Deep Belief Network Algorithm Research Articles

Occlusive target recognition method of sorting robot based on anchor-free detection network

ABSTRACT The rapid development of industry intelligence has promoted the importance of sorting robots, but the shortcomings of sorting tasks are also gradually obvious. The target detection of the sorting robot in the process of sorting and grasping is affected by the anchor frame setting, and its grasping efficiency and recognition effect are low. Therefore, a detection network based on an anchor frame-free regression algorithm is proposed in this study, and a positioning and grasping method of the sorting robot based on the Deep Belief Network (DBN) algorithm is proposed. The algorithm experiment shows that the prediction accuracy of the proposed anchor-free detection network in the PASCAL VOC dataset reaches 92.91, significantly higher than that of other detection networks. In addition, the sorting robot was researched and designed with high accuracy in occlusion target recognition. When the occlusion area reaches 70%, the accuracy rate is as high as 81.26%, which is far higher than other detection schemes. The above results show that in improving the sorting robot’s target recognition ability, the anchor-free detection network can significantly improve its recognition accuracy. At the same time, introducing the DBN network can improve the robot’s recognition ability for occluded targets, which is of great significance to the development of the sorting robot and the intelligent industry.

Monitoring the operating state of crystal growth process based on digital twin model

The reliable assessment of the operational status during the silicon single crystal growth process is a prerequisite for ensuring system safety and improving crystal quality. However, in the actual silicon single crystal growth process, due to limitations in manpower, material resources, financial resources, and current technical methods, the establishment of monitoring models is still in its infancy. To address this issue, this paper proposes a hybrid deep belief network (HDBN) algorithm aided by the digital twin (DT) model to achieve real-time monitoring of equipment operational status. Firstly, this study constructs the DT model based on the basic principles of crystal growth, mainly to achieve high-precision simulation of the actual silicon single crystal growth process and generate abnormal data for the equipment. This operation can expand the sample set, enhance the diversity and coverage of data, and effectively solve the problem of insufficient sample size. Secondly, this study uses the variational mode decomposition (VMD) algorithm to decompose the dataset composed of obtained abnormal and normal data, and constructs sub-deep belief network (DBN) for the decomposed subsequences to capture deep feature information at different frequencies of the data. Subsequently, based on the concept of ensemble learning, the outputs of each sub-DBN network are used as inputs to construct the overall DBN network, achieving monitoring of the equipment operational status. Through the combination of VMD decomposition and DBN networks, this algorithm can better capture the frequency characteristics and time-domain features of the signal, enhancing monitoring accuracy. Experimental results show that this algorithm can accurately identify abnormal equipment states, effectively improve monitoring performance, and is of significant importance for the optimization and control of the semiconductor-grade silicon single crystal growth process, contributing to increased production efficiency and product quality.

Enhanced Myocardial Infarction Prediction Using Machine Learning Algorithms and Gender-Specific Insights

A serious medical condition called myocardial infarction (MI), sometimes referred to as a "heart attack," is caused by disruptions in the blood supply to the myocardium. This research examines the efficacy of machine learning (ML) algorithms in forecasting myocardial infarction (MI) using a dataset of 350 records. The study identifies key risk factors for predicting myocardial infarction (MI), such as elevated cholesterol levels, diabetes, advanced age, overall health status, mental well-being, obesity, physical activity, smoking habits, hypertension, and depression. Significantly, gender does not manifest as a predictor of myocardial infarction (MI) when employing various classification methods. The research achieves high accuracy rates of 89.32%, 87.53%, 81.29%, and 76.59% using different machine learning algorithms, including Deep Belief Network (DBN), C4.5, Random Forest (RF), and Bayesian Network (BN), respectively. Algorithm-specific rule sets identify correlations, with the C4.5 algorithm revealing interesting connections between smoking habits and protection against myocardial infarction (MI). Performance metrics like accuracy, precision, sensitivity, and specificity attest to the effectiveness of the proposed technique. The results demonstrate the superior performance of the DBN algorithm, surpassing other algorithms in terms of accuracy (89.32%), precision (84.04%), sensitivity (86.63%), and specificity (82.45%). This paper provides crucial insights into predictive modeling for myocardial infarction (MI), highlighting the importance of various risk factors and advanced machine learning (ML) algorithms. The results offer clinicians and researchers a strong foundation for comprehending and potentially averting myocardial infarction, relying on personalized patient profiles. This paper has the potential to significantly contribute to the field by applying ensemble classifiers and machine learning models to forecast gender-specific myocardial infarctions. As a result, diagnostic precision and patient outcomes could be revolutionized.

BAGGING ENSEMBLE MINING TECHNIQUE WITH DEEP BELIEF NETWORK (DBN) ALGORITHM-BASED HEART DISEASE PREDICTION

Cardiovascular disease is the most important disease of the heart, and the stage of the disease is diagnosed, the disease can be diagnosed anytime. The following method is used to find out its status. The heart disease prediction is based on Bagging Ensemble Technique with Deep Belief Network (DBN) algorithms. The problem for heart disease prediction from the collection of the dataset. Feature extraction using the Bag of Words methods to the correct data and discontinuities collect the heart disease-based matching data's extraction from the dataset and the various combination of the data set is available in Kaggle. It is mostly used for text classification methods. The proposed system of data mining is the most important technique for data aggregation. Data mining has various methods available, and one of the techniques is the bagging Ensemble Technique. In this method using for homogeneous data are quickly collected and parallel processing for the data collections. The first process for collecting data using the bagging Ensemble Technique is based on collected preprocessing. The Cardiovascular Disease prediction using Deep Belief Network (DBN) algorithm is compared with the existing system heart disease prediction, to prediction for display the data past and present movement in classification time and prediction accuracy, sensitivity and specificity. The proposed system for classification is compared with various techniques, and the proposed methods are DBN algorithms compared to compare to 5000 data'. The performance of CNN is 89%, RNN is 90%, LSTMs is 92%, and DBM is 95.6%. Finally, the heart disease prediction or classifications are given by the DBN algorithm.

Image-driven machine learning for automatic characterization of grain size and distribution in smart vanadium dioxide thin films

This study focuses on characterizing grain size and distribution in vanadium dioxide (VO2) thin films using Atomic Force Microscope (AFM) images. Traditional segmentation methods face challenges in accurately detecting grains, and this paper addresses these limitations by employing machine learning-based classification approaches, defining three classes: grains, joints or dividing lines, and the background. Various classification methods, including supervised, unsupervised, and thresholding-based approaches, were investigated, demonstrating the superiority of machine learning-based methods over traditional segmentation techniques. Deep Learning (DL) models, such as Convolutional Neural Networks (CNN), Deep Belief Networks (DBN), and Deep Boltzmann Machines (DBM), achieved the best results in accuracy and precision. The DBN algorithm, in particular, outperformed other methods with a 93.66% accuracy in grain detection. This could be attributed to its probabilistic modeling and effective handling of challenging pixels, making it a powerful tool for accurate grain size and distribution analysis.

Robust Skin Disease Diagnosis with Deep Belief Network

The skin is one of the first lines of defenceagainst environmental influences such as sunlight, bacteriaand germs, which cause various skin diseases. In addition to the bad psychological and physical impact caused by skin disease. So,in recent years,many artificial intelligence (AI) algorithms have appeared that canrecognize Images, through which skin diseases can be diagnosed, avoiding traditional methods that rely on visual examination and self-evaluation based on experience. The paper aims to classify a group of skin diseases according to the type of disease, such as Atopic Dermatitis, Dyshidrotic Eczema and Nummular Dermatitisusing a deep belief network algorithm(DBN) thatwas built to suit the work. A global dataset was also used, obtained from the Kaggle website, and after conducting experiments on it, the algorithm achieved high accuracy in diagnosing diseases, the percentage reached 98.773%. It is possible in the future to use the network to classify other types of diseases after providing it with a large number of images of affected people

Determination and investigation of shear wave velocity based on one deep/machine learning technique

Rock physics plays an important role in the oil, gas, and water industries by providing essential data for reservoir management. This study focuses on predicting Shear Wave Velocity (Vs), a key geophysical parameter, using the Deep Belief Network (DBN) and Random Forest (RF) algorithms. The dataset is divided for training, validation, and testing, revealing that the DBN model surpasses the RF and empirical models in accuracy. With a low RMSE value of 0.0398 km/s in testing and an R2 of 0.9775, the DBN model demonstrates precision in Vs prediction. The analysis identifies specific parameters—Measure Depth (MD), Caliper (CALL), Gamma Ray (GR), Shallow Electrical Resistivity (RES-S), Medium Electrical Resistivity (RES-M), Deep Electrical Resistivity (RES-D), Compressional Wave Velocity (DCT), Neutron Porosity (NPHI), and Photoelectric Coefficient Index (PEF)—as significantly influencing Vs, while CALI and GR have a relatively lesser effect. The results of the Vs prediction indicated that the DBN algorithm excels in deep feature extraction, unsupervised pretraining, and efficient handling of complex data. Ultimately, the DBN algorithm proves to be a superior and time-saving alternative for Vs prediction and analysis in subsurface reservoirs.

Revealing the failure mechanism of high strength seawater coral aggregate reinforced concrete slabs based on acoustic emission technology: Parameter analysis and DBN-BPNN classification

In this study, high strength seawater coral aggregate concrete (SCAC) with a compressive strength of 80 MPa was prepared, and seawater coral aggregate reinforced concrete slab (SCARCS) was designed with epoxy-coated steel bars. Combining with the AE technology, monotonic flexural static tests were conducted on SCARCS specimens with three varying reinforcement ratios (i.e., 0.85%, 1.13%, and 1.41%). Results showed that the increasing the reinforcement ratio led to an increase in peak bearing capacity and a decrease in center deflections at the peak. The loading process can be divided into four stages based on the varying magnitudes of the b-value and Ib-value. The primary mode of failure was tensile microcracks at various stages, and the ratio of shear microcracks reached its maximum value in the final stage of loading. Besides, based on the signal intensity analysis, it can be observed that the scale and complexity of cracks propagation inside the SCARCS specimen increased as the reinforcement ratio rose. In addition, back propagation neural network (BPNN) classification model optimized by deep belief network (DBN) algorithm was proposed to predict the damage degree of SCARCS during loading, and the average prediction accuracy of the proposed model was shown to be over 89%. The classification outcomes demonstrated that with an increase in the reinforcement ratio of SCARCS, the proportion of AE signals in Cluster 3 increased, while the proportion of AE signals in Cluster 2 dropped. Notably, both the average frequency and energy presented an ascending trend. The findings of this study provided valuable insights for designing and assessing the safety of SCAC structures for essential island and reef engineering infrastructure.

A compensation approach for magnetic encoder error based on improved deep belief network algorithm

Magnetic encoders are an important part of industrial automation control systems and are widely used in industrial production. In this paper, a magnetic encoder is designed for angle detection during robot arm motion, but the accuracy is not high in practical applications. Through experiments, it is found that the main error compensation methods at present cannot effectively improve the accuracy of this encoder. Therefore, a hybrid prediction model based on decomposition strategy and deep learning prediction method is proposed in this paper. The hybrid prediction model structure is divided into two main parts: feature engineering based on the variational modal decomposition (VMD) method and a deep belief network prediction model based on particle swarm optimization. Through theoretical analysis, this paper introduces temperature into the prediction feature sequence, and effectively reduces the interference of errors on the prediction results through feature engineering. Experiments prove that the proposed model has excellent compensation effect, improving the accuracy from 0.22° to 0.0025°. The RMSE, Max_error and accuracy (σ) of the proposed model are optimal when compared with the mainstream error compensation methods, such as long short-term memory networks (LSTM), support vector machines (SVM) and deep belief networks (DBN). This proves that the proposed hybrid prediction model has a great improvement in the compensation effect of this magnetic encoder. Our work provides a promising approach that can provide unparalleled value in improving the accuracy of magnetic encoders.

Evolutionary Algorithm with Deep Learning based Fall Detection on Internet of Things Environment

Falling is among the most threatening event proficient by the ageing population. There is a necessity for the development of the fall detection (FD) system with the increasing ageing population. FD in an Internet of Things (IoT) platform has developed as a vital application with the rapidly increasing population of aging population and the essential for continuous health monitoring. Falls among the ageing can performance in serious injuries, decreased independence, and longer recovery periods. The FD approach can constructed on deep learning (DL) approaches, especially, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are capable in learning difficult patterns from the sensor data. The CNNs investigate the spatial features, but the RNNs approach the temporal dependencies, allowing accurate recognition of fall events. This study presents an Evolutionary Algorithm with Deep Learning based Fall Detection and Classification (EADL-FDC) methodology in the IoT platform. The projected EADL-FDC algorithm allows the DL approaches for the effective recognition and classification of falls for disabled and ageing people. In the presented EADL-FDC technique, the span-partial structure, and attention (SPA-Net) model is utilized for feature extraction purposes. In addition, the symbiotic organism search (SOS) approach was used for the parameter selection of the SPA-Net system. The deep belief network (DBN) model is applied to classify the fall events. Lastly, the moth flame optimization (MFO) algorithm can be utilized to finetune the hyperparameters related to the DBN algorithm. The stimulation analysis of the EADL-FDC method takes place on the fall detection dataset. The experimental outcome depicts the remarkable solution of the EADL-FDC technique over other existing DL methods.

Classification of EEG Signals Based on Sparrow Search Algorithm-Deep Belief Network for Brain-Computer Interface.

In brain-computer interface (BCI) systems, challenges are presented by the recognition of motor imagery (MI) brain signals. Established recognition approaches have achieved favorable performance from patterns like SSVEP, AEP, and P300, whereas the classification methods for MI need to be improved. Hence, seeking a classification method that exhibits high accuracy and robustness for application in MI-BCI systems is essential. In this study, the Sparrow search algorithm (SSA)-optimized Deep Belief Network (DBN), called SSA-DBN, is designed to recognize the EEG features extracted by the Empirical Mode Decomposition (EMD). The performance of the DBN is enhanced by the optimized hyper-parameters obtained through the SSA. Our method's efficacy was tested on three datasets: two public and one private. Results indicate a relatively high accuracy rate, outperforming three baseline methods. Specifically, on the private dataset, our approach achieved an accuracy of 87.83%, marking a significant 10.38% improvement over the standard DBN algorithm. For the BCI IV 2a dataset, we recorded an accuracy of 86.14%, surpassing the DBN algorithm by 9.33%. In the SMR-BCI dataset, our method attained a classification accuracy of 87.21%, which is 5.57% higher than that of the conventional DBN algorithm. This study demonstrates enhanced classification capabilities in MI-BCI, potentially contributing to advancements in the field of BCI.

A brief study on rice diseases recognition and image classification: fusion deep belief network and S-particle swarm optimization algorithm

In the regions of southern Andhra Pradesh, rice brown spot, rice blast, and rice sheath blight have emerged as the most prevalent diseases. The goal of this research is to increase the precision and effectiveness of disease diagnosis by proposing a framework for the automated recognition and classification of rice diseases. Therefore, this work proposes a hybrid approach with multiple stages. Initially, the region of interest (ROI) is extracted from the dataset and test images. Then, the multiple features are extracted, such as color-moment-based features, grey-level cooccurrence matrix (GLCM)-based texture, and shape features. Then, the S-particle swarm optimization (SPSO) model selects the best features from the extracted features. Moreover, the deep belief network (DBN) model trained by SPSO is based on optimal features, which classify the different types of rice diseases. The SPSO algorithm also optimized the losses generated in the DBN model. The suggested model achieves a hit rate of 94.85% and an accuracy of 97.48% with the 10-fold cross-validation approach. The traditional machine learning (ML) model is significantly less accurate than the area under the receiver operating characteristic curve (AUC), which has an accuracy of 97.48%.

Multi-Objective Seagull Optimization Algorithm with Deep Learning-Enabled Vulnerability Detection for Secure Cloud Environments

Cloud computing (CC) is an internet-enabled environment that provides computing services such as networking, databases, and servers to clients and organizations in a cost-effective manner. Despite the benefits rendered by CC, its security remains a prominent concern to overcome. An intrusion detection system (IDS) is generally used to detect both normal and anomalous behavior in networks. The design of IDS using a machine learning (ML) technique comprises a series of methods that can learn patterns from data and forecast the outcomes consequently. In this background, the current study designs a novel multi-objective seagull optimization algorithm with a deep learning-enabled vulnerability detection (MOSOA-DLVD) technique to secure the cloud platform. The MOSOA-DLVD technique uses the feature selection (FS) method and hyperparameter tuning strategy to identify the presence of vulnerabilities or attacks in the cloud infrastructure. Primarily, the FS method is implemented using the MOSOA technique. Furthermore, the MOSOA-DLVD technique uses a deep belief network (DBN) method for intrusion detection and its classification. In order to improve the detection outcomes of the DBN algorithm, the sooty tern optimization algorithm (STOA) is applied for the hyperparameter tuning process. The performance of the proposed MOSOA-DLVD system was validated with extensive simulations upon a benchmark IDS dataset. The improved intrusion detection results of the MOSOA-DLVD approach with a maximum accuracy of 99.34% establish the proficiency of the model compared with recent methods.

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction.

The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on Histopathology Images.

Histopathological grading of the tumors provides insights about the patient's disease conditions, and it also helps in customizing the treatment plans. Mitotic nuclei classification involves the categorization and identification of nuclei in histopathological images based on whether they are undergoing the cell division (mitosis) process or not. This is an essential procedure in several research and medical contexts, especially in diagnosis and prognosis of cancer. Mitotic nuclei classification is a challenging task since the size of the nuclei is too small to observe, while the mitotic figures possess a different appearance as well. Automated calculation of mitotic nuclei is a stimulating one due to their great similarity to non-mitotic nuclei and their heteromorphic appearance. Both Computer Vision (CV) and Machine Learning (ML) approaches are used in the automated identification and the categorization of mitotic nuclei in histopathological images that endure the procedure of cell division (mitosis). With this background, the current research article introduces the mitotic nuclei segmentation and classification using the chaotic butterfly optimization algorithm with deep learning (MNSC-CBOADL) technique. The main objective of the MNSC-CBOADL technique is to perform automated segmentation and the classification of the mitotic nuclei. In the presented MNSC-CBOADL technique, the U-Net model is initially applied for the purpose of segmentation. Additionally, the MNSC-CBOADL technique applies the Xception model for feature vector generation. For the classification process, the MNSC-CBOADL technique employs the deep belief network (DBN) algorithm. In order to enhance the detection performance of the DBN approach, the CBOA is designed for the hyperparameter tuning model. The proposed MNSC-CBOADL system was validated through simulation using the benchmark database. The extensive results confirmed the superior performance of the proposed MNSC-CBOADL system in the classification of mitotic nuclei.

Enhanced Lion Optimization Algorithm and deep belief network for intrusion detection with SDN enabled IoT networks

A severe problem that regularly affects cloud systems are intrusions. Ignore how the expansion of Internet of Things (IoT) devices will result in enormous intrusions. To distinguish intrusions from authorized network activity, detection is a crucial procedure. An Enhanced Lion Optimization Algorithm (ELOA) is utilized in this research, IoT intrusion detection system. Intrusions are classified using the Deep Belief Network (DBN) and an SDN controller technique. The proposed ELOA-based Intrusion Detection System uses the optimal weight in DBN to train the neurons to categorize the data in a network as normal and attacked during the training phase. In the testing step that follows training, data from nodes are examined, and by contrasting the training results, they are categorized as normal and attacked data. By using the proposed ELOA and DBN algorithms, our intrusion detection system can successfully identify intrusions. Based on the creation of blacklists for detecting IoT intrusions, the (SDN) Software Defined Networking controller can effectively prohibit harmful devices. In order to demonstrate that the proposed ELOA finds network intrusions more successfully, its performance is compared to that of other existing techniques. The node sizes of the algorithms are run and evaluated for 1000, 2000, 3000, 4000, and 5000 respectively. At highest node 5000, the Proposed ELOA and DPN have precision, recall, f-score and accuracy becomes as 97.8, 96.22, 97.5 and 98.67 respectively.

Utilize DBN and DBSCAN to detect selective forwarding attacks in event-driven wireless sensors networks

Malicious nodes launching selective forwarding attacks jeopardize network reliability in event-driven wireless sensor networks. Swift detection and exclusion of these nodes are vital, especially in harsh environments where normal nodes also suffer from decreased forwarding rates due to poor channel conditions. To solve this problem, this paper proposes a deep belief network (DBN) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) based detection scheme against selective forwarding attacks under harsh environments. The DBN algorithm extracts and analyzes behavior features of nodes, while the DBSCAN clustering algorithm effectively distinguishes between malicious and normal nodes during selective forwarding attacks. Simulation results demonstrate the scheme’s effectiveness with a missed detection rate (MDR) of approximately 2% and a false detection rate (FDR) below 5% in harsh environments, providing a robust solution for ensuring the integrity and efficiency of data transmission in event-driven wireless sensor networks.

A Comparative Study of CNN and DBN Algorithms in Oral Disease Imaging Diagnosis

Oral imaging involves imaging techniques and diagnostic methods of the internal structure of the oral cavity. Intelligent diagnostic techniques based on machine learning and medical gold standards have become a research hotspot in recent years. In order to obtain the accuracy of deep learning for oral image segmentation, this paper mainly compares the performance of deep belief network (DBN) and convolutional neural network (CNN) in image segmentation and recognition. Firstly, the CBCT image data set of the teeth of the relevant cases was established, and the image was preprocessed and marked, and the training set, test set and verification set were divided. Secondly, the DBN and CNN deep learning neural network models are built and debugged for training and testing respectively. Finally, the performance of the two models is compared and their advantages and disadvantages are analyzed from the data of graphic distortion, feature extraction accuracy, tooth discrimination and accuracy. The experimental results show that both of them have good performance in image segmentation, but the CNN model is superior to DBN in oral image classification and recognition. This study explores the application of deep learning in CBCT image processing of teeth. The research results are of positive significance for assisting doctors to judge the condition more accurately, improving the treatment effect and quality, and improving the digitization and intelligence of oral disease imaging diagnosis.

Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

Wireless sensor networks (WSNs) are widely used in various fields, and their deployment is critical to ensure area coverage and full network connectivity to achieve the maximum network lifetime. In this study, we present a mixed-integer programming (MIP) model that deeply investigates deployment parameters to optimize lifetime and analyze network connectivity. We further analyze the obtained results using Deep Belief Network (DBN) and Deep Neural Network (DNN) algorithms to achieve higher accuracy rates. Our evaluation shows that the DBN outperforms the DNN with an accuracy rate of 81.2%, precision of 81.2%, recall of 99.1%, and an F1-Score of 0.78. We also utilize two different datasets to justify the efficiency of the DBN in this research. The findings of this study emphasize the validity of our DBN algorithm and encourage further research into lifetime optimization and connectivity analysis in WSNs.

Optimal design of cold-formed steel face-to-face built-up columns through deep belief network and genetic algorithm

In this paper, a machine-learning optimisation framework for cold-formed steel (CFS) face-to-face built-up columns was proposed using Deep Belief Network (DBN) and Genetic Algorithm (GA). Firstly, an elasto-plastic non-linear finite element (FE) model was developed and validated against the available test results in the literature. Based on the validated FE model, a total of 1,100 datasets were generated, which were then used to train the DBN model. As the DBN model was proven accurate at predicting the axial strength of CFS face-to-face built-up columns, the trained DBN model was therefore used in the optimisation procedure. Through the optimisation procedure, considerable increases of up to 184.7% in axial strength were acquired for the optimised sections, when compared to the axial strength of initial CFS box section, which is commonly used in the industry. Furthermore, the optimised results obtained from the trained DBN model were compared with the results predicted by the Direct Strength Method (DSM). It was found that the DBN-GA optimised results acquired using FE datasets outperformed the results predicted by the DSM.