Artificial Neural Model Research Articles

The Engineered CRISPR-CAS System is a Beneficial Biological Tool for Detecting and Combating Antibiotic Resistance Microbes

Nowadays, the quick detection of antibiotic resistance bacteria causes a major problem in the field of the development of new antibiotics against the resistant bacteria. To overcome this problem, genome editing tools like clustered regularly interspaced short palindromic repeats (CRISPR) can be used. The CRISPR-CAS system is useful for targeting and killing antibiotic-resistant bacteria by cleaving resistance genes. It is also used to detect antibioticresistant bacteria. CRISPR is made up of a single guide RNA and the CAS 9 protein. The single guide RNA is used to guide toward the target sequence, and the CAS 9 protein is an enzyme that cuts DNA and is used in conjunction with the guide RNA. This modified sgRNA contains a complementary sequence to that of the target resistance gene and recognizes the target resistance sequence; therefore, it is cleaved by CAS-9 protein, and the removal of the resistance gene turns bacteria into antibiotic-sensitive ones. One of the delivery systems of CRISPR into bacteria is via bacteriophage.

The utility of a near-infrared spectrometer to predict the maturity of green peas (Pisum sativum)

This work evaluated the utility of an embedded diode array near-infrared reflectance spectrometer to predict the hardness (maturity) of green peas. Partial least squares, support vector machine (SVM) and artificial neural network regression models were developed to predict tenderometer reading (TR) from near-infrared spectra over the range of maturities experienced throughout a harvest season. The SVM model achieved the best performance in an independent data set collected in a second harvest season. Here, the SVM model explained 83% of the variation in TR with a root mean standard error of prediction (RMSEP) of 4.4 TR units over a range of TR from 65 to 104. The RPD of 2.3 achieved in this study exceeds the threshold for rough screening but falls short of replacing the tenderometer for quality control.

Simulation of Daily Snow Depth Data in China Based on the NEX-GDDP

In this study, a backpropagation artificial neural network snow simulation model (BPANNSIM) is built using data collected from the National Climate Reference Station to obtain simulation data of China’s future daily snow depth in terms of representative concentration pathways (RCP4.5 and RCP8.5). The input layer of the BPANNSIM comprises the current day’s maximum temperature, minimum temperature, snow depth, and precipitation data, and the target layer comprises snow depth data of the following day. The model is trained and validated based on data from the National Climate Reference Station over a baseline period of 1986–2005. Validation results show that the temporal correlations of the observed and the model iterative simulated values are 0.94 for monthly cumulative snow cover duration and 0.88 for monthly cumulative snow depth. Subsequently, future daily snow depth data (2016–2065) are retrieved from the NEX-GDPP dataset (Washington, DC/USA: the National Aeronautics and Space Administration(NASA)Earth Exchange/Global Daily Downscaled Projections data), revealing that the simulation data error is highly correlated with that of the input data; thus, a validation method for gridded meteorological data is proposed to verify the accuracy of gridded meteorological data within snowfall periods and the reasonability of hydrothermal coupling for gridded meteorological data.

Machine Learning Models for the Classification of CK2 Natural Products Inhibitors with Molecular Fingerprint Descriptors

Casein kinase 2 (CK2) is considered an important target for anti-cancer drugs. Given the structural diversity and broad spectrum of pharmaceutical activities of natural products, numerous studies have been performed to prove them as valuable sources of drugs. However, there has been little study relevant to identifying structural factors responsible for their inhibitory activity against CK2 with machine learning methods. In this study, classification studies were conducted on 115 natural products as CK2 inhibitors. Seven machine learning methods along with six molecular fingerprints were employed to develop qualitative classification models. The performances of all models were evaluated by cross-validation and test set. By taking predictive accuracy(CA), the area under receiver operating characteristic (AUC), and (MCC)as three performance indicators, the optimal models with high reliability and predictive ability were obtained, including the Extended Fingerprint-Logistic Regression model (CA = 0.859, AUC = 0.826, MCC = 0.520) for training test andPubChem fingerprint along with the artificial neural model (CA = 0.826, AUC = 0.933, MCC = 0.628) for test set. Meanwhile, the privileged substructures responsible for their inhibitory activity against CK2 were also identified through a combination of frequency analysis and information gain. The results are expected to provide useful information for the further utilization of natural products and the discovery of novel CK2 inhibitors.

Multiple Analytical Models to Evaluate the Impact of Carbon Nanotubes on the Electrical Resistivity and Compressive Strength of the Cement Paste

Cement paste is the most common construction material being used in the construction industry. Nanomaterials are the hottest topic worldwide, which affect the mechanical properties of construction materials such as cement paste. Cement pastes containing carbon nanotubes (CNTs) are piezoresistive intelligent materials. The electrical resistivity of cementitious composites varies with the stress conditions under static and dynamic loads as carbon nanotubes are added to the cement paste. In cement paste, electrical resistivity is one of the most critical criteria for structural health control. Therefore, it is essential to develop a reliable mathematical model for predicting electrical resistivity. In this study, four different models—including the nonlinear regression model (NLR), linear regression model (LR), multilinear regression model (MLR), and artificial neural network model (ANN)—were proposed to predict the electrical resistivity of cement paste modified with carbon nanotube. Furthermore, the correlation between the compressive strength of cement paste and the electrical resistivity model has also been proposed in this study and compared with models in the literature. In this respect, 116 data points were gathered and examined to develop the models, and 56 data points were collected for the proposed correlation model. Most critical parameters influencing the electrical resistivity of cement paste were considered during the modeling process—i.e., water to cement ratio ranged from 0.2 to 0.485, carbon nanotube percentage varied from 0 to 1.5%, and curing time ranged from 1 to 180 days. The electrical resistivity of cement paste with a very large number ranging from 0.798–1252.23 Ω.m was reported in this study. Furthermore, various statistical assessments such as coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and OBJ were used to investigate the performance of different models. Based on statistical assessments—such as SI, OBJ, and R2—the output results concluded that the artificial neural network ANN model performed better at predicting electrical resistivity for cement paste than the LR, NLR, and MLR models. In addition, the proposed correlation model gives better performance based on R2, RMSE, MAE, and SI for predicting compressive strength as a function of electrical resistivity compared to the models proposed in the literature.

Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage

Thermal energy stored within a rock bed thermal storage system, which is mostly used in agriculture, can be identified during the storage phase using mathematical models based on heat transfer, which concerns batteries running in a vertical setting. However, this requires the conversion of differential equations into algebraic equations, as well as knowledge about the initial and boundary conditions. Furthermore, a lack of information or incomplete information about the initial conditions makes it difficult or impossible to evaluate the volume of stored energy, or can cause significant errors during evaluation. Such situations occur in systems equipped with a rock battery, in which solar collectors act as source of energy. Considering the above, as well as the lack of a model for batteries in a vertical setting, we identified the need for research into the storage phase of rock bed thermal storage systems, working in a horizontal setting, and generating MLP-type neural models. Among these models, MLP 4-7-1 turned out to be the best both in terms of the values of regression statistics and possibilities of generalization. According to the authors, artificial neural models depicting temperature changeability in storage phase will be helpful in the development of a new methodology that can predict the heat volume in rock bed thermal storage systems.

An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront

In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level Leq,1′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of Leq,1′ falling prevalently within the range ±0.5 dB. The experimental profile of Leq,1′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations.

Mining periodic patterns from spatio-temporal trajectories using FGO-based artificial neural network optimization model

Mining periodic patterns from spatio-temporal trajectories using FGO-based artificial neural network optimization model

Artificial neural network grey-box model for design and optimization of 50 MWe-scale combined supercritical CO2 Brayton cycle-ORC coal-fired power plant

Supercritical CO2 (sCO2) Brayton cycle is a promising technology for coal-fired power generation with high efficiency and compact equipment size. However, its sophisticated construct and high-temperature waste heat rejection require a systematic design to maximize its performance. Herein, we develop a combined sCO2 Brayton cycle-organic Rankine cycle (ORC) design for coal-fired power plant. A novel glass-box model that considers the specific designs of sCO2 boiler, recuperators, coolers, and turbomachinery is formulated to optimize the power plant. A high-accuracy artificial neural network model is also developed to estimate the system’s pressure drop to reduce model complexity. As a result, the glass-box model is reformulated into a grey-box model. The model is applied to three different combined cycles’ design problem to evaluate their performance. Result shows that the grey-box model saves more than 50% of CPU time. With the turbine inlet at 620 °C/25 MPa and the main compressor inlet at 35 °C/7.38 MPa, the proposed combined cycle reaches a thermal efficiency of 45.73%, thereby achieving a 2.75 percentage point improvement compared with the standalone design. Sensitivity analysis is also carried out to evaluate the effects of ORC working fluid, flue gas temperature at the cooling wall outlet, main compressor inlet pressure, and evaporating temperature of ORC, on the system’s performance.

Modeling of Magnetic Refrigeration Device by Using Artificial Neural Networks Approach

The aim of this work is to use multi-layered perceptron artificial neural networks and multiple linear regressions models to predict the efficiency of the magnetic refrigeration cycle device operating near room temperature. For this purpose, the experimental data collection was used in order to predict coefficient of performance and temperature span for active magnetic refrigeration device. In addition, the operating parameters of active magnetic refrigerator cycle are used for solid magnetocaloric material under application 1.5 T magnetic fields. The obtained results including temperature span and coefficient of performance are presented and discussed.

Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks

Abstract Surface Temperature (ST) is important in terms of surface energy and terrestrial water balances affecting urban ecosystems. In this study, to process the nonlinear changes of climatological variables by leveraging the distinct advantages of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), we propose an LSTM-BiLSTM hybrid deep learning model which extracts multi-dimension features of inputs, i.e., backward (future to past) or forward (past to future) to predict ST. This study assessed the climatological variables, i.e., wind speed, wind direction, relative humidity, dew point temperature, and atmospheric pressure impact on ST using five major coastal cities of India: Chennai, Mangalore, Visakhapatnam, Cuddalore, and Cochin. The Recurrent Neural Networks (RNN) and hybrid LSTM-BiLSTM models have effectively predicted ST and outperformed the standalone Artificial Neural Networks (ANN), LSTM, and BiLSTM models. The RNN and LSTM-BiLSTM models have performed better in predicting ST for Mangalore (Nash-Sutcliffe efficiency (NSE)=0.91), followed by Cochin (NSE=0.89), Chennai (NSE=0.88), Cuddalore (NSE=0.88), and Vishakhapatnam (NSE=0.81). The hybrid data-driven modeling framework indicated that coupling the LSTM and BiLSTM models was proven effective in predicting the ST of coastal cities.

Spatio-temporal analysis and simulation of land cover changes and their impacts on land surface temperature in urban agglomeration of Bisha Watershed, Saudi Arabia

The present study investigates the spatiotemporal pattern of Land Use land cover (LULC) and land surface temperature (LST) in Abha for the years 1990, 2000, and 2018. This research also forecasts the future LULC and LST for the year 2028. The support vector machine (SVM) was utilised to classify the LULC for the periods 1990-. The LST for the same period was derived using the mono window algorithm. The artificial neural network-cellular automata model (ANN-CA) was employed to forecast LULC and LST for the year 2028. The results indicated that urban areas rose by 434.6% between 1990 and 2018, while the LST soared to 50 °C in 2018, covering half of the study area. The built-up area, as well as the high LST zone, will be expanded in 2028. As a result, sustainable management strategies should be implemented to limit uncontrolled urban sprawl and LST.

Artificial intelligence language models and the false fantasy of participatory language policies

Artificial intelligence neural language models learn from a corpus of online language data, often drawn directly from user-generated content through crowdsourcing or the gift economy, bypassing traditional keepers of language policy and planning (such as governments and institutions). Here lies the dream that the languages of the digital world can bend towards individual needs and wants, and not the traditional way around. Through the participatory language work of users, linguistic diversity, accessibility, personalization, and inclusion can be increased. However, the promise of a more participatory, just, and emancipatory language policy as a result of neural language models is a false fantasy. I argue that neural language models represent a covert and oppressive form of language policy that benefits the privileged and harms the marginalized. Here, I examine the ideology underpinning neural language models and investigate the harms that result from these emerging subversive regulatory bodies.

Development of a Hybrid Artificial Neural Network-Particle Swarm Optimization Model for the Modelling of Traffic Flow of Vehicles at Signalized Road Intersections

The tremendous increase in vehicular navigation often witnessed daily has elicited constant and continuous traffic congestion at signalized road intersections. This study focuses on applying an artificial neural network trained by particle swarm optimization (ANN-PSO) to unravel the problem of traffic congestion. Traffic flow variables, such as the speed of vehicles on the road, number of different categories of vehicles, traffic density, time, and traffic volumes, were considered input and output variables for modelling traffic flow of non-autonomous vehicles at a signalized road intersection. Four hundred and thirty-four (434) traffic datasets, divided into thirteen (13) inputs and one (1) output, were obtained from seven roadsites connecting to the N1 Allandale interchange identified as the busiest road in Southern Africa. The results obtained from this research have shown a training and testing performance of 0.98356 and 0.98220. These results are indications of a significant positive correlation between the inputs and output variables. Optimal performance of the ANN-PSO model was achieved by tuning the number of neurons, accelerating factors, and swarm population sizes concurrently. The evidence from this research study suggests that the ANN-PSO model is an appropriate predictive model for the swift optimization of vehicular traffic flow at signalized road intersections. This research extends our knowledge of traffic flow modelling at a signalized road intersection using metaheuristics algorithms. The ANN-PSO model developed in this research will assist traffic engineers in designing traffic lights and creation of traffic rules at signalized road intersections.

STOCK MARKET PREDICTION IN BRICS COUNTRIES USING LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK HYBRID MODELS

The BRICS (Brazil, Russia, India, China and South Africa) acronym was created by the International Monetary Foundation (IMF)–Group of Seven (G7) to represent the bloc of developing economies which crucially impact on the global economy by their potential economic growth. Most of the foreign direct investment are considering the stock markets of BRICS as the most attractive destination for foreign portfolio investment. This study aims to identify the relationship between macroeconomic variables and the stock market index values of BRICS and generate accurate predictions for index values by performing linear regression and artificial neural network hybrid models. Monthly data from January 2003 to December 2019 are used for the empirical study. The results indicate that a strong correlation exists between the stock market and macroeconomic variables in BRICS over time. The hybrid model is observed very accurate for index value prediction where the mean absolute percentage error (MAPE) value is 0.714% for the whole data set covering all BRICS countries data during the study period. Additionally, MAPE values for each of the BRICS countries are, respectively, obtained as 0.083%, 2.316%, 0.116%, 0.962% and 0.092%. Thus, the main findings of this study show that while neural network-integrated models have high performances for volatile stock market prediction, macroeconomic stabilization should be the priority of monetary policy to prevent the high volatility of stock markets.

Stochastic natural frequency analysis of varying diameter functionally graded material pipe conveying fluid

This paper investigates the uncertainty of the structure and material parameters on the natural frequency of the varying diameter functionally graded material (FGM) pipe conveying fluid. Firstly, concentrating on the spatially dependent uncertainty of geometry varying in axial direction as well as the material varying in radial direction, the random field is employed to describe the uncertainty parameters. Then, a generalized eigenvalue equation of the varying diameter FGM pipe is derived by the dynamic stiffness method while introducing the random field and variables into the deterministic governing equation. Due to the input uncertainty, the natural frequencies of the varying diameter FGM pipe change to random variables. In this respect, the stochastic characteristics of natural frequencies can be approximated by the Monte Carlo simulation. However, the enormous computational cost is unbearable. The efficient artificial neural network surrogate model is introduced and incorporated for the natural frequency uncertainty quantification. Consequently, a stochastic natural frequency analysis approach based on the neural network is proposed for the varying diameter FGM pipe conveying fluid. Some examples are investigated to illustrate the effectiveness of the proposed method and the influence of input uncertainty on the probabilistic characterization of the natural frequencies.

Soil moisture estimation in two layers for a small watershed with neural network models: Assessment of the main factors that affect the results

Soil moisture, which impacts various hydrological processes, can be estimated from point measurements in a watershed or via remote sensing. As both methods are expensive and complex, efforts have been made to develop empirical models based on data that affect the occurrence of soil moisture. However, these models have been based only on surface-layer data. We present an original approach for investigating regional empirical models of soil moisture, both for the surface (0–10 cm) and subsurface (10–20 cm) layers, and evaluate the main factors which affect the model results. Based on data about the climate, soil properties, topographic features and rainfall, we applied artificial neural network soil-moisture models for a small watershed, in southern Brazil. The models for each layer, with all selected variables, showed Nash-Suitcliffe coefficients of 0.870 and 0.893, respectively, for the surface and subsurface models. We then tested the effects of removing each variable or categories of variables. The most important variables for the surface model were the season, followed by exponential weighted moving average (EWMA) of rainfall. For the subsurface model, the most important variables were the season (although less so than for the surface model), followed by microporosity. All of the variable categories were important in the surface model. In the subsurface model, the soil-related variables were the most important, whereas the rainfall and topography variables were of little importance. It was possible to estimate soil moisture for both layers with good performance. The subsurface model, which used only the soil- and climate-related variables, explained more of the variance in soil moisture than the other models. The subsurface layer is easier to model, because the variation in moisture that is induced by recent climate and precipitation effects is attenuated by the physical features of the soil which control water infiltration.

Durability assessment of FRP-to-concrete bonded connections under moisture condition using data-driven machine learning-based approaches

The prediction of long-term performance is a crucial step in the effective structural design of adhesively bonded connections. A lack of knowledge on the durability of the FRP-to-concrete bonded connections compromises structural safety and requires employing higher safety factors in design. This study examines the capability of machine learning techniques in studying the long-term performance of FRP-to-concrete bonded connections under moisture conditions. A comprehensive database of 429 durability test results is built and used to develop various machine learning models. A Bayesian optimisation method is employed during the model development to tune the machine learning hyperparameters. Based on the detailed evaluation of prediction results, artificial neural networks (ANN) and ensemble models are proposed to study the bond strength and the failure mode of the connections. The ANN model is used to evaluate the impact of various moisture conditioning regimes on bond strength. The contribution of environmental factors in identifying the failure mode is studied by the ensemble method. Moreover, a practical equation is proposed by the M5P model tree method to predict the bond strength. The prediction results in this study indicate the capability of machine learning in predicting the durability of FRP-bonded concrete connections under moisture conditions.

Development of a method for assessment and forecasting of the radio electronic environment

Decision making support systems (DSS) are actively used in all spheres of human life. The system of the electronic environment analysis is not an exception. However, there are a number of problems in the analysis of the electronic environment, for example: the signals are analyzed in a complex electronic environment against the background of intentional and natural interference. Input signals do not match the standards, and their interpretation depends on the experience of the operator (expert), the completeness of additional information on a particular task (uncertainty condition). The best solution in this situation is found in the integration with the data of the information system analysis of the electronic environment, artificial neural networks and fuzzy cognitive models. Their advantages are also the ability to work in real time and quick adaptation to specific situations. The article develops a method for assessing and forecasting the electronic environment.
 Improving the efficiency of evaluation information processing is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, the type and parameters of the membership function. The efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information that has to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; has no accumulation of learning error of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The example of assessing the state of the electronic environment showed an increase in the efficiency of assessment at the level of 15–25 % on the efficiency of information processing

A Novel Technique for Modeling Ecosystem Health Condition: A Case Study in Saudi Arabia

The present paper proposes a novel fuzzy-VORS (vigor, organization, resilience, ecosystem services) model by integrating fuzzy logic and a VORS model to predict ecosystem health conditions in Abha city of Saudi Arabia from the past to the future. In this study, a support vector machine (SVM) classifier was utilized to classify the land use land cover (LULC) maps for 1990, 2000, and 2018. The LULCs dynamics in 1990–2000, 2000–2018, and 1990–2018 were computed using delta (Δ) change and Markovian transitional probability matrix. The future LULC map for 2028 was predicted using the artificial neural network-cellular automata model (ANN-CA). The machine learning algorithms, such as random forest (RF), classification and regression tree (CART), and probability distribution function (PDF) were utilized to perform sensitivity analysis. Pearson’s correlation technique was used to explore the correlation between the predicted models and their driving variables. The ecosystem health conditions for 1990–2028 were predicted by integrating the fuzzy inference system with the VORS model. The results of LULC maps showed that urban areas increased by 334.4% between 1990 and 2018. Except for dense vegetation, all the natural resources and generated ecosystem services have been decreased significantly due to the rapid and continuous urbanization process. A future LULC map (2028) showed that the built-up area would be 343.72 km2. The new urban area in 2028 would be 169 km2. All techniques for sensitivity analysis showed that proximity to urban areas, vegetation, and scrubland are highly sensitive to land suitability models to simulate and predict LULC maps of 2018 and 2028. Global sensitivity analysis showed that fragmentation or organization was the most sensitive parameter for ecosystem health conditions.