Search

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies. The strengths and weaknesses of various AI methodologies are examined Artificial neural networks are extensively used for modeling drying phenomena Machine learning models can simulate complex processes of food drying Deep learning has significant potential for real-time monitoring of drying Intelligent control systems can optimize food drying [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]

Despite the complex punching shear behavior of reinforced concrete slabs have been comprehensively addressed in the literature, it is further essential to develop a universal design model comprising high accuracy and the simplicity for design practicability, adaptable to diverse conditions encountered in practice. Artificial intelligence applications, artificial neural networks (ANN), and more recently, various machine learning (ML) and deep learning (DL) techniques veer off in a new direction in structural engineering context with improved accuracy and efficiency. The paper begins with the assessment of the capabilities of various artificial intelligence applications in predicting the punching shear strength of slab-column connections without shear reinforcement through the extensive database using 650 punching shear experiments from the literature. Critical parameters influencing the punching shear strength as well as the precision of the current code provisions in predicting this feature were then thoroughly examined in the paper. The results shown in this paper validated the competency of artificial intelligence applications in predicting the punching shear strength of such connections with increased accuracy and improved simplicity in practical terms. The proposed models utilizing the artificial intelligence applications encourage the ultimate rehabilitation policies to be proposed and improved code provisions to be developed for contemporary structures. [ABSTRACT FROM AUTHOR]

As the world's transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost 'drive-by' monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either 'damaged' or 'healthy'. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration. [ABSTRACT FROM AUTHOR]

This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks mainly use the integration stage to integrate money into the financial system. Fraudsters prefer to launder funds in the early hours, morning followed by the business day's afternoon time intervals. Additionally, the Naïve Bayes and Random Forest classifiers were identified as the two best-performing models to predict bank money laundering transactions. [ABSTRACT FROM AUTHOR]

For bonded Fibre Reinforced Polymer (FRP) strengthening systems in civil engineering projects, the adhesive joint performance is a key factor in the effectiveness of the strengthening; however, it is known that the material properties of structural epoxy adhesives change with temperature. This present paper examines the implied relationship between the curing regimes and the storage modulus response of the adhesive using a Machine Learning (ML) approach. A dataset containing 157 experimental data collected from the scientific papers and academic theses was used for training and testing an Artificial Neural Network (ANN) model. The sensitivity analysis reveals that the curing conditions have a significant effect on the glass transition temperatures (Tg) of the adhesive, and consequently on the storage modulus response at elevated temperatures. Curing at an extremely high temperature for a long time does not, however, guarantee a better thermal performance. For the studied adhesive, curing in a warm (≥ 45°C) and dry (near 0 % RH) environment for 21 days is recommended for practical applications. A software with a Graphical User Interface (GUI) was established, which can predict the storage modulus response of the adhesive, plot the corresponding response curve, and estimate the optimum curing condition. [ABSTRACT FROM AUTHOR]

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

An Artificial Neural Network (ANN) based Substrate Integrated Waveguide Band Pass Filter (SIW BPF) is proposed in this paper. A Feed Forward Back Propagation Neural Network (FF-BP NN) is utilized to optimize the filter dimensions. The Gradient Descent with Momentum and Adaptive Learning Rate algorithm is used to train the network at a learning rate of 0.01. The high pass characteristics of the SIW is converted into band pass by incorporating U slots on its top plane. Defected Ground Structure (DGS) is utilized in the bottom plane to improve the impedance matching. To validate, the prototype is fabricated using RT/Duroid 5880 and tested. The proposed filter has a center frequency at 14.68 GHz with a wide pass band from 12.92 to 16.43 GHz with a 3-dB Fractional Bandwidth (FBW) of 24%, return loss more than 20 dB and insertion loss of about 1.9 dB within the pass band. The filter has a small dimension of about 0.63 $ \lambda _g^2 $ λ g 2 , where λg is the guided wavelength at the center frequency. This filter offers wide passband, smaller size, low insertion loss, good return loss and it is useful in Ku-band satellite communication applications. [ABSTRACT FROM AUTHOR]

In this paper, we introduce novel instructional approaches to engage students in using modelling with data to motivate and teach differential equations. Specifically, we introduce a pedagogical framework that will execute instructional modules to teach different solution techniques for differential equations through repositories and notebook environments during real-time instruction. Each of these teaching modules employs a literate programming approach that uses the notebook environment to explain the concepts in a natural language, such as English, interspersed with snippets of macros and traditional source code on a web browser. The pedagogical approach employed is reproducible and leads to openaccess material for students to motivate and teach differential equations efficiently. We will share examples of this framework applied to teaching advanced concepts such as machine learning and neural network approaches for solving ordinary and partial differential equations as well as estimating parameters in these equations for given datasets. More details of the work can be accessed from . [ABSTRACT FROM AUTHOR]

Natural streamflow data is required in many hydrological applications. However, many basins are located in data-scarce regions or are impacted by human construction and activities. In this paper, we explore three machine learning algorithms, namely artificial neural networks, random forest and light gradient boosting machine, to simultaneously estimate all the parameters of the coupled modèle du Génie Rural à 4 paramètres Journaliers (GR4J) and snow accounting routine called CemaNeige model. A database of 675 basins in the USA and Quebec is used to train and test ensembles. After using the estimated parameters in GR4J, the resulting naturalized streamflow series are compared with those obtained by the established drainage area ratio and spatial proximity transfer methods in 11 test basins. The results indicate that the machine learning algorithms outperform the drainage area ratio and spatial proximity transfer methods. Among machine learning algorithms, random forests obtain lower (better) continuous ranked probability scores than the other methods for 10 out of 11 test basins. [ABSTRACT FROM AUTHOR]

Driving behavior monitoring by smartphone sensors is one of the most investigated approaches to ameliorate road safety. Various methods are adopted in the literature; however, to the best of our knowledge, their robustness to the prediction of new unseen data from different drivers and road conditions is not explored. In this paper, a two-phase Machine Learning (ML) method with taking advantage of high-pass, low-pass, and wavelet filters is developed to detect driving brakes and turns. In the first phase, accelerometer and gyroscope filtered time series are fed into Random Forest and Artificial Neural Network classifiers, and the suspicious intervals are extracted by a high recall. Following that, in the next phase, statistical features calculated based on the obtained intervals are used to determine the false and true positive events. To compare the predicted and real labels of the recorded events and calculate the accuracy, a method that covers the limitations of previous sliding windows is also employed. Real-world experimental result shows that the proposed method can predict new unseen datasets with average F1-scores of 71% in brake detection and 82% in turn detection which is comparable with previous works. Moreover, by sensitivity analysis of our proposed model, it is proven that implementing high-pass and low-pass filters can affect the accuracy for turn detection up to 30%. [ABSTRACT FROM AUTHOR]

The current issue of the IETE Journal of Research (Volume 69 Number 12, December 2023) contains seventy articles covering various fields of technology and engineering. These articles discuss topics such as error rate analysis, antenna development, optical DEMUX, computer science, and engineering. Each paper presents a unique study or proposal, highlighting methodologies, techniques, and findings. The articles cover a wide range of topics, including IoT security, keyphrase extraction, deep learning, object detection, emotion recognition, insulin dose prediction, antenna array synthesis, cardiac MRI segmentation, fire detection systems, fault detection in rolling bearings, medical image processing, and optimization techniques for photovoltaic systems. The authors of these articles are experts in their respective fields and have made significant contributions to their areas of study. [Extracted from the article]

This paper presents a novel approach for enhancing long-term runoff simulations through the integration of empirical mode decomposition (EMD) with four machine learning (ML) models: ensemble, support vector machine (SVM), convolutional neural networks (CNN), and artificial neural networks with backpropagation (ANN-BP). The proposed methodology uses EMD to decompose precipitation and temperature time-series into intrinsic mode functions, thereby revealing underlying data patterns. Subsequently, these components are incorporated into the ML models to simulate the runoff time-series. The effectiveness of the hybrid models is evaluated using streamflow runoff data obtained from the Grand, Winnipeg, and Moosonee Rivers in Ontario, Canada. Four widely used performance indices, namely, correlation coefficient, root mean square error (RMSE), mean absolute relative error, and Nash--Sutcliffe efficiency, are employed to assess the models' performance. The results demonstrate that the hybrid EMD-ML models exhibit significantly superior performance compared with the standalone ML methods. During the validation phase, the EMD-Ensemble, EMD-SVM, EMD-CNN, and EMD-ANN-BP models exhibit notable reductions in the RMSEs of monthly streamflow estimates for the Grand River, amounting to 11%, 22%, 8%, and 33%, respectively, compared with their non-EMD counterparts. Additionally, these hybrid models exhibit improved RMSEs for yearly simulations in the Winnipeg River, with reductions of 54%, 0.08%, 6%, and 4.5% respectively. To further enhance the accuracy of monthly and yearly streamflow estimates, an SVM-recursive feature elimination technique is employed to select a more appropriate EMD dataset in all study cases. This research underscores the potential of integrating EMD with ML models to enhance long-term runoff simulations. The outcomes highlight the superior performance of the hybrid EMD-ML models, demonstrating their ability in generating lower biases than the standalone ML methods. These findings hold significant implications for the field of computational fluid mechanics and can contribute to the understanding of hydrological processes. [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]

Artificial Neural Networks (ANNs) are emerging classes of AI algorithms, and have seen numerous applications in travel behavior research recently. However, the transferability of ANN-based travel behavior models is seldom tested. A few studies that test transferability, merely use vanilla Feedforward Neural Networks. This paper evaluates the spatial transferability of two ANN-based models: first, a Feedforward ANN-based mode choice model, and next, a Long Short Term Memory (LSTM)-based activity generation and activity-timing model, and enhances their transferability using transfer learning (TL). Both the models were found to exhibit poor transferability in case of naïve transfer. Transfer learning resulted in significant improvements with the TL-enhanced models that utilizeonly 50% of local data achieving results similar to a locally developed model. Further, ANNs performed poorer when compared with nested logit (NL) models during naïve transfer. However, the TL-enhanced ANN-based models showed significant improvement compared to transfer scaling enhanced NL models. [ABSTRACT FROM AUTHOR]

Deficits in social verbal communication in individuals with Social Communication Disorder (SCD) is of concern and SCD in the human community is prevalent in large population throughout the globe. Deficits in verbal social communication are prevalent in a large population. This paper aimed to propose internet connected multi-system architecture which is capable to support verbal communication in a social environment for individuals with social communication deficits. Implementation methodology was included with corpus collection for specific communication, deep learning based machine training for intelligent communication, and implementation of the trained algorithm on internet connected electronic multiple social communication devices. The implemented system is smart enough to initiate and maintain two types of communication; the first type includes communication between multiple individuals on the remote location and the second type includes communication with the individual present in the physical listening range. The system was investigated in terms of its algorithmic parameters and found 97% to 100% in terms of training and testing accuracy with negligible mean squared error. Vocal-Friend analysed results based on audio-bot simulative conditions provide more than 91% accuracy, interaction rate and fallback rate. On the basis of the satisfaction analysis, above average results were noticed. In terms of technical implementations and satisfaction analysis, results found acceptable with above average score. Proposed framework is easy to use by caregivers with even having little knowledge. Support individual with deficit to learn social verbal communication skill to survive in society. Aiding parents, caregivers and professionals to understand the communication needs of individuals with communication deficits. Since technology is also grooming in the domain of rehabilitation, so this system could be used in various future applications such as social robots, social virtual assistants etc. [ABSTRACT FROM AUTHOR]

This paper summarises recent advancement in applications of machine learning in sports biomechanics to bridge the lab-to-field gap as presented in the Hans Gros Emerging Researcher Award lecture at the annual conference of the International Society of Biomechanics in Sports 2022. One major challenge in machine learning applications is the need for large, high-quality datasets. Currently, most datasets, which contain kinematic and kinetic information, were collected using traditional laboratory-based motion capture despite wearable inertial sensors or standard video cameras being the hardware capable of on-field analysis. For both technologies, no high-quality large-scale databases exist. A second challenge is the lack of guidelines on how to use machine learning in biomechanics, where mostly small datasets collected on a particular population are available. This paper will summarise methods to re-purpose motion capture data for machine learning applications towards on-field motion analysis and give an overview of current applications in an attempt to derive guidelines on the most appropriate algorithm to use, an appropriate dataset size, suitable input data to estimate motion kinematics or kinetics, and how much variability should be in the dataset. This information will allow research to progress towards bridging the lab-to-field gap. [ABSTRACT FROM AUTHOR]

Wearable devices equipped with a multitude of compact, lightweight, biometric sensors are helpful in tracking the real-time physiological data for healthcare-related analysis. However, a survey of devices under the smart-wearable market segment revealed that the contemporary focus is limited to capturing and displaying some of the biometrics like pulse rate, movement of the user, calorie counter, etc. on a smart screen. Employing machine learning techniques can be particularly helpful in analyzing the trends of user-specific biometric data for pre-emptive actions. This paper presents a meaningful analysis in real-time by using machine learning approaches to interpret this physiological data and alert the user about his behavioral pattern through IoT. This research focuses on classifying the mood of the user as agitated or non-agitated, by analyzing the biometrics to help the user decipher meaningful conclusions and take suitable pre-emptive measures to refrain from any unintentional impulsive outburst. Machine learning algorithms like Polynomial regression with threshold, Decision Tree, Random Forest ensemble and variants of Deep Neural Networks (DNN) have been employed to analyse the biometric patterns from the experimental data acquired under different circumstances and detect the user's mood to assign a score to the user. The proposed approach uses a reinforcement learning algorithm to calibrate the user's current temperament by taking intermediate user feedback and comparing the score with the temperament. The results reveal that the proposed system detects the user's mood fluctuations with higher accuracy and relevance compared to any contemporary model. [ABSTRACT FROM AUTHOR]

Modelling source water quality in drinking water treatment systems could be useful for anticipating changes in specific raw water quality parameters. Those changes entail adjustments in drinking water treatment plant (DWTP) operations. Artificial intelligence (AI) has been used for modelling water quality for different purposes and has yielded reliable results. However, there has not yet been wide investigation of raw water quality modelling for treatment purposes using AI. For the first time, in this critical review, we analyzed AI models founded on machine learning techniques that are used for surface water quality modelling and which could be applied in the domain of source water treatment. In a novel approach, we convened an expert panel that helped us define the appropriate criteria for use in the selection of the papers for review. We analysed the selected papers according to several criteria, including the feasibility of input data generation, the modelled data applicability and the benefits and limitations. We evaluated whether the selected models could be applied to forecast raw water quality as decision support systems (DSS) in drinking water treatment. The highest rated were turbidity hourly models based on Support Vector Machines (SVM), as well as daily turbidity and pH models based on Artificial Neural Networks (ANN). We found there is a shortage of models used to specifically estimate raw water quality, which could assist in DSS at DWTPs. There should be an increased effort to model raw water quality, especially with AI models using hourly and sub-hourly time step. [ABSTRACT FROM AUTHOR]

In this paper, an 8-bit programmable digital metasurface is designed in the operating frequency range from 4 to 7 GHz. A monopole antenna operating at 5 GHz is characterised by using a metasurface structure as the ground plane. Various combinations of the metasurface structure are examined by altering the state of each unit-cell between 'ON (1)' and 'OFF (0)' in the coding matrix. The purpose of the 8-bit programmable digital metasurface is to control the electromagnetic wave effectively in the frequency range of interest. Dynamically controllable metasurface structure is utilised to change the unit cells' configurations. In addition, the proposed adjustable metasurface is capable to control the monopole antenna directivity, gain and main lobe magnitude, efficiently. According to various via conditions, different metasurface-based antenna parameters such as return loss (S11), radiation pattern, directivity and surface current distribution are investigated by means of the commercial numerical simulation software, CST microwave studio. Employing the simulation results of the metasurface-based antennas, the artificial neural network (ANN) data set is obtained. 128 activation conditions are trained and tested by Levenberg Marquart learning algorithm. During the ANN procedure, MATLAB is used to obtain accurate results by changing the rate of test and training data set. [ABSTRACT FROM AUTHOR]

Deep drawing involves the use of a punch to plastically deform a workpiece to create sheet metal parts such as cups and channels. Upon release of the punch, the workpiece undergoes elastic recovery and results in a change in geometry known as the springback effect. The process is highly non-linear and involves many parameters, leading to large computation times to fully simulate the process or train a regression model on the high dimensionality of the data. In this paper, dimension-reduced neural networks (DR-NNs) for efficient springback prediction in the process of deep drawing a cylindrical cup are presented with consideration for different materials. The DR-NNs are trained on FEM data from a deep drawing simulation performed on ABAQUS/CAE. Features such as the workpiece’s initial coordinates, material properties, and thickness are introduced as inputs for the dimensionality reduction. Linear and non-linear dimensionality reduction methods reduce the input to a smaller set of principal components, which are fed as inputs to the neural networks for predicting the springback after punch release. The DR-NNs are compared against a deep neural network (DNN) and show improvements in lower computation time for training, prediction uncertainty, and less storage space required while retaining prediction accuracy. [ABSTRACT FROM AUTHOR]

Predictive analytics utilizing machine learning algorithms play a pivotal role in various domains, including the profiling of carbon dioxide (CO2) emissions. This research paper delves into an extensive exploration of different algorithms, encompassing neural networks with diverse architectures, optimization, training, ensemble, and specialized algorithms. The primary objective of this research is to evaluate the efficacy of supervised and unsupervised algorithms, including Deep Belief Networks, Feed Forward Neural Networks, Gradient Boosting, and Regression, as well as Convolutional Neural Networks, Gaussian, Grey, and Markov models, and clustering and optimization algorithms. The study places particular emphasis on data-driven methodologies and cross-validation techniques with an evaluation of the learning models entailing comprehensive training, validation, and testing, employing evaluation metrics such as R2, MAE, and RMSE. The study employs correlation analysis to examine the relationship between input parameters and emission characteristics. The research highlights the advantageous attributes of these algorithms in accurately forecasting CO2 emissions, evaluating energy sources, improving prediction accuracy, and estimating emissions. Notably, deep learning, Artificial Neural Networks (ANN), and Support Vector Machines (SVM) demonstrate effectiveness across diverse industries, while the Modified Regularized Fast Orthogonal-Extreme Learning Machine (MRFO-ELM) algorithm optimizes predictions specifically related to coal chemical emissions. Hybrid techniques demonstrate accuracy in predicting carbon emissions and energy consumption, whereas gray prediction models provide reliable estimates even with limited data. However, it is important to acknowledge certain limitations, including data requirements, potential inaccuracies arising from complex factors, constraints faced by developing countries, and the impact of electric vehicle expansion on the power grid. To optimize models, a survey is conducted, involving customization of parameters and learning rates, while exploring various performance metrics to evaluate model accuracy. The research outcomes contribute to the effective monitoring of CO2 emissions in operational environments, thereby aiding executive decision-making processes. [ABSTRACT FROM AUTHOR]

In this paper, an original methodology for landslide susceptibility mapping (LSM) is presented. It consists of bagging ensembles of artificial neural networks (ANNs) and random forests (RFs), and hybrid bagging ensembles of these models. It is applied on the area of the Itajaí-Açu river valley. In December 2020, there was an extreme rainfall in the region, which triggered landslides. The RF ensemble presented slightly higher accuracy (0.941) than the ANN ensemble (0.940), but the ANN ensemble had a more balanced relation between sensitivity (0.966) and specificity (0.915) than the RF ensemble (specificity = 0.992, sensitivity = 0.891). The mixed ANN-RF ensemble presented the higher accuracy of all (0.950), and a good balance between sensitivity (0.948) and specificity (0.951), being considered the best model within those analyzed. The hybrid ensemble, together with classification threshold adjustment, removed discrepancies on the maps between both models by attenuating them. [ABSTRACT FROM AUTHOR]

3D printing has become highly applicable in modern life recently. The industry has brought a facelift to most others. However, this technology still exists some shortcomings, and it therefore has not been generalised to bring the best benefits to users. In this paper, based on multilayer perceptron and convolution neural network models, we propose a new data-driven machine learning platform for predicting optimised parameters of the 3D printing process from a model design to a complete product. This finding can open up great advances in the current 3D printing technology. Accordingly, the results obtained allow us to predict quickly and accurately some decisive parameters of the traditional 3D printing process such as time, weight and length while the input was fuzzy with a part of the initial information missing. The proposed approach does not need to account for the shape, size and material of the printed object, but it can perform the process automatically without other extra factors. After completing the model, a configurator is proposed to set the parameters for the respective printer types, which makes the 3D printing process simple and fast. [ABSTRACT FROM AUTHOR]

Convolutional Neural Network (CNN) has been proved excellent for hyperspectral image(HSI) classification on the basis of large training set. However, the lack of available samples degrades the performance of CNNs in practical HSI classification. Although transfer learning is a typical strategy to address the issue of limited training samples, such methods tend to reload source model's feature extractors directly ignoring their generalization. Furthermore, most existing adaptive transfer learning algorithms pay attention to spectral features of HSI while the obvious difference between RGB image and HSI's spatial features is overlooked. In this paper, the proposed method can solve the mentioned problems to some extent. Firstly, the proposed adaptive heterogeneous transfer (AHT) can measure their transferability and find appropriate solution to transfer knowledge. It computes feature extractors' transferability based on feature maps and influences the learning process of target model through backpropagation, therefore, target model is prone to receive knowledge from feature extractors with high generalization. Secondly, the proposed method lays emphasis on HSI's spatial features. It adopts the methodology of spatial attention and calculates the regularization loss based on spatial features. Experiments were conducted on two widely used HSI datasets. The results demonstrate the method's effectiveness when few samples are available. [ABSTRACT FROM AUTHOR]

Nowadays, breast cancer is one of the leading causes of women's death in the world. If breast cancer is detected at the initial stages, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of such cancer. However, efforts are still ongoing, given the importance of the problem. Artificial Neural Networks (ANN) are a prevalent machine learning algorithm, which is very popular for prediction and classification problems. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The proposed method consists of two stages: parameters optimisation and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimised with the help of an Evolutionary Algorithm (EA), aiming at maximising the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN with optimised parameters is applied to classify the patients. Our proposed IEC-MLP method not only reduces the complexity of MLP-NN and effectively selects the optimal subset of features but also minimises the misclassification cost. The classification results have been evaluated using the IEC-MLP over different breast cancer datasets, and the prediction results have been auspicious (98.74% accuracy on the WBCD dataset). It is noteworthy that the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP is also capable of being employed in diagnosing other cancer types. [ABSTRACT FROM AUTHOR]

In this paper, historical data about road traffic accidents are utilized to build a decision support system for emergency response to road traffic injuries in real-time. A cost-sensitive artificial neural network with a novel heuristic cost matrix has been used to build a classifier capable of predicting the injury severity of occupants involved in crashes. The proposed system was designed to be used by the medical services dispatchers to better assess the severity of road traffic injuries, and therefore to better decide the most appropriate emergency response. Taking into account that the nature of accidents may change over time due to several reasons, the system enables users to build an updated version of the prediction model based on the historical and newly reported accidents. A dataset of accidents that occurred over a 6-year period (2008-2013) has been used for demonstration purposes throughout this paper. The accuracy of the prediction model was 65%. The Area Under the Curve (AUC) showed that the generated classifier can reasonably predict the severity of road traffic injuries. Importantly, using the cost-sensitive learning technique, the predictor overcame the problem of imbalanced severity distributions which are inherent in traffic accident datasets. [ABSTRACT FROM AUTHOR]

The focus of this paper is on application of advance data analytics and deep machine learning methods for time series forecasting of injection/production data from subsurface hydrocarbon recovery processes. Injection and production data, from subsurface reservoir developed though implementation of water flooding recovery mechanism, is used with aim of developing a machine learning based forecasting methodology to eventually replace numerical simulation-based forecasting methods. Different machine learning algorithms exists for single- and multi-step time series forecasting, e.g., nonlinear regression, artificial neural network (ANN) and long-short-term-memory (LSTM) based recurrent neural networks (RNN). In this paper, RNN-LSTM algorithm is tested on real field data, comprising of a number of injection and production well patterns, and results of RNN-LSTM forecasting model are presented. Complexities related to data acquisition, analysis, and processing are also discussed in detail. The results presented in this paper are unique as it is for the first time that ML model is applied to forecast production from a tight carbonate hydrocarbon reservoir, which is developed through water flooding with help of long horizontal wells. Such an application for time series forecasting using RNN-LSTM model has never been presented, before in literature, to the best of author's knowledge. [ABSTRACT FROM AUTHOR]

In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-, and two unsupervised machine learning methods, applied to Sentinel-3 Synthetic Aperture Radar Altimeter data. For the first time, the simultaneously sensed images from the Ocean and Land Color Instrument, onboard Sentinel-3, were used for training and validation of the classifiers. This product allows to identify leads that are at least 300 meters wide. Applied to data from winter months, the supervised Adaptive Boosting, Artificial Neural Network, Naïve-Bayes, and Linear Discriminant classifiers showed robust results with overall accuracies of up to 92%. The unsupervised Kmedoids classifier produced excellent results with accuracies up to 92.74% and is an attractive classifier when ground truth data is limited. All classifiers perform poorly on summer data, rendering surface classifications that are solely based on altimetry data from summer months unsuitable. Finally, the Adaptive Boosting, Artificial Neural Network, and Bootstrap Aggregation classifiers obtain the highest accuracies when the altimetry observations include measurements from the open ocean. [ABSTRACT FROM AUTHOR]

The recent devastating earthquakes have caused severe physical, social, and financial damage worldwide and indicate that many existing buildings, especially in developing countries, are not designed to withstand seismic hazards and resulting vulnerabilities to earthquakes. It is difficult, time-consuming, and costly to investigate and inspect all the existing buildings in detail before an earthquake, especially in urban areas. Therefore, rapid methods for evaluating the vulnerability of buildings have attracted the interest of researchers. This paper investigates the seismic susceptibility through the combination of buildings' geometrical attributes that affect the vulnerability of buildings and further extended into having foresight into the damage state of reinforced concrete (RC) buildings using artificial neural network (ANN). For this purpose, a multi-layer perceptron (MLP) network has been trained and optimized using two different databases of damaged buildings from the Nepal and Ecuador earthquakes. The results show the practicability and efficacy of the selected ANN approach for classifying actual damage grade based on structural damage, which the workflow can be followed and applied into other data from other regions for the detection of highly vulnerable buildings. A prototype of a smartphone app and its implementation was later introduced for data collection and vulnerability assessment purposes based on the proposed method. [ABSTRACT FROM AUTHOR]

In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model trees, alternating model trees and random forest in estimating the CBR of reinforced soil. For this, all the models were calibrated and validated using the reliable pertinent historical data. The prognostic veracity of all the tools mentioned supra were assessed using the well-established traditional statistical indices, external model evaluation technique, multi-criteria assessment approach and independent experimental dataset. Due to the overall excellent performance of ANN, the model was converted into a trackable functional relationship to estimate the CBR of reinforced soil. Finally, the sensitivity analysis was performed to find the strength and relationship of the used parameters on the CBR value. [ABSTRACT FROM AUTHOR]

This paper proposes runoff models based on machine learning to estimate daily streamflows in Oued Sebaou watershed, a Mediterranean coastal basin located in northern Algeria. Therefore, we applied random forest (RF), artificial neural networks (ANN – under different training algorithms), and locally weighted linear regression (LWLR) using as input combinations of current and past rainfall amounts and previous values of streamflow. We selected streamflow and rainfall records to calibrate and validate the stated approaches. We used root mean square error (RMSE) and correlation coefficient (R) to evaluate the accuracy of the models. Analyses of the results show that RF provided the best outcomes for both training (RMSE = 4.7458 and R = 0.9834) and validation (RMSE = 2.3617 and R = 0.9719). The ANN calibrated with the Levenberg-Marquardt algorithm presented the second-best result, outperforming its counterparts and LWLR. [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]

Timely and accurate survey of construction and demolition waste (C&DW) distribution is of significance for managing C&DW and enhancing the quality of the urban environment, especially in rapidly urbanizing country of China. Automatic C&DW recognition using high-resolution remote sensing images is an extremely important surveying method. Existing C&DW recognition methods can only obtain a rough spatial distribution of C&DW. Mapping precise C&DW distribution on large-scale remote sensing images is still challenging. In this paper, to solve the problem of precise C&DW recognition, a novel deep learning algorithm based on high-resolution remote sensing images is proposed, which we refer to as the cross-channel multi-scale gated fusion network (CCMGNet). CCMGNet extracts depth features of C&DW from RGB and NIR bands by using two independent encoding streams. A gated fusion layer and a multi-scale attention module are designed to effectively fuse the features from two encoding streams. We manually labelled the sample set used for training and testing on 26 remote sensing images captured by the Gaofen-2 (GF-2) satellite. These images were taken in nine typical cities in China over the time period from 2019 to 2021. The results showed that the proposed method is effective in recognizing C&DW, realizing 89.42% and 84.52% in precision and IoU, and is superior to other state-of-the-art deep learning algorithms and existing C&DW recognition methods. The effectiveness of all significant components in CCMGNet was confirmed by ablation experiments. The proposed method was applied to extract C&DW regions in large-scale remote sensing images, with an example of a GF-2 image of Beijing. The high extraction efficiency and satisfactory visual effect demonstrate the potential of the proposed method for surveying the spatial distribution of C&DW. The novel method will play a crucial role in automatically surveying the spatial distribution and size of C&DW across the country. [ABSTRACT FROM AUTHOR]

Research has proven that having high level of emotional intelligence (EI) can reduce the chance of getting mental illness. EI, and its component, can be improved with training, but currently the process is less flexible and very time-consuming. Machine learning (ML), on the other hand, can analyse huge amount of data to discover useful trends and patterns in shortest time possible. Despite the benefits, ML usage in EI training is scarce. In this paper, we studied 92 journal articles to discover the trend of the ML utilisation in the study of EI and its components. This survey aims to pave way for future studies that could lead to implementation of ML in EI training, and to rope in researchers in psychology and computer science to find possibilities of having a generic ML algorithm for every EI's components. Our findings show an increasing trend to apply ML on EI components, and Support Vector Machine and Neural Network are the two most popular ML algorithms used in those researches. We also found that social skill and empathy are the least exposed EI components to ML. Finally, we provide recommendations for future research direction of ML in EI domain, and EI in ML. [ABSTRACT FROM AUTHOR]

This paper investigates how to best compare algorithms for predicting chronic homelessness for the purpose of identifying good candidates for housing programs. Predictive methods can rapidly refer potentially chronic shelter users to housing but also sometimes incorrectly identify individuals who will not become chronic (false positives). We use shelter access histories to demonstrate that these false positives are often still good candidates for housing. Using this approach, we compare a simple threshold method for predicting chronic homelessness to the more complex logistic regression and neural network algorithms. While traditional binary classification performance metrics show that the machine learning algorithms perform better than the threshold technique, an examination of the shelter access histories of the cohorts identified by the three algorithms show that they select groups with very similar characteristics. This has important implications for resource constrained not-for-profit organizations since the threshold technique can be implemented using much simpler information technology infrastructure than the machine learning algorithms. [ABSTRACT FROM AUTHOR]

Foreign-material content determination in uncleaned peanuts based on dielectric properties and bulk density measurements by microwave techniques is presented in this paper. A microwave free-space transmission technique was used at 10 GHz. Two measurement systems for measuring the dielectric properties of cleaned unshelled peanuts (nine-peanut pods) and uncleaned unshelled peanuts placed in polycarbonate sample holder (12.1 cm × 21 cm × 20.5 cm) were developed and integrated in one single measuring unit. The nine-peanut-pods system provided the cleaned unshelled peanuts moisture content which was used in the algorithms for foreign material content determination. The dielectric properties and bulk density measurements of the uncleaned unshelled peanut sample were related to the foreign-material content. These parameters, namely bulk density and dielectric properties of uncleaned peanuts and cleaned unshelled moisture content were supplied to machine learning algorithms, linear regression technique and artificial neural network algorithms. Results obtained with the artificial neural network algorithm showed the best estimate of foreign material content with a standard error of performance of 1.36% compared to that obtained with the linear regression algorithm with a standard of performance of 2.39%. [ABSTRACT FROM AUTHOR]

In order to run power plant operations smoothly, power plant faults need to be detected, located, and classified quickly. For this, artificial neural network approaches are considered significant tool in related application of power system. This research paper focuses on power plant faults detection and classification using intelligent approach of artificial neural network. There is frequent nature of power plant faults, in particular transmission line faults. The key intention of this research is to use ANN approach to detect and classify power plant faults, and in the end to compare their performance and accuracy. In this proposal, the three-phase current and voltage are use as input. Backpropagation (BPNN) technique is used in developing the learning algorithm for multi-layer neural network that can accurately and reliably classify faults. The multi-layer neural network such as Levenberg-Marquardt and Bayesian regularization are used for offline training of the data. Findings from this research show that both Levenberg-Marquardt and Bayesian regularization show good accuracy to detect and classify faults in transmission line. However, LM algorithm trained data much faster than BR. LM shows fastest detection of faults with overall mean square error value of 6.13785e-3 at the 91 epoch. Whereas, BR trained network at the 717 epochs with overall mse of 4.67123e-3. It is to be noted that the accuracy of trained network to classify all the fault types is found to be 81.1%, which is higher than previous studies using Backpropagation neural network technique. Training and test results show that these neural network-based methods can effectively detect and classify faults and faults types and have adequate performance. Matlab is used in this research because it has toolbox, which has effective features to train required data and achieve effective result. [ABSTRACT FROM AUTHOR]

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early '90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-'90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques. [ABSTRACT FROM AUTHOR]

Accurate prediction of the scour hole depth and dimensions downstream of ski-jump spillways has been an important issue among hydraulic researchers for decades. In recent years, computing methods such as Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs) and Support Vector Regression (SVR) have shown a powerful performance in the prediction of scour characteristics owing to their flexibility and learning nature. In the present paper, a new hybrid approach has been proposed for the first time in order to improve the estimation power of the SVR tool for scour hole geometry prediction below ski-jump spillways. The principal characteristics of the scour hole pattern in the equilibrium phase have been predicted using SVR optimized with Fruitfly Optimization Algorithms (FOAs). The hybrid model is compared with the corresponding simple SVR model. To evaluate the proposed hybrid model further, it is also compared with other machine learning and empirical methods, such as ANNs, ANFISs and regression equations. The results show that the proposed SVR-FOA method performs well, improves remarkably on Support Vector Machines (SVMs) results, estimates scour hole geometrical parameters more accurately than the simple SVR model, and can be applied as an alternative reliable scheme for estimations on which simple SVR and other methods demonstrate shortcomings. The proposed hybrid method improves the precision level for scour depth prediction by about 8% compared with simple SVM in terms of the correlation coefficient. [ABSTRACT FROM AUTHOR]

Crop yield prediction is an important aspect of agriculture. The timely and accurate crop yield predictions can be of great help for policy makers and farmers in planning and decision making. Generally, statistical models are employed to predict the crop yield which is time consuming and tedious. Emerging trends of deep learning and machine learning has come up as a major breakthrough in the arena. Deep learning models have the inherent ability to perform feature extraction in large dataset thus more suitable for predictions. In this paper, a deep learning-based Recurrent Neural Network (RNN) model is employed to predict wheat crop yield of northern region of India. The present study also employed LSTM to unravel the vanishing gradient problem inherent in RNN model. Experiments were conducted using 43 years benchmark dataset and proposed model results were compared with three machine learning models. Evidently, the results obtained from RNN-LSTM model(RMSE: 147.12,MAE: 60.50), Artificial Neural Network(RMSE: 732.14,MAE: 623.13), Random Forest (RMSE: 540.88, MAE: 449.36) and Multivariate Linear Regression (RMSE: 915.64,MAE: 796.07), proved the efficacy of model. Also, predicted crop yield values were found to be more close to true values for RNN-LSTM model proving efficiency of the proposed work. [ABSTRACT FROM AUTHOR]

As a key area of traffic safety research, crash severity modelling has attracted tremendous attention. Recently, there has been growing interest in applying machine learning (ML) methods in this area. However, the lessons and experience learned so far have not been systematically documented and summarised. This is the first article that surveys studies on ML applications in crash severity modelling and has the following major contributions: (1) it provides a comprehensive and critical review of current research efforts; (2) it summarises the successful experience and main challenges (e.g. data and methodology); and (3) it identifies promising research opportunities towards accurate and reliable crash severity modelling and results interpretation. The review results suggest that imbalanced data remains a major issue. Under- and over-samplings are often used to balance crash severity data despite their limitations. Some studies use local sensitivity analysis (LSA) to interpret ML modelling results but ignore the strict assumptions of LSA and omit the joint effects of risk factors. Moreover, very few studies consider the accuracy and reliability of ML model evaluation metrics. Other issues include spatiotemporal correlations, causality, model transferability and heterogeneity. This paper concludes by providing suggestions on model selection and modification to address the identified issues and recommendations for future research. For example, employing advanced ML methods such as graph convolutional networks (GCN) to model spatiotemporal correlations; exploring innovative ways of applying ML methods; and leveraging new developments in ML (e.g. interpretable ML) to derive causal relationships and interpret modelling results. [ABSTRACT FROM AUTHOR]

Understanding airfield pavement deterioration is essential for airport asset management to ensure safe and efficient airport operations. This paper employs Gradient Boosting Machine (GBM) – a machine learning method – to investigate the contributions of a variety of influencing factors to runway and taxiway pavement deterioration at Chicago O'Hare International Airport. By adopting a systematic procedure consisting of model training, validation, and testing, two separate GBM models are developed to estimate Pavement Condition Index (PCI) of runways and taxiways. The models account for various input variables that are believed to affect pavement deterioration, including pavement age and material, maintenance and rehabilitation history, weather conditions, and air traffic loading effects. The developed GBM models are shown to outperform other methods (including linear regression, nonlinear regression, artificial neural networks, and random forest) in terms of model goodness-of-fit for both runway and taxiway pavements. The GBM modelling results are subsequently used to interpret the influence of individual input variables as well as their interactions on PCI, using relative importance and partial dependence plots. With promising results, the study demonstrates the use of an approach that was not previously considered in infrastructure management and can help airport agencies enhance the ability to understand airport asset performance. [ABSTRACT FROM AUTHOR]

The assessment of the stope stability is of great importance in the underground mine design process. The instability of stopes leads to serious economic and safety problems in mining operations. This paper aims to assist in the prediction of stope stability by making use of machine learning algorithms. For this purpose, a hybrid grid search-based Artificial Neural Network method is proposed. In the experiments, a total of 215 stope cases which have been collected from six underground mining operations located across Australia are used to prove the validity of the proposed method. Then, the performance of the proposed method is compared with k-nearest neighbour (kNN), naive Bayes (NB), support vector machine (SVM), decision tree (DT) and the stability graph method. Furthermore, several performance measures, such as accuracy, precision, specificity, recall, f-measure, and g-mean are considered during the performance comparison of the methods. The accuracy values are obtained as 91.63%, 81.86%, 77.21%, 76.74%, 73.95%, and 69.77% with the proposed hybrid grid search-based ANN method, SVM, DT, NB, kNN, and the stability graph method, respectively. The empirical results from the experiments indicated that the proposed hybrid approach outperformed the other aforementioned methods, which confirms that the proposed method is a useful tool to predict stope stability. [ABSTRACT FROM AUTHOR]

High-resolution satellite images have recently been widely used for change detection of mineral resources and mining area environments. However, with their increasing resolution and number of feature sub-categories, traditional machine learning methods have become sensitive to pseudo-changes and intra-class changes, which are prone to producing misclassifications. To address these problems, a novel fully convolutional network, the Hybrid Dilated Convolutional Siamese Network (HDC-Siam) was proposed. This model combines the modular dilated convolution network with the fully convolutional Siamese networks structure, in order to reduce the commission rate. In this paper, pairs of Sentinel-2 images with an interval of about two years was used as the experimental data. The HDC-Siam model was used to detect changes, where we evaluated the accuracy in the Dongsheng coalfield in Ordos, China and the Kuznetsk coalfield in Kemerovo, Russia. We obtained F1-scores of 85% and 75% for these respective locations. In addition, we conducted comparative experiments using two other methods – Fully Convolutional Siamese – Difference (FC-Siam-diff) and Fully Convolutional Early Fusion (FC-EF) – in order to verify that the HDC-Siam works. [ABSTRACT FROM AUTHOR]

The information resulting from the use of the organization's products and services is a valuable resource for business analytics. Therefore, it is necessary to have systems to analyze customer reviews. This article is about categorizing and predicting customer sentiments. In this article, a new framework for categorizing and predicting customer sentiments was proposed. The customer reviews were collected from an international hotel. In the next step, the customer reviews processed, and then entered into various machine learning algorithms. The algorithms used in this paper were support vector machine (SVM), artificial neural network (ANN), naive bayes (NB), decision tree (DT), C4.5 and k-nearest neighbor (K-NN). Among these algorithms, the DT provided better results. In addition, the most important factors influencing the great customer experience were extracted with the help of the DT. Finally, very interesting results were observed in terms of the effect of the number of features on the performance of machine learning algorithms. [ABSTRACT FROM AUTHOR]

Traditional light water reactor simulations are usually either high fidelity, requiring hundreds of node-hours, or low fidelity, requiring only seconds to run on a common workstation. In current research, it is desirable to combine the positive aspects of both of these simulation types while minimizing their associated negative costs. Because neural networks have shown significant success when applied to other fields, they could provide a means for combining these two classes of simulation. This paper describes a methodology for designing and training neural networks to predict normalized pin powers and k e f f within a reflective two-dimensional pressurized water reactor assembly model. The developed methodology combines computer vision approaches, modular neural network approaches, and hyperparameter optimization methods to intelligently design novel network architectures. This methodology has been used to develop a novel new architecture, LatticeNet, which is capable of predicting pin-resolved powers and k e f f at a high level of detail. The results produced by this novel architecture show the successful prediction of the target neutronics parameters under a variety of typical neutronics conditions, and they indicate a potential path forward for neural network–based model development. [ABSTRACT FROM AUTHOR]

When performing microscopic traffic simulations, a premise is to have appropriately calibrated parameter values. This is often computationally expensive as it requires repeatedly running simulations. In this paper, we propose a machine learning (ML) + particle swarm optimization (PSO)-based methodology for calibrating microscopic traffic simulator parameters to improve computational efficiency. We first develop ML models that input the parameters to predict simulation outputs. Four machine learning models: decision tree, support vector machine, Gaussian process regression, and artificial neural networks are considered. The best-performing model is then embedded in PSO to seek the set of parameters that minimizes the difference between the predicted simulation outputs and the field observations. The ML+PSO methodology is applied to TransModeler using field data in Shanghai, China. We find that artificial neural networks yield the best prediction accuracy. Furthermore, PSO with embedded artificial neural networks shows superior computational efficiency and effectiveness.. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing interest in the application of machine learning for the prediction of pavement performance. Prediction models are used to predict the future pavement condition, helping to optimally allocate maintenance and rehabilitation funds. However, few studies have proposed a systematic approach to the development of machine learning models for pavement performance prediction. Most of the studies focus on artificial neural networks models that are trained for high accuracy, disregarding other suitable machine learning algorithms and neglecting the importance of models' generalisation capability for Pavement Engineering applications. This paper proposes a general machine learning approach for the development of pavement performance prediction models in pavement management systems (PMS). The proposed approach supports different machine learning algorithms and emphasizes generalisation performance. A case study for prediction of International Roughness Index (IRI) for 5 and 10-years, using the Long-Term Pavement Performance, is presented. The proposed models were based on a random forest algorithm, using datasets comprising previous IRI measurements, structural, climatic, and traffic data. [ABSTRACT FROM AUTHOR]

Machine learning models are regarded as efficient and popular models for natural disaster susceptibility prediction. However, few studies have focussed on the applications of the latest popular machine learning models in collapse susceptibility assessment (CSA). This paper proposes a 3S (RS, GPS and GIS) technology-based support vector machine (SVM) to map collapse susceptibility in the Nantian area of China. The 3S technology-based back-propagation neural network (BPNN) and radial basis function neural network (RBFNN) models are also proposed for comparison. First, 44 recorded collapses are identified through field investigation, and fourteen collapse-related causal factors are acquired using 3S technology. Second, among these recorded collapses and randomly selected 'non-collapses', 70% of the collapse and non-collapse grid cells are used to train the three models, while the remaining 30% are used to test the models. Third, the collapse susceptibility maps of the Nantian area are produced using the three models. Finally, the prediction accuracies of these models are evaluated. The results indicate that the SVM model has the highest prediction accuracy, while the RBFNN model has the lowest prediction accuracy for CSA. In addition, the distribution characteristics of collapse susceptibility in the Nantian area are produced well by all three models. [ABSTRACT FROM AUTHOR]