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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]

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]

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]

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]

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]

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]

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]

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]

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]

In recent time, heart disease has become common leading to mortality of many individuals. Hence, early and accurate prediction of this disease is vital to reduce death rate and enhance people's lives. Concurrently, Artificial Intelligence has gained more attention at present as it permits deeper understanding of the healthcare data thereby providing accurate prediction results. This efficient prediction will solve complicated queries regarding heart diseases and hence assists clinical practitioners to adopt smart medical decisions. Hence, this study intends to predict heart disease with high accuracy by proposing an improved feature selection and enhanced classification approach. The paper employs Grey-wolf with Firefly algorithm for effective feature selection and using Differential Evolution Algorithm for tuning the hyper parameters of Artificial Neural Network (ANN). Hence, it is named as Grey Wolf Firefly algorithm with Differential Evolution (GF-DE) for better classification of the selected features. This proposed classification model trains the neural network to obtain optimal weights and tunes huge number of hyper parameters in an efficiently. To prove this, the proposed system is comparatively analysed with existing methods in terms of performance metrics like accuracy, precision, recall and F1 score for Cleveland and Statlog dataset. In addition, statistical analysis is also undertaken to analyse the significance of proposed system. Outcomes revealed the efficiency of proposed method which makes it highly suitable for heart disease prediction in an efficient manner. [ABSTRACT FROM AUTHOR]

The computer vision community has proposed numerous formulations for object description based on human perceptivity and vast knowledge of the problem domain. In order to reduce human intervention, deep learning techniques are widely used to learn features automatically. However, they lack the property of explainability; that is, a human being understands the meaning of all the parts that make up the calculation of a feature. In this paper, we empirically show how Artificial Intelligence can automatically discover explainable formulations of a topological feature for binary images called the Euler characteristic. The training images are represented by bit-quad patterns, and a single-layer artificial neural network automatically learns the optimal combination of bit-quads to provide valid formulations to correctly calculate the Euler characteristic. We report the results on binary images of different complexities and sizes and compare them with state-of-the-art machine learning algorithms. Finally, we present 14 new equations to calculate the Euler number never reported in literature. [ABSTRACT FROM AUTHOR]

The road layer modulus backcalculation based on the road deflection basin obtained by the Falling Weight Deflectometer is a key issue in road engineering. Traditional Falling Weight Deflectometer backcalculation method based on Artificial Neural Network has the disadvantages of poor generalisation ability and low convergence accuracy in terms of the dynamic modulus. In this paper, a hybrid neural network structure, combined with Residual Neural Network, Recurrent Neural Network and Wide & Deep (Abbreviated as ResRNN–W&D) structure, was proposed for Falling Weight Deflectometer deflection basin backcalculation. A case study using the United States Long-Term Pavement Performance database verified that the ResRNN–W&D structure can train Falling Weight Deflectometer data on multiple roads together and achieve fast and high-precision convergence, thereby greatly improving the availability of the multi-source heterogeneous data. Moreover, two transfer learning methods for the ResRNN–W&D structure were proposed to improve the divergence issue. It was found that the ResRNN–W&D structure has stronger generalisation ability than traditional Artificial Neural Network. [ABSTRACT FROM AUTHOR]

Bridge strikes by over-height vehicles or ships are critical sudden events. Due to their unpredictable nature, many events go unnoticed or unreported, but they can induce structural failures or hidden damage that accelerates the bridge's long-term degradation. Therefore, always-on monitoring is essential for deployed systems to enhance bridge safety through the reliable detection of such events and the rapid assessment of bridge conditions. Traditional bridge monitoring systems using wired sensors are too expensive for widespread implementation, mainly due to their significant installation cost. In this paper, an intelligent wireless monitoring system is developed as a cost-effective solution. It employs ultralow-power, event-triggered wireless sensor prototypes, which enables on-demand, high-fidelity sensing without missing unpredictable impact events. Furthermore, the proposed system adopts a smart artificial intelligence (AI)-based framework for rapid bridge assessment by utilizing artificial neural networks. Specifically, it can identify the impact location and estimate the peak force and impulse of impacts. The obtained impact information is used to provide early estimation of bridge conditions, allowing the bridge engineers to prioritize resource allocation for the timely inspection of the more severe impacts. The performance of the proposed monitoring system is demonstrated through a full-scale field test. The test results show that the developed system can capture the onset of bridge impacts, provide high-quality synchronized data, and offer a rapid damage assessment of bridges under impact events, achieving the error of around 2 m in impact localization, 1 kN for peak force estimation, and 0.01 kN·s for impulse estimation. Long-term deployment is planned in the future to demonstrate its reliability for real-life impact events. [ABSTRACT FROM AUTHOR]

In this paper, we study a new model that represents the symmetric connection between capacitance–voltage and Schottky diode. This model has a symmetrical shape towards the horizontal direction. In recent times, works conducted on artificial neural network structure, which is one of the greatest actual artificial intelligence apparatuses used in various fields, stated that artificial neural networks are apparatuses that proposal very high forecast performance by the side of conventional structures. In the current investigation, an artificial neural network structure has been generated to guess the capacitance voltage productions of the Schottky diode with organic polymer edge, contingent on the frequency with a symmetrical shape. Of the dataset, 130 were grouped for training, 28 for validation, and 28 for testing. In order to evaluate the effect of the number of neurons on the prediction accuracy, three different models with different neuron numbers have been developed. This study, in which an artificial neural network model, although well-trained, could not predict the output values correctly, is a first in the literature. With this aspect, the study can be considered as a pioneering study that brings a novelty to the literature. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) can track the brain waves which contain the neural activity of the brain. EEG signals help to understand the physiological and functional details and activities of the brain. In the era of Artificial Intelligence (AI), machine learning algorithms were useful in brain disorder detection and classification. Recently, a rapid increase in using Deep Learning (DL) methods in various applications in EEG signals not only helps in the detection of brain disorders but also facilitates the recognition of human emotions and various psycho-neuro disorders. In order to offer a beneficial and broad perspective, a detailed survey on the application of deep learning architecture in EEG signals has been carried out in this paper. Different deep learning methods, using varied architecture in EEG signal analysis, offer an understanding to develop the next level of AI-based systems. This review will provide information about how deep learning methods are used in EEG signals and the challenges and limitations of each method in classification; moreover making it helpful for those who are exploring EEG signals using DL algorithms. [ABSTRACT FROM AUTHOR]

• Artificial Intelligence to predict two-phase frictional pressure gradient. • Collection of a pressure drop database for a wide range of operating conditions. • Effect of the output and input type and numerosity on prediction accuracy. • Good accuracy with non-dimensional output and mixed input values. • Conventional methods always preserve physical trend with respect to neural networks. This paper presents a discussion on several possibilities to predict the frictional pressure gradient during two-phase flow, with both the application of artificial intelligence and the implementation of conventional correlations and predictive methods. To this purpose, a huge database of approximately 8000 data points has been collected from 49 sources available in scientific literature, including 23 working fluids and the following ranges of parameters: mass fluxes from 32.7 to 2000 kg/m2s, saturation temperatures from -190°C to +120°C (reduced pressures from 0.021 to 0.780), tube diameters from 0.5 to 14.0 mm. This consolidated database has been used to train several artificial neural networks (ANNs), by using only two hidden layers (shallow neural networks) and evaluating the effect of: training and testing datasets choice (either test data included or outside the training domain), the number of neurons for each hidden layer (from 1 to 50), the type of output (either dimensional or non-dimensional), the type and number (from 1 to 22) of input parameters. The best results (MAPE of 16.8% and 88% of data within ±30%) have been obtained by using the liquid-only two-phase multiplier Φ L O 2 as non-dimensional output and 12 mixed input parameters. Compared to the statistics of well-established literature correlations for frictional pressure drop (best MAPE of 22% and 73% of data points predicted within a ±30% error range, provided by Mauro et al. mechanistic method), the ANN demonstrates therefore a higher general accuracy. However, the use of Artificial Neural Networks does not guarantee a physical trend, which is instead preserved with conventional prediction methods. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry. [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]

Resource Planning Optimization (RPO) is a common task that many companies need to face to get several benefits, like budget improvements and run-time analyses. However, even if it is often solved by using several software products and tools, the great success and validity of the Artificial Intelligence-based approaches, in many research fields, represent a huge opportunity to explore alternative solutions for solving optimization problems. To this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance. [ABSTRACT FROM AUTHOR]

This paper demonstrates the validation and prediction of the wall temperature of a building exposed to a composite climate. Two artificial intelligence models, such as multiple linear regression and artificial neural networks, have been used to predict. The wall temperature has been predicted mainly based on the parameters like ambient temperature, wind speed and relative humidity in all four directions of the buildings. Three statistical analyses were used to validate the model's outcome: R-Squared, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). In terms of train and control data, the two models yield comparable findings. The artificial neural network model has more adaptability since it was able to adjust to unexpected changes in the input data, according to a comparison of the applied mathematics of each model. The regression model was used for this investigation because it gives constant estimate values for the factors, though the neural model isn't numerically characterized. The review infers that the neural model must be utilized as an additional way to predict the wall temperature. This study can plan and adapt buildings for future work, considering the most critical climatic conditions. It also assists architects and engineers in determining the appropriate insulation for the building envelope, which improves its thermal performance. [ABSTRACT FROM AUTHOR]

Date fruit (Phoenix dactylifera L.) is known for its diversity in terms of genotypes that differ in shape, color, texture, taste, and other physical and sensory attributes, in addition to biochemical compounds. This richness generates complex, long, and tedious work at the scale of farm as well as in handling, processing, and storage facilities to prepare standardized, and homogenous final products that deal with consumer preferences and needs. To alleviate this, an increased importance and particular trend were observed recently for the use of artificial intelligence (AI) techniques and computer vision technology since they are considered as accurate, objective, non-destructive, fast, and efficient emerging technologies. In this context, this manuscript provides comprehensive insights into key applications of AI techniques and computer vision technology, using different classification models and algorithms developed on the basis (for example) of neural network classifiers, such as convolutional and artificial neural network, on harvest and postharvest assessment of date fruit. Thus, the importance of date fruit dataset used in the AI domain was discussed with link to all accessible datasets in the field of date palm. In addition, available and relevant AI tools and approaches applied to the postharvest of date fruit from maturity distinguishing until storage through other key handling operations, sorting, grading, etc., were detailed in the five sections of this paper. In terms of conclusion, this review showed that these smart technologies can play a substantial role and offer new opportunities to improve the whole value chain of date palm sector. • Harvesting and post-harvest handling impact quality and storability of dates. • These operations are tedious and subjective leading to quantitative/qualitative losses. • Artificial intelligence (AI) techniques are emergent technologies that can reduce these losses. • AI techniques are more accurate, fast, non-destructive and objective. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is nowadays an upcoming technology. It is a practice of simulating human intelligence for varied applications. When compared with the standard practices, AI is developing at a rapid rate. AI has proved its worth in several areas such as agriculture, automobile industry, banking and finance, space exploration, artificial creativity, etc. Owing to the efficiency, speed, and independence from human operations, AI is now entering the wastewater treatment sector. This technology has been used for monitoring the performance of the water treatment plants in terms of efficiency parameters, Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) determination, elimination of nitrogen and sulphur, prediction of turbidity and hardness, uptake of contaminants, etc., in the wastewater sector. Artificial Neural Networks (ANN), Fuzzy Logic Algorithms (FL), and Genetic Algorithms (GA) are the basic three models under AI predominantly used in the wastewater sector. Studies reveal that the determination coefficient values of 0.99 can be attained for COD, BOD, heavy metals and organics removal using ANN and hybrid intelligent systems. This review paper describes research with all the possible models of AI utilized in the water treatment which have enhanced the pollutant removal percentage accuracy of ranging from 84% to 90% and provided viewpoint on future directions of novel research, in the field with due focus on pollution remediation, cost effectiveness, energy economy, and water management. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) is widely used for oil-immersed power transformers as a conventional fault diagnosis tool. However, interpretation criteria from DGA assessment often depends on empirical discrimination from a specialist, which can render unreliable or ambiguous diagnoses. Intelligent fault classification algorithms can be implemented to conquer uncertainty in conventional methods, and which require feature learning of transformer condition information data rather than expert experience. In this paper, a Gaussian process multi-classification (GPMC) method is proposed, which uses multiclass recognition with a Gaussian process (GP) and renders an output with a probabilistic interpretation rather than a deterministic guess. The method is investigated using large-scale DGA field datasets to improve diagnostic accuracy and presents reliable incipient fault diagnosis ability. A kernel-based learning algorithm and versatile artificial intelligence (AI) methods, (support vector machine (SVM), artificial neural network (ANN), K-nearest neighbors (KNN), decision tree and logistic regression (LR)) have been used to obtain comparative classification accuracy in comparison to the proposed method. Additional comparison is demonstrated between conventional DGA and AI methods. The effectiveness and robustness of the proposed GPMC method are confirmed by experimental accuracy >95%, which illustrates that the proposed method is able to provide superior and reliable diagnoses for operational transformer faults. [ABSTRACT FROM AUTHOR]

This paper presents an automation process is a need in the agricultural industry specifically chili crops, that implemented image processing techniques and classification of chili crops usually based on their color, shape, and texture. The goal of this study was to develop a portable sorting machine that will be able to segregate chili based on their color by using Artificial Neural Network (ANN) and to analyze the performance by using the Plot Confusion method. A sample of ten green chili images and ten red chili images was trained by using Learning Algorithm in MATLAB program that included a feature extraction process and tested by comparing the performance with a larger dataset, which are 40 samples of chili images. The trained network from 20 samples produced an overall accuracy of 80 percent and above, while the trained network from 40 samples produced an overall accuracy of 85 percent. These results indicate the importance of further study as the design of the smart sorting machine was general enough to be used in the agricultural industry that requires a high volume of chili crops and with other differentiating features to be processed at the same time. Improvements can be made to the sorting system but will come at a higher price. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm to combine two or more techniques of maximum power point tracking (MPPT) to increase the output dc power and the efficiency of photovoltaic (PV) arrays. Different MPPT methods have dissimilar responses for the same environmental circumstances. The combination of multiple methods has the advantage of ever acquiring maximum power as the environmental conditions change. The proposed algorithm is used to enhance the selection of the most suitable duty cycle of the combined methods, based on the power-voltage characteristics, to get the best tracking response. Multiple classical and/or artificial intelligence (AI)-based MPPT methods can be combined based on this selection algorithm. To demonstrate its effectiveness, two examples are illustrated. The first one is the combination of two classical MPPT methods, which are the incremental conductance (INC) and the perturb-and-observe (P&O). The second example is to combine two AI-based MPPT, which are the artificial neural network (ANN) and the fuzzy logic control (FLC). The simulation results of the application of the proposed algorithm to a grid-connected PV system model justify its capability to acquire better static and dynamic responses than the responses of individual MPPT methods. [ABSTRACT FROM AUTHOR]

Abstract Artificial neural network has generally been used for a quantity of tasks such as classification, prediction, clustering and association analysis in different application fields. To the best of our knowledge, there are few researches on breakthrough curve used artificial neural network. In this paper, an artificial neural network model is established for breakthrough curves prediction in relation to a ternary components gas with a two-layered adsorbent bed piled up with activated carbon (AC) and zeolite, and an optimization is concluded by the artificial neural network. The performance data which acquired by Aspen model has been utilized for training artificial neural network (ANN) model. The ANN model trained has great competence for making prediction of hydrogen purification performance of PSA cycle with impressive speed and rational accuracy. On the strength of the ANN model, we implemented an optimization for seeking first-rank PSA cycle parameters. The optimization is concentrated on the effect of inlet flow rate, pressure and layer ratio of activated carbon height to zeolite height. Furthermore, this paper shows that the PSA cycle's optimal operation parameters can be obtained by use of ANN model and optimization algorithm, the ANN model has been trained according to the data generated by Aspen adsorption model. [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]

For an oil well to be said to have been successfully and conclusively drilled, the drilling fluid lies at the heart of the solution. Therefore, the guarantee to solving issues in oil well drilling is to contrive an optimal drilling fluid. However, there is usually a complex interplay of factors involved during drilling fluid formulation, property determination, its performance in the well and its relationship with other wellbore drilling parameters. This is so because drilling muds exhibit time dependent properties. This time dependency is the direct product of the synergy among the various active additives that make up the mud and the characteristic of each additive especially at downhole conditions where the effects of temperature and pressure are well pronounced. These additives are more often than not diverse in size, chemical activity, density and surface energy. Deriving knowledge from the data from these parameters in order to develop a functional relationship between them is a challenging task requiring advanced modelling techniques as well as human intuition and experience. The dependence on human intuition and on the experiential knowledge of professional mud engineers lays bare the shortcomings of traditional mud design techniques. Artificial intelligence techniques have been shown to alleviate this challenge. Exploiting the abundant literature on the various applications of artificial intelligence in oil and gas operations, several works that show how and what artificial intelligence techniques are used in the drilling fluid industry, and what have been achieved due to their use have been selected. In this paper, a review of existing artificial intelligence techniques and their applications in drilling fluid engineering is given. This paper also dug up and analyzed the strengths and pitfalls of each artificial intelligence technique. The examination of the strengths and deficiencies was done using the following virtues as the basic criteria: robustness against noise, self-organization, generalization ability, data volume requirements and the convergence speed. The artificial intelligence techniques presented in this paper include: artificial neural networks (ANNs), fuzzy logic, support vector machines (SVM), hybrid intelligent systems (HIS), genetic algorithms (GA), case based reasoning (CBR) and particle swarm algorithm (PSA). An overview of the applications of classical artificial intelligence in drilling fluid engineering is also presented. From the review, it was gathered that the ANN technique is the most widely applied in drilling fluid engineering accounting for over 54% of the papers reviewed; while lost circulation problem was the most predicted well problem related to drilling fluids accounting for over 17% of the mud problems predicted. It was also observed that a blend of AI techniques performed better than when each one of the AI techniques was used singly. Finally, judging the AI techniques on the criteria mentioned above, ANN was found to meet all the listed criteria except for its slow speed of convergence while ANN, GA, SVM and fuzzy logic were all found to be robust against noise. [ABSTRACT FROM AUTHOR]

Void fraction plays a vital role in diverse industries like oil, petrochemical, etc., which involve a wide range of fluids, including two-phase and three-phase fluids. Various procedures are used for the measurement of the void fraction, with one of the most popular being capacitance-based sensors. The characteristics of the fluid inside the pipe affect the output of this type of sensor, and every property, such as temperature, pressure, and density, plays a role. This paper presents the use of an Artificial Neural Network (ANN) and a combined capacitance-based sensor to measure the void fraction of a two-phase air-water homogeneous fluid. The aim is to develop a system that can predict void fraction independent of temperature and pressure changes. To achieve this, the COMSOL Multiphysics software was used to design and simulate two widely used sensors, concave and ring, to create a combined capacitance sensor. Simulations were conducted for the implemented sensor in different temperature ranges (275–370 K) and pressure ranges (1–500 Bar). After a large number of simulations and producing 3780 data from the combined sensor, they were used as inputs to train the proposed MLP ANN network. The presented model provides a new metering system that can accurately estimate the amount of the void fraction of a two-phase air-water homogeneous fluid independent of temperature and pressure changes and had a low error which means the MAE is equal to 4.868. • A combined capacitive sensor and ANN were used to measure the void fraction. • This system can measure void fraction independent of temperature and pressure. • COMSOL was used to design and simulate the combined capacitive sensor. • After a large number of simulations, they were used as inputs of MLP network. • The MAE of presented system is equal to 4.868 which is a low error. [ABSTRACT FROM AUTHOR]

Aim: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) researchers created new techniques, developed and applied them to solve engineering problems since two decades. Although lots of AI techniques and approaches are available in mechanical engineering, there isn’t any survey aiming to review the existing works, systems and applications in the field of fracture mechanics. In this paper, the state of the art of five AI methods which are used in the field of fracture mechanics, is surveyed. This review is performed from the technical point of view on particular applications of artificial neural networks, Bayesian networks, genetic algorithms, fuzzy logic and case-based reasoning. After an overview of AI methods, sub-domains of engineering fracture mechanics with respect to the fault and failure analysis are described. The existing works from 1990 to 2016 are analysed and discussed in four categories as sub-domains of fracture mechanics: (a) failure mode and failure mechanism identification, (b) damage and failure detection and diagnosis, (c) fault and error detection, diagnosis and (d) mechanical fracture and fracture parameters. We analyse literature based on a classification of these five AI methods in order to highlight their main concepts and explain how they are applied in these sub-domains of fracture mechanics. Our analysis and discussion in this paper shows the advantages, limitations and research gaps in this field. Finally, perspectives and future research directions are outlined. [ABSTRACT FROM AUTHOR]

This paper proposes the usage of an Artificial neural network (ANN) to predict the values of the residuary resistance per unit weight of displacement from the variables describing ship’s dimensions. For this purpose, a Multilayer perceptron (MLP) regressor ANN is used, with the grid search technique being applied to determine the appropriate properties of the model. After the model training, its quality is determined using R² value and a Bland-Altman (BA) graph which shows a majority of values predicted falling within the 95% confidence interval. The best model has four hidden layers with ten, twenty, twenty and ten nodes respectively, uses a relu activation function with a constant learning rate of 0.01 and the regularization parameter L2 value of 0.001. The achieved model shows a high regression quality, lacking precision in the higher value range due to the lack of data. [ABSTRACT FROM AUTHOR]

Simulating pulse propagation within fiber lasers is a difficult and computationally demanding task, which limits the ability to accurately capture their complex nonlinear dynamics. This paper leverages artificial intelligence (AI) and artificial neural networks (ANN) to predict the output pulse shape parameters for a ring cavity fiber laser, thus avoiding the need for traditional complex numerical calculations and reducing the overall computational cost. This study focuses on a dissipative soliton resonance (DSR) ring cavity fiber laser, incorporating a photonic crystal fiber (PCF) and mode locked by a real saturable absorber. The integration of the PCF into the laser cavity design enhances the control of both dispersion and nonlinearity, allowing for a greater range of output pulse shapes and parameters. The proposed approach demonstrates the effectiveness of artificial neural networks in predicting pulse parameters, and could pave the way for more efficient design and optimization of fiber lasers. The results of the study show that the ANN model can accurately predict the output pulse shape parameters for the DSR ring cavity fiber laser, and thus offers a promising tool for future research in the field. [Display omitted] • Fiber laser design relies on simulating pulse propagation. • The nonlinear Schrödinger Equation models pulse propagation. • Classical integration of the equation is costly. • Neural networks predict output pulse shape without integration. • Neural networks provide 11,000x faster simulation than classical integration. [ABSTRACT FROM AUTHOR]

Bearing is an important component of almost every mechanical system used in industrial environment. Hence the defect in bearing must be detected in advance to avoid catastrophic failure. This paper aims to diagnose the defect in bearing automatically using machine intelligence. A condition monitoring setup is designed for analyzing the defects in outer race, inner race and rolling element of bearing. MATLAB is used for feature extraction and neural network is used for diagnosis. It is found that the amplitude at defect frequencies may not always clearly indicate the increment; hence statistical analysis of bearing signature is a better alternative. The work presents an experimental investigation carried out on an experimental set-up for the study of bearing fault at same angular speed and load. This paper proposes an approach of damage detection in which defects in bearing are accurately analysed using vibration signal and neural network. [ABSTRACT FROM AUTHOR]

In open channels, free overfall can be used as a flow-measuring device in various flow regimes for different shapes of channels. The aim of this paper is to accurately estimate the discharge in rectangular channels by applying four soft computing models [i.e., artificial neural network (ANN), gene expression programming (GEP), multivariate adaptive regression spline (MARS) and M5 tree model]. The variables including brink depth, roughness coefficient, channel width and channel bed slope taken from earlier published works were used as inputs for the models. Data were divided according to three different splitting scenarios, 50%–50%, 60%–40% and 75%–25%, for training-testing phases to obtain more robust evaluation of the models. The computed discharges were then compared with experimental results. In order to evaluate the accuracy of discharge estimations by ANN, GEP, MARS and M5 tree models, six statistical measures including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe (NS), Willmott index (WI) and Legates and McCabe index (LMI) have been applied. For different training-testing scenarios, the performance of the ANN model is better than the other methods. Regarding 50%–50% training-testing scenario, ANN has the best accuracy with R, RMSE, MAE, NS, WI and LMI of 0.994, 0.004 m3/s/m, 0.002 m3/s/m, 0.986, 0.999 and 0.909 by considering all inputs. • Soft computing models are used in predicting discharge with free overfall. • y e , n, b and S 0 variables are used as inputs of models. • Several training-testing scenarios were used for modeling the discharge. • The ANN model provides the best estimate. [ABSTRACT FROM AUTHOR]

In this paper, intelligent technology of combined low field NMR (LF-NMR) and back propagation artificial neural network (BP-ANN) was used to monitor moisture content in carrot during microwave vacuum drying. The relationship between different drying powers (200, 300, and 400 W) and NMR signals (A21, A22, A23, and Atotal) was investigated. Results show that as the drying time elapsed, the NMR signals of Atotal and A23 decrease all drying conditions, A21 and A22 tend to increase at high moisture content and then decrease, which is consistent with the state of water while changes during drying. NMR signals can be used as indicators for online monitoring of moisture and control of the drying process. With NMR signals as input variables, a BP-ANN model was built optimized by transfer function, training function, and the number of neurons to model the moisture content (output). Compared with a linear regression model and multiple linear regression model, the BP-ANN model with the topology of 4-25-1, transfer function of tansig and purelin, and training function of trainlm outperformed the fitting performance and accuracy. This shows that the combined approach of utilizing LF-NMR and BP-ANN has great potential in intelligent online monitoring and control applications for carrot drying. [ABSTRACT FROM AUTHOR]

Abstract In this paper, numerous aero-hydro-servo-elastic coupled simulations are carried out in time-domain to observe the performance of the real-time thrust acting on the rotor of the OC3-Hywind offshore floating wind turbine. And the studying focuses on investigating the correlation between inputs (surge motion, pitch motion, wind conditions, etc.) and the targeted output (rotor thrust) in the time domain. Besides, artificial intelligence (AI) techniques are used to estimate a prediction model of real-time thrust based on the data from simulations. To predict the thrust, data for four comparative coupled environmental conditions are considered, by which the effect of turbulence and wave spectrum on the thrust force is also investigated. Moreover, a series of simulations of frequency-increasing regular wave conditions and speed-increasing wind conditions are carried out to observe their effect on the real-time rotor thrust. Additionally, the impact of the pitch and surge RAOs of the floating foundation and the wind velocity are quantitatively studied. It reveals that the high-frequency response of thrust is dominated by wave change, whereas low-frequency response is dominated by wind change. Besides, one simulation model of the thrust acting on the rotor is estimated regarding high-frequency and low-frequency response separately to account the dominating influence. [ABSTRACT FROM AUTHOR]

The Highway Safety Improvement Program (HSIP) requires state highway agencies to improve their roadway network safety through a ‘strategic’ and ‘data-driven’ approach. As part of HSIP, the Federal Highway Administration mandates that states develop a Pavement Friction Management System to reduce the rate of fatal and injury-causing crashes and prioritise their safety improvement projects based on the crash risk. This paper aims to predict the rate of wet and dry vehicle crashes based on surface friction, traffic level, and speed limit using an artificial neural network (ANN). Three learning algorithms, Levenberg-Marquardt, conjugate gradient, and resilient back-propagation, were examined to train the network. Levenberg-Marquardt produced the best precision and was used to develop the model. The results of the study suggest that the ANN model can reliably predict the rate of crashes. The prediction model can be used as a scale to prioritise safety improvement projects based on the rate of fatal and injury-causing crashes. [ABSTRACT FROM AUTHOR]

This paper proposes the expert system for accurate fault detection of bearing. The study is based upon advanced signal processing method as wavelet transform and artificial intelligence technique as artificial neural network (ANN) and K-nearest neighbor (KNN), for fault classification of bearing. An adaptive algorithm based on wavelet transform is used to extract the fault classifying features of the bearing from time domain signal. These features have been used as inputs to proposed ANN models and the same features have also been used for KNN. Dedicated experimental setup was used to perform the test upon the bearing. Single data set for four fault conditions of bearing is collected to train ANN and KNN. The processed and normalized data was trained by using backpropagation multilayer perceptron neural network. The results obtained from ANN are compared with KNN, ANN results proved to be highly effective for classification of multiple faults. [ABSTRACT FROM AUTHOR]

Purpose The aim of this paper is to redesign of morphing unmanned aerial vehicle (UAV) using neural network for simultaneous improvement of roll stability coefficient and maximum lift/drag ratio.Design/methodology/approach Redesign of a morphing our UAV manufactured in Faculty of Aeronautics and Astronautics, Erciyes University is performed with using artificial intelligence techniques. For this purpose, an objective function based on artificial neural network (ANN) is obtained to get optimum values of roll stability coefficient (Clβ) and maximum lift/drag ratio (Emax). The aim here is to save time and obtain satisfactory errors in the optimization process in which the ANN trained with the selected data is used as the objective function. First, dihedral angle (φ) and taper ratio (λ) are selected as input parameters, C*lβ and Emax are selected as output parameters for ANN. Then, ANN is trained with selected input and output data sets. Training of the ANN is possible by adjusting ANN weights. Here, ANN weights are adjusted with artificial bee colony (ABC) algorithm. After adjusting process, the objective function based on ANN is optimized with ABC algorithm to get better Clβ and Emax, i.e. the ABC algorithm is used for two different purposes.Findings By using artificial intelligence methods for redesigning of morphing UAV, the objective function consisting of C*lβ and Emax is maximized.Research limitations/implications It takes quite a long time for Emax data to be obtained realistically by using the computational fluid dynamics approach.Practical implications Neural network incorporation with the optimization method idea is beneficial for improving Clβ and Emax. By using this approach, low cost, time saving and practicality in applications are achieved.Social implications This method based on artificial intelligence methods can be useful for better aircraft design and production.Originality/value It is creating a novel method in order to redesign of morphing UAV and improving UAV performance. [ABSTRACT FROM AUTHOR]

This paper deals with the implementation of an Artificial Neural Network on reconfigurable hardware. The authors developed an application for character recognition using a feedforward backpropagation neural network, developed on an FPGA. In order to carry out such implementation the computational model of the neural network is translated into a FPGA synthesizable model using the Matlab/Simulink environment as well as the System Generator Xilinx tool. The results are illustrated in a prototype developed on an Atlys board. The authors focus the analysis and discussion on the main aspects considered to synthetize an ANN on an FPGA as well as the methodology to follow during such implementation, which are the main contributions of this paper. [ABSTRACT FROM AUTHOR]

This paper presents an affective environmental control system for Small and Medium-sized Enterprises (SMEs). The system is proposed as a technology innovation in appropriate information technology. It is defined that workplace environment set points could be controlled using worker workload. The research objectives are: 1) To design an affective environmental control model for SME; 2) To develop an Artificial Neural Network (ANN) model for predicting affective environment set points. The system consisted of 4 sub-systems as measurement, assessment, control and decision. An ANN model is developed for sub-systems of control. Training and validation data are acquired from 4 (four) samples of SME in Yogyakarta Special Region, Indonesia. The model has been developed successfully to predict temperature and light intensity set points using back -propagation supervised learning method. The research results indicated the satisfied performance of ANN with minimum error. ANN model indicated the closeness of R2 value between training and validation data. The research results could be applied to support the worker productivity in food SMEs by providing a comfort workplace environment and optimum worker workload. [ABSTRACT FROM AUTHOR]

This paper evaluates the accuracy of Support Vector Machine (SVM), Artificial Neural Network (ANN) and empirical solar radiation models with different combination of input parameters. The parameters include month, latitude, longitude, bright sunshine hours, day length, relative humidity, maximum and minimum temperature. The models are evaluated based on statistical measures. Four new empirical models are introduced and validated with experimental data. This work is focused on the prediction of monthly mean daily global solar radiation (GSR) for different cities in India with most influencing input parameters identified using Waikato Environment for Knowledge Analysis (WEKA) software. WEKA identifies month, latitude, maximum temperature and bright sunshine hours as the most influencing and relative humidity as the least influencing input parameter. SVM model with most influencing input parameter performs better than ANN and Empirical models. Exclusion of relative humidity does not affect the prediction accuracy. Therefore this work reduces the dimensionality of the data and improves the prediction accuracy. This work also attempts in assessing the solar energy potential of smart cities of Tamil Nadu, India using the SVM model. The predicted annual GSR varies from 17 to 22 MJ/m2/day which is precise enough for a wide range of solar applications. [ABSTRACT FROM AUTHOR]

Globalization is a multidimensional phenomenon that includes the commercial, financial, technological and macroeconomic factors, which have different effects on household income inequality within countries. This paper studies the interrelation between this variables to identify the main factors of each dimension and the direction of its effect through the Sensitivity Analysis of Artificial Neural Networks. This data mining tool finds out non-linear relationships with a non-parametric approach. The main factors that reduce inequality includes credit and spending on research and development, control of corruption and population growth, a credible position in the financial account and trade globalization. [ABSTRACT FROM AUTHOR]

Accurate prediction of solar radiation is essential for optimal design of solar systems. This paper presents an innovative artificial intelligence approach for the determination of the daily solar radiation. A new nonlinear model was developed to predict the daily solar radiation on horizontal surface using a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called ANN/SA. This method uses SA-based temperature cycling to improve the ANN calibration performance. A calculation procedure was presented to interpret the ANN/SA model and transform it into a practical design equation. The ANN/SA technique formulates the daily solar radiation in terms of several meteorological parameters. Thousands of daily observations during 1995–2014 in a nominal city in Iran were used to develop the solar radiation models. Validity of the model was verified through different phases. Sensitivity analysis was conducted and discussed. The ANN/SA model accurately predicts the daily solar radiation and outperforms the ANN, support vector machines (SVM), and existing regression and machine learning models. [ABSTRACT FROM AUTHOR]

Atmospheric discharges offer great risks to the population and activities that involve different systems such as telecommunications, energy distribution and transportation. Lightning prediction can contribute to minimize the risks of this natural phenomenon. Therefore the present paper presents a model for lightning prediction based on satellite atmospheric sounding data, calibrated and validated with lightning data in an Amazon region particular area through an investigation that considered five period cases for validation of lightning prediction: case 1 (one hour), case 2 (two hours), case 3 (three hours), case 4 (four hours) and case 5 (five hours). The machine learning technique used to predict lightning was the Artificial Neural Network (ANN) trained with Levenberg-Marquardt backpropagation algorithm to classify modeling related to lightning prediction. This classification relied on the possibility of lightning prediction from the vertical profile of air temperature obtained from satellite NOAA-19. Results show that ANN was capable of identifying adequately the class to which a new event belongs to in relation to categories of occurrence and absence of lightning with better performance than traditional methodologies. [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]

In this paper, the design and development of an artificial neural network (ANN) for similarity value calculation in a context-aware system is proposed. This neural network is used by the neural agent of the iConAwa system. Since iConAwa is an intelligent, context-aware, multiagent system, it provides mobile users with context-aware information and services, and also provides communication with each other. Context and points of interest are modeled in a flexible and extensible way by using ontologies. iConAwa derives high-level implicit context from low-level explicit context by inference performed over the context ontology. This approach decouples context reasoning from the source code of the system. With the addition of a neural agent, which uses an ANN, the system has learning capability. By using a neural network for similarity value calculation, the system can adapt to the needs of different people. System owners can introduce their own similarity metric considering their own requirements, which further improves the iConAwa system. Thus, the extended iConAwa system combines expert system characteristics with the capability to learn. [ABSTRACT FROM AUTHOR]