Special Issues

Advanced Machine Learning and Artificial Intelligence in Engineering Applications

Submission Deadline: 31 March 2024 (closed) View: 10

Guest Editors

Prof. Dr. Laith Abualigah, Al Al-Bayt University, Jordan.
Prof. Dr. Raed Abu Zitar, Sorbonne University Abu Dhabi, UAE.
Prof. Dr. Heming Jia, Sanming University, China.
Prof. Dr. Cuong-Le Thanh, Ho Chi Minh City Open University, Vietnam.


Artificial intelligence (AI) is a field of computer science that focuses on creating intelligent machines that can perform tasks that normally require human intelligence, such as reasoning, learning, and problem-solving. AI has a wide range of applications in engineering, and it is being used to solve complex problems, optimize processes, and improve performance and safety in a variety of industries.


Machine learning is a branch of artificial intelligence that enables machines to learn and make predictions or decisions based on data, without being explicitly programmed. Machine learning algorithms are used to build models from large datasets that can identify patterns, relationships, and trends that are not apparent to the human eye. These models can be used to make predictions and decisions in a wide range of engineering applications.


In engineering, machine learning is being used to improve processes, reduce costs, and optimize performance. For example, in manufacturing, machine learning can be used to detect defects in products, optimize manufacturing processes, and predict maintenance requirements. In autonomous vehicles, machine learning can be used to control steering, acceleration, and braking, and detect obstacles on the road. In robotics, machine learning can be used to train robots to perform complex tasks such as object recognition, grasping, and manipulation. Machine learning is also being used in other areas of engineering, such as energy, healthcare, and infrastructure. For example, in the energy industry, machine learning can be used to predict energy demand and optimize energy consumption. In healthcare, machine learning can be used to analyze patient data to improve diagnoses and treatment plans. In infrastructure, machine learning can be used to monitor and maintain buildings and bridges. Overall, machine learning is a powerful tool that is being used to solve complex engineering problems and improve efficiency, quality, and safety in a wide range of applications.


Advanced Machine Learning and Artificial Intelligence in Engineering Applications refer to the integration of cutting-edge machine learning and artificial intelligence technologies into various engineering fields. This integration aims to enhance and optimize the design, development, and operation of engineering systems and products. Engineering applications of advanced machine learning and AI include computer vision, robotics, manufacturing, aerospace and defense, energy and utilities, transportation, and healthcare, among others. These technologies can be used for tasks such as image and signal analysis, process optimization, autonomous control, predictive maintenance, and decision-making, among others. The use of advanced machine learning and AI in engineering has the potential to significantly improve the efficiency, safety, and effectiveness of engineering systems and products. However, it is important to consider the ethical and societal implications of these technologies as they become more widely used. Overall, the field of advanced machine learning and AI in engineering is rapidly growing and holds immense potential for improving various engineering domains and solving complex engineering problems.


• Computer vision
• Robotics
• Manufacturing
• Aerospace and defense
• Energy and utilities
• Transportation
• Healthcare
• Optimization
• Engineering design problems
• Real-world Problems

Published Papers

  • Open Access


    Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

    Shariar Md Imtiaz, Ki-Chul Kwon, F. M. Fahmid Hossain, Md. Biddut Hossain, Rupali Kiran Shinde, Sang-Keun Gil, Nam Kim
    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2921-2937, 2023, DOI:10.32604/csse.2023.040205
    (This article belongs to the Special Issue: Advanced Machine Learning and Artificial Intelligence in Engineering Applications)
    Abstract This article describes a novel approach for enhancing the three-dimensional (3D) point cloud reconstruction for light field microscopy (LFM) using U-net architecture-based fully convolutional neural network (CNN). Since the directional view of the LFM is limited, noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds. The existing methods suffer from these problems due to the self-occlusion of the model. This manuscript proposes a deep fusion learning (DL) method that combines a 3D CNN with a U-Net-based model as a feature extractor. The sub-aperture images obtained from the light field… More >

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