Open Access
ARTICLE
Anastasios Dounis*, Ioannis Palaiothodoros, Anna Panagiotou
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.057888
（This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)
Abstract Accurate medical diagnosis, which involves identifying diseases based on patient symptoms, is often hindered by uncertainties in data interpretation and retrieval. Advanced fuzzy set theories have emerged as effective tools to address these challenges. In this paper, new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets (q-ROFS) and interval-valued q-rung orthopair fuzzy sets (IVq-ROFS). Three aggregation operators are proposed in our methodologies: the q-ROF weighted averaging (q-ROFWA), the q-ROF weighted geometric (q-ROFWG), and the q-ROF weighted neutrality averaging (q-ROFWNA), which enhance decision-making under uncertainty. These operators are paired More >
Open Access
ARTICLE
Tmader Alballa1, Muhammad Ihsan2, Atiqe Ur Rahman2, Noorah Ayed Alsorayea3, Hamiden Abd El-Wahed Khalifa3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.057440
（This article belongs to the Special Issue: Advances in Ambient Intelligence and Social Computing under uncertainty and indeterminacy: From Theory to Applications)
Abstract The dramatic rise in the number of people living in cities has made many environmental and social problems worse. The search for a productive method for disposing of solid waste is the most notable of these problems. Many scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated ambiguity. The goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site selection. The characteristics (or sub-attributes) that… More >
Open Access
ARTICLE
Chongzhen Zhang1,2,*, Zhichen Liu3, Xiangrui Xu3, Fuqiang Hu3, Jiao Dai3, Baigen Cai1, Wei Wang3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.055596
（This article belongs to the Special Issue: Information Security and Trust Issues in the Digital World)
Abstract In the realm of Intelligent Railway Transportation Systems, effective multi-party collaboration is crucial due to concerns over privacy and data silos. Vertical Federated Learning (VFL) has emerged as a promising approach to facilitate such collaboration, allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data. However, existing works have highlighted VFL’s susceptibility to privacy inference attacks, where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client. This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems. In… More >
Open Access
ARTICLE
Diana Abi-Nader1, Hassan Harb2, Ali Jaber1, Ali Mansour3, Christophe Osswald3, Nour Mostafa2,*, Chamseddine Zaki2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.056816
Abstract Security and safety remain paramount concerns for both governments and individuals worldwide. In today’s context, the frequency of crimes and terrorist attacks is alarmingly increasing, becoming increasingly intolerable to society. Consequently, there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces, thereby preventing potential attacks or violent incidents. Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection, particularly in identifying firearms. This paper introduces a novel automatic firearm detection surveillance system, utilizing a one-stage detection… More >
Open Access
ARTICLE
Sourabh Mhaski*, G. V. Ramana
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.055963
Abstract Earthquake-induced soil liquefaction poses significant risks to the stability of geotechnical structures worldwide. An understanding of the liquefaction triggering, and the post-failure large deformation behaviour is essential for designing resilient infrastructure. The present study develops a Smoothed Particle Hydrodynamics (SPH) framework for earthquake-induced liquefaction hazard assessment of geotechnical structures. The coupled flow-deformation behaviour of soils subjected to cyclic loading is described using the PM4Sand model implemented in a three-phase, single-layer SPH framework. A staggered discretisation scheme based on the stress particle SPH approach is adopted to minimise numerical inaccuracies caused by zero-energy modes and tensile… More >
Open Access
ARTICLE
Tajmal Hussain, Jongwon Seok*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.056621
Abstract Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence (AI) and internet of things (IoT) to enhance efficiency, reduce costs, and ensure product quality. In light of the recent advancement of Industry 4.0, identifying defects has become important for ensuring the quality of products during the manufacturing process. In this research, we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network (CNN) architectures: VGG16, VGG19, Xception, and Mobile-Net V2, compensating for their… More >
Open Access
ARTICLE
Pongsakon Promsawat1, Weerapan Sae-dan2,*, Marisa Kaewsuwan3, Weerawat Sudsutad3, Aphirak Aphithana3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.057774
（This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)
Abstract The ability to accurately predict urban traffic flows is crucial for optimising city operations. Consequently, various methods for forecasting urban traffic have been developed, focusing on analysing historical data to understand complex mobility patterns. Deep learning techniques, such as graph neural networks (GNNs), are popular for their ability to capture spatio-temporal dependencies. However, these models often become overly complex due to the large number of hyper-parameters involved. In this study, we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks (DMST-GNODE), a framework based on ordinary differential equations (ODEs) that autonomously discovers effective spatial-temporal… More >
Open Access
ARTICLE
Jiawei Tian1, Yu Zhou1, Xiaobing Chen2, Salman A. AlQahtani3, Hongrong Chen4, Bo Yang4,*, Siyu Lu4, Wenfeng Zheng3,4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.057032
Abstract Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination, hindering accurate three-dimensional lesion reconstruction by surgical robots. This study proposes a novel end-to-end disparity estimation model to address these challenges. Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions, integrating multi-scale image information to enhance robustness against lighting interferences. This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison, improving accuracy and efficiency. The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot, comprising More >
Open Access
ARTICLE
Duc-Dam Nguyen1, Nguyen Viet Tiep2,*, Quynh-Anh Thi Bui1, Hiep Van Le1, Indra Prakash3, Romulus Costache4,5,6,7, Manish Pandey8,9, Binh Thai Pham1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.056576
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
Abstract This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand, India, using advanced ensemble models that combined Radial Basis Function Networks (RBFN) with three ensemble learning techniques: DAGGING (DG), MULTIBOOST (MB), and ADABOOST (AB). This combination resulted in three distinct ensemble models: DG-RBFN, MB-RBFN, and AB-RBFN. Additionally, a traditional weighted method, Information Value (IV), and a benchmark machine learning (ML) model, Multilayer Perceptron Neural Network (MLP), were employed for comparison and validation. The models were developed using ten landslide conditioning factors, which included slope, aspect, elevation, curvature, land cover, geomorphology,… More >
Open Access
ARTICLE
Suganya Athisayamani1, A. Robert Singh2, Gyanendra Prasad Joshi3, Woong Cho4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.056129
Abstract In radiology, magnetic resonance imaging (MRI) is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures. MRI is particularly effective for detecting soft tissue anomalies. Traditionally, radiologists manually interpret these images, which can be labor-intensive and time-consuming due to the vast amount of data. To address this challenge, machine learning, and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans. This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods. There are three… More >
Open Access
ARTICLE
Nermin Özcan1,2,*, Semih Utku3, Tolga Berber4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.055860
（This article belongs to the Special Issue: Advances in Swarm Intelligence Algorithms)
Abstract Metaheuristics are commonly used in various fields, including real-life problem-solving and engineering applications. The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm (ACSA). The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this process. The work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions, identified as classical benchmark functions. The method was subsequently examined by application to 12 CEC 2022 benchmark problems of different complexities. Furthermore, the paper evaluates ACSA in comparison to 64 metaheuristic… More >
Open Access
ARTICLE
Petr Opěla1,*, Josef Walek1,*, Jaromír Kopeček2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.055219
（This article belongs to the Special Issue: Computer Aided Simulation in Metallurgy and Material Engineering)
Abstract In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression More >
Open Access
ARTICLE
Lakshmi S V V1, Shiloah Elizabeth Darmanayagam1,*, Sunil Retmin Raj Cyril2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.056424
（This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)
Abstract Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of… More >
Open Access
ARTICLE
Bhanu Pratap Reddy Bhavanam, Prashanth Ragam*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.052355
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
Abstract The Internet of Things (IoT) has orchestrated various domains in numerous applications, contributing significantly to the growth of the smart world, even in regions with low literacy rates, boosting socio-economic development. This study provides valuable insights into optimizing wireless communication, paving the way for a more connected and productive future in the mining industry. The IoT revolution is advancing across industries, but harsh geometric environments, including open-pit mines, pose unique challenges for reliable communication. The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency… More >
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