Special lssues
Table of Content

AI-Driven Engineering Applications

Submission Deadline: 31 July 2023 (closed)

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


    An Approach for Human Posture Recognition Based on the Fusion PSE-CNN-BiGRU Model

    Xianghong Cao, Xinyu Wang, Xin Geng, Donghui Wu, Houru An
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 385-408, 2024, DOI:10.32604/cmes.2024.046752
    (This article belongs to the Special Issue: AI-Driven Engineering Applications)
    Abstract This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit (PSE-CNN-BiGRU) fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments. Firstly, the deep convolutional network is integrated with the Mediapipe framework to extract high-precision, multi-dimensional information from the key points of the human skeleton, thereby obtaining a human posture feature set. Thereafter, a double-layer BiGRU algorithm is utilized to extract multi-layer, bidirectional temporal features from the human… More >

  • Open Access


    Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

    Kolli Ramujee, Pooja Sadula, Golla Madhu, Sandeep Kautish, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1455-1486, 2024, DOI:10.32604/cmes.2023.043384
    (This article belongs to the Special Issue: AI-Driven Engineering Applications)
    Abstract Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems. Its attributes as a non-toxic, low-carbon, and economical substitute for conventional cement concrete, coupled with its elevated compressive strength and reduced shrinkage properties, position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure. In this context, this study sets out the task of using machine learning (ML) algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field. To achieve this goal,… More >

  • Open Access


    Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

    Smita Khade, Shilpa Gite, Sudeep D. Thepade, Biswajeet Pradhan, Abdullah Alamri
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 323-345, 2023, DOI:10.32604/cmes.2023.023674
    (This article belongs to the Special Issue: AI-Driven Engineering Applications)
    Abstract Contactless verification is possible with iris biometric identification, which helps prevent infections like COVID-19 from spreading. Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses, replayed the video, and print attacks. The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness. Seven assorted feature creation ways are studied in the presented solutions, and these created features are explored for the training of eight distinct… More >

    Graphic Abstract

    Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

  • Open Access


    Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

    Shixin Tu, Yuanyuan Jia, Jinglong Du, Baoru Han
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 293-321, 2023, DOI:10.32604/cmes.2023.022308
    (This article belongs to the Special Issue: AI-Driven Engineering Applications)
    Abstract The field of healthcare is considered to be the most promising application of intelligent sensor networks. However, the security and privacy protection of medical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention. Fortunately, digital watermarking provides an effective method to solve this problem. In order to improve the robustness of the medical image watermarking scheme, in this paper, we propose a novel zero-watermarking algorithm with the integer wavelet transform (IWT), Schur decomposition and image block energy. Specifically, we first use IWT to extract low-frequency information and… More >

  • 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, Vol.135, No.2, pp. 1053-1089, 2023, DOI:10.32604/cmes.2022.021755
    (This article belongs to the 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 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 the 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 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 the 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… More >

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