A Review on Machine Learning Strategies for Real-World Engineering Applications

Abstract

Huge amounts of data are circulating in the digital world in the era of the Industry 5.0 revolution. Machine learning is experiencing success in several sectors such as intelligent control, decision making, speech recognition, natural language processing, computer graphics, and computer vision, despite the requirement to analyze and interpret data. Due to their amazing performance, Deep Learning and Machine Learning Techniques have recently become extensively recognized and implemented by a variety of real-time engineering applications. Knowledge of machine learning is essential for designing automated and intelligent applications that can handle data in fields such as health, cyber-security, and intelligent transportation systems. There are a range of strategies in the field of machine learning, including reinforcement learning, semi-supervised, unsupervised, and supervised algorithms. This study provides a complete study of managing real-time engineering applications using machine learning, which will improve an application's capabilities and intelligence. This work adds to the understanding of the applicability of various machine learning approaches in real-world applications such as cyber security, healthcare, and intelligent transportation systems. This study highlights the research objectives and obstacles that Machine Learning approaches encounter while managing real-world applications. This study will act as a reference point for both industry professionals and academics, and from a technical standpoint, it will serve as a benchmark for decision-makers on a range of application domains and real-world scenarios.

An overview memristor based hardware accelerators for deep neural network

“…107 Machine learning algorithms, by intelligently analyzing data, are the key to developing smart, real-time engineering applications for real-world problems. 108 Machine learning is categorized into Supervised, Unsupervised, and Reinforcement Learning. In memristor-based studies, [109][110][111][112] these learning algorithms are used in conjunction with various deep learning methods.…”

“…Algorithms for machine learning learn and improve from experiences automatically without being explicitly programmed 107 . Machine learning algorithms, by intelligently analyzing data, are the key to developing smart, real‐time engineering applications for real‐world problems 108 . Machine learning is categorized into Supervised, Unsupervised, and Reinforcement Learning.…”

YOLOv5-PD: A Model for Common Asphalt Pavement Defects Detection

“…Jhaveri et al [2] discusses and summarizes the applicability and applications of machine learning to various problems in the real world. This study can be used as a benchmark for machine learning in a variety of applications and real-world situations.…”

Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning

“…The lexicon-or corpus-based technique uses a dictionary that contains words and their corresponding polarities [10], whereas machine learning-based techniques require a massive labeled dataset with manual annotation [11]. Deep learning's (DL) success in a variety of fields, including computer vision, reveals its use in the field of natural language processing [12][13][14]. Because DL does not use feature engineering to learn continuous text representations from data, it outperforms traditional machine learning.…”

“…With the use of various word embedding approaches, deep contextual properties of words are extracted in a lower-dimensional space [15]. For NLP, many DL approaches are used, such as convolutional neural networks (CNNs) [12,16] and long short-term memory (LSTM) [17]. Although such DL techniques are frequently applied to English corpora, Arabic Sentiment Analysis deep learning models are not getting as much attention [18][19][20].…”