Journal of Computational Science最新文献

{"title":"AFF-BPL: An adaptive feature fusion technique for the diagnosis of autism spectrum disorder using Bat-PSO-LSTM based framework","authors":"","doi":"10.1016/j.jocs.2024.102447","DOIUrl":"10.1016/j.jocs.2024.102447","url":null,"abstract":"<div><div>Autism spectrum disorder (ASD) is a neurological condition revealed by deficiencies in physical well-being, social communication, hyperactive behavior, and increased sensitivity. The delayed diagnosis of ASD showcases a significant obstacle in mitigating the severity of its impact. Individuals with ASD often exhibit restricted and repetitive behavioral patterns. In this context, we proposed a novel adaptive feature fusion technique with a BAT-PSO-LSTM-based network for the diagnosis of autism spectrum disorder. Our focus is on three distinct autism screening datasets namely, Toddlers, Children, and Adults for comprehensive analysis of techniques. Bat and PSO concurrently select features and the selected features will go through an adaptive feature fusion algorithm and an LSTM-based classifier. This research addresses various challenges encountered in the existing techniques including concerns related to overfitting, faster training, interpretability, generalization capability, and reduced computation time. The work incorporates baseline techniques like a neural network, CNN, and LSTM with evaluations based on key parameters like precision, specificity, accuracy, sensitivity, and f1-score. The experimental simulations reveal that AFF-BPL outperforms considered baseline techniques achieving remarkable accuracy on all three datasets. Specifically, the model attains the accuracy of 0.992, 0.989, and 0.986 on toddler, children, and adult datasets respectively. Additionally, the exploration of functional and structural images will provide deeper insights into the underlying mechanism of ASD.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven robust optimization in the face of large-scale datasets: An incremental learning approach","authors":"","doi":"10.1016/j.jocs.2024.102432","DOIUrl":"10.1016/j.jocs.2024.102432","url":null,"abstract":"<div><div>One of the most significant current discussions in optimization under deep uncertainty is integrating machine learning and data science into robust optimization, which has led to the emergence of a new field called Data-Driven Robust Optimization (DDRO). When creating data-driven uncertainty sets, it considers a dataset’s complexity, hidden information, and inherent form. One of the more practical machine learning algorithms for creating data-driven uncertainty sets is support vector clustering (SVC). This algorithm has no prerequisites for preliminary information to generate uncertainty sets with arbitrary geometry. More scenarios can reduce risk when developing SVC-based uncertainty sets. However, the lack of a systematic way to manage the large number of these scenarios hinders the employment of SVC. This paper puts forward an incremental learning algorithm based on support vector clustering, called Incremental Support Vector Clustering (ISVC), to construct an uncertainty set incrementally and efficiently using large datasets. This approach’s novelty and main contributions include incrementally constructing uncertainty sets and dynamic management of outliers. In order to update the temporarily stored Bounded Support Vectors (BSV) and identify outliers, the idea of BSV-archive is offered, where the revision-and-recycle operation is tailored to do just that. As a result, some of the newly acquired information is preserved. Experiments on large-scale datasets demonstrate that the proposed ISVC approach can create an uncertainty set comparable to that of an SVC-based method while using significantly less time.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VEGF-ERCNN: A deep learning-based model for prediction of vascular endothelial growth factor using ensemble residual CNN","authors":"","doi":"10.1016/j.jocs.2024.102448","DOIUrl":"10.1016/j.jocs.2024.102448","url":null,"abstract":"<div><p>Vascular Endothelial Growth Factor (VEGF), a signaling protein family, is essential in angiogenesis, regulating the growth and survival of endothelial cells that create blood vessels. VEGF is critical in osteogenesis for coordinating blood vessel growth with bone formation, resulting in a well-vascularized environment that promotes nutrition and oxygen delivery to bone-forming cells. Predicting VEGF is crucial, yet experimental methods for identification are both costly and time-consuming. This paper introduces VEGF-ERCNN, an innovative computational model for VEGF prediction using deep learning. Two datasets were generated using primary sequences, and a novel feature descriptor called multi fragmented-position specific scoring matrix-discrete wavelet transformation (MF-PSSM-DWT) was developed to extract numerical characteristics from these sequences. Model training is performed via deep learning techniques such as generative adversarial network (GAN), gated recurrent unit (GRU), ensemble residual convolutional neural network (ERCNN), and convolutional neural network (CNN). The VEGF-ERCNN outperformed other competitive predictors on both training and testing datasets by securing the highest 92.12 % and 83.45 % accuracies, respectively. Accurate prediction of VEGF therapeutic targeting has transformed treatment techniques, establishing it as a crucial participant in both health and disease.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new space–time localized meshless method based on coupling radial and polynomial basis functions for solving singularly perturbed nonlinear Burgers’ equation","authors":"","doi":"10.1016/j.jocs.2024.102446","DOIUrl":"10.1016/j.jocs.2024.102446","url":null,"abstract":"<div><div>In this paper, the singularly perturbed nonlinear Burgers’ problem (SPBP) with small kinematic viscosity <span><math><mrow><mn>0</mn><mo>&lt;</mo><mi>ϵ</mi><mo>≪</mo><mn>1</mn></mrow></math></span> is solved using a new Space–Time Localized collocation method based on coupling Polynomial and Radial Basis Functions (STLPRBF). To our best knowledge, it is the first time that the solution of SPBP is accurately approximated using the space–time meshless method without applying any adaptive refinement technique. The method is based on solving the problem without distinguishing between space and time variables, which eliminates the need for time discretization schemes. To address the inherent non-linearity of the problem, the method employs an iterative algorithm based on quasilinearization technique. The efficiency and accuracy of the proposed method are demonstrated by solving different examples of one- and two-dimensional SPBP with very small <span><math><mi>ϵ</mi></math></span> up to <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>10</mn></mrow></msup></mrow></math></span>. Additionally, the numerical convergence of the method with respect to <span><math><mi>ϵ</mi></math></span> and also to the number of collocation points has been investigated. The comparison of the STLPRBF results with other published ones is presented.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of the emulator-based component analysis","authors":"","doi":"10.1016/j.jocs.2024.102437","DOIUrl":"10.1016/j.jocs.2024.102437","url":null,"abstract":"<div><div>We present a PyTorch-powered implementation of the emulator-based component analysis used for ill-posed numerical non-linear inverse problems, where an approximate emulator for the forward problem is known. This emulator may be a numerical model, an interpolating function, or a fitting function such as a neural network. With the help of the emulator and a data set, the method seeks dimensionality reduction by projection in the variable space so that maximal variance of the target (response) values of the data is covered. The obtained basis set for projection in the variable space defines a subspace of the greatest response for the outcome of the forward problem. The method allows for the reconstruction of the coordinates in this subspace for an approximate solution to the inverse problem. We present an example of using the code provided as a Python class.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877750324002308/pdfft?md5=48eb0727ba0e208a45b58ba3c8f2bac4&pid=1-s2.0-S1877750324002308-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feasible numerical computation based on matrix operations and collocation points to solve linear system of partial differential equations","authors":"","doi":"10.1016/j.jocs.2024.102445","DOIUrl":"10.1016/j.jocs.2024.102445","url":null,"abstract":"<div><p>In this work, a system of linear partial differential equations with constant and variable coefficients via Cauchy conditions is handled by applying the numerical algorithm based on operational matrices and equally-spaced collocation points. To demonstrate the applicability and efficiency of the method, four illustrative examples are tested along with absolute error, maximum absolute error, RMS error, and CPU times. The approximate solutions are compared with the analytical solutions and other numerical results in literature. The obtained numerical results are scrutinized by means of tables and graphics. These comparisons show accuracy and productivity of our method for the linear systems of partial differential equations. Besides, an algorithm is described that summarizes the formulation of the presented method. This algorithm can be adapted to well-known computer programs<strong>.</strong></p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Markov random field model for change points detection","authors":"","doi":"10.1016/j.jocs.2024.102429","DOIUrl":"10.1016/j.jocs.2024.102429","url":null,"abstract":"<div><p>Detecting Change Points (CPs) in data sequences is a challenging problem that arises in a variety of disciplines, including signal processing and time series analysis. While many methods exist for PieceWise Constant (PWC) signals, relatively fewer address PieceWise Linear (PWL) signals due to the challenge of preserving sharp transitions. This paper introduces a Markov Random Field (MRF) model for detecting changes in slope. The number of CPs and their locations are unknown. The proposed method incorporates PWL prior information using MRF framework with an additional boolean variable called Line Process (LP), describing the presence or absence of CPs. The solution is then estimated in the sense of maximum a posteriori. The LP allows us to define a non-convex non-smooth energy function that is algorithmically hard to minimize. To tackle the optimization challenge, we propose an extension of the combinatorial algorithm DPS, initially designed for CP detection in PWC signals. Also, we present a shared memory implementation to enhance computational efficiency. Numerical studies show that the proposed model produces competitive results compared to the state-of-the-art methods. We further evaluate the performance of our method on three real datasets, demonstrating superior and accurate estimates of the underlying trend compared to competing methods.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepDetect: An innovative hybrid deep learning framework for anomaly detection in IoT networks","authors":"","doi":"10.1016/j.jocs.2024.102426","DOIUrl":"10.1016/j.jocs.2024.102426","url":null,"abstract":"&lt;div&gt;&lt;p&gt;The presence of threats and anomalies in the Internet of Things infrastructure is a rising concern. Attacks, such as Denial of Service, User to Root, Probing, and Malicious operations can lead to the failure of an Internet of Things system. Traditional machine learning methods rely entirely on feature engineering availability to determine which data features will be considered by the model and contribute to its training and classification and “dimensionality” reduction techniques to find the most optimal correlation between data points that influence the outcome. The performance of the model mostly depends on the features that are used. This reliance on feature engineering and its effects on the model performance has been demonstrated from the perspective of the Internet of Things intrusion detection system. Unfortunately, given the risks associated with the Internet of Things intrusion, feature selection considerations are quite complicated due to the subjective complexity. Each feature has its benefits and drawbacks depending on which features are selected. Deep structured learning is a subcategory of machine learning. It realizes features inevitably out of raw data as it has a deep structure that contains multiple hidden layers. However, deep learning models such as recurrent neural networks can capture arbitrary-length dependencies, which are difficult to handle and train. However, it is suffering from exploiting and vanishing gradient problems. On the other hand, the log-cosh conditional variational Autoencoder ignores the detection of the multiple class classification problem, and it has a high level of false alarms and a not high detection accuracy. Moreover, the Autoencoder ignores to detect multi-class classification. Furthermore, there is evidence that a single convolutional neural network cannot fully exploit the rich information in network traffic. To deal with the challenges, this research proposed a novel approach for network anomaly detection. The proposed model consists of multiple convolutional neural networks, gate-recurrent units, and a bi-directional-long-short-term memory network. The proposed model employs multiple convolution neural networks to grasp spatial features from the spatial dimension through network traffic. Furthermore, gate recurrent units overwhelm the problem of gradient disappearing- and effectively capture the correlation between the features. In addition, the bi-directional-long short-term memory network approach was used. This layer benefits from preserving the historical context for a long time and extracting temporal features from backward and forward network traffic data. The proposed hybrid model improves network traffic’s accuracy and detection rate while lowering the false positive rate. The proposed model is evaluated and tested on the intrusion detection benchmark NSL-KDD dataset. Our proposed model outperforms other methods, as evidenced by the experimental results. The overall accuracy of","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877750324002199/pdfft?md5=499c056a20080f0138b115130d2376c9&pid=1-s2.0-S1877750324002199-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142149749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable wildfire spreading model for real-time predictions","authors":"","doi":"10.1016/j.jocs.2024.102435","DOIUrl":"10.1016/j.jocs.2024.102435","url":null,"abstract":"<div><p>Forest fires are a key component of natural ecosystems, but their increased frequency and intensity have devastating social, economic, and environmental implications. Thus, there is a great need for trustworthy digital tools capable of providing real-time estimates of fire evolution and human interventions. This work develops an interpretable, physics-based model that will serve as the core of a broader wildfire prediction tool. The modeling approach involves a simplified description of combustion kinetics and thermal energy transfer (averaged over local plantation height) and leads to a computationally inexpensive system of differential equations that provides the spatiotemporal evolution of the two-dimensional fields of temperature and combustibles. Key aspects of the model include the estimation of mean wind velocity through the plantation and the inclusion of the effect of ground inclination. Predictions are successfully compared to benchmark literature results concerning the effect of flammable bulk density, moisture content, and the combined influence of wind and slope. Simulations appear to provide qualitatively correct descriptions of firefront propagation from a localized ignition site in a homogeneous or heterogeneous canopy, of acceleration resulting from the collision of oblique firelines, and of firefront overshoot or arrest at fuel break zones.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142149748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface feature extraction method for cloud data of aircraft wall panel measurement points","authors":"","doi":"10.1016/j.jocs.2024.102427","DOIUrl":"10.1016/j.jocs.2024.102427","url":null,"abstract":"<div><p>In the cloud, users need to connect to the data server to perform the file transmission via the Internet, and the Server transmits data to many servers. A machine or vehicle that can fly with the assistance of the air is known as an Aircraft. As an alternative to the downward thrust of jet engines, it uses either static lift or an airfoil's dynamic lift to combat gravity's pull. Drawing wall panel measurement points in the model is easy using the Aircraft Wall Panels (AWP) button. Draw wall panels between existing nodes or on the drawing grid using the relevant wall panel specifications. The technique intends to discover and extract information about undesirable defects such as dents, protrusions, or scratches based on local surface attributes gathered from a 3D scanner. Defects from a perfectly smooth surface include indentations and bumps on the surface. An image's features may be extracted by reducing the number of pixels in the picture to a manageable size so that the most exciting sections of the image can be recorded with Surface Feature Extraction (SFE). Some of the problems are the threat of drones and composite materials that do not break easily in oxymoronic. The aircraft's inner structure may have been damaged, although this is impossible to determine. A runway incursion severely threatens aviation safety because of the rise in aircraft movement on the airport surface and other human factors. An electronic moving map of airport runways and taxiways is shown to the pilot through a head-up display in the cockpit's head-down position. A practical feature extraction approach is required to ensure the safety of the airport scene in runway incursion prevention systems. All the drawbacks are rectified by AWP-SFE sensors installed along the runway centerline to detect magnetic signals generated by surface-moving targets, and this information is utilized to compute the target's length. The target length may extract peak features after regularizing the time domain data. Differentiation of target characteristics is used to determine the similarities between distinct targets. The suggested method's signal characteristics are more easily recognized than time domain or frequency domain feature methods. The experimental results show the proposed method AWP-SE to achieve a high-efficiency ratio of 88.2 %, activity ratio of 73.3 %, Analysis of aircraft in wall plane measurement point of 87.8 % and an error rate of 32.3 % compared to other methods.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142149750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}