Table of Content

AI-Driven Engineering Applications

Submission Deadline: 31 December 2022 Submit to Special Issue

Guest Editors

Prof. Biswajeet Pradhan, University of Technology Sydney, Australia
Assoc. Prof. Dr. Shilpa Gite, Symbiosis International (Deemed University), India


In the last few decades, Artificial Intelligence (AI) has been an integral part of our life. AI technology is important because it enables human capabilities – understanding, reasoning, planning, communication and perception – to be undertaken by software increasingly effectively, efficiently and at low cost. There is a growing need for AI technologies like machine learning, specifically, deep neural networks, knowledge graphs, neuro-fuzzy models etc., in the most of the applications that can affect masses. The purpose of this Special Issue is to provide a platform for engineers, data scientists, researchers and practitioners to present new academic research and industrial development on machine learning for engineering applications. It aims at original research papers in the field, covering new theories, algorithms, systems, as well as new implementations and applications incorporating state-of-the-art machine learning techniques. This special issue invites authors to submit their contributions in the following areas, but not limited to:


·        AI solutions for medical diagnosis

·        AI-driven security and privacy

·        AI-driven marketing and finance applications

·        AI-driven industry use cases

·        AI-driven health monitoring systems and wearable system

·        AI-driven usability applications

·        AI in biomedical engineering

·        AI in imaging

·        AI in biometric identification

·        AI in smart cities

·        AI in smart transportation 

·        AI in genomics

·        AI in humanities

·        AI applications in mental health/sentiment analysis

·        AI case studies, experience reports, benchmarking, best practices, and worst practices


Both research articles and extensive review articles are allowed.

Published Papers

  • Open Access


    Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

    Sneha Basak, Himanshi Agrawal, Shreya Jena, Shilpa Gite, Mrinal Bachute, Biswajeet Pradhan, Mazen Assiri
    CMES-Computer Modeling in Engineering & Sciences
    (This article belongs to this Special Issue: AI-Driven Engineering Applications)
    Abstract Speech recognition systems have become a unique human-computer interaction (HCI) family. Speech is one of the most naturally developed human abilities; speech signal processing opens up a transparent and hand-free computation experience. This paper aims to present a retrospective yet modern approach to the world of speech recognition systems. The development journey of ASR (Automatic Speech Recognition) has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper. A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented, along with a brief discussion of various modern-day developments and applications in… More >

  • Open Access


    Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems

    Mohamed Hassan Essai Ali, Fahad Alraddady, Mo’ath Y. Al-Thunaibat, Shaima Elnazer
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 755-778, 2023, DOI:10.32604/cmes.2022.022246
    (This article belongs to this Special Issue: AI-Driven Engineering Applications)
    Abstract For a 5G wireless communication system, a convolutional deep neural network (CNN) is employed to synthesize a robust channel state estimator (CSE). The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state information. Also, it utilizes pilots to offer more helpful information about the communication channel. The proposed CNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory (BiLSTM/LSTM) NNs-based CSEs. The CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based estimators. Using three different loss function-based classification layers and… More >

  • Open Access


    Explainable Artificial Intelligence–A New Step towards the Trust in Medical Diagnosis with AI Frameworks: A Review

    Nilkanth Mukund Deshpande, Shilpa Gite, Biswajeet Pradhan, Mazen Ebraheem Assiri
    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 843-872, 2022, DOI:10.32604/cmes.2022.021225
    (This article belongs to this Special Issue: AI-Driven Engineering Applications)
    Abstract Machine learning (ML) has emerged as a critical enabling tool in the sciences and industry in recent years. Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks–thanks to advancements in technique, the availability of enormous databases, and improved computing power. Deep learning models are at the forefront of this advancement. However, because of their nested nonlinear structure, these strong models are termed as “black boxes,” as they provide no information about how they arrive at their conclusions. Such a lack of transparencies may be unacceptable in many applications, such as the medical domain. A… More >

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