Performance Of Artificial Neural Network Research Articles

A novel deep learning strategy to optimize Al2O3–SiO2 porous ceramics with phosphate tailings as raw material

The Al2O3–SiO2 porous ceramics (ASPC) prepared from phosphorus tailings usually showed a large fluctuation in material properties due to the complex composition of the raw material, which required extensive experimental exploration to optimize their properties. To solve the above problem, a novel deep learning strategy for automatically adjust hyperparameters had been proposed to predict the property of ASPC based on the small experimental datasets in this work. Initially, phosphate tailings and bauxite were used as raw materials. Active carbon, ranging from 0 to 15 wt%, was added as a pore-forming agent. After mixing the raw materials, they were shaped under a pressure of 5 MPa and sintered in the temperature range of 1000–1200 °C to produce ASPC, the density and compressive strength of the ASPC were measured to train the artificial neural network (ANN). An exhaustive search was conducted to identify the optimal combination of hyperparameters suitable for small datasets, then the influence of each hyperparameter on the training performance of ANN was analyzed. Subsequently, the trained model was used to predict the feature space of ASPC, and the key processes for regulating ASPC performance were explored, thereby the experimental schemes that meet target performance (density and compressive strength) were identified. Three groups of experimental schemes were predicted, whose properties were compared with the experimental ones. The results showed that the mean squared errors (MSE) of 0.03 and 0.85 for density and compressive strength, respectively. Furthermore, the ASPC exhibited excellent comprehensive properties when 70 wt% phosphorus tailings and 30 wt% bauxite were added, whose apparent porosity was 59.11 %, density was 1.16 g/cm3 and cold compressive strength was 6.49 MPa. And the predicted data trend influence of the pore-forming agent content and sintering temperature on the performance of ASPC is the same as the experimental data. This research provides valuable guidance for addressing the limited controllability of traditional experimental design processes and accelerate the preparation of ASPC from solid waste.

Unveiling optimal half-cell potentials in RCC slabs through cutting-edge ANFIS, ANN and genetic algorithm integration

Purpose The purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to chloride ingress.The models for HCP using 1,134 data set values based on experimentation are developed and compared using ANFIS, artificial neural network (ANN) and integrated ANN-GA algorithms. Design/methodology/approach In this study, RCC slabs, 1000 mm × 1000 mm × 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x- and y-axes, temperature, relative humidity and age of the slab in days were the input parameters, while the HCP values with reference to the Standard Calomel Electrode were the output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model. Findings In this study, the premise and consequent parameters are trained, validated and tested using ANFIS, ANN and by using ANN as fitness function of GA. The MAPE, RMSE and MAE of the ANFIS model were 24.57, 1702.601 and 871.762, respectively. Amongst the ANN algorithms, Levenberg−Marquardt (LM) algorithm outperforms the other methods, with an overall R-value of 0.983. GA with ANN as the objective function proves to be the best means for the development of prediction model. Originality/value Based on the original experimental values, the performance of ANFIS, ANN and GA with ANN as objective function provides excellent results.

Adsorption simulation of 2,4-D pesticide on novel zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complexes using machine learning approach.

The capacity of zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complex (Zn(NH2-TBA)2) and modified Zn(NH-TBA)2COMe complex for removal of 2,4-dichlorophenoxyacetic acid (2,4-D) from aqueous solutions was investigated through adsorption modeling and artificial intelligence tools. Analyzing the adsorption characteristics of pesticides helps in studying the groundwater pollution by pesticides in agriculture area.In this study, Zn(NH2-TBA)2 was synthesized using Schiff base and its surface was modified using acetic anhydride group and their physical characteristics were identified using proton NMR, FTIR, and XRD. NMR results showed maximum modification yield obtained was 65% after 5days. The porous structure and surface area monitored using nitrogen isotherm and BET surface area analysis presented relatively less surface area and porosity after modification. Adsorption modelling indicated that Toth model with a maximum adsorption capacity of 150.8mg/g and 100.7mg/g represents the homogenous adsorption systems which satisfy both low- and high-end boundary of adsorbate concentration in all settings according to the optimum point, while the kinetics and rate of 2,4-D adsorption follow the pseudo-first-order kinetic model in all situations. Artificial neural network (ANN), support vector regression, and particle swarm optimized least squares-support vector regression (PSO-LSSVR) were used for the optimization and modelling of adsorbent mass, adsorbate concentration, contact time, and temperature to develop predictive equations for the simulation of the adsorption efficiency of 2,4-D pesticide. The obtained results exhibited the better performance of ANN and PSO-LSSVR for prediction of adsorption results. The mean square error values of ANN (0.001, 0.012) and PSO-LSSVR (0.121, 0.105) were obtained for Zn(NH2-TBA)2 and Zn(NH-TBA)2COMe, respectively, while their respective coefficient of determination (R2) obtained were 0.999 and 0.988 for ANN and 0.980 and 0.825 for PSO-LSSVR. The study specified that machine learning predictive behavior performed better for Zn(NH2-TBA)2 compared to Zn(NH-TBA)2COMe that is also supported by theoretical kinetics and isotherm models. The research concludes that artificial intelligence models are the most efficient tools for studying the predictive behavior of adsorption data.

Performance and rotational study of solar box cooker utilizing PCM and hybrids deep algorithm approach

The cooking performance of the solar box cooker (SBC) is enhanced by utilizing magnesium chloride hexahydrate (MgSO4·7H2O) with plastic balls (PBs) encasing the phase-change material (PCM). Accurate predictions of SBC efficiency were achieved using an Artificial Neural Network (ANN) simulation and the incorporation of thermal behavior utilizing the tree-seed metaheuristic algorithm (TSA). Therefore, improving ANN performance may be necessary to reap the benefits associated with adopting a variation in engineering design. The design incorporates a copper bar plate (CBP) that is 50% larger than a silver bar plate (SBP), and the Tree-Seed Algorithm (TSA) is used to establish an optimal influence for neurons. For the SBC with CBP design, food cooking efficiency was functional, with R 2, RMSE, MRE, and MAE values of 0.990, 0.0475, 0.228, and 0.050, respectively. Similarly, for SBP, the values are 0.98, 0.086, 0.007, and 0.053, respectively, simulated using the ANN/TSA technique. For the CBP in the training set “R,” testing set “R,” and overall set “R,” the respective values were 0.999, 0.995, and 0.997. Similarly, for the SBP in SBC cooking performance, the values were 1.000, 0.964, and 0.996. The performance of CBP in the SBC design with PCMPBs is found to enhance cooking efficiency, improving the system’s ability to prepare rice and eggs within 2 to 3 h.

Numerical analysis and ANN performance prediction of solar composite ventilation wall for built environment regulation

With the aim to achieve winter heating, summer heat insulation and year-round power generation as well as indoor pollutant purification, photovoltaic (PV) combined with phase change heat storage technology is ingeniously embedded in an adjustable solar composite ventilation wall with entire room model (SPVW-PV/PCM) in this study. Then, the effects of key parameters e.g. PCM melting temperature Tm and PV coverage ratio Cr on solar energy conversion and utilization with indoor environment creation are comprehensively illustrated by indoor temperature, exergy efficiency as well as power generation amount. In addition, to further clarify the application potential of the proposed model applied in different typical places in China, the artificial neural network (ANN) has been rarely adopted to predict the annual heating amount, power generation as well as its economic and environmental benefits. Research results show that the three numerical submodels closely align with previously published results, with only minor discrepancies observed in the decoupling model regarding Cout, vroom and PCM melt fraction, well within acceptable numerical tolerances of 9.87% RMSE. The thermal performance is significantly influenced by the key parameters of Tm and Cr, with at least 110 min of advance heating start-up time revealed, and excellent indoor temperature improvement of 1.59 K and 3.02 K are achieved in the heating season at Tm = 318 K or Cr = 0 % simulated group. The highest annual total energy conversion of 1367.39 kWh is surveyed in Guangzhou, corresponding to the lowest levelized cost of electricity (LCOE) of 0.18 $/kWh and the highest CO2 saving cost of 19.03 $/year. The total energy conversion effect and the CO2 saving cost can be effectively improved by 13.88 % and 13.82 % in Guangzhou, respectively, compared to the coupling off-grid PV and conventional wall in building. These research findings offer valuable insights for optimizing the design of SPVW-PV/PVM in indoor environmental construction and enhancing its overall applicability.

Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition

This study addresses the concerns regarding the performance of Handwritten Character Recognition (HCR) systems, focusing on the classification stage. It is widely acknowledged that the development of the classification model significantly impacts the overall performance of HCR. The problems identified specifically pertain to the classification model, particularly in the context of the Artificial Neural Network (ANN) learning problem, leading to low accuracy in recognizing handwritten characters. The objective of this study is to improve and refine the ANN classification model to achieve better HCR. To achieve this goal, this study proposed a hybrid Flower Pollination Algorithm with Artificial Neural Network (FPA-ANN) classification model for HCR. The FPA is one of the metaheuristic approaches is utilized as an optimization technique to enhance the performance of ANN, particularly by optimizing the network training process of ANN. The experimentation phase involves using the National Institute of Standards and Technology (NIST) handwritten character database. Finally, the proposed FPA-ANN classification model is analysed based on generated confusion matrix and evaluated performance of the classification model in terms of precision, sensitivity, specificity, F-score and accuracy.

The Effect of Biotic Stress in Plant Species Induced by ‘Candidatus Phytoplasma solani’—An Artificial Neural Network Approach

Infections with phytoplasma present one of the most significant biotic stresses influencing plant health, growth, and production. The phytoplasma ‘Candidatus Phytoplasma solani’ infects a variety of plant species. This pathogen impacts the physiological and morphological characteristics of plants causing stunting, yellowing, leaf curling, and other symptoms that can lead to significant economic losses. The aim of this study was to determine biochemical changes in peony (Paeonia tenuifolia L.), mint (Mentha × piperita L.), and dill (Anethum graveolens L.) induced by ‘Ca. Phytoplasma solani’ in Serbia as well as to predict the impact of the biotic stress using artificial neural network (ANN) modeling. The phylogenetic position of the Serbian ‘Ca. Phytoplasma solani’ strains originated from the tested hosts using 16S rRNA (peony and carrot strains) and plsC (mint and dill strains) sequences indicated by their genetic homogeneity despite the host of origin. Biochemical parameters significantly differed in asymptomatic and symptomatic plants, except for total anthocyanidins contents in dill and the capacity of peony and mint extracts to neutralize superoxide anions and hydroxyl radicals, respectively. Principal Component Analysis (PCA) showed a correlation between different chemical parameters and revealed a clear separation among the samples. Based on the ANN performance, the optimal number of hidden neurons for the calculation of TS, RG, PAL, LP, NBT, •OH, TP, TT, Tflav, Tpro, Tant, DPPH, and Car was nine (using MLP 8-9-13), as it produced high r2 values (1.000 during the training period) and low SOS values. Developing an effective early warning system for the detection of plant diseases in different plant species is critical for improving crop yield and quality.

Sleep-Like Unsupervised Replay Improves Performance When Data Are Limited or Unbalanced (Student Abstract)

The performance of artificial neural networks (ANNs) degrades when training data are limited or imbalanced. In contrast, the human brain can learn quickly from just a few examples. Here, we investigated the role of sleep in improving the performance of ANNs trained with limited data on the MNIST and Fashion MNIST datasets. Sleep was implemented as an unsupervised phase with local Hebbian type learning rules. We found a significant boost in accuracy after the sleep phase for models trained with limited data in the range of 0.5-10% of total MNIST or Fashion MNIST datasets. When more than 10% of the total data was used, sleep alone had a slight negative impact on performance, but this was remedied by fine-tuning on the original data. This study sheds light on a potential synaptic weight dynamics strategy employed by the brain during sleep to enhance memory performance when training data are limited or imbalanced.

Nondestructive Quantification of Isoflavones in Cotyledons by Near-Infrared Spectroscopy and Potential and Limits for Sustainable Soybean Breeding

The isoflavones in the cotyledon of soybean seeds mimic human estrogen in structure, conferring them complex effects on health. Their regulation represents a major challenge for the sustainable breeding of new varieties with lower levels of potential endocrine disruptors. To develop a rapid, nondestructive, and eco-friendly analysis method, this study explores how sample grinding affects the results of near-infrared spectroscopy (NIRS) and the preprocessing methods. The prediction of the daidzein and genistein content would help the specific reduction in isoflavones in the cotyledon without harming seed development. The potential of a nonlinear approach (artificial neural network) is also compared with the more conventional partial least squares (PLS) regression. The isoflavone content of cotyledons from 529 soybean samples (65 genotypes) was quantified by HPLC, and the NIR spectra of these samples were collected using a Brucker multi-purpose analyzer. The spectra of whole and ground cotyledons were also collected for 155 samples. The results show that grain fragmentation improves the model calibration, although spectral preprocessing can harmonize this effect. Although the best PLS regression in cross-validation did not suffice to quantify the daidzein and genistein percentages, the artificial neural network (ANN) approach allowed us to develop much more reliable models than PLS. The performance of ANNs in external validation is remarkable in terms of both precision and applicability (R2 = 0.89 and a ratio of prediction to deviation of 2.92), making ANNs suitable in the breeding context for screening soybean grains regarding their isoflavone content.

A Grain Size Profile Prediction Method Based on Combined Model of Extreme Gradient Boosting and Artificial Neural Network and Its Application in Sand Control Design

Summary The grain size distribution along the well depth is of great significance for the prediction of the physical properties and the staged sand control design of the unconsolidated or weakly consolidated sandstone reservoir. In this paper, a new method for predicting the formation median grain size profile based on the combination model of extreme gradient boosting (XGBoost) and artificial neural network (ANN) is proposed. The machine learning algorithm and weighted combination model are applied to the prediction and analysis of reservoir grain size. The prediction model is improved from two aspects: First, the feature engineering of the XGBoost-ANN model is constructed by using the data of multiple sampling points on the logging curve. Second, the prediction accuracy is improved by increasing the dimension of the prediction model, that is, the XGBoost and ANN single-prediction models are weighted by the error reciprocal method and a combined prediction model containing multidimensional information is established. The research results show that compared with the single-point mapping model, the prediction accuracy of the multipoint mapping model considering the vertical geological continuity of the reservoir is higher than that of the single-point prediction and the coefficient of determination in the testing set can be improved up to 14.5%. The influence of different weighting methods on prediction performance is studied, and the prediction performance of original XGBoost, ANN, and XGBoost-ANN combined models is compared. The combined prediction model has a higher prediction accuracy than the single XGBoost and ANN models with the same number of sampling points and the coefficient of determination can be improved by up to 16.5%. The prediction accuracy and generalization ability of the XGBoost-ANN combined model are evaluated comprehensively. The combined model is used to design layered sand control of a well in an adjacent block, and good results have been achieved in production practice. This study provides a new method with high accuracy and efficiency for the prediction of unconsolidated sand median grain size profile.

Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks.

The utilization of numerical methods, such as computational fluid dynamics (CFD), has been widely established for modeling patient-specific hemodynamics based on medical imaging data. Hemodynamics assessment plays a crucial role in treatment decisions for the coarctation of the aorta (CoA), a congenital heart disease, with the pressure drop (PD) being a crucial biomarker for CoA treatment decisions. However, implementing CFD methods in the clinical environment remains challenging due to their computational cost and the requirement for expert knowledge. This study proposes a deep learning approach to mitigate the computational need and produce fast results. Building upon a previous proof-of-concept study, we compared the effects of two different artificial neural network (ANN) architectures trained on data with different dimensionalities, both capable of predicting hemodynamic parameters in CoA patients: a one-dimensional bidirectional recurrent neural network (1D BRNN) and a three-dimensional convolutional neural network (3D CNN). The performance was evaluated by median point-wise root mean square error (RMSE) for pressures along the centerline in 18 test cases, which were not included in a training cohort. We found that the 3D CNN (median RMSE of 3.23mmHg) outperforms the 1D BRNN (median RMSE of 4.25mmHg). In contrast, the 1D BRNN is more precise in PD prediction, with a lower standard deviation of the error (±7.03mmHg) compared to the 3D CNN (±8.91mmHg). The differences between both ANNs are not statistically significant, suggesting that compressing the 3D aorta hemodynamics into a 1D centerline representation does not result in the loss of valuable information when training ANN models. Additionally, we evaluated the utility of the synthetic geometries of the aortas with CoA generated by using a statistical shape model (SSM), as well as the impact of aortic arch geometry (gothic arch shape) on the model's training. The results show that incorporating a synthetic cohort obtained through the SSM of the clinical cohort does not significantly increase the model's accuracy, indicating that the synthetic cohort generation might be oversimplified. Furthermore, our study reveals that selecting training cases based on aortic arch shape (gothic versus non-gothic) does not improve ANN performance for test cases sharing the same shape.

A counting-time optimization method for artificial neural network (ANN) based gamma-ray spectroscopy

With advancements in machine learning technologies, artificial neural networks (ANNs) are being widely used to improve the performance of gamma-ray spectroscopy based on NaI(Tl) scintillation detectors. Typically, the performance of ANNs is evaluated using test datasets composed of actual spectra. However, the generation of such test datasets encompassing a wide range of actual spectra representing various scenarios often proves inefficient and time-consuming. Thus, instead of measuring actual spectra, we generated virtual spectra with diverse spectral features by sampling from categorical distribution functions derived from the base spectra of six radioactive isotopes: 54Mn, 57Co, 60Co, 134Cs, 137Cs, and 241Am. For practical applications, we determined the optimum counting time (OCT) as the point at which the change in the Kullback–Leibler divergence (ΔKLDV) values between the synthetic spectra used for training the ANN and the virtual spectra approaches zero. The accuracies of the actual spectra were significantly improved when measured up to their respective OCTs. The outcomes demonstrated that the proposed method can effectively determine the OCTs for gamma-ray spectroscopy based on ANNs without the need to measure actual spectra.

Utilizing neural networks to supplant chemical kinetics tabulation through mass conservation and weighting of species depletion

Artificial Neural Networks (ANNs) have emerged as a powerful tool in combustion simulations to replace memory-intensive tabulation of integrated chemical kinetics. Complex reaction mechanisms, however, present a challenge for standard ANN approaches as modeling multiple species typically suffers from inaccurate predictions on minor species. This paper presents a novel ANN approach which can be applied on complex reaction mechanisms in tabular data form, and only involves training a single ANN for a complete reaction mechanism. The approach incorporates a network architecture that automatically conserves mass and employs a particular loss weighting based on species depletion. Both modifications are used to improve the overall ANN performance and individual prediction accuracies, especially for minor species mass fractions. To validate its effectiveness, the approach is compared to standard ANNs in terms of performance and ANN complexity. Four distinct reaction mechanisms (H2, C7H16, C12H26, OME34) are used as a test cases, and results demonstrate that considerable improvements can be achieved by applying both modifications.

A robust neural network model for fault detection in the presence of mislabelled data

AbstractSeveral data‐driven methodologies for process monitoring and detection of faults or abnormalities have been developed for the safety of processing systems. The effectiveness of data‐based models, however, is impacted by the volume and quality of training data. This work presents a robust neural network model for addressing the mislabelled and low‐quality data in detecting faults and process abnormalities. The approach is based on harnessing data quality features along with supervisory labels in the network training. The data quality has been computed using the Mahalanobis distances and trusted centres of each class of data such as normal and faulty data. The method has been examined for detecting abnormalities in two case studies; a continuous stirred tank heater problem for detecting leaks and the Tennessee Eastman chemical process for detecting step and sticking faults. The performance of the proposed robust artificial neural networks (ANN) model is evaluated in terms of accuracy, fault detection rate, false alarm rate, and classification index at varying extents of mislabelling, namely, 1%, 5%, and 10% mislabelled data. The proposed model demonstrates higher detection performance, especially at increased labels of mislabelled data where the performance of the conventional ANN is severely impacted. The proposed methodology can be advantageous in handling mislabelled and low‐quality data issues which are crucial in the data‐driven modelling of processing systems.

Bimodal alteration of cognitive accuracy for spintronic artificial neural networks.

Spintronics-based artificial neural networks (ANNs) exhibiting nonvolatile, fast, and energy-efficient computing capabilities are promising neuromorphic hardware for performing complex cognitive tasks of artificial intelligence and machine learning. Early experimental efforts focused on multistate device concepts to enhance synaptic weight precisions, albeit compromising on cognitive accuracy due to their low magnetoresistance. Here, we propose a hybrid approach based on the tuning of tunnel magnetoresistance (TMR) and the number of states in the compound magnetic tunnel junctions (MTJs) to improve the cognitive performance of an all-spin ANN. A TMR variation of 33-78% is controlled by the free layer (FL) thickness wedge (1.6-2.6 nm) across the wafer. Meanwhile, the number of resistance states in the compound MTJ is manipulated by varying the number of constituent MTJ cells (n = 1-3), generating n + 1 states with a TMR difference between consecutive states of at least 21%. Using MNIST handwritten digit and fashion object databases, the test accuracy of the compound MTJ ANN is observed to increase with the number of intermediate states for a fixed FL thickness or TMR. Meanwhile, the test accuracy for a 1-cell MTJ increases linearly by 8.3% and 7.4% for handwritten digits and fashion objects, respectively, with increasing TMR. Interestingly, a multifarious TMR dependence of test accuracy is observed with the increasing synaptic complexity in the 2- and 3-cell MTJs. By leveraging on the bimodal tuning of multilevel and TMR, we establish viable paths for enhancing the cognitive performance of spintronic ANN for in-memory and neuromorphic computing.

Sentiment Analysis of Self Driving Car Dataset: A comparative study of Deep Learning approaches

Sentiment Analysis (SA) is a crucial task in understanding public opinions and perceptions towards emerging technologies. In this study, we focus on SA for a self-driving car dataset as it provides valuable insights into public perceptions and opinions towards a transformative technology. The dataset consists of textual reviews associated with sentiment labels, providing insights into how people perceive self-driving car technology. Our objective is to analyze the sentiments expressed in these reviews using Deep Learning (DL) models, namely, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). We compared our results with an existing technique in the field of self-driving car sentiment classification, that implemented various Machine Learning (ML) and DL models, including Support Vector Machines (SVM), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), CNN, and LSTM. In our study, we expanded upon this research by evaluating the performance of ANN, BiLSTM, GRU, and BiGRU. Results reveal that BiLSTM, GRU, and BiGRU exhibit superior performance in sentiment classification within the self-driving car dataset. These findings offer valuable insights into public sentiment towards self-driving cars, contributing significantly to the advancement of SA techniques in the domain of autonomous vehicles. Additionally, the results are statistically tested and are statistically significant.

Predictive modelling of water clarity based on remote sensing data using artificial neural network (ANN): A case study in Saguling Reservoir, West Java, Indonesia

Indonesia faced several challenges regarding water quality such as water exploitation and contamination caused by human activities. Comprehensive and sustainable water management is required to ensure its availability for the society. Ecosystem quality monitoring is needed to make sure the availability of water resource all year round by using modelling to assist. This paper presented application of Artificial Neural Network (ANN) utilizing multilayer perception model with a backpropagation algorithm to predict water clarity in Saguling Reservoir provided by PT Indonesia Power. ANN performance of predicting water clarity level were evaluated using regression analysis (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE). Based on the results, prediction data during rainy season shows better performance than dry season with R2 value of 0.94, MAE value of 0.035, and MSE value of 0.0032 meanwhile dry season data of R2, MAE, and MSE are 0.83, 0.041, and 0.0045 respectively. ANN prediction model demonstrated a relatively good prediction capability of water clarity and may be used as one of references in classifying the water quality level of Saguling Reservoir quantity and quality integrated maintenance.

Artificial Neural Network for Classification Task in Tabular Datasets and Image Processing: A Systematic Literature Review

Artificial Neural Network (ANN) is one of the machine learning algorithms that is widely used for classification cases. Some examples of classification cases that can be handled with ANN include classifications in the health sector, banking, and classification in image processing. This study presents a systematic literature review (SLR) of the ANN algorithm to find a research gap that can be used in future research. There are 3 phases used in preparing the SLR. Those are planning, conducting, and reporting. Formulation of research questions and establishing a review protocol is carried out in the planning phase. The second phase is conducted. In this phase, searching for relevant articles is carried out, determining the quality of the literature found and selecting particles according to what has been formulated in the planning phase. The selected literature is then carried out by the process of extracting data and information and then synthesizing the data. Writing SLR articles based on existing findings is carried out in the last phase, namely reporting. The results of data and information extraction from the 13 reviewed articles show that the ANN algorithm is powerful enough with satisfactory results to handle classification cases that use tabular datasets or image datasets. The challenges faced are the need for extensive training data so that ANN performance can be better, the use of appropriate evaluation measures based on the cases studied does not only rely on accuracy scores, and the determination of the correct hyperparameters to get better performance in the case of image processing.

Fluoranthene biotreatment using prominent freshwater microalgae: physiological responses of microalgae and artificial neural network modeling of the bioremoval process

Due to the intensified industrial activities and other anthropogenic actions, contamination of polycyclic aromatic hydrocarbons (PAHs) has been growing at an alarming rate, turning in to a serious environmental concern. Bioremediation, as an eco-friendly and sustainable removal technology, can be used by organisms to reduce the resulting contaminations. In the present study, the ability of Tetradesmus obliquus to remove of fluoranthene (FLA) was evaluated. It was confirmed that FLA removal efficiency was managed by various environmental parameters and pH was found to be one of the most important influencial factors. The reusability of the algae in long-term repetitive operations confirmed the occurrence of biodegradation along with other natural attenuation and 10 intermediate compounds were identified in the FLA biodegradation pathway by GC-MS. As a result of physiological assays, induced antioxidant enzymes activities and augmentation of phenol and flavonoids contents, after the treatment of the microalgae by a high concentration of FLA, confirmed the ability of the microalgae to upregulate its antioxidant defense system in response to the toxic effects of FLA. An artificial neural network (ANN) model was then developed to predict FLA biodegradation efficiency and the appropriate predictive performance of ANN was confirmed by comparing the experimental FLA removal efficiency with its predicted amounts (R2 = 0.99).

Hybrid approach for gas viscosity in Yemeni oil fields

The estimation of a gas viscosity experimentally is often difficult. So, accurate determination of gas viscosity has been the main challenge in a gas reservoir development. There are many correlations to estimate this property. Often time, the results of these correlations do not agree with experimental data, thereby causing a considerable amount of error. The difficulty of these correlations can be propagated simply by tuning against some experimental data using artificial intelligent model. Currently, the achievements of artificial neural networks (ANN) techniques alone to predict gas viscosity open the door to use the hybrid system. In this model, the Particle Swarm Optimization (PSO) algorithm is employed to search for optimal connection weighs and thresholds for the neural networks (NN), then the back-propagation learning rule and training algorithm is used to adjust the final weights. A total of about 868 data points obtained from the laboratory measurements of gas viscosity were used. The data include measured gas viscosity, specific gas gravity, temperature, pressure, molecular weight, pseudo-critical temperature and pressure and non-hydrocarbon components (H2S, CO2, and N2). The performance of the PSONN model is compared with performance of ANN and other empirical model to show the most general and accurate model for predicting gas viscosity. From the results of this study, we found that the PSONN model is more reliable and accurate with the absolute present relative (APRE) error and mean square error (RMS) of 2.76 and 5.49 respectively.