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  • Open Access

    ARTICLE

    Employing a Backpropagation Neural Network for Predicting Fear of Cancer Recurrence among Non-Small Cell Lung Cancer Patients

    Man Liu1, Zhuoheng Lv1,#, Hongjing Wang2,*, Lu Liu1,*

    Psycho-Oncologie, Vol.18, No.4, pp. 305-316, 2024, DOI:10.32604/po.2024.054098 - 04 December 2024

    Abstract Objective: Non-small cell lung cancer (NSCLC) patients often experience significant fear of recurrence. To facilitate precise identification and appropriate management of this fear, this study aimed to compare the efficacy and accuracy of a Backpropagation Neural Network (BPNN) against logistic regression in modeling fear of cancer recurrence prediction. Methods: Data from 596 NSCLC patients, collected between September 2023 and December 2023 at the Cancer Hospital of the Chinese Academy of Medical Sciences, were analyzed. Nine clinically and statistically significant variables, identified via univariate logistic regression, were inputted into both BPNN and logistic regression models developed… More >

  • Open Access

    ARTICLE

    Virtual Assembly Collision Detection Algorithm Using Backpropagation Neural Network

    Baowei Wang1,2,*, Wen You2

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1085-1100, 2024, DOI:10.32604/cmc.2024.055538 - 15 October 2024

    Abstract As computer graphics technology continues to advance, Collision Detection (CD) has emerged as a critical element in fields such as virtual reality, computer graphics, and interactive simulations. CD is indispensable for ensuring the fidelity of physical interactions and the realism of virtual environments, particularly within complex scenarios like virtual assembly, where both high precision and real-time responsiveness are imperative. Despite ongoing developments, current CD techniques often fall short in meeting these stringent requirements, resulting in inefficiencies and inaccuracies that impede the overall performance of virtual assembly systems. To address these limitations, this study introduces a… More >

  • Open Access

    ARTICLE

    Deep Learning: A Theoretical Framework with Applications in Cyberattack Detection

    Kaveh Heidary*

    Journal on Artificial Intelligence, Vol.6, pp. 153-175, 2024, DOI:10.32604/jai.2024.050563 - 18 July 2024

    Abstract This paper provides a detailed mathematical model governing the operation of feedforward neural networks (FFNN) and derives the backpropagation formulation utilized in the training process. Network protection systems must ensure secure access to the Internet, reliability of network services, consistency of applications, safeguarding of stored information, and data integrity while in transit across networks. The paper reports on the application of neural networks (NN) and deep learning (DL) analytics to the detection of network traffic anomalies, including network intrusions, and the timely prevention and mitigation of cyberattacks. Among the most prevalent cyber threats are R2L,… More >

  • Open Access

    ARTICLE

    Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

    Jayaraj Thankappan1, Delphin Raj Kesari Mary2, Dong Jin Yoon3, Soo-Hyun Park4,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1053-1079, 2023, DOI:10.32604/cmc.2023.038437 - 31 October 2023

    Abstract Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world. Therefore, timely and accurate decision-making is essential for mitigating flood-related damages. The traditional flood prediction techniques often encounter challenges in accuracy, timeliness, complexity in handling dynamic flood patterns and leading to substandard flood management strategies. To address these challenges, there is a need for advanced machine learning models that can effectively analyze Internet of Things (IoT)-generated flood data and provide timely and accurate flood predictions. This paper proposes a novel approach-the Adaptive Momentum and Backpropagation (AM-BP) algorithm-for… More >

  • Open Access

    REVIEW

    Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics

    Loc Vu-Quoc1,*, Alexander Humer2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130 - 26 June 2023

    Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting… More >

  • Open Access

    ARTICLE

    Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

    Deye Jiang1, Yiguang Wang2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 295-309, 2023, DOI:10.32604/cmc.2023.039244 - 08 June 2023

    Abstract In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of… More >

  • Open Access

    ARTICLE

    Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy: Combined machine learning and experimental validation in mice

    WEI LIANG1,2,*, ZONGWEI ZHANG1,2, KEJU YANG1,2,3, HONGTU HU1,2, QIANG LUO1,2, ANKANG YANG1,2, LI CHANG4, YUANYUAN ZENG4

    BIOCELL, Vol.47, No.6, pp. 1253-1263, 2023, DOI:10.32604/biocell.2023.027373 - 19 May 2023

    Abstract Background: Diabetic nephropathy (DN) is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide. Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN. Kidney biopsy is the gold standard for diagnosing DN; however, its invasive character is its primary limitation. The machine learning approach provides a non-invasive and specific criterion for diagnosing DN, although traditional machine learning algorithms need to be improved to enhance diagnostic performance. Methods: We applied high-throughput RNA sequencing to obtain the genes… More >

  • Open Access

    ARTICLE

    A Stochastic Framework for Solving the Prey-Predator Delay Differential Model of Holling Type-III

    Naret Ruttanaprommarin1, Zulqurnain Sabir2,3, Rafaél Artidoro Sandoval Núñez4, Emad Az-Zo’bi5, Wajaree Weera6, Thongchai Botmart6,*, Chantapish Zamart6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5915-5930, 2023, DOI:10.32604/cmc.2023.034362 - 28 December 2022

    Abstract The current research aims to implement the numerical results for the Holling third kind of functional response delay differential model utilizing a stochastic framework based on Levenberg-Marquardt backpropagation neural networks (LVMBPNNs). The nonlinear model depends upon three dynamics, prey, predator, and the impact of the recent past. Three different cases based on the delay differential system with the Holling 3rd type of the functional response have been used to solve through the proposed LVMBPNNs solver. The statistic computing framework is provided by selecting 12%, 11%, and 77% for training, testing, and verification. Thirteen numbers of neurons More >

  • Open Access

    ARTICLE

    An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

    Wajaree Weera1, Thongchai Botmart1,*, Charuwat Chantawat1, Zulqurnain Sabir2,3, Waleed Adel4,5, Muhammad Asif Zahoor Raja6, Muhammad Kristiawan7

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2711-2724, 2023, DOI:10.32604/cmc.2023.033233 - 31 October 2022

    Abstract The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure. The stochastic procedures mainly depend on the combination of the artificial neural network (ANNs) along with the Levenberg-Marquardt Backpropagation (LMB) i.e., ANNs-LMB technique. The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional order α. The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between… More >

  • Open Access

    ARTICLE

    Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

    Narongsak Yotha1, Qusain Hiader2, Zulqurnain Sabir3, Muhammad Asif Zahoor Raja4, Salem Ben Said5, Qasem Al-Mdallal5, Thongchai Botmart6, Wajaree Weera6,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2415-2430, 2023, DOI:10.32604/cmc.2023.031352 - 31 October 2022

    Abstract This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using the normal and dysregulated bone remodeling of the myeloma bone disease (MBD). For the more precise performance of the model, fractional-order derivatives have been used to solve the disease model numerically. The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts (OC) and bone formation or osteoblasts (OB). The connections of OC and OB are represented by a nonlinear differential system based on the cellular components, which depict stable fluctuation in the usual bone case and… More >

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