Artificial Neural Network System Research Articles

The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates.

Inferences made about objects via vision, such as rapid and accurate categorization, are core to primate cognition despite the algorithmic challenge posed by varying viewpoints and scenes. Until recently, the brain mechanisms that support these capabilities were deeply mysterious. However, over the past decade, this scientific mystery has been illuminated by the discovery and development of brain-inspired, image-computable, artificial neural network (ANN) systems that rival primates in these behavioral feats. Apart from fundamentally changing the landscape of artificial intelligence, modified versions of these ANN systems are the current leading scientific hypotheses of an integrated set of mechanisms in the primate ventral visual stream that support core object recognition. What separates brain-mapped versions of these systems from prior conceptual models is that they are sensory computable, mechanistic, anatomically referenced, and testable (SMART). In this article, we review and provide perspective on the brain mechanisms addressed by the current leading SMART models. We review their empirical brain and behavioral alignment successes and failures, discuss the next frontiers for an even more accurate mechanistic understanding, and outline the likely applications.

Novel Two-Terminal Synapse/Neuron Based on an Antiferroelectric Hafnium Zirconium Oxide Device for Neuromorphic Computing.

Functionally diverse devices with artificial neuron and synapse properties are critical for neuromorphic systems. We present a two-terminal artificial leaky-integrate-fire (LIF) neuron based on 6 nm Hf0.1Zr0.9O2 (HZO) antiferroelectric (AFE) thin films and develop a synaptic device through work function (WF) engineering. LIF neuron characteristics, including integration, firing, and leakage, are achieved in W/HZO/W devices due to the accumulated polarization and spontaneous depolarization of AFE HZO films. By engineering the top electrode with asymmetric WFs, we found that Au/Ti/HZO/W devices exhibit synaptic weight plasticity, such as paired-pulse facilitation and long-term potentiation/depression, achieving >90% accuracy in digit recognition within constructed artificial neural network systems. These findings suggest that AFE HZO capacitor-based neurons and WF-engineered artificial synapses hold promise for constructing efficient spiking neuron networks and artificial neural networks, thereby advancing neuromorphic computing applications based on emerging AFE HZO devices.

Artificial Neural Network and Adaptive Neuro Fuzzy Inference System Hybridized Models in the Sustainable Integration of Language and Mathematics Skills: The Case of Singapore and Hong Kong

The four basic language skills, listening, speaking, reading, and writing play, a crucial role in the development of an individual’s skills in other disciplines. The current study aims to underpin the relationship between language skills and mathematics skills by focusing on the language and mathematics curricula of two consistently high-achieving countries, Hong Kong and Singapore, in the Program for International Student Assessment (PISA) rankings. In the current study, the convergent parallel mixed method was utilized that qualitative and quantitative data were composed together. Primarily, the outcomes of four language skills were determined in the native language teaching curricula of the two countries. The topics and themes related to four basic language skills were determined from the two mathematics curricula. The curricula were examined by document analysis from qualitative research methods. The analysis was conducted by examining the native language teaching and the mathematics curricula of both countries by the content analysis method. Later, the findings of the document analysis were used to develop machine learning models to find a possible positive relationship between language skills and the PISA scores. Although a number of previous studies have found a reasonable relationship between language skills and mathematics skills, the current study results were contradictory to the ones performed previously in the literature, and considering the curricula no positive relationship between the language and mathematics skills was found. The findings of the current study were further supported by the artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) model performance metrics. Compared to an acceptable level of 0.80, significantly low R2 values of 0.35 and 0.39 for the ANN and ANFIS models, respectively, indicated very little relationship between the language and mathematics skills.

Optimization and prediction of biogas yield from pretreated Ulva Intestinalis Linnaeus applying statistical-based regression approach and machine learning algorithms

A statistical-based regression approach and machine learning (ML) algorithms (response surface methodology (RSM), feed-forward backpropagation artificial neural network (ANN) and multi-layer adaptive neuro-fuzzy inference system (ANFIS)) were explored for the optimization and prediction of biogas resulting from the anaerobic digestion (AD) of pretreated Ulva Intestinalis Linnaeus (UIL). ANFIS model was found to better predict and model the process of biogas production from the AD of pretreated UIL owing to low mean square error (MSE) and RMSE values (0.8841 and 0.9402-US, 0.9628 and 0.9812-O3, 0.1387 and 0.3724-MW and 0.3018 and 1.1410-Fe3O4) and highest values of R2 (0.9998-US, 0.9996-O3, 0.9996-MW and 0.9995-Fe3O4). Optimum conditions for biogas yield as a result of the various pretreatment processes based on the ANFIS model were US-15 power/time, time-40 min and the biogas yield-181.0 mL.gVS−1, O3-15.0 mg/min, time-40.0 min and biogas yield of 164.0 mL.gVS−1, MW-2.3 power/time, time-40.0 min and biogas yield-81.7 mL.gVS−1 and Fe3O4-20.0 mg L−1, time-40.0 min and biogas yield-154 mL.gVS−1. The results obtained show that the ML and statistical-based models were effective in approximating the biogas yield with high precision and low error and could be beneficial for the biogas production scale-up process.

Training Artificial Neural Network with a Cultural Algorithm

Artificial neural networks are amongst the artificial intelligence techniques with their ability to provide machines with some functionalities such as decision making, comparison, and forecasting. They are known for having the capability of forecasting issues in real-world problems. Their acquired knowledge is stored in the interconnection strengths or weights of neurons through an optimization system known as learning. Several limitations have been identified with commonly used gradient-based optimization algorithms, including the risk of premature convergence, the sensitivity of initial parameters and positions, and the potential for getting trapped in local optima. Various meta-heuristics are proposed in the literature as alternative training algorithms to mitigate these limitations. Therefore, the primary aim of this study is to combine a feed-forward artificial neural network (ANN) with a cultural algorithm (CA) as a meta-heuristic, aiming to establish an efficient and dependable training system in comparison to existing methods. The proposed artificial neural network system (ANN-CA) evaluated its performance on classification tasks over nine benchmark datasets: Iris, Pima Indians Diabetes, Thyroid Disease, Breast Cancer Wisconsin, Credit Approval, Glass Identification, SPECT Heart, Wine and Balloon. The overall experimental results indicate that the proposed method outperforms other methods included in the comparative analysis by approximately 12% in terms of classification error and approximately 7% in terms of accuracy.

Product Colour Variation Management with Artificial Intelligence

This research focuses on the topic of using AI in color variant management in products to enhance the appeal and performance of the products in the marketplace by incorporating artificial intelligence, deep learning, and neural network systems. Real-time consumer and product information, preferences, buying history, and sales history; I created an AI model to predict and change product colors in real-time. The complete workflow used comprises data gathering, processing, and feature extraction, model training, integration of the color adjustment software tools, and finally, testing and validation. The efficiency of such AI-driven interventions was analyzed through the consumer satisfaction indices, the sales results, and the engagement data based on the consumption of digital platforms. This study demonstrates valuable potential of AI to improve product design application and development while providing valuable suggestions for Businesses adapting and improving market outcomes according to the changing consumer trends. Such an application of AI implements a new best practice in ways of enhancing futuristic consumer-oriented marketing approaches. The paper was first completed in 2021 and later I have modified the paper with latest updates till date 2024.

Application of triple-branch artificial neural network system for catalytic pellets combustion

On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to move to biofuels, including biomass. This paper examines the impact of placing a catalytic system in a retort boiler to minimize emissions of greenhouse gases, dust and other pollutants when burning pellets. The effect of platinum, and oxides of selected metals placed on the deflector as a solid catalyst was studied. Based on the experimental data, a branched artificial neural network was constructed and trained. The routing of three parallel topologies made it possible to achieve high accuracy while keeping the input data relatively simple. The system showed an average error of 3.54% against arbitrary test data. On the basis of experimental data as well as predictions returned by the artificial neural network, recommendations were shown for the catalysts used and their amounts. Depending on the biomass from which the pellet was produced, the experiment suggested the use of titanium or copper oxides. In the case of the neural network, it was able to select a better system, based on platinum, improving emission reductions by up to more than 19%, depending on the type of pellet used.

Quadrotor height control system using LQR and recurrent artificial neural networks

The quadorotor is a type of unmanned flying vehicle known as Unmanned Aerial Vehicle (UAV). In recent years, quadrotors have attracted much attention from researchers around the world due to their excellent maneuverability. A good control system in this quadrotor system is needed for ease of use of this quadrotor. One control system that is often used is the Linear Quadratic Regulator (LQR) control system. This control system has challenges for dynamic system disturbances in quadrotor control. Researchers proposed a recurrent artificial neural network (RNN) system to address these challenges.RRN is used to change the value of the feedback component in the LQR control system. The nature of the feedback component in LQR, which is static, is changed based on the system error value based on changes in the error value entered into the RNN. The result of this RNN is a change in the value of the LQR feedback component based on the input of the system. The results of this research show that LQR control with RNN produces a faster system response of 0.075 seconds and a faster settling time of 0.221 seconds. Compensation for the system response speed produces a higher overshot value.

Molybdenum Disulfide Memristors for Next Generation Memory and Neuromorphic Computing: Progress and Prospects

AbstractIn the last 15 years memristors have been investigated as devices for high‐density, low‐power, non‐volatile, resistive random access memory (ReRAM) beyond Moore's law. They also show potential in neuromorphic logic architectures to overcome the Von–Neumann bottleneck of classical circuitry facilitating better hardware for artificial intelligence (AI) and artificial neural network (ANN) systems. Molybdenum disulfide (MoS2) has emerged as a promising material for memristor devices of monolayer thickness due to its direct bandgap, high carrier mobility and environmental stability. In this review, recent progress in the development of MoS2 memristors the current understanding of the mechanisms behind their function are examined. The remaining obstacles to a commercially viable device principle and how these may be surmounted in light of the rapid progress that has already been made are also discussed.

Modeling and prediction of flame retardant performance of alkyl phosphinate using artificial neural network and adaptive neural fuzzy inference system

AbstractThe flame retardancy of polymer materials is closely linked to the types and compositions of flame retardants used. However, due to the complex relationships involved, it is difficult to quantify and predict flame retardancy using linear equations and models. Alkyl phosphinate flame retardants have emerged as promising candidates for their environmentally friendly, low toxicity, and excellent flame retardant properties. In this study, we aimed to develop a novel, low‐cost alkyl phosphinate flame retardant by predicting the quantitative impact of flame retardant composition on flame retardancy. To establish prediction models for the limiting oxygen index (LOI) value of alkyl phosphinate flame retardants, we utilized artificial neural network (ANN) and adaptive neural fuzzy inference system (ANFIS) programs, namely ANN, ANFIS‐grid partition (GP), and ANFIS‐subtractive clustering (SC). The prediction models were trained and validated, and their reliability was assessed based on the root mean square error (RMSE), coefficient of determination (R2), and mean absolute percentage error (MAPE) criteria. Our results indicate that the ANFIS‐SC prediction model exhibits the highest reliability, with high accuracy and ideal prediction results.

Multi-factors-regulated memristor based on Sm-doped Pb(Mg1/3Nb2/3)O3–PbTiO3 for artificial neural network

The memristor, characterized by its resistive switching (RS) behavior, has garnered significant interest within the scientific community, particularly because of its vast potential applications in the fields of artificial intelligence (AI) and information storage. This is attributed to its unique properties, which align well with the requirements of advanced computational and memory systems. Ferroelectric memristors are currently a thriving area of research, and this study uses Sm-doped Pb(Mg1/3Nb2/3)O3–PbTiO3 (Sm-PMN-PT) and polyvinylidene difluoride (PVDF) as the functional layer. A multi-factor responsive memristor based on a Ag/Sm-PMN-PT:PVDF/ITO sandwich structure is fabricated, for which the RS behavior of the memristor can be adjusted by multi-factors such as voltage scanning rate, bias voltage amplitude, temperature and environmental humidity. Specifically, this device is sensitive to changes in environmental humidity and exhibits the properties of an artificial neural synapse. These advantageous characteristics endow this device with great potential for use in environmental sensors and artificial neural network (ANN) systems.

Estimation of optimal process parameters for minimum wear in the application of SiC/RHA reinforced Al7075 hybrid composites using ANN, ANFIS, and GA

Increasing demand for high-performance materials has led to the exploration of composite materials for enhanced mechanical properties. In this study, a composite of silicon carbide particulate and rice husk ash (RHA) in varying proportions was utilized to reinforce an aluminum alloy (Al7075) hybrid composite fabricated through the stir casting technique. Microstructure examination via an optical microscope ensured the homogeneous distribution of reinforced particles. Wear was evaluated using a pin-on-disc apparatus, considering material factors (% of SiC and % of RHA) and mechanical wear factors (load applied, speed of rotation, and sliding distance). Experimental data were used to develop artificial neural network and adaptive neural fuzzy inference system models, which demonstrated high predictive accuracy. An objective function, formulated to minimize wear via regression analysis, guided the application of a genetic algorithm to determine optimal process parameters. The optimal combination, resulting in a minimum wear of 34.5 µm, comprised 12% SiC, 7% RHA, a sliding speed of 1.9 m/s, an applied load of 11.5 N, and a sliding distance of 715 mm. This study concludes with recommendations for further research and implications for composite material design and optimization.

Inverse kinematics analysis of a wrist rehabilitation robot using artificial neural network and adaptive Neuro-Fuzzy inference system

Inverse kinematics analysis of a wrist rehabilitation robot using artificial neural network and adaptive Neuro-Fuzzy inference system

The Effect of Varying Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Parameters on Wind Energy Prediction: A Comparative Study

Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other.

Multi-objective optimization and performance assessment of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy interfence system (ANFIS) for estimation of fouling in phosphoric acid/steam heat exchanger

Fouling is a common occurrence in industrial heat exchangers, leading to a decrease of the thermal efficiency. This research addresses the challenge of applying various machine learning algorithms including Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to effectively model the fouling resistance in a heat exchanger in phosphoric acid concentration loop. Subsequently, a multi-objective optimization approach was employed to minimize the fouling resistance. Confirmatory experiments are then conducted in the phosphoric acid concentration plant using optimized variables.The findings of this study indicate that the three models accurately align with one year of operational data, achieving a remarkably high coefficient of correlation (R). Among the models utilized, RSM demonstrates the highest level of prediction accuracy, with an R of 0.9998, accompanied by the lowest mean square error (MSE) of 5.9388 10−13 and root mean squared error (RMSE) of 7.7064 10−7. The RSM optimization process identifies the optimal conditions for variables such as time, acid inlet and outlet temperature, steam temperature, acid density, and volume flow rate, which are determined to be 114.805 h; 72.214 °C; 80.407 °C; 116.784 °C; 1642.47 kg/m3; and 2308.1 m3/h, respectively. The predicted fouling resistance demonstrates a strong correlation with the actual data, with a negligible percentage difference of 2.19 %, further validating the accuracy and reliability of the RSM model.

Predicting the precipitated calcium carbonate and unconfined compressive strength of bio-mediated sands through robust hybrid optimization algorithms

Biological soil stabilization through Microbial-Induced Calcite Precipitation (MICP) demonstrates the potential to be an eco-friendly and sustainable approach to enhance the strength properties of cohesionless granular soils. In this study, based on a comprehensive set of experimental data compiled from literature, the amounts of Precipitated Calcium Carbonate (PCC) and Unconfined Compressive Strength (UCS) of MICP-treated sands are estimated through hybridizing Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models with metaheuristic algorithms. During the data extraction procedure, a total of 70 and 115 datasets are used to predict the PCC and UCS, respectively. Accordingly, five key features of urea concentration (Mu), calcium chloride (Mcc), optical density of bacterial suspension (OD600), pH and total volume of injection per volume of sample (Vt), are first selected as the inputs of the hybrid models to estimate the amount of calcium carbonate precipitation within the porous structure of MICP-treated granular medium. The experimental data on the PCC along with the basic characteristics of the host sand including mean grain size (D50), uniformity coefficient (Cu) and void ratio (e), are then adopted as the inputs of the numerical models in the second phase whilst the UCS of bio-mediated sands is set as the output. The performance and accuracy of implemented models are rigorously assessed through analyzing various statistical indices and performance criteria. According to the numerical analysis, the ANN-PSO and ANFIS-PSO hybrid models show the best performance in predicting PCC and UCS of bio-mediated sands, respectively. Using the well-established Gene Expression Programming (GEP) algorithm, practical correlations are developed to predict the PCC and UCS values of MICP-mediated soils based on the characteristics of biological binding agents as well as the host material. The Machine Learning (ML)-based models presented in this study provide engineers with a fruitful framework for the rough and preliminary estimation of the amount of calcite precipitation as well as the UCS of bio-mediated sands in the field prior to stabilization and performing complementary tests.

Modelling and optimizing the transesterification process of shea butter via CD-BaCl-IL catalyst using soft computing algorithms

Shortage and environmental threats posed by fossil fuel have become a critical issue requiring searching for alternative energy sources. This study utilized clay-doped barium chloride and ionic liquid (CD-BaCl-IL) as a catalyst for optimizing biodiesel production from shea butter. 10% barium chloride and ionic liquid were blended with clay, dried, and then calcined for 4 h at 600 °C to develop the catalyst. The synthesized catalyst and the biodiesel were suitably characterized. Response surface methodology (RSM) implementing Central Composite Design (CCD) and Genetic Algorithm (GA) were employed to model and optimize the effect of the process parameters on the response. The model's capabilities were evaluated using coefficient of determination (R2) and mean square error (MSE). The second-order polynomial model is shown in the Analysis of Variance (ANOVA) with an (R2 -0.9945, Adjusted R2-0.9846, Predicted R2-0.8694) demonstrating the model's acceptance. Artificial Neural Network (ANN) and Adaptive Neurofuzzy Inference System (ANFIS) were used to assess the model's capability. The obtained statistical results - (R2 = 0.8694, MSE = 0.7035), ANN (R2 = 0.999, MSE = 0.0001026), ANFIS (R2 = 0.99, MSE = 0.000041), showed that ANFIS had the best prediction with the lowest MSE. ANFIS integrated with GA (GA-ANFIS) gave the best optimization with a biodiesel yield (96.72%) at a catalyst concentration of 4 wt%, a methanol/mol ratio of 10:1, a time of 2.5 h, a temperature of 70 °C, and an agitation speed of 400 rpm. The optimal developed biodiesel properties were successfully evaluated within the ASTM D 6751 standards.

Pseudograin decomposition of short fiber-reinforced plastics for two-step homogenization using machine learning approach

Decomposing the representative volume element (RVE) of short fiber-reinforced plastics (SFRPs) into several pseudograins (PGs) is essential for understanding its effective mechanical behavior. However, conventional PG decomposition methodologies are limited by their high computational costs due to iteration-based algorithms. To address this, we propose a machine learning-assisted PG decomposition procedure that utilizes a series–parallel artificial neural network (ANN) system to facilitate the time-consuming decomposition process. To validate the effectiveness of our proposal, we implemented a two-step homogenization framework of SFRP that consists of the series–parallel ANN system, Mori-Tanaka model, and Voigt model into ABAQUS user material subroutine (UMAT). The elastic modulus values predicted by the UMAT are found to be in good agreement with both DIGIMAT-MF and experimental values, while also maintaining low computational time.

Crime Prevention Through Environmental Design: Numerical investigation of environmental factors affecting security in sports facilities

Sports are one of the most essential categories of developed and developing societies, among which football, as the most popular sport in the world, receives special attention from governments. Therefore, to increase the attendance of people in stadiums, it is necessary for the managers to identify the factors influencing the attendance of spectators, both psychological and internal factors, as well as external and environmental factors, and to design and implement a written and specific strategy and plan. Having a sense of security is one of the influential factors in creating satisfaction in the audience. Due to the high cost of building sports facilities and spaces, care should be taken about many factors and variables, especially the correct location of such spaces, to enable easier access, fairer distribution, and greater security. Therefore, this research aims to analyze the environmental factors that affect security in sports facilities with the approach of crime prevention through environmental design in stadiums. The instrument in this research is a questionnaire on security in sports facilities with the effectiveness of crime prevention through environmental design. Finally, the artificial neural network system method was used in MATLAB (A 2018) and SPSS (V24) software to analyze the data and answer the research questions. Environmental factors of access, light and illumination, readability, visibility and natural surveillance, repair and maintenance, dynamics, functional disorders, and guide signs respectively had a positive effect on security and crime prevention through environmental design in sports facilities. Therefore, sports directors and architects must pay attention to these indicators in the design of sports stadiums. Keywords: Stadiums, Sport Management, Sports Events, CPTED Approach, Neural Networks.

Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing.

Realization of dendric signal processing in the human brain is of great significance for spatiotemporal neuromorphic engineering. Here, we proposed an ionic dendrite device with multichannel communication, which could realize synaptic behaviors even under an ultralow action potential of 80 mV. The device not only could simulate one-to-one information transfer of axons but also achieve a many-to-one modulation mode of dendrites. By the adjustment of two presynapses, Pavlov's dog conditioning experiment was learned successfully. Furthermore, the device also could emulate the biological synaptic competition and synaptic cooperation phenomenon through the comodulation of three presynapses, which are crucial for artificial neural network (ANN) implementation. Finally, an ANN was further constructed to realize highly efficient and anti-interference recognition of fashion patterns. By introducing the cooperative device, synaptic weight updates could be improved for higher linearity and larger dynamic regulation range in neuromorphic computing, resulting in higher recognition accuracy and efficiency. Such an artificial dendric device has great application prospects in the processing of more complex information and the construction of an ANN system with more functions.