Deep Artificial Neural Networks Research Articles

Decoding green food safety information dependency in the digital era: An intelligent validation using SEM-ANN framework

Dependence on mobile phone information in the Internet era is a crucial issue, and psychological perception and personalized and accurate pushing significantly impact this field. This introduction of rational and planned behaviour combined with the theoretical framework of Structural Equation Modelling (SEM) and Artificial Neural Networks (ANN) are intricacy analytical tools to explore the key factors affecting the information dependence on green food safety. The key factors affecting the reliance on green food safety information were investigated. The survey covered 630 participants aged 17 to 70 in Guangdong Province, China. The study showed that perceived risk, regulation, price volatility, innovativeness, and anticipatory confirmation significantly influence the perceived usefulness of green food information, affecting satisfaction with green food. The study also highlights the significant impact of the degree of anticipatory confirmation by users on satisfaction and perceived usefulness of green food. Model path analysis revealed that anticipatory confirmation and perceived usefulness explained 61% and 74% of the variance in financial sustainability, respectively. The application of deep ANN multilayer perceptron improved the prediction accuracy of perceived usefulness with 87.54% training accuracy and 90.34% testing accuracy, further deepening the understanding of the mechanism of relying on green food safety information. This study provides strong support for a deeper understanding of the relationship between consumer behaviour and green food safety information dependence, which helps promote enterprises and market camps to more effectively formulate product or service promotion strategies and provides a solid theoretical and empirical foundation for future green food safety and personalized and accurate push thus enhancing information dependence.

A unifying framework for functional organization in early and higher ventral visual cortex.

A key feature of cortical systems is functional organization: the arrangement of functionally distinct neurons in characteristic spatial patterns. However, the principles underlying the emergence of functional organization in the cortex are poorly understood. Here, we develop the topographic deep artificial neural network (TDANN), the first model to predict several aspects of the functional organization of multiple cortical areas in the primate visual system. We analyze the factors driving the TDANN's success and find that it balances two objectives: learning a task-general sensory representation and maximizing the spatial smoothness of responses according to a metric that scales with cortical surface area. In turn, the representations learned by the TDANN are more brain-like than in spatially unconstrained models. Finally, we provide evidence that the TDANN's functional organization balances performance with between-area connection length. Our results offer a unified principle for understanding the functional organization of the primate ventral visual system.

Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains

AbstractEffective solar energy utilization demands improvements in forecasting due to the unpredictable nature of solar irradiance (SI). This study introduces and rigorously tests two innovative forecasting models across different locations: the Sequential Deep Artificial Neural Network (SDANN) and the Deep Hybrid Random Forest Gradient Boosting (RFGB). SDANN, leveraging deep learning, aims to identify complex patterns in weather data, while RFGB, combining Random Forest and Gradient Boosting, proves more effective by offering a superior balance of efficiency and accuracy. The research highlights the SDANN model's deep learning capabilities along with the RFGB model's unique blend and their comparative success over existing models such as eXtreme Gradient Boosting (XGBOOST), Categorical Boosting (CatBOOST), Gated Recurrent Unit (GRU), and a K‐Nearest Neighbors (KNN) and XGBOOST hybrid. With the lowest Mean Squared Error (147.22), Mean Absolute Error (8.77), and a high R2 value (0.80) in a studied region, RFGB stands out. Additionally, detailed ablation studies on meteorological feature impacts on model performance further enhance accuracy and adaptability. By integrating cutting‐edge AI in SI forecasting, this research not only advances the field but also sets the stage for future renewable energy strategies and global policy‐making.

Intelligent algorithm-based maximum power point tracker for an off-grid photovoltaic-powered direct-current irrigation system

Abstract This research aims to enhance the performance of photovoltaic (PV) systems on a 2-fold basis. Firstly, it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking, ensuring optimal extraction of electrical power from PV arrays. Secondly, it proposes the use of 96-V, 2.98-kW direct-current (DC) water pumps for farm irrigation, aiming to improve efficiency, reduce cost and complexity, and overcome challenges associated with connecting faraway farm irrigation systems to the utility grid. In this study, it has been demonstrated that the use of DC pumps greatly improves system performance and efficiency by eliminating the need for isolation transformers, power passive filters and inverters, therefore simplifying the architecture of the system. The efficacy of the proposed methodology is confirmed by MATLAB®/Simulink® simulation results, whereby the proposed algorithm attains a mean squared error of 6.5705 × 10–5 and a system efficiency approaching 99.8%, ensuring a steady voltage under varying load conditions.

Data Representation for Deep Learning - Based Arabic Text Summarization Performance Using Python Results

A sequence-to-sequence model is used as the foundation for a suggested abstractive Arabic text summarizing system. Our goal is to create a sequence-to-sequence model by utilizing multiple deep artificial neural networks and determining which one performs the best. The encoder and decoder have been developed using several layers of recurrent neural networks, gated recurrent units, recursive neural networks, convolutional neural networks, long short-term memory, and bidirectional long short-term memory. We are re-implementing the fundamental summarization model in this study, which uses the sequence-to-sequence framework. Using a Google Colab Jupiter notebook that runs smoothly, we have constructed these models using the Keras library. The results further demonstrate that one of the key techniques that has led to breakthrough performance with deep neural networks is the use of Gensim for word embeddings over other text representations by abstractive summarization models, along with FastText, a library for efficient learning of word representations and sentence classification.

A Hierarchy of Variables That Influence the Force–Velocity Profile of Acrobatic Gymnasts: A Tool Based on Artificial Intelligence

Jumping performance is considered an overall indicator of gymnastics ability. Acrobatic Gymnastics involves base and top gymnasts, considering the type of training that is performed and the distinct anthropometric traits of each gymnast. This work aims to investigate a hierarchy of variables that influence the force–velocity (F-V) profile of top and base acrobatic gymnasts through a deep artificial neural network model. Twenty-eight first division and elite acrobatic gymnasts (eleven tops and seventeen bases) performed two evaluations to assess the F-V profile during the Countermovement Jump and its mechanical variables, using My Jump 2 (a total of 56 evaluations). A training background survey and anthropometric assessments were conducted. The final model (R = 0.97) showed that the F-V imbalance (F-Vimb) increases with higher force and decreases with higher maximal power, fat percentage, velocity, and height. Coaches should prioritize the development of force, followed by maximal power, and velocity for the optimization of gymnasts’ F-Vimb. For training planning, the influences of body mass and push-off height are higher for the bases, and the influences of years of practice and competition level are higher for the tops.

Forecasting and characterization of composite pipeline based on experimental modal analysis and YUKI-gradient boosting

The surging demand for fiberglass pipe materials in the Oil and Gas transportation sectors are driven by their exceptional durability and ease of use. This study combined improved experimental and numerical analyses to assess the behavior of composite pipe and focused on characterizing it, especially in terms of identifying the frequency based on different crack lengths. Various tests, such as impact hammer tests and modal analysis, determine the pipe properties and natural frequencies. The study also investigates the impact of varying fiber volume fractions to refine material properties. To understand crack effects behavior, different crack lengths have been introduced into the pipes and analyzing the modal analysis using frequency and mode shapes. The effect of cracks into frequency based on experimental and developed numerical models is investigated. A predective model is developed based on YUKI-Gradient Boosting algorithms and Deep Artificial Neural Networks. To test the robustness of developed algorithm several tests are provided and the results show the effectiveness of YUKI-Gradient Boosting outperforms YUKI-Deep-ANN in accuracy and consistency.

A bilinear transformer interactive neural networks-based approach to fine-grained recognition and protection of plant diseases for gardening design

Landscape gardening design is an important part of urban ecology and urban image, which is of great significance for urban plant protection. Plant diseases are a major challenge that must be faced in the conservation of landscape plants. Having timely and comprehensive information about the types and developmental stages of plant diseases is crucial for effective disease control. Traditional plant disease differentiation, which relies on manual inspection by professionals, is both time-consuming and labor-intensive. The rapid development of deep artificial neural network technology, especially the rise of computer vision technology, provides efficient and accurate technical means for plant disease identification. In this paper, we introduce the Interactive Bilinear Transformer Network (IBTN) model, which utilizes fine-grained recognition technology for landscape plant disease identification. We validate the effectiveness of model using several public datasets, demonstrating its competitive performance.

A Source Identification Problem in Magnetics Solved by Means of Deep Learning Methods

In this study, a deep learning-based approach is used to address inverse problems involving the inversion of a magnetic field and the identification of the relevant source, given the field data within a specific subdomain. Three different techniques are proposed: the first one is characterized by the use of a conditional variational autoencoder (CVAE) and a convolutional neural network (CNN); the second one employs the CVAE (its decoder, more specifically) and a fully connected deep artificial neural network; while the third one (mainly used as a comparison) uses a CNN directly operating on the available data without the use of the CVAE. These methods are applied to the magnetostatic problem outlined in the TEAM 35 benchmark problem, and a comparative analysis between them is conducted.

Organic-Inorganic Hybrid Synaptic Transistors: Methyl-Silsesquioxanes-Based Electric Double Layer for Enhanced Synaptic Functionality and CMOS Compatibility.

Electrical double-layer (EDL) synaptic transistors based on organic materials exhibit low thermal and chemical stability and are thus incompatible with complementary metal oxide semiconductor (CMOS) processes involving high-temperature operations. This paper proposes organic-inorganic hybrid synaptic transistors using methyl silsesquioxane (MSQ) as the electrolyte. MSQ, derived from the combination of inorganic silsesquioxanes and the organic methyl (-CH3) group, exhibits exceptional thermal and chemical stability, thus ensuring compatibility with CMOS processes. We fabricated Al/MSQ electrolyte/Pt capacitors, exhibiting a substantial capacitance of 1.89 µF/cm2 at 10 Hz. MSQ-based EDL synaptic transistors demonstrated various synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, signal pass filtering, and spike-number-dependent plasticity. Additionally, we validated synaptic functions such as information storage and synapse weight adjustment, simulating brain synaptic operations through potentiation and depression. Notably, these synaptic operations demonstrated stability over five continuous operation cycles. Lastly, we trained a multi-layer artificial deep neural network (DNN) using a handwritten Modified National Institute of Standards and Technology image dataset. The DNN achieved an impressive recognition rate of 92.28%. The prepared MSQ-based EDL synaptic transistors, with excellent thermal/chemical stability, synaptic functionality, and compatibility with CMOS processes, harbor tremendous potential as materials for next-generation artificial synapse components.

Integrative processing in artificial and biological vision predicts the perceived beauty of natural images.

Previous research shows that the beauty of natural images is already determined during perceptual analysis. However, it is unclear which perceptual computations give rise to the perception of beauty. Here, we tested whether perceived beauty is predicted by spatial integration across an image, a perceptual computation that reduces processing demands by aggregating image parts into more efficient representations of the whole. We quantified integrative processing in an artificial deep neural network model, where the degree of integration was determined by the amount of deviation between activations for the whole image and its constituent parts. This quantification of integration predicted beauty ratings for natural images across four studies with different stimuli and designs. In a complementary functional magnetic resonance imaging study, we show that integrative processing in human visual cortex similarly predicts perceived beauty. Together, our results establish integration as a computational principle that facilitates perceptual analysis and thereby mediates the perception of beauty.

Probing the Structure and Functional Properties of the Dropout-Induced Correlated Variability in Convolutional Neural Networks.

Computational neuroscience studies have shown that the structure of neural variability to an unchanged stimulus affects the amount of information encoded. Some artificial deep neural networks, such as those with Monte Carlo dropout layers, also have variable responses when the input is fixed. However, the structure of the trial-by-trial neural covariance in neural networks with dropout has not been studied, and its role in decoding accuracy is unknown. We studied the above questions in a convolutional neural network model with dropout in both the training and testing phases. We found that trial-by-trial correlation between neurons (i.e., noise correlation) is positive and low dimensional. Neurons that are close in a feature map have larger noise correlation. These properties are surprisingly similar to the findings in the visual cortex. We further analyzed the alignment of the main axes of the covariance matrix. We found that different images share a common trial-by-trial noise covariance subspace, and they are aligned with the global signal covariance. This evidence that the noise covariance is aligned with signal covariance suggests that noise covariance in dropout neural networks reduces network accuracy, which we further verified directly with a trial-shuffling procedure commonly used in neuroscience. These findings highlight a previously overlooked aspect of dropout layers that can affect network performance. Such dropout networks could also potentially be a computational model of neural variability.

Deep learning for predicting the residual concentration of sodium hypochlorite in the cooling water OF nuclear power plants

Sea water is used as cooling water in several operating nuclear power plants, as well as in other industries such as petrochemicals. Biofouling is a common problem in systems that use sea water, causing corrosion in pipes and equipment, blockages, and loss of efficiency in heat exchangers. The use of sodium hypochlorite has proven to be effective in minimizing the damages caused by biofouling, provided that the residual chlorine concentration remains within a specific range, as low dosages will not protect the equipment and high dosages can cause environmental damage. With the aim of reducing maintenance and operation costs of equipment, and particularly ensuring the operability of systems related to the safety of nuclear power plants that use sea water, this study developed deep artificial neural networks (ANN) models capable of predicting the residual chlorine concentration in the main cooling and safety systems of a nuclear power plant. This prediction is based on environmental and process parameters that influence the residual chlorine concentration. Proposed model achieved average mean squared error of 0.002 and correlation coefficient (R2-Score) of 0.934, demonstrating to be a promising tool for predicting residual chlorine concentration.

Hierarchical-taxonomy-aware and attentional convolutional neural networks for acoustic identification of bird species: A phylogenetic perspective

The study of bird populations is crucial for biodiversity research and conservation. Deep artificial neural networks have revolutionized bird acoustic recognition; however, most methods overlook inherent relationships among bird populations, resulting in the loss of biological information. To address this limitation, we propose the Phylogenetic Perspective Neural Network (PPNN), which incorporates hierarchical multilevel labels for each bird. PPNN uses a hierarchical semantic embedding framework to capture feature information at different levels. Attention mechanisms are employed to extract and select common and distinguishing features, thereby improving classification accuracy. We also propose a path correction strategy to rectify inconsistent predictions. Experimental results on bird acoustic datasets demonstrate that PPNN outperforms current methods, achieving classification accuracies of 90.450%, 91.883%, and 89.950% on the Lishui-Zhejiang Birdsdata (100 species), BirdCLEF2018-Small (150 species), and BirdCLEF2018-Large (500 species) datasets respectively, with the lowest hierarchical distance of a mistake across all datasets. Our proposed method is applicable to any bird acoustic dataset and presents significant advantages as the number of categories increases.

Метод прогнозирования оттока клиентов банка на основе ансамблевой модели машинного обучения

The results of research are presented, the purpose of which was to develop a method for predicting the outflow of clients of a commercial bank based on the use of machine learning models (including deep artificial neural networks) for processing client data, as well as the creation of software tools that implement this method. The object of the study is a commercial bank, and the subject of the study is its activities in the B2C segment, which includes commercial interaction between businesses and individuals. The relevance of the chosen area of research is determined by the increased activity of banks in the field of introducing digital services to reduce non-operating costs associated, in particular, with retaining clients, since the costs of attracting new ones are much higher than maintaining existing clients. The scientific novelty of the research results is the developed method for predicting the outflow of commercial bank clients, as well as the algorithm underlying the software that implements the proposed method. The proposed ensemble forecasting model is based on three classification algorithms: k-means, random forest and multilayer perceptron. To aggregate the outputs of individual models, it is proposed to use a learning tree of fuzzy inference systems of the Mamdani type. Training of the ensemble model is carried out in two stages: first, the listed three classifiers are trained, and then, based on the data obtained from their outputs, a tree of fuzzy inference systems is trained. The ensemble model in the proposed method implements a static version of the forecast, the results of which are used in a dynamic forecast performed in two versions – based on the recurrent least squares method and based on a convolutional neural network. Model experiments carried out on a synthetic dataset taken from the Kaggle website showed that the ensemble model has a higher quality of binary classification than each model individually.

Electromyography-Based Biomechanical Cybernetic Control of a Robotic Fish Avatar

This study introduces a cybernetic control and architectural framework for a robotic fish avatar operated by a human. The behavior of the robot fish is influenced by the electromyographic (EMG) signals of the human operator, triggered by stimuli from the surrounding objects and scenery. A deep artificial neural network (ANN) with perceptrons classifies the EMG signals, discerning the type of muscular stimuli generated. The research unveils a fuzzy-based oscillation pattern generator (OPG) designed to emulate functions akin to a neural central pattern generator, producing coordinated fish undulations. The OPG generates swimming behavior as an oscillation function, decoupled into coordinated step signals, right and left, for a dual electromagnetic oscillator in the fish propulsion system. Furthermore, the research presents an underactuated biorobotic mechanism of the subcarangiform type comprising a two-solenoid electromagnetic oscillator, an antagonistic musculoskeletal elastic system of tendons, and a multi-link caudal spine composed of helical springs. The biomechanics dynamic model and control for swimming, as well as the ballasting system for submersion and buoyancy, are deduced. This study highlights the utilization of EMG measurements encompassing sampling time and μ-volt signals for both hands and all fingers. The subsequent feature extraction resulted in three types of statistical patterns, namely, Ω,γ,λ, serving as inputs for a multilayer feedforward neural network of perceptrons. The experimental findings quantified controlled movements, specifically caudal fin undulations during forward, right, and left turns, with a particular emphasis on the dynamics of caudal fin undulations of a robot prototype.

Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazilian Market

Global environmental impacts such as climate change require behavior from society that aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels with other energy sources. An important aspect of efficient and sustainable management of the electricity supply in Brazil is the prediction of some variables of the national electric system (NES), such as the price of differences settlement (PLD) and wind speed for wind energy. In this context, the present study investigated two distinct forecasting approaches. The first involved the combination of deep artificial neural network techniques, long short-term memory (LSTM), and multilayer perceptron (MLP), optimized through the canonical genetic algorithm (GA). The second approach focused on machine committees including MLP, decision tree, linear regression, and support vector machine (SVM) in one committee, and MLP, LSTM, SVM, and autoregressive integrated moving average (ARIMA) in another. The results indicate that the hybrid AG + LSTM algorithm demonstrated the best performance for PLD, with a mean squared error (MSE) of 4.68. For wind speed, there is a MSE of 1.26. These solutions aim to contribute to the Brazilian electricity market’s decision making.

Prediction of CBR by Deep Artificial Neural Networks with Hyperparameter Optimization by Simulated Annealing

AbstractThe construction of pavements requires the complete identification of the soils in place and of the added materials. This identification consists in determining the class of the soils and in evaluating their bearing capacity through the California bearing ratio (CBR) index. Obtaining the CBR index is very costly in terms of time and financial resources, especially when it is a large-scale project. It thus leaves prospects of obtaining it by simpler processes; hence, it arises the need to find simpler processes compared to classical processes. This study develops models for predicting the CBR index from physical properties that are less complex to obtain, based on deep neural networks. To achieve this, three databases were used. A first database consists of the proportion of fines, the Atterberg limits and the Proctor references of the soils. A second database uses the methylene blue value instead of the Atterberg limits, and a third database uses only the proportion of fines and the Proctor soil reference. On each of the databases, a deep neural network model was developed using dense layers, regularization layers, residual blocks and parallelization in TensorFlow to predict the CBR value. Each model was formed by combining several deep neural networks developed according to specific architectures. To expedite training, the simulated annealing method was employed to optimize hyperparameters and define the optimal configuration for each network. The predictions obtained are correlated with the true values from 83.6 to 96.5%. In terms of performance, the models have a mean deviation ranging from 3.74 to 5.96%, a maximum deviation ranging from 12.43 to 16.2% and a squared deviation ranging from 0.781 to 2.189. The results suggest that the variable VBS has a negative impact on the accuracy of the networks in predicting the CBR index. The developed models respect the confidence threshold (± 10%) and can be used to set up a local or regional geotechnical platform.

Renewable energy sources integration via machine learning modelling: A systematic literature review

The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms of costs and technology, expecting a massive diffusion in the near future and placing several challenges to the power grid. Since RESs depend on stochastic energy sources —solar radiation, temperature and wind speed, among others— they introduce a high level of uncertainty to the grid, leading to power imbalance and deteriorating the network stability. In this scenario, managing and forecasting RES uncertainty is vital to successfully integrate them into the power grids. Traditionally, physical- and statistical-based models have been used to predict RES power outputs. Nevertheless, the former are computationally expensive since they rely on solving complex mathematical models of the atmospheric dynamics, whereas the latter usually consider linear models, preventing them from addressing challenging forecasting scenarios. In recent years, the advances in machine learning techniques, which can learn from historical data, allowing the analysis of large-scale datasets either under non-uniform characteristics or noisy data, have provided researchers with powerful data-driven tools that can outperform traditional methods. In this paper, a systematic literature review is conducted to identify the most widely used machine learning-based approaches to forecast RES power outputs. The results show that deep artificial neural networks, especially long-short term memory networks, which can accurately model the autoregressive nature of RES power output, and ensemble strategies, which allow successfully handling large amounts of highly fluctuating data, are the best suited ones. In addition, the most promising results of integrating the forecasted output into decision-making problems, such as unit commitment, to address economic, operational and managerial grid challenges are discussed, and solid directions for future research are provided.

Deep artificial neural network based multilayer gated recurrent model for effective prediction of software development effort

Deep artificial neural network based multilayer gated recurrent model for effective prediction of software development effort