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The color gamut of imaging media is significant for the reproduction of color images because its magnitude directly affects the degree to which colors change during the printing process. Over the last few years, digital impression technology has started to play a substantial role in the printing industry due to the quest for short runs and variable information printing. The color gamut of electrophotographic digital printing depends on various parameters including the printer and toner, but especially the properties (whiteness, roughness, and gloss) of the paper, which influence the final printed color gamut and replication quality. Artificial intelligence approaches are applied herein for the first time to choose and predict the performance of a paper with appropriate properties to achieve the maximum color gamut. A genetic algorithm-based computer code is developed to optimize the architecture of an artificial neural network, thereby yielding an accurate model to predict the color gamut achievable in electrophotographic color printing. The gamut volume was generated using an Eye-One spectrophotometer, ProfileMaker, and ColorThink software. The properties of 11 dissimilar types of paper were assessed by atomic force microscopy, spectrophotometer, and goniophotometer. The results indicate that the reproducibility depended considerably on the features of the paper. Although high whiteness and gloss increased the color gamut volume, and high roughness decreased the reproducibility of the printing machine, the artificial intelligence approach provided the opportunity to achieve a high gamut volume with low gloss and high roughness. [ABSTRACT FROM AUTHOR]

In the recent era, Transportation considers the most powerful component of the business logistics system. Likewise, there is an interdependent relationship between the transportation and logistics systems. This paper aims to make a comparative study of logistics transportation problems based on intelligence algorithms. The researchers surveyed the previous studies conducted in the Artificial Intelligent field to solve complex problems. In this research study, the authors focused on techniques that are mostly applied in transportation and logistics systems, especially, Artificial Neural Network, Genetic Algorithm, and Fuzzy Logic models. Also, a proposed model and algorithm was done to obtain customers' and organizations' satisfaction. Artificial Neural Network uses as a decision tool that combines the system stat sets and the operation state-dependent sets. As well, the genetic algorithm combines the best parameters as a method to finds the best evaluation solutions. And fuzzy logic uses a fuzzy set to help decision-makers in making the best decisions in multiple fields. Finally, authors recommended to work in two new areas which are FGA, NFGA Algorithms to solve complex and multimodal problems that faces transportation logistics sector. [ABSTRACT FROM AUTHOR]

The retinal impulse signal is the basic carrier of visual information. It records the distribution of light on the retina. However, its direct conversion to a scene image is difficult due to the nonlinear characteristics of its distribution. Therefore, the use of artificial neural network to reconstruct the scene from retinal spikes has become an important research area. This paper proposes the architecture of a neural network based on vector quantization, where the feature vectors of spike trains are extracted, compressed, and stored using a feature extraction and compression network. During the decoding process, the nearest neighbour search method is used to find the nearest feature vector corresponding to each feature vector in the feature map. Finally, a reconstruction network is used to decode a new feature map composed of matching feature vectors to obtain a visual scene. This paper also verifies the impact of vector quantization on the characteristics of pulse signals by comparing experiments and visualizing the characteristics before and after vector quantization. The network delivers promising performance when evaluated on different datasets, demonstrating that this research is of great significance for improving relevant applications in the fields of retinal image processing and artificial intelligence. [ABSTRACT FROM AUTHOR]

Abnormal behavior is called an abnormal behavior that deviates from the same normal standard as the average. The installation of public CCTVs to prevent crimes is increasing, but the crime rate is rather increasing recently. In line with this situation, artificial intelligence research using deep learning that automatically finds abnormal behavior in CCTV is increasing. Deep learning is a type of artificial intelligence designed based on artificial neural networks, and the quality of learning data is important for high accuracy in the development of artificial intelligence through deep learning. This paper verifies whether learning data for abnormal behavior detection is suitable as learning data which is being constructed using an MPED-RNN model for binary classification to determine whether there is an abnormal behavior by frame using skeleton data of a person based on an autoencoder. As a result of the experiment, the unsupervised learning-based MPED-RNN model used in this paper is not suitable for verifying images with a similar number of frames with and without abnormal behavior, such as the corresponding data, and it is judged that appropriate results can be derived only when verified with a supervised learningbased model. [ABSTRACT FROM AUTHOR]

To address the problem that the traditional detection method for litchi sugar content is time-consuming and laborious and will destroy the tested sample, this paper proposed a non-destructive detection method for litchi sugar content based on near-infrared spectroscopy (NIR) and artificial intelligence algorithm. Firstly, to remove noise and other interference, the preprocessing methods for spectral data are studied. Nine preprocessing methods, such as moving average smoothing (MA), standard normal variate transform (SNV), and multiplicative scatter correction (MSC), are adopted to preprocess the spectral data. Then, to reduce the input dimension of the model and overcome the interference of redundant bands, the feature extraction methods for spectral data are examined. Two feature extraction methods, including Monte-Carlo uninformative variable elimination (MCUVE) and competitive adaptive reweighted sampling (CARS), are utilized to extract the features of spectral data. Finally, partial least squares regression (PLSR) and stochastic configuration network (SCN) are adopted to establish the prediction model of litchi sugar content. The experimental results show that the SNV-CARS-SCN prediction model has the highest accuracy. The coefficient of determination ( R 2 ), RMSE, and MAE of the training dataset are 0.9996, 0.1145, and 0.1154, respectively. R 2 , RMSE, and MAE of the test dataset are 0.9740, 0.4962, and 0.3818, respectively. The NIR detection system and SCN prediction model designed in this paper are of great significance for the design of litchi automatic sorting system. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field. [ABSTRACT FROM AUTHOR]

The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks will be easier to implement in small-scale solutions, like those for the Internet of Things, in the future. This paper presents test results where a real system was monitored, and anomalies were detected and predicted. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) management has become a highly challenging issue for many countries at different levels of development since the growth of population and urbanisation has resulted in a large increase in MSW generation. It is difficult to forecast the solid waste generated and its characteristics due to the non-linear nature of the MSW system, hence the importance of introducing artificial intelligence (AI) techniques and machine learning (ML) methods. This paper discusses a systematic literature review (SLR) on the application of AI techniques in MSW management, including waste generation prediction, waste collection and transportation and waste treatment and final disposal. The study reviewed and analysed the research studies published between 2000 and 2021, and investigated the current challenges faced by researchers in implementing AI approaches in the MSW system. It was concluded that artificial neural networks are the most used approach in various MSW-related problems. However, the lack and reliability of data are limiting the advancement of AI techniques in this field. Additionally, most studies claimed that their results are accurate and can be implemented in real-life scenarios, with an absence of a clear baseline to assess the performance of the adopted approaches. The detailed gaps and future suggestions for AI techniques in MSW systems are also discussed for further research. [ABSTRACT FROM AUTHOR]

The rising population of millennials, coupled with Digital Assistants (DA) and online purchasing trends among consumers have gained increasing attention by global marketers. The study evaluates the influence of DA attributes on the purchasing intention (PUI) of millennials. A combined approach of PLS-SEM, Artificial Neural Network (ANN) and Fuzzy-set Qualitative Comparative Analysis (fsQCA) is used to predict the PUI of 345 millennials. Also, multi-group analysis is employed to uncover the influence of gender on the relationship between PUI and DA attributes. The findings suggest that DA attributes may amplify purchasing intention among millennials, especially through perceived interactivity and anthropomorphism. Further, the moderating role of gender was found significant on the inter-relationship of perceived interactivity and PUI. This research is a pioneer study in the area of artificial intelligence, conversational commerce, DA and AI-powered chatbots. This study will help marketers and practitioners to predict millennial purchasing intentions. An evaluation of this paper may help them to foster immersive and effective engagement through DA. [ABSTRACT FROM AUTHOR]

Obtaining mathematical models of nonlinear cyber–physical systems for use in controller design is both difficult and time consuming. In this paper, an ANN-based method is proposed to design a controller for a nonlinear system that does not require a mathematical model. The developed ANN-based control algorithm is implemented directly on a real-time field controller, and its performance is evaluated without the use of auxiliary devices, such as PCs or workstations. By executing machine learning algorithms on local devices or embedded systems, edge artificial intelligence (Edge AI) with transfer learning gives priority to processing data at the source, minimizing the necessity for continuous connectivity to remote servers. The control algorithm was developed using the Matlab Simulink environment. The first and second ANNs were cascaded, wherein the first ANN computes the appropriate pressure signal for the given displacement, while the second predicts the force based on the pressure value from the first ANN. Subsequently, the ANN-based control algorithm was converted to SCL code using the Simulink PLC Coder and deployed on the PLC for operation. The algorithm was tested using two different scenarios. The conducted tests demonstrated the successful prediction of pressure signals corresponding to the targeted displacement values and accurate estimation of force values. Experimental work was carried out on PAM manipulators as a nonlinear model application, and the obtained results were discussed. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

Rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs posing growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. utilities are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metrics for gauging the effect of distributed renewable energy on distribution networks is reliability analysis. Reliability analysis in distribution systems involves evaluating the performance and robustness of electrical distribution networks. An artificial intelligence approach is implemented in this paper to improve reliability analysis with dispersed generations in distribution network. Deep belief neural networks (DBNNs) are a type of artificial neural network that can be used for various tasks, including analyzing complex data such as those found in power distribution systems. This paper integrated a DBNN using a particle swarm optimization (PSO) technique. The proposed model performance is assessed using mean square error, mean absolute error, root mean square error, and R squared error. The findings reveal that reliability analysis with this novel technique is more accurate. [ABSTRACT FROM AUTHOR]

The main goal of this work is to combine the usage of the numerical homogenization technique for determining the effective properties of representative volume elements with artificial neural networks. The effective properties are defined according to the classical laminate theory. The purpose is to create and train a rapid surrogate model for the quick calculation of the mechanical properties of hollow concrete slabs. First, the homogenization algorithm was implemented, which determines membrane, bending and transverse shearing properties of a given parametrized hollow-core precast slab reinforced with steel bars. The algorithm uses the finite element mesh but does not require a formal solution of the finite element method problem. Second, the learning and training artificial intelligence framework was created and fed with a dataset obtained by optimal Latin hypercube sampling. In the study, a multilayer perceptron type of artificial neural network was used. This allows for obtaining rapid calculations of the effective properties of a particular hollow-core precast slab by using a surrogate model. In the paper, it has been proven that such a model, obtained via complex numerical calculations, gives a very accurate estimation of the properties and can be used in many practical tasks, such as optimization problems or computer-aided design decisions. Above all, the efficient setup of the artificial neural network has been sought and presented. [ABSTRACT FROM AUTHOR]

This paper deals with the detection of single- and double-switching faults (open-circuit type "O-C") which appear in the inverter of a photovoltaic solar pumping system. Our system as a whole contains a photovoltaic module, a DC/DC step-up converter controlled by perturbation and observation maximum power point tracking technique, a three-phase DC/AC inverter controlled by the sinusoidal pulse width modulation technique, a three-phase induction motor, and a water pump. The used techniques to detect this type of faults are based on artificial intelligence (AI) (neural networks and neuro-fuzzy networks); we use AI as an observer to the inverter in order to detect the faults using extracted features from the inverter output currents. Both of the proposed fault diagnosis techniques show a good performance and high accuracy with less than ±5% of error for neuron-fuzzy and ±7% for artificial neural network and a response time of less than 0.1 s, which is a satisfying speed to detect the faults before a total degradation or any undesirable effects. This paper fulfills an identified need for faults diagnosis of a three-phase inverter in photovoltaic solar pumping systems using AI. The effectiveness of the AI techniques was evaluated for O-C fault detection by simulation tests using the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANN), an Artificial Intelligence (AI) technique, are both bio-inspired and nature-inspired models that mimic the operations of the human brain and the central nervous system that is capable of learning. This paper is based on a system that optimizes the performance of an uncertain unmanned nonlinear Multi-Input Multi-Output (MIMO) aerodynamic plant called Twin Rotor MIMO System (TRMS). The pitch and yaw angles which are challenging to control and optimize in practice, are being used as the input to the Nonlinear Auto-Regressive with eXogenous (NARX) model, and eventually trained. The training features use the Matlab Deep Learning Toolbox. The NARX structure has its core in the neural networks' architecture. Data is collected from the TRMS testbed which is used to train the network. ANN as a Hybrid intelligent control strategy of ANN in combination with Pattern Search and Genetic Algorithm, is then utilized to optimize the parameters of the neural networks. At the end it was validated, tested and the optimized system run in simulation and compared with other intelligent and conventional controllers, with the proposed controller outperforming them, giving a very fast-tracking control, stable and optimal performance that satisfactorily met all our design requirements. [ABSTRACT FROM AUTHOR]

Purpose: Taiwan experiences frequent seismic activity. Major earthquakes in recent history have seriously damaged the school buildings. School buildings in Taiwan are intended to serve both as places of education and as temporary shelters in the aftermath of major earthquakes. Therefore, the seismic performance assessments of school buildings are critical issues that deserve investigation. Design/methodology/approach: This paper develops a methodology that uses principal component analysis to generalize the seismic factors from the basic seismic parameters of school buildings, uses data mining to cluster different school building sizes and uses grey theory to analyze the relationship between seismic factors and the seismic performance of school buildings. Additionally, this paper employs the Artificial Neural Network (ANN) to deduce the seismic assessment model for school buildings. Finally, it adopts support vector machine to validate the ANN's deductive results. Findings: An empirical study was conducted on 326 school buildings in the central area of Taichung City, Taiwan, to illustrate the effectiveness of the proposed approach. Results show that thickness of wall and width of middle-row column relate significantly with school-building seismic performance. Originality/value: This paper provides a model that structural engineers or architects may use to design school buildings that are adequately resistant to earthquakes as well as a reference for future academic research. [ABSTRACT FROM AUTHOR]

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset. [ABSTRACT FROM AUTHOR]

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In this paper, a technique for optical performance monitoring (OPM) using deep learning-based artificial neural network (ANN) is implemented. ANN is trained with parameters derived from eye-diagram for the identification of optical signal to noise ratio (OSNR), chromatic dispersion (CD) and polarisation mode dispersion (PMD) simultaneously and independently in a 10 Gb/s system with non-return-to-zero (NRZ) on-off keying (OOK) data signal. ANN-based OPM confirms that the proposed approach can provide reliable estimated results. The mean squared errors for OSNR, CD and differential group delay (DGD) are found to be 4.6071 dB, 0.0417 ps/nm/km and 0.0016 ps/km, respectively. The proposed technique may be utilized in analyzing the signals of future heterogeneous optical communication networks intelligently. [ABSTRACT FROM AUTHOR]

With an ever-increasing competitive world of engineering services, and with always thinner profit margins and decreasing market shares, the cost of a project is one of the significant criteria in decision making at the early stages of the construction industry. To remain competitive in the market, companies must have an accurate estimate of their projects. With the rise of computing power, there is now a tendency to use Machine Learning (ML)-based methods, such as Artificial Neural Networks (ANNs), 2for more accurate cost estimation that can remain reliable in the face of insufficient details during the tendering phase. This technical paper will review an artificial neural network approach to the cost estimate of engineering services and construction activities. While developing the mentioned model, firstly, the influential factors that affect the costs of construction and engineering services are identified, after this, a model developed using data of multiple projects from the author experience. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to simplify the design of class-E power amplifier (PA) using hybrid artificial neural-optimization network modeling. The class-E PA is designed for wireless power transfer (WPT) applications to be used in biomedical or internet of things (IoT) devices. Artificial neural network (ANN) models are combined with optimization algorithms to support the design of the class-E PA. In several amplifier circuits, the closed form equations cannot be extracted. Hence, the complicated numerical calculations are needed to find the circuit elements values and then to design the amplifier. Therefore, for the first time, ANN modeling is proposed in this paper to predict the values of the circuit elements without using the complex equations. In comparison with the other similar models, high accuracy has been obtained for the proposed model with mean absolute errors (MAEs) of 0.0110 and 0.0099, for train and test results. Moreover, root mean square errors (RMSEs) of 0.0163 and 0.0124 have been achieved for train and test results for the proposed model. Moreover, the best and the worst-case related errors of 0.001 and 0.168 have been obtained, respectively, for the both design examples at different frequencies, which shows high accuracy of the proposed ANN design method. Finally, a design of class-E PA is presented using the circuit elements values that, first, extracted by the analyses, and second, predicted by ANN. The calculated drain efficiencies for the designed class-E amplifiers have been obtained equal to 95.5% and 91.2% by using analyses data and predicted data by proposed ANN, respectively. The comparison between the real and predicted values shows a good agreement. [ABSTRACT FROM AUTHOR]

Bubble point oil formation volume factor (Bob) and solution gas–oil ratio (Rs) are two crucial PVT parameters used for modeling and volumetric calculations in petroleum industry. They are usually determined in laboratory or estimated using empirical correlations. Experimental methods are time-consuming and expensive where empirical correlations have limitations. Artificial intelligence can be sued overcome these limitations to develop more accurate, robust, and quick predictive tools. In this paper, we used three artificial neural network algorithms to develop intelligent models to predict Bob and Rest using 465 experimental data. Application of the Elman neural network (ENN) for this purpose is being reported for the first time. A variety of input parameters were selected based on a sensitivity analysis which include reservoir temperature (T), oil API gravity (°API), bubble point pressure (Pb), gas-specific gravity (γg), and Rs was used to predict the Bob. T, °API, Pb, γg, and Bob was used to predict the Rs. The ENN model was found superior to the other developed smart models and the empirical correlations with coefficient of determination (R2) of 0.993, root mean square error (RMSE) of 0.0093, and average absolute percent relative error (AAPRE) of 0.93% for the Bob and 0.999, 0.016, and 6.72% for the Rs, respectively. The ENN network has fewer adjustable parameters and provides faster training capabilities using fewer neurons and hidden layers compared to other ANN algorithms. The developed smart predictive tools can be safely used instead of laboratory methods and empirical correlations for a much wider ranges of input parameters and with higher accuracy and confidence. [ABSTRACT FROM AUTHOR]

Low-cost inertial measurement units (IMUs) are commonly used to determine the orientation of objects, such as unmanned aerial vehicles (UAVs) and smartphones. They calculate yaw by measuring Earth's magnetic field's horizontal components. However, in the presence of tilt (pitch or roll), a tilt-compensation operation is necessary. This is usually done by projecting measurements onto a horizontal plane. This method has limitations, particularly for large tilt angles and when the IMU is pointing toward the east or west directions. In this paper, we expose the shortcomings of this conventional approach and propose a novel machine learning–based solution employing an artificial neural network (ANN). This method eliminates the need to determine tilt angles and uses accelerometer and magnetometer measurements as its inputs. The dataset for training and testing the ANN was collected based on a 3D nonmagnetic scaled platform, using a low-cost IMU and a Raspberry Pi platform. On one hand, our method outperforms the conventional tilt-compensation technique and other complementary filters (Madgwick and Mahony) in terms of accuracy, as evidenced by the root mean square error (RMSE = 1.95°). However, this superiority comes at the expense of a more complex system that consumes more processing time. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) refers to the recreation of human intelligence in machines that have been designed to think like humans and mimic their actions. AI has been used in many fields such as image processing, health care, education, and marketing. Machine Learning (ML) has been the sub-division of AI, and deep learning has been the subdivision of ML. Artificial Neural Network has been the most predominantly used deep learning technique. While implementing the ANN technique, knowing whether the implementation could have been done in hardware or software becomes necessary, which is essential to achieve the expected performance. This paper gives a survey on the available methods in which the ANN architecture has been implemented to achieve efficient output with minimal resources. It is vital to study and analyze various strategies for implementation and their functionality. This paper has also explained the advantages and disadvantages of different implementation techniques that allow selecting the most appropriate hardware and respective methodology for optimizing the hardware. [ABSTRACT FROM AUTHOR]

Despite advancements in smart grid (SG) technology, effective load forecasting utilizing big data or large-scale datasets remains a complex task for energy management, planning, and control. The Saudi SGs, in alignment with the Saudi Vision 2030, have been envisioned as future electrical grids with a bidirectional flow of power and data. To that end, data analysis and predictive models can enhance Saudi SG planning and control via artificial intelligence (AI). Recently, many AI methods including deep learning (DL) algorithms for SG applications have been published in the literature and have shown superior time series predictions compared with conventional prediction models. Current load-prediction research for the Saudi grid focuses on identifying anticipated loads and consumptions, on utilizing limited historical data and the behavior of the load's consumption, and on conducting shallow forecasting models. However, little scientific proof on complex DL models or real-life application has been conducted by researchers; few articles have studied sophisticated large-scale prediction models for Saudi grids. This paper proposes hybrid DL methods to enhance the outcomes in Saudi SG load forecasting, to improve problem-relevant features, and to accurately predict complicated power consumption, with the goal of developing reliable forecasting models and of obtaining knowledge of the relationships between the various features and attributes in the Saudi SGs. The model in this paper utilizes a real dataset from the Jeddah and Medinah grids in Saudi Arabia for a full year, 2021, with a one-hour time resolution. A benchmark strategy using different conventional DL methods including artificial neural network, recurrent neural network (RNN), conventional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and different real datasets is used to verify the proposed models. The prediction results demonstrate the effectiveness of the proposed hybrid DL models, with CNN–GRU and CNN–RNN with NRMSE obtaining 1.4673% and 1.222% improvements, respectively, in load forecasting accuracy. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as 'bradykinesia', loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation. [ABSTRACT FROM AUTHOR]

This paper proposes a self-driving car model also called autonomous, robotic or driverless car, which is the one that operates and navigates using its intelligence. The primary objective of our prototype is to navigate safely, quickly, efficiently and comfortably through our virtual environment using computer vision. Detection of lanes, traffic cars, obstacles, signals have been performed. [ABSTRACT FROM AUTHOR]

Artificial neural network is revolutionizing business and everyday life, bringing us to the next level in artificial intelligence. It has a unique ability to extract meaning from complex data to find patterns and detect trends that are too convoluted for the human brain. This paper analyzes the artificial neural network impact on different computational organizations by using the innovative structure of bipolar complex spherical fuzzy relation which is any subset of the Cartesian product of two bipolar complex spherical fuzzy sets. This notion has a comprehensive structure that consists of membership grade, abstinence grade, and non-membership grade. Furthermore, various kinds of bipolar complex spherical fuzzy relation with suitable examples are given and some authentic results also have been proved. These newly defined structures are used to investigate the impact of artificial neural network work on a variety of organizations. The innovative framework is also compared with the existing structure in the field of fuzzy set theory to prove its superiority. [ABSTRACT FROM AUTHOR]

Heading and position control system of ships has remained a challenging control problem. It is a nonlinear multiple input multiple output system. Moreover, the dynamics of the system vary with operating as well as environmental conditions. Conventionally, simple Proportional Integral Derivative controller is used which has well known limitations. Other conventional control techniques have also been investigated but they require an accurate mathematical model of a ship. Unfortunately, accuracy of mathematical models is very difficult to achieve. During the past few decades computational intelligence techniques such as artificial neural networks have been very successful when an accurate mathematical model is not available. Therefore, in this paper, an artificial neural network controller is proposed for heading and position control system. For simulation purposes, a mathematical model with four effective thrusters have been chosen to test the performance of the proposed controller. The final closed loop system has been analysed and tested through simulation studies. The results are very encouraging. [ABSTRACT FROM AUTHOR]

Load forecasting (LF) is an essential factor in power system management. LF helps the utility maximize the utilization of power-generating plants and schedule them both reliably and economically. In this paper, a novel and hybrid forecasting method is proposed, combining a long short-term memory network (LSTM) and neural prophet (NP) through an artificial neural network. The paper aims to predict electric load for different time horizons with improved accuracy as well as consistency. The proposed model uses historical load data, weather data, and statistical features obtained from the historical data. Multiple case studies have been conducted with two different real-time data sets on three different types of load forecasting. The hybrid model is later compared with a few established methods of load forecasting found in the literature with different performance metrics: mean average percentage error (MAPE), root mean square error (RMSE), sum of square error (SSE), and regression coefficient (R). Moreover, a guideline with various attributes is provided for different types of load forecasting considering the applications of the proposed model. The results and comparisons from our test cases showed that the proposed hybrid model improved the forecasting accuracy for three different types of load forecasting over other forecasting techniques. [ABSTRACT FROM AUTHOR]

This paper focuses on developing a computationally economic lightweight artificial intelligence (AI) technology for smartphones. Until date, no commercial system is available on this technology. Thus the developed breakthrough technology can enhance the capability of users on the field for monitoring the agricultural vehicles (AgV)s health by analyzing the acoustic noise using smartphone's app. This paper can enable the user of AgVs to optimize their farming by management at edge devices: smartphones. Since smartphones use a small integrated computing unit with computational limited resources, thus lighter the system, more favorable to work on. Artificial neural network (ANN) is one of the most favorite AI techniques, but its lightweight architecture—attributed by the number of inputs and hidden layers and neurons—, is one of the most important issues in the context of smartphones. Under the framework of bi-level optimization, we aim to analyze the tournament selection operator based genetic algorithm with hybrid crossover operators, at level-I, to evolve ANN, at level-II to design the lightweight edge device enabled AI technique. The obtained results and numerical evaluative analysis indicate that the optimized design of the lightweight fault detection system responds well to the soundtracks received via microphones. We present the evaluation of the proposed technology on serial programming, parallel programming (PP), GPU programming, and GPU with PP. The results show that PP is enough efficient for the proposed technology and can save the cost of GPU for large scale implementation of the technology. [ABSTRACT FROM AUTHOR]

In recent years, order-reduction techniques based on artificial intelligence algorithms have become a topic of interest in the structural dynamics community. In this paper, we describe a new artificial intelligence technique based on the artificial neural network used to reduce a large interval system. Applied to reduce the degree of the polynomial numerator and denominator each separately, by allowing them to learn automatically from the original system, this machine learning phase allows the algorithm to improve over time and control performance of the approximation, maintaining as much as possible the stability of the dynamic system, and reducing errors between the original system and the reduced system that are presented as a very acceptable approximation, a comparison study is presented between existing works and the proposed technique, with the help of examples from literature. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has grown considerably in the present environment and acts as a subject still possessing substantial space for development, making most of its noteworthy advancements in the current era. While considering the chess game based on AI, the end-to-end pipeline was employed, in which the prior knowledge concerning chess is not essential, but in contrast, the efficient learning makes the method more efficient for making the optimal move. This paper proposes the Hybrid Fly-based artificial neural network (Hybridfly-ANN) model for attaining the maximum possible legal moves by rectifying the drawbacks of conventional position evaluation strategies. The Portable Game Notation (PGN) file acts as the input database comprising the details and the games played by the players over 1 month. The possible legal moves corresponding to the opponent's single move are evaluated through the proposed method and the mini-max algorithm is utilized for evaluating the best optimal move. The implication of the research relies on the proposed Hybrid Fly optimization algorithm that tunes the ANN classifier's weight optimally, leading to enhanced system performance by hybridizing the behavior of the Photinus and ephemera flies. Thus, the ANN's weights tuned with the proposed Hybrid Fly optimization's global best solution reduces the training loss thereby, improving the prediction accuracy. The effectiveness of the proposed method is examined in terms of the evaluation metrics which showed the average accuracy above 98% and minimal computation time of 180 secs. [ABSTRACT FROM AUTHOR]

Rock mechanical properties play a crucial role in fracturing design, wellbore stability and in situ stresses estimation. Conventionally, there are two ways to estimate Young's modulus, either by conducting compressional tests on core plug samples or by calculating it from well log parameters. The first method is costly, time-consuming and does not provide a continuous profile. In contrast, the second method provides a continuous profile, however, it requires the availability of acoustic velocities and usually gives estimations that differ from the experimental ones. In this paper, a different approach is proposed based on the drilling operational data such as weight on bit and penetration rate. To investigate this approach, two machine learning techniques were used, artificial neural network (ANN) and support vector machine (SVM). A total of 2288 data points were employed to develop the model, while another 1667 hidden data points were used later to validate the built models. These data cover different types of formations carbonate, sandstone and shale. The two methods used yielded a good match between the measured and predicted Young's modulus with correlation coefficients above 0.90, and average absolute percentage errors were less than 15%. For instance, the correlation coefficients for ANN ranged between 0.92 and 0.97 for the training and testing data, respectively. A new empirical correlation was developed based on the optimized ANN model that can be used with different datasets. According to these results, the estimation of elastic moduli from drilling parameters is promising and this approach could be investigated for other rock mechanical parameters. [ABSTRACT FROM AUTHOR]

Application of artificial neural network (ANN) models has been reported to solve variety of water resources and environmental related problems including prediction, forecasting and classification, over the last two decades. Though numerous research studies have witnessed the improved estimate of ANN models, the practical applications are sometimes limited. The black box nature of ANN models and their parameters hardly convey the physical meaning of catchment characteristics, which result in lack of transparency. In addition, it is perceived that the point prediction provided by ANN models does not explain any information about the prediction uncertainty, which reduce the reliability. Thus, there is an increasing consensus among researchers for developing methods to quantify the uncertainty of ANN models, and a comprehensive evaluation of uncertainty methods applied in ANN models is an emerging field that calls for further improvements. In this paper, methods used for quantifying the prediction uncertainty of ANN based hydrologic models are reviewed based on the research articles published from the year 2002 to 2015, which focused on modeling streamflow forecast/prediction. While the flood forecasting along with uncertainty quantification has been frequently reported in applications other than ANN in the literature, the uncertainty quantification in ANN model is a recent progress in the field, emerged from the year 2002. Based on the review, it is found that methods for best way of incorporating various aspects of uncertainty in ANN modeling require further investigation. Though model inputs, parameters and structure uncertainty are mainly considered as the source of uncertainty, information of their mutual interaction is still lacking while estimating the total prediction uncertainty. The network topology including number of layers, nodes, activation function and training algorithm has often been optimized for the model accuracy, however not in terms of model uncertainty. Finally, the effective use of various uncertainty evaluation indices should be encouraged for the meaningful quantification of uncertainty. This review article also discusses the effectiveness and drawbacks of each method and suggests recommendations for further improvement. [ABSTRACT FROM AUTHOR]

One of the most common pavement distresses in flexible pavement is rutting, which is mainly caused by heavy wheel load and various other factors. The prediction of rutting depth is important for safe travel and the long-term performance of pavements. Factors that are considered in this paper for the prediction of rut depth are Temperature, Equivalent Single Axle Load, Resilient modulus, and Thickness of hot mixed asphalt. The input data for all factors are collected from the Long-Term Pavement Performance Information Management System for the state of Texas. Regression analysis is performed for dependent and independent variables to obtain the empirical relationship. In various fields of civil engineering, artificial neural networks have recently been utilized to model the qualities and behavior of materials and to determine the complicated relationship between various properties. An Artificial Neural Network is used to develop a predictive model to predict the rutting depth. A total number of 70 observations were considered for the predictive model. A mathematical relation is developed and verified between rut depth and variable input data. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment modalities and prognostic outcomes. This paper targets oral pathologists, oral medicinists, and head and neck surgeons to provide them with a theoretical and conceptual foundation of artificial intelligence‐based diagnostic approaches, with a special focus on convolutional neural networks, the state‐of‐the‐art in artificial intelligence and deep learning. Methods: The authors conducted a literature review, and the convolutional neural network's conceptual foundations and functionality were illustrated based on a unique interdisciplinary point of view. Conclusion: The development of artificial intelligence‐based models and computer vision methods for pattern recognition in clinical and histopathological image analysis of head and neck cancer has the potential to aid diagnosis and prognostic prediction. [ABSTRACT FROM AUTHOR]

Affected by shooting angle and light intensity, shooting through transparent media may cause light reflections in an image and influence picture quality, which has a negative effect on the research of computer vision tasks. In this paper, we propose a Residual Attention Based Reflection Removal Network (RABRRN) to tackle the issue of single image reflection removal. We hold that reflection removal is essentially an image separation problem sensitive to both spatial and channel features. Therefore, we integrate spatial attention and channel attention into the model to enhance spatial and channel feature representation. For a more feasible solution to solve the problem of gradient disappearance in the iterative training of deep neural networks, the attention module is combined with a residual network to design a residual attention module so that the performance of reflection removal can be ameliorated. In addition, we establish a reflection image dataset named the SCAU Reflection Image Dataset (SCAU-RID), providing sufficient real training data. The experimental results show that the proposed method achieves a PSNR of 23.787 dB and an SSIM value of 0.885 from four benchmark datasets. Compared with the other most advanced methods, our method has only 18.524M parameters, but it obtains the best results from test datasets. [ABSTRACT FROM AUTHOR]

Developing accurate and interpretable models to forecast concrete's self-healing behavior is of interest to material engineers, scientists, and civil engineering contractors. Machine learning (ML) and artificial intelligence are powerful tools that allow constructing high-precision predictions, yet often considered "black box" methods due to their complexity. Those approaches are commonly used for the modeling of mechanical properties of concrete with exceptional accuracy; however, there are few studies dealing with the application of ML for the self-healing of cementitious materials. This paper proposes a pioneering study on the utilization of ML for predicting post-fire self-healing of concrete. A large database is constructed based on the literature studies. Twelve input variables are analyzed: w/c, age of concrete, amount of cement, fine aggregate, coarse aggregate, peak loading temperature, duration of peak loading temperature, cooling regime, duration of cooling, curing regime, duration of curing, and specimen volume. The output of the model is the compressive strength recovery, being one of the self-healing efficiency indicators. Four ML methods are optimized and compared based on their performance error: Support Vector Machines (SVM), Regression Trees (RT), Artificial Neural Networks (ANN), and Ensemble of Regression Trees (ET). Monte Carlo analysis is conducted to verify the stability of the selected model. All ML approaches demonstrate satisfying precision, twice as good as linear regression. The ET model is found to be the most optimal with the highest prediction accuracy and sufficient robustness. Model interpretation is performed using Partial Dependence Plots and Individual Conditional Expectation Plots. Temperature, curing regime, and amounts of aggregates are identified as the most significant predictors. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) refers to the development of computer systems capable of performing tasks that normally require human intelligence. AI and its subsets, machine learning and deep learning, have been incorporated into several aspects of dentistry including pediatric dentistry. However, there is a lack of documentation and analysis of the current applications of AI in pediatric dentistry. Aim: The aim of this systematic review was to evaluate the effectiveness of AI as a diagnostic tool in pediatric dentistry. Materials and Methods: The literature for this paper was identified by performing a thorough search in electronic databases such as PubMed, Google Scholar, and Cochrane Library from the years 2011 to 2021. The following keywords and Boolean operators were used: AI AND pediatric dentistry, artificial neural networks AND pediatric dentistry, convolutional neural networks AND pediatric dentistry, and machine learning AND pediatric dentistry. After applying appropriate inclusion and exclusion criteria, 13 articles were selected, fully read, and systematically analyzed as per a specific research question. Results: Among the 13 selected articles, it was found that AI is a useful tool for dental diagnosis/classification, cephalometric landmark identification, identification of early childhood caries patterns, chronological age assessment in children, assessment of facial attractiveness in cleft patients, dental plaque detection, and oral health education. Conclusion: The selected articles indicate that AI is an effective diagnostic tool and has the potential for assisting several aspects of pediatric dentistry. However, further studies are required to assess the clinical effectiveness of these AI models. [ABSTRACT FROM AUTHOR]

Permeability is an important parameter for oil and gas reservoir characterization. Permeability can be traditionally determined by well testing and core analysis. These conventional methods are very expensive and time-consuming. Permeability estimation in heterogeneous carbonate reservoirs is a challenge task to be handled accurately. Many researches tried to relate permeability and reservoir properties using complex mathematical equations which resulted in inaccurate estimation of the formation permeability values. Permeability prediction based on well logs using artificial intelligent techniques was presented by many authors. They used several wire-line logs such as gamma ray, neutron porosity, bulk density, resistivity, sonic, spontaneous potential, hole size, depths, and other logs. The objective of this paper is to develop an artificial neural network (ANN) model that can be used to predict the permeability of heterogeneous reservoir based on three logs only, namely resistivity, bulk density, and neutron porosity. In addition to the ANN model, in this paper and for the first time a mathematical equation from the ANN model will be extracted that can be used for permeability prediction for any data set without the need for the ANN model. Also, in this study and for the first time we introduced a new term which is the mobility index that can be used effectively in the permeability prediction. Mobility index term is derived from the mobile oil saturation that occurred due to the drilling fluid filtrate invasion. The obtained results showed that ANN model gave a comparable results with support vector machine and adaptive neuro-fuzzy inference system model. The developed mathematical equation from ANN model can be used to estimate the permeability for heterogamous carbonate reservoir based only on three parameters: bulk density, neutron porosity, and mobility index. Actual core data points (1223 points) with the three logs were used to train (857 data points, 70% of the data) and test the model for unseen data (366 data points, 30% of the data). The correlation coefficient for training and testing was 0.95, and the root-mean-square error was 0.28. The developed mathematical equation will help the engineers to save time and predict the permeability with a high accuracy using inexpensive technique. Introducing the new parameter, mobility index, in the prediction process greatly improved the permeability prediction from the log data compared to the actual measured data. [ABSTRACT FROM AUTHOR]

Artificial intelligence is increasingly used in various branches of engineering. In this article, artificial neural networks are used to predict the crush resistance of corrugated packaging. Among the analysed packages were boxes with ventilation openings, packages with perforations and typical flap boxes, which make the proposed estimation method very universal. Typical shallow feedforward networks were used, which are perfect for regression problems, mainly when the set of input and output parameters is small, so no complicated architecture or advanced learning techniques are required. The input parameters of the neural networks are selected so as to take into account not only the material used for the production of the packaging but also the dimensions of the box and the impact of ventilation holes and perforations on the load capacity of individual walls of the packaging. In order to maximize the effectiveness of neural network training process, the group of input parameters was changed so as to eliminate those to which the sensitivity of the model was the lowest. This allowed the selection of the optimal configuration of training pairs for which the estimation error was on the acceptable level. Finally, models of neural networks were selected, for which the training and testing error did not exceed 10%. The demonstrated effectiveness allows us to conclude that the proposed set of universal input parameters is suitable for efficient training of a single neural network model capable of predicting the compressive strength of various types of corrugated packaging. [ABSTRACT FROM AUTHOR]

One of the most challenging tasks in project management is estimating the duration of a project. The unknowns that accompany projects, the different risks, the uniqueness of each project, and the differences between organizations' culture and management techniques, hinder the ability to build one project duration prediction tool that can fit all types of projects and organizations. When machine learning (ML) techniques are used for project duration prediction, the challenge is even greater, as each organization has a different dataset structure, different features, and different quality of data. This hinders the ability to create one ML model that fits all types of organizations. This paper presents a new dynamic ML tool for improving the prediction accuracy of project duration. The tool is based on an artificial neural network (ANN) which is automatically adapted and optimized to different types of prediction methods and different datasets. The tool trains the ANN model multiple times with different architectures and uses a genetic algorithm to eventually choose the architecture which gives the most accurate prediction results. The validation process of the prediction accuracy is performed by using real-life project datasets supplied by two different organizations which have different project management approaches, different project types, and different project features. The results show that the proposed tool significantly improved the prediction accuracy for both organizations despite the major differences in the size, type, and structure of their datasets. [ABSTRACT FROM AUTHOR]

Purpose of the article: To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings: Artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction. Conclusions: It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise. Conclusions resulting from the paper are beneficial for further research. [ABSTRACT FROM AUTHOR]

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