Artificial Neural Network Architecture Research Articles

Predicting photovoltaic parameters of perovskite solar cells using machine learning

Perovskite solar cells (PSCs) have garnered significant attention owing to their highly power conversion efficiency (PCE) and cost-effectiveness. Traditionally, screening for PSCs with superior photovoltaic parameters relies on resource-intensive trial-and-error experiments. Nowadays, time-saving machine learning (ML) techniques serve as an artificial intelligence approach to expedite the prediction of photovoltaic parameters using accumulated research datasets. In this study, we employ seven supervised ML methods to forecast key photovoltaic parameters for PSCs such as PCE, short-circuit current density (J sc), open-circuit voltage (V oc), and fill factor (FF). Particularly, we design an artificial neural network (ANN) architecture that incorporates residual connectivity and layer normalization after the linear layers to enhance the scope and adaptability of the network. For PCE and J sc, ANN demonstrates superior prediction accuracy, yielding root mean square errors of 2.632% and 2.244 mA cm−2, respectively. The Random Forest (RF) model exhibits exceptional prediction performance for V oc and FF. Additionally, an interpretability analysis of the model is conducted to elucidate the impact of features on PCE prediction, offering a novel approach for accurate and interpretable ML methods in the context of PSCs.

ANN model of co-gasification process in bubbling fluidized bed: Contribution ratio analysis

Co-gasification of coal and biomass in a bubbling fluidized bed may offer an effective and economical method to partially replace coal to produce hydrogen, thereby addressing the objectives of achieving a carbon dioxide emissions peak and carbon neutrality. An Artificial neural network (ANN) model was utilized to simulate the hydrogen and other performance of co-gasification in the bubbling fluidized bed. Five types of ANN architectures were tested, including FFBP, CFBP, EFBP, LR, and NARX. Five optimization algorithms were also applied, including L-M, GA, GD, GDX, and PSO, to identify the optimal model to give a more accurate predicate the hydrogen production. The input data were derived from the ultimate analysis of the mixed coal and biomass, the proximate analyzes of mixed coal and biomass, and the operational conditions. The output data consisted of gas composition, carbon conversion, gasification efficiency, total gas yield, syngas yield, low and high heating values of the gas. The LR-PSO with 6 hidden neurons (R2 = 0.9995 and MSE = 0.1357) outperformed other ANN types and optimization algorithms. To further understand the performance of co-gasification, the influences of biomass to coal ratio, equivalence ratio, steam to carbon ratio and temperature on ratio of H2 to CO and syngas yield were predicted. The highest ratio of H2 to CO for the work of Li et al. (2010) [26], Song et al. (2013) [27] and Valdés et al. (2015) [28] were 0.7, 1.2 and 0.88, respectively. And those for syngas yield were 0.85, 1.09 and 1.55 m3/kg, respectively. To reveal the important factor on the ratio of H2 to CO, a detailed analyzed of the contribution ratio was conducted. The most critical factor affecting co-gasification performance in the work of Song et al. (2013) [27] were identified as SC ratio and temperature, which was higher than those of W and ER. The effect of SC exceeded that of T at SC ratio of 0.4, while it came to a reverse at temperature above 700 °C. These findings may serve as a valuable tool for adjusting the various factors to achieve the desired performance in the co-gasification of coal and biomass.

Developing a High-Performance Sports Results Prediction Artificial Neural Network: Case Study on World Championship Boxing

Abstract Major sports events are watched by millions around the world and the prediction of event outcomes is a subject of interest to many stakeholders which underlines the relevance of continuous development and improvement of prediction models. This study uses a factorial design methodology to develop and test 18 Artificial Neural Network (ANN) models for the prediction of world championship boxing matches. The methodology was applied to evaluate the individual and collaborative effects of feature selection, ANN architecture and training data selection on the prediction performance of ANNs. Feature selection was found to be the most influential factor on prediction performance with a statistically significant Analysis of Variance (ANOVA) between the feature selection levels and the test accuracy (p-value of 0.012). The collaborative effect of training data selection and feature selection on prediction performance was found to be statistically significant with ANOVA p-value of 0.007. The best performing model achieved a test accuracy of 81.53% which is an improvement to current benchmarks for sports prediction. The findings of this study contribute to the development of future machine learning sports prediction models.

Machine learning-based ensemble model for groundwater quality prediction: A case study

ABSTRACT Groundwater quality is vital for public health and environmental sustainability. As managing large datasets is challenging for traditional methods, this study combines the hidden Markov model (HMM) and the artificial neural network (ANN), a machine learning-based ensemble model to predict groundwater quality in Kanyakumari District, Tamil Nadu, India. In order to train the model, the acquired data is cleaned and normalized. HMM is used to find hidden patterns while the ANN architecture is used to forecast groundwater quality categories. Accuracy, precision, sensitivity, and F1-scores calculation are necessary to evaluate the model's performance. The effectiveness of the approach can be analyzed by k-fold cross-validation scores. The study demonstrates the effectiveness of the HMM–ANN approach in groundwater quality prediction with an accuracy of 97.41%. Thus, the research contributes to groundwater quality assessment by offering a unique methodology that facilitates informed decision-making for water resource management and environmental conservation.

Investigation of SnO2-based Thick Film Gas Sensors through Optimization of Artificial Neural Networks Parameters for Hazardous Gas Detection

In the present study, a systematic approach for optimizing artificial neural network parameters for the detection of a greenhouse gas carbon di-oxide (CO2) through SnO2-based thick film gas sensors for a wide range of concentrations is presented. The un-doped and Pd-doped SnO2 thick film is used as the sensor. The approach includes dataset preparation, input and output variable selection, artificial neural network architecture selection, training, validation, hyperparameter tuning, and testing. The input variables were selected based on their relevance to the problem at hand, such as the concentrations of the gas. The output variables were the changes in resistance of the sensor in response to the corresponding input variables. The artificial neural network architecture was carefully chosen, considering factors like the number of layers, neurons per layer, activation functions, and learning rate. Using the prepared dataset, the artificial neural network was trained by adjusting the connection weights between neurons to minimize the disparity between actual and predicted sensor responses. After training, the accuracy of the artificial neural network and generalization ability were assessed using a separate dataset for validation. The approach in the presented study can be used for different types of gases, and can be utilized in various applications

Enhancing tribological performance of hybrid fiber-reinforced composites through machine learning and response surface methodology

This study delves into the significant effects of sodium hydroxide (NaOH) treatment on the tribological properties of hybrid fiber-reinforced composites, specifically focusing on the combination of paddy straw (PS) and pineapple leaf (PALF) in a polyester matrix. By leveraging Artificial Neural Networks (ANNs) to predict the Specific Wear Rate (SWR) and Coefficient of Friction (COF), the research employs a grid search approach for hyperparameter optimization. This optimization process results in an optimal ANN architecture with impressive accuracy, showcasing low mean absolute error and high R-squared values of 0.991 and 0.986 for SWR and COF predictions, respectively. Utilizing the Design of Experiments (DOE), the study systematically analyzes the intricate interplay of disc speed, wear duration, and NaOH treatment percentage, with a specific focus on SWR and COF as pivotal tribological metrics. The Analysis of Variance (ANOVA) results underscore the substantial impact of duration and treatment percentage on wear characteristics. Additionally, quadratic regression models reveal nuanced correlations, highlighting the sensitivity of SWR to NaOH percentage and the influence of disc speed, duration, and treatment percentage on COF. This outcome emphasizes the efficacy of these parameters in achieving superior tribological performance in hybrid composites. Beyond contributing to a profound understanding of wear characteristics, this work introduces an innovative dimension through optimized ANN modeling, ensuring a more accurate and fine-tuned predictive model.

Artificial neural network-based modeling of Malachite green adsorption onto baru fruit endocarp: insights into equilibrium, kinetic, and thermodynamic behavior

In this study, artificial neural network (ANN) tools were employed to forecast the adsorption capacity of Malachite green (MG) by baru fruit endocarp waste (B@FE) under diverse conditions, including pH, adsorbent dosage, initial dye concentration, contact time, and temperature. Enhanced adsorption efficiency was notably observed under alkaline pH conditions (pH 10). Kinetic analysis indicated that the adsorption process closely followed a pseudo-second-order model, while equilibrium studies revealed the Langmuir isotherm as the most suitable model, estimating a maximum adsorption capacity of 57.85 mg g−1. Furthermore, the chemical adsorption of MG by B@FE was confirmed using the Dubinin–Radushkevich isotherm. Thermodynamic analysis suggested that the adsorption is spontaneous and endothermic. Various ANN architectures were explored, employing different activation functions such as identity, logistic, tanh, and exponential. Based on evaluation metrics like the coefficient of determination (R 2) and root mean square error (RMSE), the optimal network configuration was identified as a 5–11-1 architecture, consisting of five input neurons, eleven hidden neurons, and one output neuron. Notably, the logistic activation function was applied in both the hidden and output layers for this configuration. This study highlights the efficacy of B@FE as an efficient adsorbent for MG removal from aqueous solutions and demonstrates the potential of ANN models in predicting adsorption behavior across varying environmental conditions, emphasizing their utility in this field.

Comparing a polynomial DOE model and an ANN model for enhanced geranyl cinnamate biosynthesis with Novozym® 435 lipase

Central Composite Rotatable Design (CCRD), a type of factorial design of experiment is among the most traditional methods used for optimizing bioprocesses, but, in recent years artificial neural networks (ANNs) have emerged as a promising approach for data modeling in bioprocesses. A comparative study between CCRD and ANN modeling for data treatment in the optimization of geranyl cinnamate biosynthesis using Novozym® 435 lipase was conducted. The most effective ANN architecture identified for predicting and maximizing the enzymatic synthesis of geranyl cinnamate was a 3-3-1 neurons model utilizing logsig activation function in the hidden layer. The ANN was trained using all available experimental data and demonstrated a strong fit to the experimental data, coefficient of determination near 1 (R2 = 0.9948) and low Sum-squared Error (SSE = 43.06). The polynomial model (CCRD; R2 = 0.9806; SSE = 161.56) indicated the same optimal conditions as the ANN model, predicting a temperature of 90.2 °C, molar ratio of 1:5.68, and enzyme concentration of 18.6% w/w. Comparison of R2 and SSE values due to lack of fit between both models suggests that ANN predictions closely align with experimental conversion values of geranyl cinnamate. Although the polynomial model is feasible to be applied in enzymatic synthesis, it may be less precise in its predictions than ANN models.

Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques

Methane steam reforming is the foremost method for hydrogen production, and it has been studied through experiments and diverse computational models to enhance its energy efficiency. This study focuses on employing an artificial neural network as a model of the methane steam reforming process. The proposed data-driven model predicts the output mixture’s composition based on reactor operating conditions, such as the temperature, steam-to-methane ratio, nitrogen-to-methane ratio, methane flow, and nickel catalyst mass. The network, a feedforward type, underwent training with a comprehensive dataset augmentation strategy that augments the primary experimental dataset through interpolation and theoretical simulations of the process, ensuring a robust model training phase. Additionally, it introduces weights to evaluate the relative significance of different data categories (experimental, interpolated, and theoretical) within the dataset. The optimal artificial neural network architecture was determined by evaluating various configurations, with the aim of minimizing the mean squared error (0.00022) and maximizing the Pearson correlation coefficient (0.97) and Spearman correlation coefficient (1.00).

Tradescantia response to air and soil pollution, stamen hair cells dataset and ANN color classification.

Tradescantia plant is a complex system that is sensible to environmental factors such as water supply, pH, temperature, light, radiation, impurities, and nutrient availability. It can be used as a biomonitor for environmental changes; however, the bioassays are time-consuming and have a strong human interference factor that might change the result depending on who is performing the analysis. We have developed computer vision models to study color variations from Tradescantia clone 4430 plant stamen hair cells, which can be stressed due to air pollution and soil contamination. The study introduces a novel dataset, Trad-204, comprising single-cell images from Tradescantia clone 4430, captured during the Tradescantia stamen-hair mutation bioassay (Trad-SHM). The dataset contain images from two experiments, one focusing on air pollution by particulate matter and another based on soil contaminated by diesel oil. Both experiments were carried out in Curitiba, Brazil, between 2020 and 2023. The images represent single cells with different shapes, sizes, and colors, reflecting the plant's responses to environmental stressors. An automatic classification task was developed to distinguishing between blue and pink cells, and the study explores both a baseline model and three artificial neural network (ANN) architectures, namely, TinyVGG, VGG-16, and ResNet34. Tradescantia revealed sensibility to both air particulate matter concentration and diesel oil in soil. The results indicate that Residual Network architecture outperforms the other models in terms of accuracy on both training and testing sets. The dataset and findings contribute to the understanding of plant cell responses to environmental stress and provide valuable resources for further research in automated image analysis of plant cells. Discussion highlights the impact of turgor pressure on cell shape and the potential implications for plant physiology. The comparison between ANN architectures aligns with previous research, emphasizing the superior performance of ResNet models in image classification tasks. Artificial intelligence identification of pink cells improves the counting accuracy, thus avoiding human errors due to different color perceptions, fatigue, or inattention, in addition to facilitating and speeding up the analysis process. Overall, the study offers insights into plant cell dynamics and provides a foundation for future investigations like cells morphology change. This research corroborates that biomonitoring should be considered as an important tool for political actions, being a relevant issue in risk assessment and the development of new public policies relating to the environment.

A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts.

Spintronic Artificial Synapses Using Voltage‐Controlled Multilevel Magnetic States

AbstractNeuromorphic computing offers energy‐efficient computations for large‐scale data processing compared to conventional von Neumann computing. The artificial synapse, a key element for learning and memory operations in neuromorphic computing, requires multi‐state characteristics and the capability to change and store its states. The implementation of hardware‐based artificial synapses using nonvolatile memory provides significant advantages in terms of energy consumption and circuit area compared to their currently employed CMOS‐based counterparts. In this regard, spintronic devices have emerged as a promising candidate due to their desirable properties for artificial synapses, including multilevel formability, non‐volatility, and outstanding writing performance. In this study, spintronic artificial synapses utilizing voltage‐controlled multilevel magnetic states and energy‐ and area‐efficient artificial neural network architectures associated with them are demonstrated. The multilevel states are created by gradually modulating the magnetic easy‐axis orientation from perpendicular to in‐plane and vice versa, which is achieved either by sequentially applying gate voltage pulses or by adjusting the pulse width of the gate voltage. Based on these spintronic artificial synapses, convolutional neural network (CNN) and spiking neural network (SNN) architectures are constructed, demonstrating high recognition accuracy for the MNIST dataset with improved energy efficiency and a reduced circuit area.

A Neural Network Approach for Software Reliability Prediction

The increasing reliance on computer software has raised significant concerns regarding software reliability evaluation. Over the past four decades, various software reliability models have been developed, encompassing both parametric and nonparametric approaches. However, no single model has demonstrated effectiveness in handling all types of datasets. In response to this challenge, the deep neural network (DNN), a powerful deep learning model, has emerged as a promising solution. By leveraging the flexibility and adaptability of artificial neural networks (ANN), the DNN model exhibits remarkable prediction performance by capturing training variables and exploring deeper layers. This study presents an approach that utilizes a DNN model based on an ANN architecture with multiple activation functions. This approach aims to estimate software reliability and predict the frequency of software flaws. Through extensive experimental analysis and validation, the proposed DNN model surpasses the predictive accuracy achieved by existing parametric and nonparametric models. These results highlight the potential of the DNN model in effectively addressing the complexities involved in software reliability evaluation. By combining the power of deep learning with the adaptability of ANN, the proposed model offers a promising and accurate solution for software reliability prediction.

Deep-Learning-Based Strong Ground Motion Signal Prediction in Real Time

Processing ground motion signals at early stages can be advantageous for issuing public warnings, deploying first-responder teams, and other time-sensitive measures. Multiple Deep Learning (DL) models are presented herein, which can predict triaxial ground motion accelerations upon processing the first-arriving 0.5 s of recorded acceleration measurements. Principal Component Analysis (PCA) and the K-means clustering algorithm were utilized to cluster 17,602 accelerograms into 3 clusters using their metadata. The accelerograms were divided into 1 million input–output pairs for training, 100,000 for validation, and 420,000 for testing. Several non-overlapping forecast horizons were explored (1, 10, 50, 100, and 200 points). Various architectures of Artificial Neural Networks (ANNs) were trained and tested, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and CNN-LSTMs. The utilized training methodology applied different aspects of supervised and unsupervised learning. The LSTM model demonstrated superior performance in terms of short-term prediction. A prediction horizon of 10 timesteps in the future with a Root Mean Squared Error (RMSE) value of 8.43 × 10−6 g was achieved. In other words, the LSTM model exhibited a performance improvement of 95% compared to the baseline benchmark, i.e., ANN. It is worth noting that all the considered models exhibited acceptable real-time performance (0.01 s) when running in testing mode. The CNN model demonstrated the fastest computational performance among all models. It predicts ground accelerations under 0.5 ms on an Intel Core i9-10900X CPU (10 cores). The models allow for the implementation of real-time structural control responses via intelligent seismic protection systems (e.g., magneto-rheological (MR) dampers).

Dynamically reconfigurable all-optical neural network based on a hybrid graphene metasurface array

In recent years, optical neural networks (ONNs) have received considerable attention for their intrinsic parallelism and low energy consumption, making them a vital area of research. However, the current passive diffractive ONNs lack dynamic tunability after fabrication for specific tasks. Here, we propose a dynamically reconfigurable diffractive deep neural network based on a hybrid graphene metasurface array, wherein the transmission and refractive index of each pixel can be finely adjusted via gate voltage. This capability enables the tailored modulation of the incident light’s amplitude and phase at each pixel, aligning with specific task requirements. The simulation results show the attainability of a dynamic modulation range of 7.97dB (ranging from −8.56dB to −0.591dB). Additionally, this proposed diffractive neural network platform incorporates an ultrathin structure comprising a one-atom-thick graphene layer and nanoscale metallic metastructures, rendering it compatible with complementary metal-oxide-semiconductor technology. Notably, a classification accuracy of 92.14% for a single-layer neural network operating in the terahertz spectrum is achieved based on the calculation result. This proposed platform presents compelling prospects for constructing various artificial neural network architectures with applications ranging from drug screening to automotive driving and vision sensing.

Environmental Lighting towards Growth Effect Monitoring System of Plant Factory using ANN

Malaysia is currently driven to become another most developed country in the world. Among other priority sector is Food Sustainability. Along the process, our vegetable supply-demand keeps increasing by year. Compared to traditional systems, closed systems or its other name called hydroponic is getting more important for plant production, with artificial light which has many potential advantages, including better quality transplants, shorter production time and less resource use. To gain full profit from it, the quality of vegetables needs to be controlled efficiently. Climate conditions, especially temperature and light intensity, have a significant impact on vegetable growth and yield, as well as nutritional quality. Plant growth and development are influenced by a variety of environmental factors, the most important one is light intensity. Among the problems to be tackled in this research are plant growth manual observation, light intensity variation and abundance of growth-related data to be evaluated manually. Therefore, to solve these problems, the specific type of vegetable used here is lettuce. The proposed methods are, observation of plant growth conducted automatically round the clock in intervals of 15 minutes for the whole month (estimated mature period of lettuce), using images captured. At the same time, the proposed light intensity which is red & white to the ratio of 2:1 (optimum ratio recommended by previous researchers) will be used. The issue of data to be evaluated manually will be solved using Artificial Neural Network (ANN) architecture, in specific Deep Learning. Concisely, the results & analysis shows the research is successfully developed for plant growth monitoring by using artificial neural network which, reached 80% to 90% accuracy in the training and validation session that made the architecture sufficient for determining the growth of the said vegetable. This is indeed foreseen, will highly assist the farmer in better monitoring the growth rate of the plant.

Predicting total biogas potential of food waste using the initial output of biogas potential tests as input data to train an artificial neural network

Quantification of biogas potential is important for predicting anaerobic digestion operability and price. This study uses data from 446 biogas potential tests to train and test a multilayer perceptron artificial neural network (ANN) to forecast total biogas production using the evolution at the start of the experiment (3–14 days) as input data. ANN architecture (training algorithm, activation function, hidden nodes, regularisation, and input data) was optimised using response surface methodology. Best conditions (accuracy/computational speed) were obtained using adaptive moment estimation (adam) training algorithm and rectified linear unit (ReLU) activation function. When using three days of biogas production data, the accuracy of the model was reasonable (r2test = 0.881, r2validation = 0.879), although this increased significantly for 7 days (r2test = 0.953, r2validation = 0.925), or 14 days (r2test = 0.971, r2validation = 0.953). The highest accuracy was reported for readily digestible substrates (sugars and carbohydrates) and macronutrient mixtures. The methodology could be used to shorten prediction times of biogas potential tests.

Exam Assessor Tool: An Automated System for Efficient Answer Sheet Evaluation

With Education 4.0 and four quadrant approach number of innovations have gone into academics for efficient, experiential, and outcome-based education however assessment schemes are still very much dependent on manual assessment methods which are time-consuming and cumbersome. The grading system can sometimes be irrational, with diversified schemes for the same course and can also be biased. Covid 19 pandemic caused a global economic avalanche like we’ve never experienced in our lifetime. Many countries have implemented control measures such as blockades and curfews. The education system in this chaos saw a silver lining with academics shifting to online mode, with paradigm shift in teaching, assessment techniques too need to evolve. Work done is an effort to ease the process of assessment, a machine learning assisted model is developed that automates subjective answer evaluation in the education sector. Our project involved several crucial steps, including grayscale conversion, Natural Language Processing (NLP) for data cleansing, data splitting, and training an artificial neural network (ANN) to predict scores based on extracted features. ANN-based system grades subjective responses without human intervention, reducing the workload of teachers and professors. Model constructed an ANN architecture with three layers using Rectified Linear Activation Unit (ReLU) and Sigmoid activation functions. Trained model was incorporated into a user-friendly web application using the Streamlit library. Model design gives a major boost in grading efficiency and accuracy while providing valuable feedback to students. Research surveys were conducted, and a dataset was constructed for training and testing the model. study yielded an accuracy of 83.14% after employing techniques such as text cleaning, preprocessing, and feature extraction.

Assessment of mechanical and physicochemical properties of palm fiber composites: Effect of alkaline treatment and volume alterations

This study assesses the impact of alkaline treatments and volume fractions on biocomposites composed of a high-density polyethylene (HDPE) matrix reinforced with date palm tree fibers (FPDS). Tensile tests were conducted on both untreated and NaOH-treated biocomposites. Additionally, fiber analysis was performed using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The results reveal higher strength and stiffness compared to HDPE, albeit with limited plasticity making the material brittle. The NaOH treatment enhances certain mechanical properties. Further assessments encompassed hardness, density, melt index, and Izod impact tests. Two volume fractions, 20% and 25%, of FPDS were tested. The study establishes a correlation between empirical predictions and artificial neural network (ANN) models. Notably, an ANN architecture consisting of two input factors, 10 hidden nodes, and one output provides the analysis of mechanical properties. This investigation highlights the potential of FPDS-reinforced HDPE biocomposites, emphasizing their mechanical performance under various treatments and fiber levels.

Prediction of the discharge capacity of piano key weirs using artificial neural networks

ABSTRACT The discharge capacity of the piano key weir (PKW) is an important flow feature which ultimately decides the design of PKWs. In the present research work, the different architecture of artificial neural networks (ANNs) was employed to predict the discharge capacity of the trapezoidal piano key weir (TPKW) by varying geometric parameters. Furthermore, adaptive neuro-fuzzy interference system (ANFIS), support vector machines (SVMs) and non-linear regression (RM) techniques were also applied to compare the performance of best ANN models. The performance of each model was evaluated using statistical indices including scatter-index (SI); coefficient of determination (R2) and mean square error (MSE). The prediction capability of all the models was found to be satisfactory. However, results predicted by ANN-22(H-15) [R2 = 0.998, MSE= 0.0024, SI = 0.0177] were more accurate than ANFIS (R2 = 0.995, MSE = 0.00039, SI=0.0256), SVM (R2 = 0.982, MSE = 0.000864, SI =0.0395) and RM (R2 = 0.978, MSE = 0.001, SI = 0.0411). It was observed that Si/So, Wi/Wo and L/W ratios have the greatest effect on the discharge performance of TPKW. Furthermore, sensitivity analysis confirmed that h/P is the most influencing ratio which may considerably affect the discharge efficiency of the TPKW and ANN architecture having a single hidden layer and keeping neurons three times of input parameters in hidden layers generated better results.