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Numerical oil spill models, which predict the transport and behavior of oil spills, are an essential tool for risk assessment and clean-up during an actual accident. The existing numerical oil spill models are mainly applied to large-scale oil spills, while few models on small-scale oil spills exist. Therefore, this study focuses on the prediction model of small-scale oil spills. Oil diffusion experiments in seawater using different oil types, including heavy oil, light oil, and gasoline, at different addition amounts under various kinds of wind were carried out, and these diffusion processes were recorded by a camera. The experimental images were processed to obtain the spread oil film area. The oil film edge processing based on genetic algorithm (GA) and back propagation artificial neural network optimized by a particle swarm optimization (PSO-BP) is proposed. Numerical prediction models were then constructed using the BP artificial neural network, the genetic algorithm-optimized back propagation neural network (GA-BP), and the PSO-BP. Among the three methods, the PSO-BP has the fastest convergence speed and the highest stability, which can usually achieve the goal. The PSO-BP reduces the possibility of the BP-ANN and the GA-BP falling into a local optimum instead of reaching global optimization. The prediction performance evaluation data are R2 = 1 and MSE = 3.58e−9 – 8.87e−8. Results show that the GA and the PSO-BP provide a new approach to small-scale oil spill prediction.

Background Digital connectivity drives global innovation, ensuring computer network security is more critical than ever. With increasing interconnectivity, cyber threats are becoming more sophisticated, highlighting the urgent need for advanced defense mechanisms. Objective This research proposed the investigation of the Genetic Algorithm with Back Propagation Neural Network (GA-BPNN) model for computer network safety. Methodology The investigation utilized the CIC-IDS-2017 dataset to evaluate the GA-BPNN model. Data preprocessing was performed using min–max normalization to standardize the dataset. Independent component analysis (ICA) was employed for feature extraction to improve the model’s efficiency. The GA-BPNN model was then implemented and analyzed for its performance in detecting network threats. Results The GA-BPNN approach demonstrated strong performance metrics for computer network security, achieving a recall (95%), F1 score (96.5%), precision (98.5%), and accuracy (98%). Conclusion The GA-BPNN model enhances network security by improving threat detection and incident response. Its high accuracy and precision indicate its potential for strengthening cybersecurity defenses in interconnected systems. Implementing GA-BPNN in network security frameworks could lead to more effective protection against cyber threats. Highlights • Its analysis of the GA-BPNN model for computer network safety was suggested. The researchers collected data from the CIC-IDS-2017 dataset. • To safeguard data networks, the researchers preprocessed the data using min–max normalization and the GA-BPNN. • The GA-BPNN is the suggested method we utilize. Accuracy and precision are included in the GA-BPNN technique for computer network security, as well as recall rate and F1 score. • When applied to computer network security, the GA-BPNN technique may enhance threat identification, incident response, and cybersecurity.

Due to the limitation of sample size in predicting the torsional strength of Reinforced Concrete (RC) beams, this paper aims to discuss the feasibility of employing a novel machine learning approach with K-fold cross-validation in a small sample range, which combines the advantages of a Genetic Algorithm (GA) and a Neural Network (NN) to predict the torsional strength of RC beams. This research study not only utilizes the application of a Back Propagation (BP) neural network and the Gene Algorithm-Back Propagation (GA-BP) neural network in the prediction of the torsional strength of the RC beam, but it also investigates neural network parameter optimization, including connection weights and thresholds, using K-fold cross-validation. The root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and correlation coefficient (R2) are among the evaluation metrics used to assess the performance of the trained model. To elaborate on the superiority of the proposed network models in predicting the torsional strength of RC beams, a parametric study is conducted by comparing the proposed model to three commonly used empirical formulae from existing design codes. The comparative findings of this research study demonstrate that the performance of the BP neural network is highly similar to that of design codes; however, its accuracy is inadequate. After improving the weights and thresholds by k-fold cross-validation and GA, the prediction of the BP neural network shows higher consistency with the actual measured values. The outcome of this study can be used as a theoretical reference for the optimal design of RC beams in practical applications.

PurposeAn algorithm for diagnostic system with neural network is developed for diagnosis of dental caries in digital radiographs. The diagnostic performance of the designed system is evaluated.MethodsThe diagnostic system comprises of Laplacian filtering, window based adaptive threshold, morphological operations, statistical feature extraction and back-propagation neural network. The back propagation neural network used to classify a tooth surface as normal or having dental caries. The 105 images derived from intra-oral digital radiography, are used to train an artificial neural network with 10-fold cross validation. The caries in these dental radiographs are annotated by a dentist. The performance of the diagnostic algorithm is evaluated and compared with baseline methods.ResultsThe system gives an accuracy of 97.1%, false positive (FP) rate of 2.8%, receiver operating characteristic (ROC) area of 0.987 and precision recall curve (PRC) area of 0.987 with learning rate of 0.4, momentum of 0.2 and 500 iterations with single hidden layer with 9 nodes.ConclusionsThis study suggests that dental caries can be predicted more accurately with back-propagation neural network. There is a need for improving the system for classification of caries depth. More improved algorithms and high quantity and high quality datasets may give still better tooth decay detection in clinical dental practice.

Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.

Debris flows have been always a serious problem in the mountain areas. Research on the assessment of debris flows susceptibility (DFS) is useful for preventing and mitigating debris flow risks. The main purpose of this work is to study the DFS in the Shigatse area of Tibet, by using machine learning methods, after assessing the main triggering factors of debris flows. Remote sensing and geographic information system (GIS) are used to obtain datasets of topography, vegetation, human activities and soil factors for local debris flows. The problem of debris flow susceptibility level imbalances in datasets is addressed by the Borderline-SMOTE method. Five machine learning methods, i.e., back propagation neural network (BPNN), one-dimensional convolutional neural network (1D-CNN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) have been used to analyze and fit the relationship between debris flow triggering factors and occurrence, and to evaluate the weight of each triggering factor. The ANOVA and Tukey HSD tests have revealed that the XGBoost model exhibited the best mean accuracy (0.924) on ten-fold cross-validation and the performance was significantly better than that of the BPNN (0.871), DT (0.816), and RF (0.901). However, the performance of the XGBoost did not significantly differ from that of the 1D-CNN (0.914). This is also the first comparison experiment between XGBoost and 1D-CNN methods in the DFS study. The DFS maps have been verified by five evaluation methods: Precision, Recall, F1 score, Accuracy and area under the curve (AUC). Experiments show that the XGBoost has the best score, and the factors that have a greater impact on debris flows are aspect, annual average rainfall, profile curvature, and elevation.

In this paper, an algorithm for multiple watermarking based on discrete wavelet transforms (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) has been proposed for healthcare applications. For identity authentication purpose, the proposed method uses three watermarks in the form of medical Lump image watermark, the doctor signature/identification code and diagnostic information of the patient as the text watermarks. In order to improve the robustness performance of the image watermark, Back Propagation Neural Network (BPNN) is applied to the extracted image watermark to reduce the noise effects on the watermarked image. The security of the image watermark is also enhanced by using Arnold transform before embedding into the cover. Further, the symptom and signature text watermarks are also encoded by lossless arithmetic compression technique and Hamming error correction code respectively. The compressed and encoded text watermark is then embedded into the cover image. Experimental results are obtained by varying the gain factor, different sizes of text watermarks and the different cover image modalities. The results are provided to illustrate that the proposed method is able to withstand a different of signal processing attacks and has been found to be giving excellent performance for robustness, imperceptibility, capacity and security simultaneously. The robustness performance of the method is also compared with other reported techniques. Finally, the visual quality of the watermarked image is evaluated by the subjective method also. This shows that the visual quality of the watermarked images is acceptable for diagnosis at different gain factors. Therefore the proposed method may find potential application in prevention of patient identity theft in healthcare applications.

Recommender system (RS) are a type of suggestion to the information overload problem suffered by user of websites that allow the rating of particular item. The movie RS are one of the most efficient, useful, and widespread applications for individual to watch movie with minimum decision time. Many attempts made by the researchers to solve these issues like watching movie, purchasing book etc., through RS, whereas most of the study fails to address cold start problem, data sparsity and malicious attacks. This study address these problems, we propose trust matrix measure in this paper, which combines user similarity with weighted trust propagation. Non cold user passed through different models with trust filter and a cold user generated an optimal score with their preferences for recommendation. Four different recommendation models such as Backpropagation (BPNN) model, SVD (Singular Value Decomposition) model, DNN (Deep Neural Network model) and DNN with Trust were compared to recommend the suitable movie to the user. Results imply that DNN with trust model proved to be the best model with high accuracy of 83% with 0.74 MSE value and can be used for best movie recommendation.

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.

Due to increasingly serious deterioration of surface water quality, effective water quality prediction technique for real-time early warning is essential to guarantee the emergency response ability in advance for sustainable water management. In this study, an effective data-driven model for surface water quality prediction is developed to analyze the inherent water quality variation tendencies and provide real-time early warnings according to the historical observation data. The developed data-driven model is integrated by an improved genetic algorithm (IGA) for selecting optimal initial weight parameters of neural a network and a back-propagation neural network (BPNN) for adjusting appropriate connection architectures of neural network. First, improved genetic algorithm is used to optimize the reasonable initial weight parameters and prevent the developed model from selecting a local optimal result. Second, BPNN is applied to adjust appropriate connection architectures and identify the features of water quality variation. The developed model is then applied to forecast the surface water quality variations for real-time early warning in Ashi River, China, comparing with simple BPNN model. The prediction results demonstrate that the developed data-driven model can significantly improve the prediction performance both in prediction accuracy and reliability, and effectively provide real-time early warning for emergency response.