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This paper proposes a new framework for leak diagnosis in pipelines using leak-augmented scalograms and deep learning. Acoustic emission (AE) scalogram images obtained from the continuous wavelet transform have been useful for pipeline health diagnosis, particularly when combined with deep learning. However, background noise has a significant impact on AE signals, which can reduce the accuracy of pipeline health identification using classification models. To address this issue, a new type of scalograms called leak-augmented scalogram is introduced, which enhances the variation in colour intensities of AE scalogram images. The leak-augmented scalograms are obtained by pre-processing them using image-enhancing Gaussian and Laplacian filters. The proposed method utilizes convolutional neural networks (CNNs) and convolutional autoencoders (CAEs) for feature extraction. The CNN extracts patterns specific to local changes, while the CAE extracts holistic patterns from the leak-augmented scalograms. The resulting leak susceptible and leak holistic indicators are merged into a single feature pool and provided as input to a shallow artificial neural network (ANN) to evaluate pipeline health conditions. The proposed method achieves high classification as well as accuracy, precision, F-1 Score and recall, compared to existing state of the art methods. [ABSTRACT FROM AUTHOR]

The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many different prediction methods, both complex and simple, have been put out in recent years and used to solve a wide variety of issues. Several case studies have highlighted factors that impede energy and resource usage in green buildings. The utilisation, trends, and consequences of wall and thermal insulation materials are examined. The main scope of this investigation is to predict buildings' heat loss by applying artificial neural networks according to the heat transfer coefficients of walls and coating materials, as well as indoor, outdoor, and external surface temperatures. The data has been normalised and presented to two selected neural networks (Harmony search (HS) and particle swarm optimisation are used and contrasted (PSO)). For evaluating the accuracy of models, two statistical indexes are used (R² and RMSE). Model performance of PSO-MLP is shown by R² amounts of 0.97055 and 0.87381, respectively, and RMSE amounts of 0.02534 and 0.09685. Similarly, HS-MLP model accuracy is also indicated by R² amounts of 0.93839 and 0.84176 and RMSE amounts of 0.03635 and 0.10753. The analysis in this paper shows that PSO-MLP predicts heat loss with higher accuracy and improved performance. [ABSTRACT FROM AUTHOR]

Heating and thermal comfort have remarkable share of final energy consumption. Until now, most of the demand for heating applications in buildings is supplied by fossil fuels and electrical technologies. Concerning the exhaustion of fossil fuels in the future and the environmental problems related to their consumption, making use of renewable energy sources can be a practical alternative. On this point, solar energy is an appropriate source to be applied for heating by utilizing different technologies. The function and output of solar heaters depends on numerous factors, and this causes difficulties in the prediction of their performance and modelling. In this scenario, intelligent techniques are helpful and have been used by several scholars in recent years. This paper reviews proposed models for the prediction of the performance of different solar heaters. The literature review reveals that artificial neural Networks represent one of the most used approaches for the performance prediction of solar heaters; however, other intelligent techniques, namely support vector machines, have been used for this purpose too. Moreover, it is found that these methods have the ability to predict with great precision by applying the appropriate approach and architecture. In addition, it can be noted that the function of the models generated based on intelligent techniques are associated with some elements such as the employed function and architecture of the model. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to predict the maximum pressure of journal bearing using ANN. The maximum pressure values are analysed using FEM and predicted using ANN. The FEM analysis is performed for micropolar lubricated hybrid journal bearing, and the results are used for training and testing the ANN model. Feedforward backpropagation algorithm is used for ANN model development. The externally applied load and rotational speed of the journal are considered as input parameters for performance predictions. ANN predictions are made within and outside the prescribed range of input parameters. This approach shows better predictions for journal bearing performance. Results obtained from FEM and ANN are in close agreement with each other. The percentage error less than 0.5 is observed for training and testing of the ANN model. The prediction error is in the range of −0.6% to 0.48%. In this paper, a mathematical model is established using FEM. The performance predictions obtained using ANN are very useful because much time can be saved which would be otherwise consumed in the experimental or theoretical bearing analysis. [ABSTRACT FROM AUTHOR]

The shear behaviour of fibre-reinforced polymer reinforced concrete (FRP-RC) beams without web reinforcement suffers from low strength, low stiffness, more brittleness and wide and quick propagated cracks. Fortunately, the addition of various types of fibres could improve most of these weaknesses. On the other hand, the shear strength prediction of FRP-RC beams with various types of fibres is one of the most complex cases in structural engineering applications. Developing generalised, precise and consistent prediction models are necessary and very limited. This paper investigates the impacts of various types of fibres on shear strength and presents proposing four new prediction models, utilising artificial neural networks and empirical nonlinear regression analysis, and modifying the combination of available models based on a collected database of 49 shear test results of FRP-RC members with various types of fibres. The comparison of the developed models with the available equations from the literature indicates that the developed models yielded excellent performance, great efficiency and a high level of accuracy over all other existing models. Additionally, the parametric study confirmed that all the developed models have great abilities to accurately predict the actual response of each parameter, in spite of its complexity, on the shear strength of FRP-reinforced fibrous concrete beams without stirrups. [ABSTRACT FROM AUTHOR]

Neuromorphic computing based on brain-inspired frameworks has shown its potential to perform computation with remarkable power and speed efficiency. The spintronic device has been considered a promising candidate for neuromorphic computing because of its intrinsic non-volatility, ultrafast switching dynamics and scalability. This article will review the current status of neuromorphic computing techniques employing spintronic materials and devices. We will begin with a concise introduction to the biological structure of neurons and then go through several important models of artificial neural networks. The revolutionary breakthroughs of spintronic-based neuromorphic computation will be demonstrated together with their challenges and outlook. This paper will systematically review the status of neuromorphic computing techniques employing spintronic materials and devices. After reading this article, readers can grasp the revolutionary breakthroughs of spintronic-based neuromorphic computation. [ABSTRACT FROM AUTHOR]

Self-optimizing control (SOC) is a technique for selecting appropriate controlled variables (CVs) and maintaining them constant such that the plant runs at its best. Some tough challenges in this subject, such as how to select CVs when the active constraint set changes remains unsolved since the notion of SOC was presented. Previous work had some drawbacks such as structural complexity and control inaccuracy when dealing with constrained SOC problems due to the elaborate control structures or the limitation of local SOC. In order to overcome the deficiency of previous methods, this paper developed a constrained global SOC (cgSOC) approach to implement self-optimizing controlled variable selection and control structure design. The constrained variables that may change between inactive and active are represented as a nonlinear function of available measurement variables under optimal operations. The unknown function is then intelligently learnt over the whole operating region through neural network training. The difference between the nonlinear function and the actual constrained variables measured in real-time is then used as CVs. When the CVs are controlled at zero in real-time, near-optimal operation can be ensured globally whenever active constraint changes. The efficacy of the proposed approach is demonstrated through an evaporator case study. [ABSTRACT FROM AUTHOR]

In this paper, surface roughness prediction models are developed for turning of Inconel 718 using untreated and cryogenically treated inserts by using Dimensional Analysis, Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Performance of untreated and treated tools is analysed using SEM, Energy-dispersive X-ray analysis, Vicker hardness test and electrical conductivity. For the established surface roughness models by dimensional analysis, RSM and ANN, the mean absolute errors for confirmation tests are 5.32%, 8.28% and 4.15% for untreated inserts and 4.95%, 6.01% and 4.20% for treated inserts, respectively. The effect of cutting parameters on surface roughness is analysed using the main effect plot and 3D surface plots. Based on correlation coefficient (R2) values, ANN modelling technique (R2 = 99.68%) is more accurate for predicting surface roughness. Thus, it can be an effective tool for analysing machining responses. The study also noted that while cutting at v= 60 m/min, f= 0.1 mm/rev and d= 0.5 mm, surface roughness and flank wear values are 0.5 µm and 0.45 µm and 0.777 mm and 0.627 mm for untreated and treated inserts, respectively. The use of treated tools resulted in 10% and 19% improvement in surface quality and tool life than the untreated tools. [ABSTRACT FROM AUTHOR]

Due to a lack of solar radiation measurement instruments in various regions across the world, particularly in Ethiopia, many empirical models have been developed to estimate global solar radiation. Reliable global solar radiation infor- mation is essential for the design and development of solar energy systems.Thus, the aim of this paper is to investigate the feasibility of using Artificial Neural Network (ANN) to predict mean daily global solar radiation (GSR) and to compare the performance of ANN and empirical models based on sunshine in estimating mean daily global solar irradiation on a horizontal surface.For the ANN model, network inputs were daily data for a number of days, average daily data (sunshine hours, maximum temperature, minimum temperature, wind speed, relative humidity, and pressure), and while daily averaged global solar radiation was the network output.After training, the model had R values of 0.891 for training, 0.932 for validation, 0.920 for testing, and 0.903 for all, with validation performance of 0.0797.The results of the ANN model were compared to the empirical equations using statistical error evaluation (MBEANN = 0.0005, RMSEANN = 0.3310, and R2ANN = 0.7998).The agreement between the values of NASA and estimated values reveals that the ANN exhibits good performance in studying daily GSR. The result also shows that the mean daily GSR is varying between 3.675KWh/m² (August-1) and 6.935 KWh/m² (April-19) in the study site. [ABSTRACT FROM AUTHOR]

This paper presents the design of a grid-connected hybrid system using modified Z source converter, bidirectional converter and battery storage system. The input sources for the proposed system are fed from solar and wind power systems. A modified high gain switched Z source converter is designed for supplying constant DC power to the DC-link of the inverter. A hybrid deep learning (HDL) algorithm (CNN-BiLSTM) is proposed for predicting the output power from the hybrid systems. The HDL method and the PI controller generate pulses to the proposed system. A closed loop control framework is implemented for the proposed grid integrated hybrid system. A 1.5 Kw hybrid system is designed in MATLAB/SIMULINK software and the results are validated. A prototype of the proposed system is developed in the laboratory and experimental results are obtained from it. From the simulation and experimental results, it is observed that the ANN controller with SVPWM (Space vector Pulse width Modulation) gives a THD (Total harmonic distortion) of 2.2% which is within the IEEE 519 standard. Therefore, from the results, it is identified that the ANN-SVPWM method injects less harmonic currents into the grid than the other two controllers. [ABSTRACT FROM AUTHOR]

Recently, the use of artificial neural networks (ANN) in the offshore exploration and production industry to optimize decision-making and reduce costs and non-productive time have been increasing. Despite this trend, there have been only 11 reviews of ANN in offshore engineering published on the Scopus database. Therefore, this article aims to provide an update on the relevance of ANN in offshore engineering over the past 18 years (2005-2023) through a bibliometric analysis using Excel and VOS Viewer software. This analysis highlights the yearly increase in publications related to ANN implementations in offshore engineering and identifies the most cited publications, citation network analysis, authors, keywords, journals, institutions, and leading countries. The objective of this bibliometric analysis is to assist subsequent research and collaboration in this field by shedding light on ANN's potential and identifying areas for further application. The identified cluster area publications encompass a range of topics, including drilling systems and the assessment of pipes. Furthermore, the significant fourfold increase in publications since 2005 indicates a growing interest among researchers in adapting ANN for various applications within this field. This could lead to further advancements, innovations, and improved solutions to promote collaboration and knowledgesharing among researchers in this domain. [ABSTRACT FROM AUTHOR]

Due to the wide application of evolutionary science in different engineering problems, the main aim of this paper is to present two novel optimizations of multi-layer perceptron (MLP) neural network, namely dragonfly algorithm (DA) and biogeography-based optimization (BBO) for landslide susceptibility assessment at a study area, West of Iran. Utilizing 14 landslide conditioning factors, namely elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), and topographic wetness index (TWI) and rainfall as the input variables, and 208 historical landslides as target variable, the required spatial database is created. Then, the MLP is synthesized with the mentioned algorithms to develop the proposed DA-MLP and BBO-MLP ensembles. Three accuracy criteria of mean square error, mean absolute error, and area under the receiving operating characteristic curve are used to evaluate the performance of the models and also to develop a score-based ranking system. As the first outcome, the application of the DA and BBO metaheuristic algorithms enhances the accuracy of the MLP. Moreover, referring to the calculated total ranking scores of 6, 14, and 16, it was revealed that the BBO performs more efficiently than DA in optimizing the MLP. [ABSTRACT FROM AUTHOR]

Air bending is a frequently used process for sheet metal fabrication. Spring-back and bend force are the two essential parameters to be predicted for the design of tooling and selection of press. In air bending of electro-galvanised steel sheets, these two parameters are affected by the factors such as strain hardening exponent, coating thickness, die opening, die radius, punch radius, punch travel and punch velocity. This paper presents the development of a predictive model of spring-back and bend force using artificial neural network (ANN) approach. A central composite design of 88 experiments is conducted to obtain the data required for training the network. A new set of data combinations, not belonging to the training data, is used to assess the generalisation performance of the neural network. The network is validated with a set of validation data and it is found that ANN is capable of predicting the two parameters. [ABSTRACT FROM AUTHOR]

One of the most significant issues in urban design is energy-related CO2 emissions, which are rising quickly as cities expand. The GDP of the Central European countries (from 1990 to 2016) based on several energy sources, such as coal, oil, natural gas, and renewable energy, are used as inputs in this study. To develop a reliable predictive network considering the problem complexity, multi-layer perceptron (MLP) is combined with several nature-inspired optimization algorithms, namely, black hole algorithm (BHA), future search algorithm (FSA), backtracking search algorithm (BSA), biogeography-based optimization (BBO), and shuffled complex evolution (SCE). By applying the approaches mentioned above to the synthesis of the MLP, the recommended BBO, BHA, BSA, FSA, and SCE ensembles are obtained. A series of parametric studies are performed to improve the effectiveness of the employed models. It is found that, by combining the BBO, BHA, BSA, FSA, and SCE algorithms, the MLP's accuracy is increased. The result from this parametric analysis showed that SCE and BBO perform better than the other three algorithms as the CO2 emission was computed with the highest level of accuracy using R² = 0.9999 and 0.9998, RMSE = 1.6781 and 2.0539 for SCE, and R² = 0.9999 and 0.9998, RMSE = 1.8689 and 2.3833 for BBO. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG) has proven to be one of the most efficient ways of tracking heart defects in athletes. However, the interpretation of electrocardiograms often require the expertise of a cardiologist. Meanwhile, an automated heart monitoring system could be used to ensure early heart defect detection in athletes, even in the absence of a cardiologist. In this paper, an automated heart defect detection model is proposed for athletes using ECG and Artificial Neural Network (ANN). We developed an ECG biomedical equipment to acquire 400 ECG data vectors from 40 participants, who comprises of athletes and non-athletes. Four classes of possible heart conditions among athletes, namely: normal, tachyarrhythmia, bradyarrhythmia and hypertrophic cardiomyopathy were considered. The ECG data collected were pre-processed and features were extracted based on first order moment. Different ANNs were trained to correctly classify the ECG data. By and large, the performances of ANNs that were trained based on Levenberg-Marquardt learning algorithm outperformed those trained based on Scale Conjugate Gradient learning algorithm. The network architecture with tansig activation function at both hidden and output layers and ten neurons in the hidden layer (TTLM) produced the best performance that cut across all the key performance indicators. The generalization testing of the developed TTLM model with new input data (that were excluded from the training dataset) produced acceptable results with classification accuracy, sensitivity and specificity of 90.00, 91.96 and 97.06% respectively. In essence, the implementation of the developed model in this study could potentially assist in reducing sudden cardiac death among athletes. [ABSTRACT FROM AUTHOR]

The hybrid electric vehicles (HEVs) market has grown tremendously in the past few years which, as a result, has led to an exponential growth in the spare parts (SPs) market. Therefore, there is a strong need, nowadays, to predict the demand as well as the price of these SPs. However, ascertaining such an aim is not as easy as it may seem, this being due to the facts that (i) the demand is highly uncertain as it depends on many uncertain variables, and (ii) the price does not follow the normal value chain methods. In this research work, the artificial neural network (ANN) is utilized to develop models that can map 15 vehicles and SPs- related variables to the demand and the price of the HEV SPs. It has been demon- strated that the ANN models have the ability to predict both the demand and the price of the HEV SPs. In addition, the developed ANN models outperform the linear regression models by minimizing the root mean square error values by approxi- mately 4 and 5 times for the demand and the price, respectively. Neural network- based models have been employed to accurately predict the demand as well as the price of the HEV SPs by mapping them to 15 vehicles and SPs-related variables. [ABSTRACT FROM AUTHOR]

The insinuations of the ailments associated with the unrestrained and disorganized proliferation of artificial impervious materials over natural surfaces are prevalent among city dwellers. These impacts can be comprehended by estimating land surface temperature (LST), as it is vital for evaluating urban climate, particularly to explain the intensity of urban heat islands and to define the health and welfare of the planet as well as the living beings. Urbanization-driven landscape changes severely disrupt comfortable living in almost every city, necessitating monitoring and modelling historical, current, and likely future LSTs. This research article proposes two forecasting techniques: Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. These models have been widely accepted for the efficient prediction of climatic parameters, including LST, over an urban area. The landscape, elevation, and LST trend served as input to the models for an accurate prediction of LST. The analysis was performed over the Kolkata Metropolitan Area (KMA) with an additional 10 km buffer to understand urban growth and its effect on the LST of the entire region. The two developed models (MLR and ANN) effectively anticipated the LST over the KMA region. A continual increment in the surface temperatures ranging from 1 ℃ to 4 ℃, over existing and likely-predicted urban areas was comprehended. It was anticipated that the regions near the urban areas will also experience severe discomfort and heat waves without proper mitigation measures. This scientific literature provides essential insights for decision-makers, stakeholders, and government officials to articulate new policies and modify the existing ones to create a sustainable and livable urban environment for the inhabitants. [ABSTRACT FROM AUTHOR]

Owing to the use of nonlinear loads in the distribution side, there are power quality issues such as voltage swell/sag, harmonics, flickers, voltage imbalance, and outage. The harmonics in power system affect the quality of power and hence a suitable methodology is vital to mitigate the harmonics and compensation of reactive power. In this paper, CNN (Convolutional Neural Network)-based harmonic mitigation is performed. A 5-level cascaded H-bridge inverter is employed as a shunt active filter in which the reference current is generated by the SRF theory, incorporating CNN for harmonic extraction. The DC-link potential across capacitor is retained by means of ANN (Artificial Neural Network) controller whose behaviour is compared with a proportional controller as well as FLC. The gating pulse for the cascaded inverter is generated by means of PWM generator incorporated with Hysteresis Current Controller (HCC). By this control strategy, the harmonics in the current and voltage get mitigated; subsequently, the reactive power compensation is achieved with unity power factor. By implementing the five-level inverter, the THD and the settling time are minimized. The performance of the system is analysed using MATLAB for nonlinear load and the hardware is implemented with FPGA Spartan 6E. The THD of 0.93% is accomplished in simulation and 1.4% in the hardware execution. [ABSTRACT FROM AUTHOR]

Advancements in technology and the compulsion to use environment-friendly materials have been challenging tasks for researchers for the past two decades. Researchers have been focusing on the utilization of plant fibers to produce good quality fiber-reinforced polymer/polyester composites for automobile, structural, and building applications. Researchers have been looking for high-quality and cost-effective drilling processes. The primary goal of this study is to identify optimal drilling conditions for CCFRP composite laminates, affecting thrust force and torque. This is achieved by manipulating drilling process variables using Taguchi's Design of Experiments (TDOE), Analysis of variance (ANOVA), Response Surface Methodology (RSM), Desirability Function Analysis (DFA) and Artificial Neural Network (ANN). From the results, it was observed that the spindle speed of 2000 rpm, feed of 15 mm/min, point angle of 90°, fiber length of 6 mm, fiber volume of 30%, and fiber diameter of 7 microns gave the optimum results for obtaining minimum thrust force and torque. Further RSM revealed that an increase in fiber vol % and a decrease in spindle speed resulted in an increase in thrust force and torque. From DFA optimization results, the minimum thrust force of 24.0042N and minimum torque of 0.8001N-m was obtained. Finally, the experimental values of thrust force and torque were compared with the corresponding values predicted by the MLP-ANN model. The average error percentage for thrust force and torque was 1.75% and 6.56% respectively. [ABSTRACT FROM AUTHOR]

The nature of paratransit services makes for increased uncertainty in trip time, leading to reported unreliability and dissatisfaction by the users. While providing travel information has proved helpful in formal bus services and has been recommended for paratransit setup, little is reported about efforts at providing information to paratransit users. This study focused on one strand of possible travel information that can be provided - Travel Time. An artificial neural network (ANN)-based model was developed to predict paratransit travel times, geared towards providing information to improve user experiences. The developed model was tested on a real-world paratransit bus route (minibus taxi) in Kumasi. A travel time survey that employed a mobile phone application was used to collect data onboard the vehicles on the study route. Two ANN models were trained. The first used only historical datasets, while the second incorporated real-time information. The results show that the model in which real-time information was included performed better than that trained with only historical data. The developed models were compared with a historical average model and a regression-based model, and the results showed that the ANN models outperformed the others. The study showed that the nature of paratransit services and the limitations of continuous data collection, notwithstanding, travel times of paratransit trips can be predicted to a reasonable level of accuracy, as can be relied upon in providing information to the users. [ABSTRACT FROM AUTHOR]

Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R= −0.43), tree cover (R= 0.93) and temperature (R= 0.42) were strongly correlated with forest fire probability in the USB region. [ABSTRACT FROM AUTHOR]

The automatic pattern recognition of single and multiple power quality (PQ) disturbances is a very important task for the detection and monitoring of multiple faults and events in electrical power system. This paper presents an automatic classification algorithm for PQ disturbances based on wavelet norm entropy features and probabilistic neural network (PNN) as an effective pattern classifier. The proposed method employs the discrete wavelet transform based on multi-resolution analysis technique to extract the most important and constructive features of PQ disturbances at various resolution levels. The distinctive norm entropy features of the PQ disturbances have been extracted and were employed as inputs to the PNN. Various other architectures of artificial neural network such as multilayer perceptron and radial basis function neural network have also been employed for comparison. The PNN is found the most suitable pattern recognition tool for the classification of the PQ disturbances. Various PQ disturbances used for analysis were generated by simulating a typical 11-kV distribution system. The simulation results obtained show that the proposed approach can detect and classify the PQ disturbances effectively and can be implemented successfully in real-time electrical power distribution networks. [ABSTRACT FROM PUBLISHER]

This paper presents the potential of generating wind power using soft computing model and ground station data. In reality, the process of wind resource assessment is to set up an experiment in the targeted locations, and measure the wind speed and direction. In this paper, a prediction model based on the terrain based neural network named terrain nonlinear autoregressive neural network (TNARX) is proposed to forecast the wind speed in the areas not covered by measurements using a ground station located nearby. The model has meteorological, physical and topographical as input, while the wind speed is the target variable. The suitability of the proposed model was judged using statistical measures. The paper shows characteristics of wind speed and the most prevailing wind directions. The variation of wind speed at 10-40 m heights was obtained and presented. Wind speed distribution modelling was carried out using five statistical models. It was found that Weibull and Gamma fits the wind speed of the studied areas. Wind power and energy density results show the areas falls within class 1, which is possible for harnessing energy content in wind for small scale purposes. [ABSTRACT FROM AUTHOR]

This paper deals with the prediction of flank wear of the cutting tool insert and surface roughness of the machined surface in hard turning using artificial intelligence technique. During machining of hard materials, the flank wear and surface roughness are favoured by both machining parameters and geometrical parameters. Prediction of output responses is much more important since surface roughness produced depends on the wear occurring at the flank face of the cutting tool insert. Based on the chosen input control parameters an L18 orthogonal array is chosen for seven input parameters varied through three levels to perform experiments using Taguchi's design of experiments. An artificial neural network (ANN) model is developed to predict the flank wear and surface roughness, which is compared with the predicted values from the developed empirical equations using multiple linear regression models. The results obtained from the ANN model shows a good prediction when comparing with the regression models showing the superiority of the generated neural network model. [ABSTRACT FROM AUTHOR]

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sown biodiverse pastures (SBP) are a pasture system developed in Portugal. Until 2014, farmers were supported in installing and maintaining SBP, but tracking their locations has been lacking. To survey the country, remote sensing tools with machine learning were used. Here, we developed the first algorithm that combines remote sensing data with machine learning algorithms to identify SBP areas. The algorithm combines Landsat-7 and night-light spectral data with terrain and bioclimatic data. Remotely sensed data offer higher spatial resolution compared to bioclimatic data and also cover interannual variability. Gradient-boosted decision trees (XGB) and artificial neural networks (ANN) were the machine learning methods used. The overall classification accuracy, on an independent validation dataset, was 94%, with 82% producer accuracy and 85% user accuracy. The total estimated area of SBP in the Portuguese region of Alentejo region was 1300 km2 in 2013, which is similar to the total known installed area (approximately 1000 km2). The estimated spatial distribution is in accordance with the known distribution at the municipal level. These results are a critical first step towards the future development of remote systems for assessing the state of SBP and for compliance checks of farmer commitments. [ABSTRACT FROM AUTHOR]

Precisely forecasting air temperature as a significant meteorological parameter has a critical role in environment quality management. Hence, this study employs a hybrid intelligent model for accurately monthly temperature forecasting for one to three times ahead in the hottest and coldest regions of the world. The hybrid model contains the artificial neural network (ANN) hybridized with the powerful hetaeristic Honey Badger Algorithm (HBA-ANN). The average mutual information (AMI) technique is employed to find the optimal time delay values for the temperature variable for different time horizons. Finally, the performance of the developed hybrid model is compared with the classical ANN and the Gene Expression Programming (GEP) using some statistical criteria, and the Taylor and scatter diagrams. Results indicated that in each time horizon, the HBA-ANN model with the lowest distance from observation points based on Taylor diagram, high values for NSE and R², and low values for RMSE, MAE, and RSR outperformed the ANN and GEP models in both training and testing phases. Hence, using the Honey Badger Algorithm could increase the accuracy of the model. This model's precise performance supports the case for it to be employed to forecast other environmental parameters. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study into the use of pattern recognition as a method for detecting damage in structures. Pattern recognition is achieved by the use of artificial neural networks (ANNs), however, these require careful design because the number of hidden layers and the number of neurons in each hidden layer are critical to the ANN's performance. In the current study, a Bayesian model class selection method was employed to select an optimal ANN model class that avoids ad hoc assumptions and subjective decisions in the ANN design. The objective of the research was to provide an extended study of the proposed method using the IASC-ASCE Structural Health Monitoring Phase II Simulated Benchmark Structure. Damage-induced modal parameter changes were used as a pattern feature in damage detection. Analysis showed that the proposed method is able to successfully identify damages in the benchmark structure. [ABSTRACT FROM AUTHOR]

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas. [ABSTRACT FROM AUTHOR]

This study explores the use of gene expression programming (GEP) and artificial neural networks (ANNs) to estimate flood index values based on the unit flood response (UFR) method in two adjacent watersheds (Ardak and Kardeh) located in northeast Iran. The performances of the studied data-driven models were compared according to certain statistical measures such as Root Mean Square Error (RMSE). The findings indicate that GEP models were more accurate than ANNs (RMSE = 0.0986 vs. 0.1512 for Ardak and RMSE = 0.1024 vs. 0.1112 for Kardeh, respectively). Another advantage of the GEP models was providing an explicit relationship between flood index values and physical attributes. As flood index values derived via the UFR method were close to each other, flood contribution area maps were developed using a geographic information system (GIS) to consider uncertainty. Then, fusion algorithms including ordinary averaging, linear regression, and GEP were applied to develop a flexible regional model. [ABSTRACT FROM AUTHOR]

In this paper, an optimal model is developed for path loss predictions using the Feed-Forward Neural Network (FFNN) algorithm. Drive test measurements were carried out in Canaanland Ota, Nigeria and Ilorin, Nigeria to obtain path loss data at varying distances from 11 different 1,800 MHz base station transmitters. Single-layered FFNNs were trained with normalized terrain profile data (longitude, latitude, elevation, altitude, clutter height) and normalized distances to produce the corresponding path loss values based on the Levenberg-Marquardt algorithm. The number of neurons in the hidden layer was varied (1-50) to determine the Artificial Neural Network (ANN) model with the best prediction accuracy. The performance of the ANN models was evaluated based on different metrics: Mean Absolute error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), standard deviation, and regression coefficient (R). Results of the machine learning processes show that the FNN architecture adopting a tangent activation function and 48 hidden neurons produced the least prediction error, with MAE, MSE, RMSE, standard deviation, and R values of 4.21 dB, 30.99 dB, 5.56 dB, 5.56 dB, and 0.89, respectively. Regarding generalization ability, the predictions of the optimal ANN model yielded MAE, MSE, RMSE, standard deviation, and R values of 4.74 dB, 39.38 dB, 6.27 dB, 6.27 dB, and 0.86, respectively, when tested with new data not previously included in the training process. Compared to the Hata, COST 231, ECC-33, and Egli models, the developed ANN model performed better in terms of prediction accuracy and generalization ability. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new diagnosing technique for chronic kidney disease by using Artificial Neural Network (ANN). Where, the required data for the computational health-care system is collected from various hospitals at Jazan region, Saudi Arabia. Furthermore, in order to prove the convergence of this method, a ridge function is used in the hidden layer as a basis for the neurons. The technique applied for different number of neurons, and in each case a least square error is provided for choosing the best possible approximation. [ABSTRACT FROM AUTHOR]

This paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM) to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs): line MVA performance index (PIMVA) and Voltage-reactive power performance index (PIVQ). SVM works in two steps. Step I is the estimation of both standard indices (PIMVA and PIVQ) that is carried out under different operating scenarios and Step II contingency ranking is carried out based on the values of PIs. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus (New England system). The approach can be beneficial tool which is less time consuming and accurate security assessment and contingency analysis at energy management center. [ABSTRACT FROM AUTHOR]

This study presents a comparison of prediction performances by an artificial neural network (ANN), well-known deep convolutional neural network (D-CNN) models, and four proposed shallow convolutional neural network (S-CNN) models to forecast three key physicochemical properties (PCPs): salinity, °Brix, and moisture content of kimchi sauce (KS). The S-CNN models effectively minimized underfitting issues found in D-CNN models, predicting PCPs with a low error rate even with small image datasets. Furthermore, the ANN model using color values also allowed for competitive predictions. We used two nondestructive prediction strategies: (i) using color values with ANNs for immediate application in small-scale enterprises and (ii) using photographs as input for S-CNN models, allowing for faster and more accurate quality prediction. These results highlight the potential for image-based quality prediction in food science, possibly enhancing the efficiency and accuracy of real-time quality control. Future enhancements could incorporate additional data sources for improved predictive performance. [ABSTRACT FROM AUTHOR]

The prestressed concrete structures are taking the forefront in recent years due to the innovations in the construction industry. However, corrosion is one of the barriers to the serviceability of the prestressed structures. Therefore, a detailed investigation of the prestressed concrete structure under a corrosive environment is essential. This study uses Resilient Back Propagation with BackTracking Neural Network (RBPBTNN) to estimate the flexural strength of the corroded prestressed concrete beam. Three RBPBTNN-based prediction models are proposed to predict the ultimate load, ultimate moment and deflection. The datasets involving multiple influencing parameters are collected from experimentally verified literature. The best possible RMSE and R² values obtained during the training phase for ultimate load prediction are 3.2834 and 0.9964 and for ultimate moment prediction are 2.6128 and 0.9987 and for deflection prediction are 0.8252 and 0.9992 when K-fold cross-validation is three and training repetition is ten. The final performance measures (MAE, R², RMSE etc) of the prediction results are presented in comparison with other artificial neural network algorithms and it is found that the proposed models are the best fit for the collected datasets. [ABSTRACT FROM AUTHOR]

In the precision machining industry, machine tools are usually affected by various factors during machining, and various machining errors generated accordingly. Where thermal error is one of the most common and difficult to control factors for machine tools. Therefore, in this study, six temperature sensors and an eddy current displacement meter are provided in a machine tool with 4-axis for dataset collection required for the model training, then data are organized and normalized. Next, data are introduced into a variety of learning models and validated by k -Fold cross-validation for predicting those nonlinear factors that affect the errors. At the end, predicted results are summarized and compared to find out the best two model with better predictive performance for pre-trained model of transfer learning. It observes better predicted results from a retraining conducted through applying Multilayer Perceptron (MLP) on these two pre-trained models, wherein MAE value as 0.40, RMSE as 0.52625 and R 2 score as 0.99696 respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Fibers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Natural Fibers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Flexoelectric effect can be used to design actuators to control engineering structures including beams, plates, and shells. Multiple flexoelectric actuators method has the advantage of less stress concentration and better control effect, but the mode-dependent optimal actuator locations could influence the flexoelectric actuation effect significantly. In this work, a neural network model is established to study the optimal combinations of multiple flexoelectric actuators on a rectangular plate. In the physical model, an atomic force microscope (AFM) probe was employed to generate an electric field gradient in the flexoelectric patch, so that flexoelectric control force and moment can be obtained. Multiple flexoelectric actuators on the plate was considered. Case studies showed that the flexoelectricity induced stress mainly concentrate near the probe, the size and shape of the flexoelectric patch have limited effect on the actuation, hence, only the actuator positions were choosing as the input of the ANN model. Using the prediction of the neural network model, the driving effect of a large number of actuators at different positions can be quickly obtained, and the optimal position of the actuator can be analyzed more accurately. [ABSTRACT FROM AUTHOR]

To broaden the efficient operating zone and increase the energy efficiency of a multi-stage double-suction centrifugal pump, a multi-component and multi-condition optimization design method involving high-precision performance predictions, a flow loss visualization technique based on entropy production theory, and machine learning is proposed. First, the accuracy of the baseline pump numerical methodology is verified via a grid convergence analysis and experiments. Thereafter, nine design parameters of the impeller and double volute are selected as design variables. Subsequently, 150 designs are created according to the Latin hypercube sampling method (LHS) and numerically simulated using an automatic simulation program. A backpropagation neural network (BPNN) and a multi-objective genetic algorithm (MOGA) are adopted to maximize the efficiency at 0.6Qd, 1.0Qd, and 1.2Qd. Finally, the optimal results are verified via numerical calculations and analyzed. The results indicate that the efficiency of the optimized pump is increased by 2.05%, 3.56%, and 5.36% at 0.6Qd, 1.0Qd, and 1.2Qd, respectively. The comparative analysis of the energy characteristics reveals that the improved performance of the optimized pump can be attributed to the improved matching between the rotor and stator. This research further demonstrates the accuracy and reliability of the optimization method using an artificial neural network (ANN). [ABSTRACT FROM AUTHOR]

Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural network (ANN) and random forest (RF) algorithms in this field. However, more recent studies have demonstrated promising PV solar power output forecasting performances by the decision tree (DT), extreme gradient boosting (XGB), and long short-term memory (LSTM) algorithms. Therefore, the present study aims to address a research gap in this field by determining the best performer among these 5 algorithms. A data set from the United States' National Renewable Energy Laboratory (NREL) consisting of weather parameters and solar power output data for a monocrystalline silicon PV module in Cocoa, Florida was utilized. Comparisons of forecasting scores show that the ANN algorithm is superior as the ANN16 model produces the best mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2) with values of 0.4693, 0.8816 W, and 0.9988, respectively. It is concluded that ANN is the most reliable and applicable algorithm for PV solar power output forecasting. [ABSTRACT FROM AUTHOR]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The application of artificial neural network (ANN) to predict the wear rate of the surface composites produced using a solid-state technique called friction stir processing (FSP) is presented in this work. The copper surface composites were prepared by incorporating different sort of ceramic particles such as SiC, TiC, Al2O3, WC and B4C. The design of experiments (DOE) strategy was utilised to direct the experimental work. The considered operating parameters were sort of ceramic particle, traverse speed, tool rotational speed and groove width, whereas wear rate is the response. An approximation mechanism having an arbitrary function, the ANN was consequently used for simulating the wear rate of the surface composites. The feedforward back propagation technique was employed to alter the weights of the network to minimise the mean squared error for the development of ANN models. The predicted trends were explained and studied the influence of the considered factors with the aid of observed micro structures. The lower wear rate was observed with B4C-reinforced surface composites. [ABSTRACT FROM AUTHOR]

Stock market is considered too uncertain to be predictable. Many individuals have developed methodologies or models to increase the probability of making a profit in their stock investment. The overall hit rates of these methodologies and models are generally too low to be practical for real-world application. One of the major reasons is the huge fluctuation of the market. Therefore, the current research focuses in the stock forecasting area is to improve the accuracy of stock trading forecast. This paper introduces a system that addresses the particular need. The system integrates various data mining techniques and supports the decision-making for stock trades. The proposed system embeds the top-down trading theory, artificial neural network theory, technical analysis, dynamic time series theory, and Bayesian probability theory. To experimentally examine the trading return of the presented system, two examples are studied. The first uses the Taiwan Semiconductor Manufacturing Company (TSMC) data-set that covers an investment horizon of 240 trading days from 16 February 2011 to 23 January 2013. Eighty four transactions were made using the proposed approach and the investment return of the portfolio was 54% with an 80.4% hit rate during a 12-month period in which the TSMC stock price increased by 25% (from $NT 78.5 to $NT 101.5). The second example examines the stock data of Evergreen Marine Corporation, an international marine shipping company. Sixty four transactions were made and the investment return of the portfolio was 128% in 12 months. Given the remarkable investment returns in trading the example TSMC and Evergreen stocks, the proposed system demonstrates promising potentials as a viable tool for stock market forecasting. [ABSTRACT FROM AUTHOR]

Numerical simulation of sediment transport in alluvial rivers often becomes untenable due to the absence of requisite data. Over the years, artificial neural network (ANN) models have been successfully employed for tackling such scenarios. However, the effect of input and target datasets on the simulation of sediment transport using ANN has not yet been adequately addressed in the literature so far. To study the effect of input-target dataset on the performance of ANN models, the present study employs seven input-target datasets for the development of several ANN models and assessment of their performance. The limited study carried out reveals that there are multiple ANN models – having different combinations of training algorithms, transfer functions and input/target datasets – that can reliably be employed to estimate bed elevation changes in alluvial rivers. [ABSTRACT FROM AUTHOR]

Hot weather concreting is unavoidable in the construction industry. Producing desirable workability in different temperatures needs careful recognition of temperature effects on workability. Several studies have shown that slump flow model of concrete is not only specified by the content of its ingredients, but that is also determined by fresh concrete placement temperature, ambient temperature, humidity, wind speed, etc. In this study slump flow prediction of conventional concretes using artificial neural network (ANN) models are presented. This paper discusses that to what extent inclusion of fresh concrete temperature can influence slump flow prediction of ANN models. It is also detailed that how variations in the "as placed" concrete temperature change the workability and influence of ingredients on workability. The results show that: (i) the ANN models of slump flow that incorporate the concrete temperature are a little more accurate and slightly has higher prediction precision than those they do not; and (ii) the ANN models are more precise in predicting slump flow rather than non-linear and linear regression models. [ABSTRACT FROM AUTHOR]

In this study, a supervised artificial neural network (ANN) trained by back propagation (BP) algorithms was developed to predict the transpiration of poplar based on six input variables. Based on the transpiration characteristics of trees, a three-layer BP network was constructed with six input units and one output unit, Daily average temperature, relative humidity, photosynthetic affective radiation, wind speed, sunlight duration and the soil water content 50 cm below the soil surface were considered as the six input variables of the network, which primarily affected the transpiration of poplar. The prediction of daily transpiration of poplar in Heilonggang region, Hebei province was conducted. The research results indicated that R² equalled 0.9534 between measured values and predicted values. The maximum relative error, the minimum relative error and the average relative error were 16.85, 1.49, and 4.2%, respectively. The proposed model could describe the relationship between the daily transpiration of poplar, the meteorological factors and soil moisture conditions with a relatively high accuracy. The research results had potential values for the production and management in this polar stand. [ABSTRACT FROM AUTHOR]

The paper presents an experimental-based study of abrasive jet machining (AJM) considering the effect of changing process parameters. A series of drilling tests were carried out on glass workpieces using sand as the abrasive powder. The influence of each process parameter; applied air pressure, standoff distance, nozzle diameter, particle grain size and impact angle on the machining performance was determined in terms of the resultant material removal rate (MRR). The experimental results revealed that MRR was highly dependent on the kinetic energy of the abrasive particles, with the applied pressure the dominant parameter. The experimental results were compared with an erosion rate model previously published by Jafar et al. Though correct trends were predicted, there was a large discrepancy between model and measured values. An artificial neural network (ANN) was utilised to model the MRR more precisely, particularly to establish relationships between applied machining parameters and experimentally measured MRR and achieved a maximum error of only 5.3%. A Genetic Algorithm (GA) was applied to optimise the model and identify the conditions to maximise the MRR. The results were experimentally validated and good agreement found between the experimental results obtained and the ANN and GA predictions. [ABSTRACT FROM PUBLISHER]

This study aims to analyze the potential of the computer vision (CV) approach to classify eight canola varieties. The input images of eight canola varieties were CON-I, CON-II, CON-III, Pakola, Canola Raya, Rainbow, PARC Canola Hybrid, and Tarnab-III. A digital camera acquired these images on an open sunny day without any complex laboratory setup. First-order histogram features, second-order statistical texture features, binary features, spectral features of three bands were, blue (B), green (G), and red (R), were employed in the artificial neural network (ANN). A 10-fold stratified cross-validation method was used for classification. The best results with accuracy ranging from 95% to 98% observed when the data of regions of interest (512 × 512) deployed to the classifier. [ABSTRACT FROM AUTHOR]