics.
Analyzing the IT Job Market and Classifying IT Jobs Using Machine Learning Algorithms: Machine Learning (ML) algorithms are used to classify analogous jobs. Convolutional Networks (CNNs) have been used to establish job classification systems based on job requirement text. The main challenge of a job classification system is data accumulation. Recruiter skill preferences change from time to time. So, data collection from up-to-date CVs is a challenging issue. Here, in our system, jobs are recommended based on the skill and qualification of a candidate. However, sometimes recruiters also give equal importance to experiences and educational history. They want to find a generalist instead of a specialist. In this work, two datasets including “Job Posts Dataset” and “multipleChoiceResponses Dataset” are used. Several ML approaches such as Gradient Boosting, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN) have been applied for classifying IT jobs.
Machine Learning and Blockchain-based Privacy-aware: Cognitive Radio Internet of Things Features: Spectrum sensing and Dynamic Spectrum Access (DSA) in Cognitive Radio Internet of Things (CR-IoT) networks based on Cooperative Spectrum Sensing (CSS) rely on a centralized Fusion Center (FC), which is vulnerable because of the potential attacks of Malicious Users (MUs) and a single point of failure. To address this issue, this research chapter introduces a promising Machine Learning (ML) model that uses a K-means clustering algorithm to detect and cluster malicious CR-IoT users and a blockchain verification protocol as a function for secure spectrum sharing in CR-IoT networks. Each CR-IoT user in the proposed CR-IoT network appears as a sensing node and a mining node in the blockchain protocol, which leads to energy consumptions. Simulations checked the effectiveness of the suggested K-means clustering algorithm and blockchain-based privacy-aware protocol in CR-IoT networks.
Discusses advance data mining, feature extraction and classification algorithms for disease detection, cyber security detection and prevention, soil quality assessment and other industrial applications
Includes the parameter optimization and explanation of intelligent approaches for business applications
Presents context-aware smart insights and energy efficient and smart computing for the next-generation of smart industry
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