Search

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 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]

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]

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]

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning describe a broad range of algorithm types that can be trained based on datasets to make predictions. The increasing sophistication of AI has created new opportunities to apply these algorithms within within trauma care. Our paper overviews the current uses of AI along the continuum of trauma care, including injury prediction, triage, emergency department volume, assessment, and outcomes. Starting at the point of injury, algorithms are being used to predict severity of motor vehicle crashes, which can help inform emergency responses. Once on the scene, AI can be used to help emergency services triage patients remotely in order to inform transfer location and urgency. For the receiving hospital, these tools can be used to predict trauma volumes in the emergency department to help allocate appropriate staffing. After patient arrival to hospital, these algorithms not only can help to predict injury severity, which can inform decision-making, but also predict patient outcomes to help trauma teams anticipate patient trajectory. Overall, these tools have the capability to transform trauma care. AI is still nascent within the trauma surgery sphere, but this body of the literature shows that this technology has vast potential. AI-based predictive tools in trauma need to be explored further through prospective trials and clinical validation of algorithms. [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]

• A comprehensive, explainable accident-data-based AI artifact design is presented. • Utilize aggregate SHAP scores to classify crash data into high-level causal factors. • A systems-level taxonomy of driver behaviors linked to injury severity is discussed. • Separating explanation from models yields managerial insights. Automobile traffic accidents represent a significant threat to global public safety, resulting in numerous injuries and fatalities annually. This paper introduces a comprehensive, explainable artificial intelligence (XAI) artifact design, integrating accident data for utilization by diverse stakeholders and decision-makers. It proposes responsible, explanatory, and interpretable models with a systems-level taxonomy categorizing aspects of driver-related behaviors associated with varying injury severity levels, thereby contributing theoretically to explainable analytics. In the initial phase, we employed various advanced techniques such as data missing at random (MAR) with Bayesian dynamic conditional imputation for addressing missing records, synthetic minority oversampling technique for data imbalance issues, and categorical boosting (CatBoost) combined with SHapley Additive exPlanations (SHAP) for determining and analyzing the importance and dependence of risk factors on injury severity. Additionally, exploratory feature analysis was conducted to uncover hidden spatiotemporal elements influencing traffic accidents and injury severity levels. We developed several predictive models in the second phase, including eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural networks (DNN), and fine-tuned parameters. Using the SHAP approach, we employed model-agnostic interpretation techniques to separate explanations from models. In the final phase, we provided an analysis and summary of the system-level taxonomy across feature categories. This involved classifying crash data into high-level causal factors using aggregate SHAP scores, illustrating how each risk factor contributes to different injury severity levels. [ABSTRACT FROM AUTHOR]

• More accurate and explanatory model is proposed for natural gas storage deliverability. • A hybrid Sparse Autoencoder and Random Forest model shows on average of 5.7% increase in prediction accuracy. • The first use of machine learning and explanatory methods (XAI) in deliverability prediction for natural gas storage. Numerous subsurface factors, including geology and fluid properties, can affect the connectivity of the storage spaces in depleted reservoirs; hence, fluid flow simulations become more complicated, and predicting their deliverability remains challenging. This paper applies Machine Learning (ML) techniques to predict the deliverability of underground natural gas storage (UNGS) in depleted reservoirs. First, three baseline models were developed based on Support Vector Regression (SVR), Artificial Neural Network (ANN), and Random Forest (RF) algorithms. To improve the accuracy of the RF model as the best-performing baseline model, a unified framework, referred to as SARF, was developed. SARF combines the capabilities of Sparse Autoencoder (SA) and that of Random Forest (RF). To achieve this, the internal representations of the SA, which constitute extracted features of the input variables, are used in RF to develop the proposed SARF framework. The predictive capabilities of the baseline models and the proposed SARF model were validated using 3744 real-world storage data samples of 52 active storage reservoirs in the United States. The experimental result of this study shows that the proposed SARF model achieved an average 5.7% increase in accuracy on four separate data partitions over the baseline RF model. Furthermore, a set of eXplainable Artificial Intelligence (XAI) methods were developed to provide an intuitive explanation of which factors influence the deliverability of reservoir storage. The visualizations developed using the XAI method provide an easy-to-understand interpretation of how the SARF model predicted the deliverability values for separate reservoirs. [ABSTRACT FROM AUTHOR]

Estimating the ocean subsurface thermal structure (OSTS) based on multisource sea surface data in the western Pacific Ocean is of great significance for studying ocean dynamics and El Niño phenomenon, but it is challenging to accurately estimate the OSTS from sea surface parameters in the area. This paper proposed an improved neural network model to estimate the OSTS from 0–2000 m from multisource sea surface data including sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). In the model experiment, the rasterized monthly average data from 2005–2015 and 2016 were selected as the training and testing set, respectively. The results showed that the sea surface parameters selected in the paper had a positive effect on the estimation process, and the average RMSE value of the ocean subsurface temperature (OST) estimated by the proposed model was 0.55 °C. Moreover, there were pronounced seasonal variation signals in the upper layers (the upper 200 m), however, this signal gradually diminished with increasing depth. Compared with known estimation models such as the random forest (RF), the multiple linear regression (MLR), and the extreme gradient boosting (XGBoost), the proposed model outperformed these models under the data conditions of the paper. This research can provide an advanced artificial intelligence technique for estimating subsurface thermohaline structure in major sea areas. [ABSTRACT FROM AUTHOR]

• FFR prediction algorithm is optimized by performance within gray zone. • Synthetic models and CFD analysis are used to improve data quality and quantity. • Biometric features and flow features are used as input in FFR prediction algorithm. • Algorithm optimization is based on outlier elimination and k-fold cross validation. • Among eight algorithms, multilayer perceptron has shown highest performance. Recent attempts on adopting artificial intelligence algorithm on coronary diagnosis had limitations on data quantity and quality. While most of previous studies only used vessel image as input data, flow features and biometric features should be also considered. Moreover, the accuracy should be optimized within gray zone as the purpose is to decide stent insertion with estimated fractional flow reserve. The main purpose of this study is to develop an artificial intelligence-based coronary vascular diagnosis system focused on performance in the gray zone, from CT image extraction to FFR estimation. Three main issues should be considered for an algorithm to be used for pre-screening: algorithm optimization in the gray zone, minimization of labor during image processing, and consideration of flow and biometric features. This paper introduces a full FFR pre-screening system from automatic image extraction to an algorithm for estimating the FFR value. The main techniques used in this study are an automatic image extraction algorithm, lattice Boltzmann method based computational fluid dynamics analysis of a synthetic model and patient data, and an AI algorithm optimization. For feature extraction, this study focused on an automatic process to reduce manual labor. The algorithm consisted of two steps: the first algorithm calculates flow features from geometrical features, and the second algorithm estimates the FFR value from flow features and patient biometric features. Algorithm selection, outlier elimination, and k-fold selection were included to optimize the algorithm. Eight types of algorithms including two neural network models and six machine learning models were optimized and tested. The random forest model shows the highest performance before optimization, whereas the multilayer perceptron regressor shows the highest gray zone accuracy after optimization. [Display omitted] [ABSTRACT FROM AUTHOR]

For thin-walled beams in fire, the development of simple analytical models is hindered by the complexity of the interaction between local and lateral-torsional buckling combined with the temperature reduction of steel properties, resulting in overly conservative fire design rules. This paper investigates the application of machine learning models to predict the capacity of steel beams with thin-walled sections at elevated temperatures. Machine learning models provide a pathway to deliver fast and accurate methods to predict complex non-linear problems, which may overcome limitations of existing design methods, time-consuming finite element simulations, and expensive laboratory tests; yet these models remain unexplored in this particular field of study. This work describes the development, validation, and application of artificial neural networks, support vector machines, polynomial regression and random forests using an extensive dataset of numerical results from previously validated finite element models. It is shown that these models can also predict the capacity for beams with loading and boundary conditions outside the training dataset range. Finally, the machine learning models are compared against existing design proposals to demonstrate the benefits of using these advanced techniques to calculate the capacity of thin-walled beams at elevated temperatures. [ABSTRACT FROM AUTHOR]

The rate of penetration (ROP) accounts for a substantial portion of the overall drilling cost. The drilling optimization process, which mostly involves the adjustment of the mechanical drilling parameters, is therefore of prime importance in ensuring efficient drilling. However, drilling formations with assorted types of lithology necessitate the involvement of more parameters to reduce uncertainty and enhance confidence when predicting the ROP. The objective of this paper is to introduce an ensemble model based on random forest (RF), in which artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are the base learner models, to predict the ROP across different lithological formations In this study, two types of actual field data of Well-1 were employed to build the model: (i) mechanical drilling parameters collected from real-time sensors allocated at the rig site, and (ii) petrophysical properties obtained from conventional well logs. Well-2 with more than 2300 unseen data points was used to compare the capability of the base learners (i.e., ANN and ANFIS), standalone RF, and RF-meta model in predicting the ROP with two of the earliest published ROP empirical models (Maurer's and Bingham's models). The results showed that the RF-meta model outperformed the base learners and Maurer's and Bingham's empirical models in predicting the ROP in Well-2 with a low absolute average percentage error (AAPE) of 7.8 % and a high coefficient of determination (R 2) of 0.94. • Prediction of the rate of penetration (ROP) while drilling different formation lithologies. • ROP prediction using an ensemble learning model based on RF, in which ANN, and ANFIS were served as base learner models. • Investigation of the relevance importance for the input parameters by employing principal component analysis (PCA). [ABSTRACT FROM AUTHOR]