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The study focuses on optimizing biomethane yield in the anaerobic digestion of alkali-pretreated groundnut shells, involving varied input parameters. Biomethane optimization will improve the economy of the technology, which will assist in managing the environmental challenges of fossil fuel combustion. Traditional methods prove challenging, inaccurate, and uneconomical, necessitating efficient optimization models. This research hybridizes particle swarm optimization (PSO) and genetic algorithms (GA) with adaptive neuro-fuzzy inference system (ANFIS) models, assessing input parameters' influence on biomethane yield through renowned performance metrics. Comparing the best model in the hybrid analysis, encompassing pretreatments A-E, the PSO-ANFIS (RMSE = 1.1719, MADE = 0.6525, MAE = 0.9314, Theil's U = 0.1844, and SD = 0.7737) outperformed the GA-ANFIS (RMSE = 1.9338, MADE = 0.9318, MAE = 1.6557, Theil's U = 0.2734, SD = 1.0598), using the same cluster radius of 0.50. Furthermore, compared to the GA-ANFIS model, the PSO-ANFIS model demonstrated significant improvements across various metrics: RMSE by 39.40%, MADE by 29.97%, MAE by 43.75%, Theil's U by 32.56%, and SD by 27.00%. Results indicate that the PSO-ANFIS model outperforms the GA-ANFIS model, emphasizing the importance of suitable clustering algorithms and precise parameter adjustment for optimal performance in predicting biomethane yield from pretreated lignocellulose feedstocks. [ABSTRACT FROM AUTHOR]

Computational models of tumor growth are valuable for simulating the dynamics of cancer progression and treatment responses. In particular, agent-based models (ABMs) tracking individual agents and their interactions are useful for their flexibility and ability to model complex behaviors. However, ABMs have often been confined to small domains or, when scaled up, have neglected crucial aspects like vasculature. Additionally, the integration into tumor ABMs of precise radiation dose calculations using gold-standard Monte Carlo (MC) methods, crucial in contemporary radiotherapy, has been lacking. Here, we introduce AMBER, an Agent-based fraMework for radioBiological Effects in Radiotherapy that computationally models tumor growth and radiation responses. AMBER is based on a voxelized geometry, enabling realistic simulations at relevant pre-clinical scales by tracking temporally discrete states stepwise. Its hybrid approach, combining traditional ABM techniques with continuous spatiotemporal fields of key microenvironmental factors such as oxygen and vascular endothelial growth factor, facilitates the generation of realistic tortuous vascular trees. Moreover, AMBER is integrated with TOPAS, an MC-based particle transport algorithm that simulates heterogeneous radiation doses. The impact of radiation on tumor dynamics considers the microenvironmental factors that alter radiosensitivity, such as oxygen availability, providing a full coupling between the biological and physical aspects. Our results show that simulations with AMBER yield accurate tumor evolution and radiation treatment outcomes, consistent with established volumetric growth laws and radiobiological understanding. Thus, AMBER emerges as a promising tool for replicating essential features of tumor growth and radiation response, offering a modular design for future expansions to incorporate specific biological traits. [ABSTRACT FROM AUTHOR]

Accurate forecasting of dissolved oxygen (DO) levels is vital for river ecosystem health. A novel methodology, MVMD-TSA-GPR, combines Multivariate Variational Mode Decomposition (MVMD), the Tunicate Swarm Algorithm (TSA), and Gaussian Process Regression (GPR) to improve DO level predictions. This study also incorporated Generalized Additive Model and Regression Bagged Ensemble (RBE) for 1- and 3-month forecasts using monthly data (1974–2023) from 16 water quality parameters across five Mississippi River basin sites. Key predictors identified through cross-correlation include lagged values and parameters like water temperature, discharge, pH, total phosphorus, potassium, and sulfate, which significantly influence DO levels. The MVMD-TSA-GPR model outperformed others, especially at site 5, showing substantial improvements in accuracy with decreased RMSE values across various scenarios. Model ranking via the Taylor Diagram indicated MVMD-TSA-GPR had the highest performance, followed by MVMD-TSA-RBE and others. Notably, the GPR model's RMSE at site 3 decreased from 2.11 to 1.01 (109% reduction) for the 1-month forecast, while at site 4 for the 3-month forecast, it dropped from 1.85 to 1.04 (106% reduction). The results revealed that the MVMD-TSA-GPR model demonstrated the highest performance for DO (t + 1), achieving R = 0.90, PBIAS = 0.73%, and WI = 0.804, as well as for DO (t + 3), with R = 0.88, PBIAS = 0.54%, and WI = 0.779, at the Lower Mississippi site during the test phase. Additionally, the MVMD-TSA-RBE model excelled for DO (t + 1) in the Missouri River at the Hermann site, achieving R = 0.91, PBIAS = 0.86%, and WI = 0.805 during the test phase. These results underscore the effectiveness of the MVMD-TSA hybrid approach. Interpretative analysis using SHapley Additive exPlanations (SHAP) revealed water temperature, pH, and potassium as key factors affecting DO levels. The speed and accuracy of MVMD-TSA-GPR make it a promising tool for monitoring river water quality. [ABSTRACT FROM AUTHOR]

Skin cancer, one of the most dangerous cancers, poses a significant global threat. While early detection can substantially improve survival rates, traditional dermatologists often face challenges in accurate diagnosis, leading to delays in treatment and avoidable fatalities. Deep learning models like CNN and transfer learning have enhanced diagnosis from dermoscopic images, providing precise and timely detection. However, despite the progress made with hybrid models, many existing approaches still face challenges, such as limited generalization across diverse datasets, vulnerability to overfitting, and difficulty in capturing complex patterns. As a result, there is a growing need for more robust and effective hybrid models that integrate multiple architectures and advanced mechanisms to address these challenges. Therefore, this study aims to introduce a novel multi-architecture hybrid deep learning model called "RvXmBlendNet," which combines the strengths of four individual models: ResNet50 (R), VGG19 (v), Xception (X), and MobileNet (m), followed by "BlendNet" to signify their fusion into a unified architecture. The integration of these models is achieved through a synergistic combination of architectures, incorporating self-attention mechanisms using attention layers and adaptive content blocks. This study used the HAM10000 dataset to refine dermoscopic image preprocessing and enhance deep learning model accuracy. Techniques like OpenCV-based hair removal, min–max scaling, and adaptive histogram equalization were employed to improve image quality and feature extraction. A comparative study between the proposed hybrid "RvXmBlendNet" and individual models (CNN, ResNet50, VGG19, Xception, and MobileNet) demonstrated that "RvXmBlendNet" achieved the highest accuracy of 98.26%, surpassing other models. These results suggest that the system can facilitate earlier interventions, improve patient outcomes, and potentially lower healthcare costs by reducing the need for invasive diagnostic procedures. [ABSTRACT FROM AUTHOR]

Abstract Skin cancer, one of the most dangerous cancers, poses a significant global threat. While early detection can substantially improve survival rates, traditional dermatologists often face challenges in accurate diagnosis, leading to delays in treatment and avoidable fatalities. Deep learning models like CNN and transfer learning have enhanced diagnosis from dermoscopic images, providing precise and timely detection. However, despite the progress made with hybrid models, many existing approaches still face challenges, such as limited generalization across diverse datasets, vulnerability to overfitting, and difficulty in capturing complex patterns. As a result, there is a growing need for more robust and effective hybrid models that integrate multiple architectures and advanced mechanisms to address these challenges. Therefore, this study aims to introduce a novel multi-architecture hybrid deep learning model called "RvXmBlendNet," which combines the strengths of four individual models: ResNet50 (R), VGG19 (v), Xception (X), and MobileNet (m), followed by "BlendNet" to signify their fusion into a unified architecture. The integration of these models is achieved through a synergistic combination of architectures, incorporating self-attention mechanisms using attention layers and adaptive content blocks. This study used the HAM10000 dataset to refine dermoscopic image preprocessing and enhance deep learning model accuracy. Techniques like OpenCV-based hair removal, min–max scaling, and adaptive histogram equalization were employed to improve image quality and feature extraction. A comparative study between the proposed hybrid "RvXmBlendNet" and individual models (CNN, ResNet50, VGG19, Xception, and MobileNet) demonstrated that "RvXmBlendNet" achieved the highest accuracy of 98.26%, surpassing other models. These results suggest that the system can facilitate earlier interventions, improve patient outcomes, and potentially lower healthcare costs by reducing the need for invasive diagnostic procedures.

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions. [ABSTRACT FROM AUTHOR]

In late 2023, the United Nations conference on climate change (COP28), which was held in Dubai, encouraged a quick move from fossil fuels to renewable energy. Solar energy is one of the most promising forms of energy that is both sustainable and renewable. Generally, photovoltaic systems transform solar irradiance into electricity. Unfortunately, instability and intermittency in solar radiation can lead to interruptions in electricity production. The accurate forecasting of solar irradiance guarantees sustainable power production even when solar irradiance is not present. Batteries can store solar energy to be used during periods of solar absence. Additionally, deterministic models take into account the specification of technical PV systems and may be not accurate for low solar irradiance. This paper presents a comparative study for the most common Deep Learning (DL) and Machine Learning (ML) algorithms employed for short-term solar irradiance forecasting. The dataset was gathered in Islamabad during a five-year period, from 2015 to 2019, at hourly intervals with accurate meteorological sensors. Furthermore, the Grid Search Cross Validation (GSCV) with five folds is introduced to ML and DL models for optimizing the hyperparameters of these models. Several performance metrics are used to assess the algorithms, such as the Adjusted R2 score, Normalized Root Mean Square Error (NRMSE), Mean Absolute Deviation (MAD), Mean Absolute Error (MAE) and Mean Square Error (MSE). The statistical analysis shows that CNN-LSTM outperforms its counterparts of nine well-known DL models with Adjusted R2 score value of 0.984. For ML algorithms, gradient boosting regression is an effective forecasting method with Adjusted R2 score value of 0.962, beating its rivals of six ML models. Furthermore, SHAP and LIME are examples of explainable Artificial Intelligence (XAI) utilized for understanding the reasons behind the obtained results. [ABSTRACT FROM AUTHOR]

The estimation of a certain threshold beyond which an extreme value distribution can be fitted to the tail of a data distribution remains one of the main issues in the theory of statistics of extremes. While standard Peak over Threshold (PoT) approaches determine this threshold graphically, we introduce in this paper a general framework which makes it possible for one to determine this threshold algorithmically by estimating it as a free parameter within a composite distribution. To see how this threshold point arises, we propose a general framework for generating three-component hybrid distributions which meets the need of data sets with right heavy-tail. The approach involves the combination of a distribution which can efficiently model the bulk of the data around the mean, with an heavy-tailed distribution meant to model the data observations in the tail while using another distribution as a link to connect the two. Some special examples of distributions resulting from the general framework are generated and studied. An estimation algorithm based on the maximum likelihood method is proposed for the estimation of the free parameters of the hybrid distributions. Application of the hybrid distributions to the S &P 500 index financial data set is also carried out. [ABSTRACT FROM AUTHOR]

In recent decades, demand for freshwater resources has increased the risk of severe water stress. With the growing prevalence of artificial intelligence (AI), many researchers have turned to it as an alternative to linear methods to assess water consumption (WC). Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, this study utilized 229 screened publications identified through database searches and snowball sampling. This study introduces novel aspects of AI's role in water consumption assessment by focusing on innovation, application sectors, sustainability, and machine learning applications. It also categorizes existing models, such as standalone and hybrid, based on input, output variables, and time horizons. Additionally, it classifies learnable parameters and performance indexes while discussing AI models' advantages, disadvantages, and challenges. The study translates this information into a guide for selecting AI models for WC assessment. As no one-size-fits-all AI model exists, this study suggests utilizing hybrid AI models as alternatives. These models offer flexibility regarding efficiency, accuracy, interpretability, adaptability, and data requirements. They can address the limitations of individual models, leverage the strengths of different approaches, and provide a better understanding of the relationships between variables. Several knowledge gaps were identified, resulting in suggestions for future research. [ABSTRACT FROM AUTHOR]

Forecasting solar power production accurately is critical for effectively planning and managing renewable energy systems. This paper introduces and investigates novel hybrid deep learning models for solar power forecasting using time series data. The research analyzes the efficacy of various models for capturing the complex patterns present in solar power data. In this study, all of the possible combinations of convolutional neural network (CNN), long short-term memory (LSTM), and transformer (TF) models are experimented. These hybrid models also compared with the single CNN, LSTM and TF models with respect to different kinds of optimizers. Three different evaluation metrics are also employed for performance analysis. Results show that the CNN–LSTM–TF hybrid model outperforms the other models, with a mean absolute error (MAE) of 0.551% when using the Nadam optimizer. However, the TF–LSTM model has relatively low performance, with an MAE of 16.17%, highlighting the difficulties in making reliable predictions of solar power. This result provides valuable insights for optimizing and planning renewable energy systems, highlighting the significance of selecting appropriate models and optimizers for accurate solar power forecasting. This is the first time such a comprehensive work presented that also involves transformer networks in hybrid models for solar power forecasting. [ABSTRACT FROM AUTHOR]

Industrial and agricultural development, population increase, limitations in water resources renewability, lack of timely management of water resources, and the recent years' droughts have caused pressure on groundwater. One of the aquifers that have faced a sharp drop in water level in recent years is the Aspas aquifer in Fars province. In this study, the condition of the groundwater level (GWL) in this aquifer was analyzed using the data of the gravity recovery and climate experiment (GRACE) Satellite. In addition, pre-processing tools, such as complementary ensemble empirical mode and decomposition (CEEMD) and wavelet transform (WT), were utilized. The support vector regression (SVR) and artificial neural networks (ANN) models were used in two simple and hybrid ways with pre-processing tools. According to the results, combining the models with pre-processing tools has improved their efficiency. As a result, the coefficient of determination (R2) has been improved from 0.927 in ANN to 0.938 in W-ANN and 0.998 in CEEMD-ANN. The R2 has reached from 0.918 in the SVR to 0.949 in the W-SVR and 0.948 in the CEEMD-SVR. The comparison between the results of processing algorithms of GRACE satellite in the test phase determined that the GFZ processing algorithm shows the best performance. CEEMD-ANN performance was compared to GFZ algorithm. In addition, a new approach was utilized to forecast the GWL shifts. The results indicated that the new approach provides a suitable estimate of the groundwater in the shortest time with the lowest cost. Therefore, this approach can be used to predict the GWL in other aquifers. [ABSTRACT FROM AUTHOR]

Substantial deterioration of surface water quality, mainly caused by human activities and climate change, makes the assessment of water quality a global priority. Thus, in this study, four metaheuristic algorithms, namely the particle swarm optimization (PSO), differential evolution (DE), ant colony optimization algorithm (ACOR), and genetic algorithm (GA), were employed to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in the evaluation of surface water total dissolved solids (TDS). Monthly and annual TDS were considered as target variables in the analysis. In order to evaluate and compare the authenticity of the models, an economic factor (convergence time) and statistical indices of the coefficient of determination (R2), Kling Gupta efficiency (KGE), root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliff efficiency (NSE) were utilized. The results revealed that the hybrid methods used in this study could enhance the classical ANFIS performance in the analysis of the monthly and annual TDS of both stations. For more clarification, the models were ranked using the TOPSIS approach by simultaneously applying the effects of statistical parameters, temporal and spatial change factors, and convergence time. This approach significantly facilitated decision-making in ranking models. The ANFIS-ACOR annual model considering discharge had the best performance in the Vanyar Station; Furthermore, the ANFIS-ACOR monthly model ignoring discharge was outstanding in the Gotvand Station. In total, after utilizing two defined and proposed temporal and spatial change factors, the ANFIS-ACOR and ANFIS-DE hybrid models had the best and worst performance in TDS prediction, respectively. [ABSTRACT FROM AUTHOR]

This paper aims to highlight in a relevant way the interest of hybrid models (coupling of ARIMA processes and machine learning models) for economic or financial agents. These models are likely to allow better consideration of certain stylized facts (not necessarily taken into account by often-used models such as ARIMA-GARCH), observed in the analysis of financial time series. Most hybrid models assume that the random perturbation of the chosen ARIMA process follows a normal distribution. Thus, the literature on hybrid models remains on this Gaussian framework and it would be necessary to consider a non-Gaussian and much more realistic framework. We consider hybrid models with the assumption that the random disturbance process follows a Student's distribution (denoted by ARIMA T), allowing us to take into account the leptokurticity often observed when analyzing financial time series returns. Under certain assumptions, the empirical results show the power of the hybrid models considered. [ABSTRACT FROM AUTHOR]

The state of New York admitted 143 million metric tons of carbon emissions from fossil fuels in 2020, prompting the ambitious goal set by the CLCPA to achieve carbon neutrality. The paper focused on analyzing and predicting carbon emissions using four different machine-learning algorithms. It examined emissions from fossil fuel combustion from 1990 to 2020 and validated four different algorithms to choose the most effective one for predicting emissions from 2020 to 2050. The analysis covered various economic sectors including transportation, residential, commercial, industrial, and electric power. By analyzing policies, the paper forecasted emissions for 2030 and 2050, leading to the identification of different pathways to reach carbon neutrality. The research concluded that in order to achieve neutrality, radical measures must be taken by the state of New York. Additionally, the paper compared the most recent data for 2021 with the forecasts, showing that significant measures need to be implemented to achieve the goal of carbon neutrality. Despite some studies assuming a trend of decreased emissions, the research revealed different results. The paper presents three pathways, two of which follow the ambitious plan to reach carbon neutrality. As a result, the emission amount by 2050 for the different pathways was projected to be 31.1, 22.4, and 111.95 of MMt CO2 e, showcasing the need for urgent action to combat climate change. [ABSTRACT FROM AUTHOR]

The use of a convolutional neural network with transfer learning is a strategy that defines high-level features, commonly explored to study patterns in medical images. These features can be analyzed via different methods in order to design hybrid models with more useful and accurate solutions for clinical practice. In this paper, a computational scheme is presented to define hybrid models through deep features by transfer learning, selection by ranking and a robust ensemble classifier with five algorithms. The obtained models were applied to classify histological images from breast, colorectal and liver tissue. The strategy developed here allows knowing important results and conditions to improve models of computer-aided diagnosis, even exploring classic CNN models. The features were defined using layers from the AlexNet and ResNet-50 architectures. The attributes were organized into subsets of the most relevant features and submitted to a k-fold cross-validation process. The best hybrid models were obtained with deep features from the ResNet-50 network, using distinct layers (activation_48_relu and avg_pool) and a maximum of 35 descriptors. These hybrid models provided 98.00% and 99.32% of accuracy values, with emphasis on histological images of breast cancer, indicating the best solution among those available in the specialized Literature. Also, these models provided more relevant results for classifying UCSB and LG datasets than regularized techniques and CNN architectures, exploring data augmentation or not. The computational scheme with detailed information regarding the main hybrid models is a relevant contribution to the community interested in the study of machine learning techniques for pattern recognition. [ABSTRACT FROM AUTHOR]

Big data delivers high velocity, high volume, and high veracity data in vast quantities. Analyzing and handling this data requires new software, fast and high-capacity hardware, and trained individuals. One can determine the quality of such data models by assessing the software's reliability at a certain level after considering all the external and environmental factors affecting the software functionality. There are many existing reliability models, but the performance of these models regarding Big Data is still questionable. Thus, we need to develop a model that best suits the big data environment and accurately predicts the fault likely to occur. We developed a hybrid model to determine the software reliability using big fault data. The model included external factors, like human interaction and hardware malfunctioning resulting from the volume, veracity, and velocity of Big Data while determining software reliability, giving better performance and estimation accuracy than the existing well-known models. This paper proposes ten new hybrid models used for handling software reliability of big data. The intensity function and mean value of the proposed hybrid models were derived by combining well-known models' mean value and intensity value function. The parameter evaluation was done using the maximum likelihood method and ranking algorithm within the specified range of the guess vector. The data was collected using Big Data analysis. The best model out of the proposed ten is selected based on estimation accuracy and weighted function value. The chosen model was validated using a set of comparison criteria and estimation accuracy comparison. A t-test was also done to show that both the developed model's estimated and observed values were drawn from the same population pool. The comparisons and experiment results strengthen our claim regarding the better performance of our developed model over existing ones. [ABSTRACT FROM AUTHOR]

Glaucoma is an eye disease caused by damage to the optic nerves and is a common cause of incurable blindness worldwide. If glaucoma is diagnosed early, vision loss can be prevented with regular exams and treatment. If diagnosed too late, the disease can cause severe damage to the optic nerve that cannot be reversed, leading to loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Most of the studies conducted in recent years have presented Deep Learning (DL) based architectures that use an automatic computerized system based on segmentation of hand-made features in fundus images. In this study, we seek to help experts detect glaucoma using a model that combines Deep Learning and Machine Learning using raw fundus images. Deep features are extracted using a new Convolutional Neural Networks (CNN) model. Deep features have been used in popular traditional Machine Learning methods (ML) for classification such as Adaboost, k Nearest Neighbor (kNN), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machines (SVM), and Naive Bayes (NB). The performances of the hybrid models were evaluated using the ACRIMA dataset of 705 images. The dataset is reserved for 80% training and 20% testing data. Experimental results show that the hybrid model of CNN and Adaboost has the highest success rate with 92.96% accuracy, 93.75% F1 score and an AUC value of 0.928. [ABSTRACT FROM AUTHOR]

Fast and automated recognizing of abnormal behaviors in crowded scenes is significantly effective in increasing public security. The traditional procedure of recognizing abnormalities in the Web of Thing (WoT) platform comprises monitoring the activities and describing the crowd properties such as density, trajectory, and motion pattern from the visual frames. Accordingly, incorporating real-time security monitoring based on the WoT platform and machine learning algorithms would significantly enhance the influential detection of abnormal behaviors in the crowds. This paper addresses various automatic and real-time surveillance methods for abnormal event detection to recognize the dynamic crowd behavior in security applications. The critical aspect of security and protection of public places is that we cannot manually monitor the unpredictable and complex crowded environments. The abnormal behavior algorithms have attempted to improve efficiency, robustness against pixel occlusion, generalizability, computational complexity, and execution time. Similar to the state-of-the-art abnormal behavior detection of crowded scenes, we broadly classified methods into different categories such as tracking, classification based on handcrafted extracted features, classification based on deep learning, and hybrid approaches. Hybrid and deep learning methods have been found to have more satisfactory results in the classification stage. A set of video frames called Motion Emotion Dataset (MED) is employed in this study to examine the various conditions governing these methods. Incorporating an appropriate real-time approach with considering WoT platform can facilitate the analysis of crowd and individuals' behavior for security screening of abnormal events. [ABSTRACT FROM AUTHOR]

Accurately estimating river water flow during floods is crucial to water resource management, dam reservoir operation, and flood mitigation strategies. Although hydrological models for flood prediction have improved, they still face constraints and make inaccurate forecasts. Hydraulic models face uncertainties related to riverbed Manning roughness coefficient and energy slope. This study employs a novel Friction-Slope (α parameter) method to estimate flood discharge. Investigation focuses on three alluvial rivers in Golestan, Iran. The computation method uses the Manning formula and accounts for river energy slope and riverbed Manning roughness coefficient. The α parameter is calculated using easy-to-access river cross-section variables: flow depth, area, and hydraulic radius. SVR-PSO, SVR-GWO, and SVR-RSM hybrid methods are used to achieve this. Calculated river flow discharges are compared to measured data. Statistical evaluation criteria like R2, MAE, RMSE, and conformity index determined the hybrid models' optimal structures. The SVR-RSM model had the highest accuracy during testing, with an R2 value of 0.97, MAE of 0.22, RMSE of 1.66, and d of 0.99. Once the α parameter was determined using the RSM-SVR model, river flow discharges were calculated and compared to observed values. The testing phase produced the most accurate results, with R2 = 0.88, MAE = 0.15, RMSE = 0.41, and d = 0.98. [ABSTRACT FROM AUTHOR]

Monitoring water resources requires accurate predictions of rainfall data. Our study introduces a novel deep learning model named the deep residual shrinkage network (DRSN)—temporal convolutional network (TCN) to remove redundant features and extract temporal features from rainfall data. The TCN model extracts temporal features, and the DRSN enhances the quality of the extracted features. Then, the DRSN–TCN is coupled with a random forest (RF) model to model rainfall data. Since the RF model may be unable to classify and predict complex patterns and data, our study develops the RF model to model outputs with high accuracy. Since the DRSN–TCN model uses advanced operators to extract temporal features and remove irrelevant features, it can improve the performance of the RF model for predicting rainfall. We use a new optimizer named the Gaussian mutation (GM)–orca predation algorithm (OPA) to set the DRSN–TCN–RF (DTR) parameters and determine the best input scenario. This paper introduces a new machine learning model for rainfall prediction, improves the accuracy of the original TCN, and develops a new optimization method for input selection. The models used the lagged rainfall data to predict monthly data. GM–OPA improved the accuracy of the orca predation algorithm (OPA) for feature selection. The GM–OPA reduced the root mean square error (RMSE) values of OPA and particle swarm optimization (PSO) by 1.4%–3.4% and 6.14–9.54%, respectively. The GM–OPA can simplify the modeling process because it can determine the most important input parameters. Moreover, the GM–OPA can automatically determine the optimal input scenario. The DTR reduced the testing mean absolute error values of the TCN–RAF, DRSN–TCN, TCN, and RAF models by 5.3%, 21%, 40%, and 46%, respectively. Our study indicates that the proposed model is a reliable model for rainfall prediction. [ABSTRACT FROM AUTHOR]

Climate change poses significant challenges globally, demanding accurate forecasting methodologies to comprehend and address its consequences. This paper presents a comparative analysis of hybrid models for forecasting carbon dioxide ( C O 2 ) emissions in drought-prone Somalia. The study employs 15 time series models, including six single time series models AutoRegressive Integrated Moving Average (ARIMA), Error Trend Seasonality (ETS), Trigonometric seasonality, Box-Cox transformation (TBATS), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), and Neural Network AutoRegression (NNAR) and nine hybrid models (ARIMA-ETS, ARIMA-TBATS, ARIMA-Theta, ARIMA-NNAR, ARIMA-ETS-TBATS, ARIMA-NNAR-Theta, ARIMA-NNAR-TBATS, ARIMA-ETS-Theta, and ARIMA-ETS-NNAR). The dataset spans from 1950 to 2020, with a training set from 1950 to 2010 and a testing/validation set from 2011 to 2020. The models are evaluated using various metrics, including Mean Absolute Scaled Error (MASE), Symmetric Mean Absolute Percentage Error (SMAPE), Mean Absolute Percentage Error (MAPE), and Theil's U statistic. The data are found to be non-stationary, requiring the application of differencing techniques. Among the single time series models, TBATS(0.717, 0,0, 1, -) performs the best, while the ARIMA-TBATS hybrid model outperforms the other hybrid models. The forecasts provide valuable insights for addressing the challenges posed by charcoal burning and exporting in Somalia. The study concludes with recommendations for future research, including the incorporation of socio-economic and policy variables, spatial analysis, and long-term forecasting. [ABSTRACT FROM AUTHOR]

One of the key elements of the hydrogeological cycle and a variable used by many water resource operating models is the variation in groundwater level (GWL). One of the biggest obstacles to the drawdown analysis and GWL forecasts is the absence of accurate and complete data. The application of diverse numerical models has been regarded as a reliable approach in recent years. Such models are able to determine the GWL for any given region by utilising a wide variety of statistics, data, and field measurements such as pumping experiments, geophysics, soil and land use maps, topography and slope data, a plethora of boundary conditions, and the application of complex equations. Artificial intelligence-based models need significantly less information. The purpose of this research is to predict the changes of GWL of Shazand plain by using the PSO-ANN, ACA-ANN hybrid methods and deep learning methods LSTM, LS-SVM, and ORELM and comparing with GMS numerical model. The model's accuracy is evaluated using a two-stage validation and verification process. Then Taylor's diagram was used to select the best model. Results show that ORELM with R, Nash, RMSE and NRMSE values equal to 0.977, 0.955, 0.512 and 0.058 respectively was the best performance in the test stage. After that is the PSO-ANN model. Using the Taylor diagram is another certain way to guarantee that you've picked the best possible model. The research results show that there is a link between the ORELM and the place that is most central to the reference point. Since the GMS model is complex and requires a large amount of data and a time-consuming calibration and validation process, the ORELM model can be utilised with certainty to predict the GWL across the entire plain. This research suggests that instead of using numerical models with a complex and time-consuming structure, deep learning methods with the least required data and with high accuracy should be used to forecast the groundwater level. [ABSTRACT FROM AUTHOR]

Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive networks (FCNs) arose from traditional fuzzy cognitive maps (FCMs) to have the advantage of guaranteed convergence to equilibrium points, thus being more suitable than conventional FCMs for a variety of pattern recognition and system identification tasks. Moreover, recent developments led to FCNs with functional weights (FCNs-FW), as a significant FCNs enhancement in terms of storage requirements, efficiency and less human intervention requirements. In this paper we proceed further by introducing hybrid deep learning structures, interweaving FCNs-FW with well established deep neural network (DNN) representative structures and apply the new schemes on a variety of pattern recognition and time series prediction tasks. More specifically, after discussing general issues related to the construction of deep learning structures using FCNs-FW we present three hybrid models, which combine the FCN-FW with convolutional neural networks (CNNs), echo state networks (ESNs) and AutoEncoder (AE) schemes, respectively. The hybrid schemes are tested on diverse benchmark data sets and prove that FCN-FW based hybrid schemes perform equally well or better than state-of-the-art DNN-based schemes, paving thus the way for using cognitive networks to deep learning representative structures. [ABSTRACT FROM AUTHOR]

Globally, but especially in Sichuan Province (Southwest China), landslides are considered to be one of the most common geological hazards. The purpose of the current study is to develop a novel model based on fractal theory, GeoDetector, and random forest, namely, the "GD-FFR-RF" model, for landslide susceptibility modeling and identifying areas prone to landslides with a case study of Sichuan Province, China. On this basis, fourteen landslide conditioning factors, including slope angle, slope aspect, relief amplitude, surface roughness, cutting depth, elevation, river density, rainfall, stream power index, road density, plan curvature, profile curvature, peak ground acceleration, and lithology, are selected. The information gain ratio is used to determine the significance of different conditioning factors. The overall performance of the three resulting models is assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical indices. The GD-FFR-RF model yields better overall performance and more accurate results than the FFR-RF and FR-RF models. Therefore, in conclusion, the GD-FFR-RF model can be applied as a promising comprehensive model for the spatial prediction of landslide occurrence in subsequent studies. The generated landslide susceptibility maps will be useful for developing strategies for landslide hazard prevention and mitigation. [ABSTRACT FROM AUTHOR]

A method for constructing models of the scenario simulation of special situations in biophysical systems with allowance for the effect of changes in the physics of the Sun is proposed. Tools for representing hybrid computational structures for computer models of scenarios of invasion of species with a high reproductive potential into a new habitat by analogy with physical phase transitions are being intentionally developed. Our analysis shows that the dynamics of population insect invasions has much in common with the development of the modern pandemic: many series of repeated activity outbreaks are observed in both forest pests and new strains of coronavirus. After a long, but false and deceptive, damping, local peaks reappear, as has been observed for the invasive North American Lymantria dispar moth pest. Physical models of oscillators need to be significantly improved, since biosystems are self-regulating. We have formed a calculation scheme from variants of the right-hand sides of differential equations with predicates for their situational switching at the event transformation points. We have simulated the extreme development and analyzed options in the sequence of stages of crisis processes. The developed invasion modeling principle is based on the description of stages and phase changes during the evolution of processes occurring under the dispersal of alien species. We have developed switching schemes for a set of forms for the right-hand sides of the equations correlated with the invasion stages. The modeled process is divided into stages: introduction of a small group of individuals, hidden existence, adaptive takeover of a habitat, population explosion, crisis, and new oscillating equilibrium. There is a hypothesis that changes in the pest population are influenced by the physical effect of cyclic solar activity. Computer-simulation experiments in scenarios for situations of pulsating pest invasions include the factor of periodic changes in the solar constant, for example, in the Hale cycle. The factor of cyclicity of the solar constant, according to the Schatten's model, has been included in the auxiliary equation of our hybrid system. Using the method for organizing hybrid structures, we have studied the phase variants of the invasive phenomena leading to pulsating outbreaks of the Lymantria dispar pest. Analysis of the hybrid model with a lag argument does not allow us to say that the periodic solar activity is the most important physical factor in triggering pulsating outbreaks. The rates of biota recovery and adaptation of natural enemies are more important factors of triggering the outbreak activity. [ABSTRACT FROM AUTHOR]

Floods are one of the natural disasters that occur most frequently in the Brahmani River of Eastern India. Frequent floods in the area, results from anthropogenic activities, climate change, unpredictable weather, and heavy rainfall cause loss of life, property, and resources. Flood hazard susceptibility maps will help in identifying risk-prone locations, which aids in flood management and the decision-making process. The main objective of this study is to evaluate the performance of bivariate statistical and hybrid models such as Frequency ratio, Weight of Evidence (WoE), Evidential Belief Function (EBF), Index of Entropy, Analytical Hierarchy Process (AHP), AHP–TOPSIS and AHP VIKOR to prepare flood susceptibility maps of the Brahmani River basin, India. The flood conditioning factor includes Slope, Aspect, Elevation, Curvature, Geology, Geomorphology, Topographic Wetness Index, Topographic Ruggedness Index, Distance from Streams, Distance from Roads, Rainfall, Normalized Difference Vegetation Index (NDVI), Stream Power Index, Soil, Drainage Density, and Land use Land cover. The flood inventory was prepared using various atlas and news reports. Multicollinearity among all the causative parameters is measured using the Variance Inflation factor (VIF) and Tolerance (TOL). The collection of a spatial database and flood inventory from various sources is used to prepare flood susceptibility maps. The flood susceptibility map is categorised into very high, high, moderate, low, and very low classes using the natural break method. The models are validated using flood training (70%) and testing (30%) points. The accuracy of flood susceptibility maps is determined using Area Under Receiver Operating Characteristics (AUROC) curves and Seed Cell Area Index (SCAI) values. The success rate of Frequency ratio (AUC 0.91), EBF (AUC 0.90) and WoE (AUC 0.86) show the highest accuracy among all other models in AUC. WoE, IoE, and FR gave reliable results in the SCAI validation process. The susceptibility maps will help in identifying hazard-prone areas, which will help policy makers better assessment of natural hazards. [ABSTRACT FROM AUTHOR]

Sentiment Analysis (SA) using machine learning approaches is a fascinating field of research to unravel the opinion expressed by online users. It deals with the classification problem of opinions expressed on a product or service in review texts as positive or negative or neutral. Classification beyond binary classes poses even greater challenges using machine learning. In this paper two sets of approaches, first set of approach on traditional machine learning and second one on deep learning technique was and evaluated and analyzed for multiclass polarity classification at fine-grain level on cross-domain review dataset. The analysis of performance of traditional machine learning approaches that comprise ensemble models and incorporate semantic and diverse statistical features, shows that ensemble models are better in their category. With the proposed sentiment-document model coupled with the robustness of the proposed ensemble models, we were able to investigate and establish that the document polarity could serve as the latent mutual information that could leverage the model performance of predicting sentiments at sentence and document levels. Furthermore, the evaluation of deep learning models infers that they are more viable option for multi-class polarity classification of cross-domain dataset. They outperformed the traditional approaches based on feature extraction and feature engineering and achieved better accuracy and F1 scores. [ABSTRACT FROM AUTHOR]

In many parts of semiarid and arid regions, qanats are the leading supplier of water demand for agricultural and drinking usage. Qanat is an ancient collecting water system, and qanat water flow (QWF) varies in different seasons and decreases gradually by pumping groundwater wells. The present research utilized a set of supervised machine learning (ML) models to predict the QWF in the Chaghlondi Aquifer in Iran using monthly intervals of 14 years (2007–2021). The wavelet transform (WT) technique was also applied to enhance the QWF prediction quality of ML models for three lead months utilizing QWF, precipitation, evapotranspiration, temperature and GWL signal datasets as input. The five widely used ML models, i.e., artificial neural network (ANN), adaptive neuro-fuzzy inference system, group method of data handling (GMDH), gene expression programming and least square support vector machine, were applied and then compared with their hybrid wavelet models. To assess the performance of the models, the following four evaluation criteria were employed: correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), root means squared error (RMSE) and mean absolute error (MAE). The outcomes showed that the hybrid-wavelet ML considerably improved the standalone model performance. The best QWF predictions for a one-month ahead QWF prediction were acquired from the WT-GMDH model results from input scenario 3 with RMSE, MAE, R and NSE equal to 14.46, 10.78, 0.93 and 0.85, respectively. In addition, the result of this study indicates that ML's performance was improved by using wavelet transform for two and three months ahead of QWF predictions. [ABSTRACT FROM AUTHOR]

A reliable traffic noise prediction model is one of the decision-making tools used in providing a noise friendly environment. In this study, four linear–nonlinear hybrid models were proposed to capture both linear and nonlinear patterns of the data by summing up the predicted traffic noise from the multilinear regression (MLR) and estimated residuals from four artificial intelligence-based models. The input variables for the models were volumes of cars, medium vehicles, buses, heavy vehicles, and average speed. Prior to the development of the hybrid model, the potential of boosted regression tree, feed forward neural network, Gaussian process regression (GPR), support vector regression and linear regression models for traffic noise prediction was evaluated and compared with each other. The performances of the single and hybrid models were evaluated using the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE) and relative root mean square error (rRMSE). MLR-GPR hybrid demonstrated better prediction capability than all other models with NSE, RMSE, MAE and rRMSE values of 0.9312, 0.0427, 0.0347 and 7.4%, respectively. The study found that the efficiency of the linear models could be improved up to 27.26% when they are hybridized with the nonlinear models. [ABSTRACT FROM AUTHOR]

Precipitation forecast is key for water resources management in semi-arid climates. The traditional hybrid models simulate linear and nonlinear components of precipitation series separately. But they do not still provide accurate forecasts. This research aims to improve hybrid models by using an ensemble of linear and nonlinear models. Preprocessing configurations and each of the Gene Expression Programming (GEP), Support Vector Regression (SVR), and Group Method of Data Handling (GMDH) models were used as in the traditional hybrid models. They were compared against the proposed hybrid models with a combination of all these three models. The performance of the hybrid models was improved by different methods. Two weather stations of Tabriz and Rasht in Iran with respectively annual and monthly time steps were selected to test the improved models. The results showed that Theil's coefficient, which measures the inequality degree to which forecasts differ from observations, improved by 9% and 15% for SVR and GMDH relative to GEP for the Tabriz station. The applied error criteria indicated that the proposed hybrid models have a better representation of observations than the traditional hybrid models. Mean square error decreased by 67% and Nash Sutcliffe increased by 5% in the Rasht station when we combined the three machine learning models using genetic algorithm instead of SVR. Generally, the representation of the nonlinear models within the improved hybrid models showed better performance than the traditional hybrid models. The improved models have implications for modeling highly nonlinear systems using the full advantages of machine learning methods. [ABSTRACT FROM AUTHOR]

Hypertension is a primary or contributing cause for premature death in the entire world. As a matter of fact, there is a high prevalence and low control rates in low- and middle-income countries, such that the prevention and treatment of hypertension should remain a top priority in global health. In the recent years, the awareness, treatment, and control rates of hypertension patients in China have been significantly improved to 51.6%, 45.8%, and 16.8%, respectively. However, those rates are still far from a satisfactory level. Clinical studies suggest that for people in the pre-clinical stage of hypertension or having the risk of hypertension, the progression of the disease may be significanly reduced through a change in lifestyle, or by an effective drug therapy. In this paper, we address risk prediction for hypertension in the next five years, and put forward a model merging KNN and LightGBM. Our approach allows us to predict the hypertension risk for a specific individual using features such as the age of the subject and blood indicators. Results shows that our model is reliable and achieves accuracy and recall rate over 86% and 92%, respectively. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is a key factor in the hydrologic cycle and quantifying ETo for future periods is essential for the efficient management of water resources. The objective of this research was to project the possible changes in ETo in future periods in Iran. For this purpose, observed climate data in 40 stations across Iran were collected from 1984 to 2014 and ETo was calculated using these data and the FAO56–PM method. A multi-model ensemble of 27 CMIP6 models under two scenarios, SSP1-2.6 and SSP5-8.5, for the periods 2031–2060 and 2061–2090, was used. A linear regression (LR) model was applied for downscaling and bias correction of CMIP6 temperature using the observed temperature data. The output of the LR model was entered into an artificial neural network (ANN) model with an optimized structure, trained by the observed temperature and the calculated ETo, to project the future ETo based on the downscaled and bias-corrected CMIP6 future temperature. Results showed that ETo will increase in both 2031–2060 and 2061–2090 periods under SSP1–2.6 and SSP5–8.5 scenarios, although the rate of increment was higher under SSP5-8.5 in all stations. Considering all stations, the average of ETo changes was ~ 0.9 mm/day for 2061–2090 under SSP5-8.5. It was found that the ETo rising rate was higher in arid environments, and therefore, shifting to drier conditions with higher water demands will be expectable in such climates in the future. This could cause severe water crises in these environments. [ABSTRACT FROM AUTHOR]

Studies on the amino acid sequences, protein structure, and the relationships of amino acids are still a large and challenging problem in biology. Although bioinformatics studies have progressed in solving these problems, the relationship between amino acids and determining the type of protein formed by amino acids are still a problem that has not been fully solved. This problem is why the use of some of the available protein sequences is also limited. This study proposes a hybrid deep learning model to classify amino acid sequences of unknown species using the amino acid sequences in the plant transcription factor database. The model achieved 98.23% success rate in the tests performed. With the hybrid model created, transcription factor proteins in the plant kingdom can be easily classified. The fact that the model is hybrid has made its layers lighter. The training period has decreased, and the success has increased. When tested with a bidirectional LSTM produced with a similar dataset to our dataset and a ResNet-based ProtCNN model, a CNN model, the proposed model was more successful. In addition, we found that the hybrid model we designed by creating vectors with Word2Vec is more successful than other LSTM or CNN-based models. With the model we have prepared, other proteins, especially transcription factor proteins, will be classified, thus enabling species identification to be carried out efficiently and successfully. The use of such a triplet hybrid structure in classifying plant transcription factors stands out as an innovation brought to the literature. [ABSTRACT FROM AUTHOR]

A reliable prediction of the spatio-temporal drought variation can lead to a reduction in vulnerability and an improvement in the management of drought-dependent businesses. In this study, three clustering algorithms, K-mean, Hierarchical and Expectation–Maximization, were first used to divide Southern Italy into homogeneous drought regions, based on gridded data of the Standardized Precipitation Evapotranspiration Index forecasting with a 6 months' time scale (SPEI6). The Hierarchical algorithm identified five well-distinct clusters characterized by drought events of different duration and severity, considering the different morphoclimatic characteristics of the study area. Then, the mean SPEI6 time series were evaluated for each cluster and used to assess the evolutionary drought trends. In addition, two Machine Learning (ML) algorithms, M5P and Support Vector Regression (SVR), were also used to develop forecasting models for the SPEI6, without the need for additional exogenous inputs. Moreover, the Stacking ML technique was used to develop a hybrid model based on both individual M5P and SVR algorithms. The clustering-forecasting combination makes it possible to identify the evolutionary trends taking place in the various homogeneous areas in a concise but effective manner. The hybrid M5P-SVR model (R2 up to 0.91, minimum Root Mean Square Error RMSE = 0.38) outperformed both M5P (R2 up to 0.87, minimum RMSE = 0.42) and SVR (R2 up to 0.89, minimum RMSE = 0.39) models, showing to be particularly suitable for drought forecasting in areas with long and severe drought events. [ABSTRACT FROM AUTHOR]

Droughts cause severe damage to the economy, society, and environment. Drought forecasting plays an important role in establishing mitigation drought damage plans. In this study, a hybrid model involving long short-term memory and a climate model (LSTM-CM) is constructed for drought prediction. LSTM-CM was compared to the long short-term model stand-alone (LSTM-SA) and climate prediction model GloSea5 (GS5). The performance of models was evaluated based on the Pearson correlation coefficient (CC), mean absolute error (MAE), root mean squared error (RMSE), and skill score (SS). GS5 displayed physical robustness in predictions and did not reduce the amplitude or shift results. However, GS5 prediction tends to have a large bias caused by the inputs, model structure, and parameters. The MAEs of GS5 at 1, 2 and 3 months (0.41, 0.68, and 0.89) were higher than those of LSTM-SA (0.38, 0.61, and 0.89). The LSTM-SA reduced bias, but predictions were characterized by shifts, small variance, and failure to capture drought occurrences in long-lead-time cases. LSTM-CM yielded enhanced drought predictions by encompassing the low bias of LSTM-SA and the physical process simulation ability of GS5; thus, it inherited the good features of these models and limited the poor features. The SS values based on the CC, MAE, and RMSE of LSTM-CM compared to those of GS5 for 1-, 2-, and 3-month lead time predictions were improved from 29.17 to 54.29, 22.47 to 34.15, and 1.75 to 35.09%, respectively. LSTM-CM can accurately detect drought events and displayed less uncertainty in prediction than LSTM-SA and GS5. [ABSTRACT FROM AUTHOR]

Recognition of handwritten Arabic characters remains a major challenge for researchers, given the significant differences in handwriting. This paper presents a hybrid method based on combining the most efficient classification techniques. A trained convolutional neural network (CNN) was applied to extract features from character images. Then, a support vector machine (SVM) was used for classification. By combining CNN and SVM, the aim is to exploit both technologies' strengths. Four hybrid models are proposed in this work. Several databases such as HACDB, HIJJA, AHCD, and MNIST were used to evaluate them. The results obtained are satisfactory compared to similar studies in the literature, with a test accuracy of 89.7, 88.8, 97.3, and 99.4%, respectively. [ABSTRACT FROM AUTHOR]

Accurate prediction of stock trends is of great crucial to guiding investment management and financial policymaking. In recent years, text content generated by investors on various media platforms has had a big impact on stock movements. However, most of the existing studies apply equal weight to the text content to construct the sentiment index and ignore the fact that the influence of the sentiment index on the stock market will decrease with the increase of time interval. In this paper, we propose a model based on dual classifier coupling and sentiment analysis to study the trend of stock index. (1) We come up with a sentiment index weighted based on the reading volume to predict the trend of the stock index. (2) The exponentially weighted moving average (EWMA) model is used to modify the weighted sentiment index to obtain the modified sentiment index. (3) The maximum information coefficient (MIC) is applied to calculate the correlation between the closing price of the stock index and the modified sentiment index to obtain the best-modified sentiment index. (4) Two frequently used classifiers, convolutional neural network (CNN) and support vector machine (SVM), are used to build a dual-classifier coupling prediction model and adopt it for the final classification prediction. Two Chinese stock indexes (SSE 50 and CSI 300) are used to evaluate the predictive effect of the proposed model. In the practice case of the SSE 50 Index, the precision, accuracy, recall rate, F1-score, and AUC reached 81.38%, 80.99%, 80.99%, 81.04%, and 81.22%, respectively, after adding the modified sentiment index. In the practical case of the CSI 300 Index, the precision, accuracy, recall rate, F1-score, and AUC reached 80.07%, 80.07%, 80.07%, 80.06%, and 80.03%, respectively, after adding the modified sentiment index. It can be found through the experimental results that adding the investor sentiment index can advance the trend prediction of the stock index, and adding the modified sentiment index has a more obvious improvement effect. After adding the modified investor sentiment index, the prediction results of our proposed two-classifier coupled CNN-SVM model are much better than other benchmark models. [ABSTRACT FROM AUTHOR]

Drought is a prolonged dry period that has a serious impact on health, agriculture, economies, energy, and the environment. Thus, there have been numerous attempts to make this phenomenon more predictable for preventing the aforementioned effects. The present study aims to determine the best combination of input data sets and predict the Standardized Precipitation Evapotranspiration Index (SPEI) in 1,6, and 12-month time scales using Artificial Intelligence (AI) models (Multilayer Perceptron Neural Network (MLPNN), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Ensemble Decision Tree (EDT)), which all models are hybridized with a wavelet transformation at three synoptic stations named Ardebil, Khalkhal, and Moghan. To this end, monthly lags of precipitation, temperature, and SPEI were used in northwestern Iran from 1987 to 2018. The methods were classified into single parameter and multiparameter, and each sub-method was designed based on a combination of the parameters. Moreover, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Effective Factor Elimination Technique (EFET), and Feature Scaling (FS) were used to determine the best lags of parameters. In this regard, Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Nash–Sutcliffe Efficiency Index (NSE) were used as the statistical criteria to assess AI models, methods, and sub-methods. The results revealed that the sub-method (D) with W-MLPNN in the 1-month and 6-month time scales and the sub-method (D, P, T) with W-SVR in the 12-month time scale were the best models and sub-methods of this area, respectively. Moreover, based on the results, the efficiency of the AI was enhanced in longer time scales (more than the 6th month) and the longer the time scale, the more the number of lags (under the 6th month) in input data is decreased. [ABSTRACT FROM AUTHOR]

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models. [ABSTRACT FROM AUTHOR]

In the present study, seasonal autoregressive integrated moving average (SARIMA) time series models, nonlinear BL model, multi-layer perceptron artificial neural network and SARIMA-bilinear hybrid models were employed to predict the quality parameters of total dissolved solids (TDS), sodium adsorption ratio (SAR), electrical conductivity (EC) in the Maroon basin in Khuzestan Province. For fitting the mentioned models, the monthly data were used for the calibration (1970–2000), confirmation (2001–2011) and prediction of the model (2012–2018). The appropriate models of SARIMA, the bilinear BL and SARIMA-BL hybrid models of the rationalized parameters of the above-mentioned quality parameters were selected based on the adequacy tests, such as the Akaike criterion and the independence test of the model residuals (Ljung–Box). To determine the effective input parameters of the network, the partial mutual information (PMI) algorithm was used to model the three monthly EC, TDS, and SAR parameters. Also, for input and output layers linear transfer function and for hidden layer various active functions with the back-propagation learning algorithm were used for modeling and predicting this water quality parameters. Comparison of the models showed the tangible superiority of SARIMA-bilinear hybrid models than the artificial neural network with effective input parameters based on PMI algorithm, SARIMA and bl models in performed prediction of the all three monthly qualitative parameters in all three stages, training-validation-test in Maroon basin. [ABSTRACT FROM AUTHOR]

Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions. [ABSTRACT FROM AUTHOR]

Automatic readability assessment (ARA) aims to determine the cognitive load of a reader to comprehend a given text. ARA research has been mostly conducted at the text level, with numerous studies showing strong performance of neural and hybrid models. ARA at the sentence level, however, has received less attention, even though many applications in natural language processing (NLP) require assessment of the difficulty of individual sentences. This article compares the performance of neural models, hybrid models and large language models (LLMs) for sentence-level ARA, making three main contributions. First, we construct the first Chinese sentence-level ARA datasets, with nearly 70K sentences, to facilitate evaluation on Chinese. Second, we present the first experimental results on applying LLMs to sentence-level ARA. Finally, while previous work focused mostly on English data, we show that hybrid models outperform traditional classifiers, neural models, and LLMs in both English and Chinese data. The best hybrid model obtained state-of-the-art results on the Wall Street Journal dataset, surpassing the previous best result by almost 15% absolute. It also achieved competitive results on the CEFR-SP dataset. In detailed analyses, we identify the linguistic features that most significantly contributed to the performance of the hybrid model. We show that 10 linguistic features are correlated to readability across all datasets, and models trained on this reduced feature set achieve performance that rivals the full set. These results not only yield new insights into hybrid models for sentence-level ARA, but also set new benchmarks for future research in both English and Chinese. [ABSTRACT FROM AUTHOR]

Soil temperature (ST) is one of the vital parameters of soil, which can affect many of the processes occurring in soil. Accurate techniques are therefore needed to know the ST regime at different soil depths. The present study strived to model the daily ST of the Isfahan and Rasht stations, Iran, at four multiple depths (5, 10, 50, 100 cm). To achieve this goal, the standalone gene expression programming (GEP) and support vector machine (SVM) models were applied. In this context, two types of input patterns were taken into consideration including weather parameters- and lagged ST data-based patterns. Additionally, two different pre-processing methods comprising of entropy and τ-Kendall were utilized to discern the most important weather parameters under the weather parameters-based patterns. It was found that both the employed pre-processing techniques introduced various input combinations as the most important weather parameters. In general, entropy-based GEP and SVM models slightly presented higher accuracy compared to the τ-Kendall-based standalone models at different depths. Then, wavelet (W)-based coupled models were proposed by hybridizing the W theory on the GEP and SVM in order to develop the hybrid W-GEP and W-SVM approaches. The outcomes demonstrated that the implemented coupled models outperformed the single GEP and SVM in modeling the ST of various depths under both the defined input patterns. [ABSTRACT FROM AUTHOR]

Achieving the desired accuracy in time series forecasting has become a binding domain, and developing a forecasting framework with a high degree of accuracy is one of the most challenging tasks in this area. Combining different forecasting methods to construct efficient hybrid models has been widely reported in the literature regarding this challenge. Various types of hybrid models have been developed and successfully employed to improve forecasting accuracy. The well-known hybrid models can be generally categorized into four classes: (1) preprocessing-based, (2) parameter optimization-based, (3) components combination-based, and (4) postprocessing-based hybrid models. Despite the significant successes of hybrid models, efforts to access more accurate results face continued growth. Hybridization of hybrid models is a novel idea proposed to obtain extreme accuracy in recent literature, in which two or more hybrid classes are combined instead of conjoining the conventional individual forecasting methods. Although, in many publications, the aforementioned classes of hybrid models have been reviewed and analyzed in a wide variety of forecasting fields; no study is conducted to review the hybridization of hybrid models. This paper's main contribution is to fill this gap and provide classification and comprehensive review of the current endeavors done in the hybridization of hybrid models in time series forecasting areas. Our searches indicate that more than 250 papers have been published in recent years utilizing hybridization of hybrid models. In this paper, these published papers have been classified regarding their different used combination strategies into four main categories, including (1) Hybridization with preprocessing-based hybrid models (HPH), (2) Hybridization with parameter optimization-based hybrid models (HOH), (3) Hybridization with components combination-based hybrid models (HCH) and, (4) Hybridization with postprocessing-based hybrid models (HSH). Each hybridization of the hybrid class is evaluated regarding the usage frequency, specific merits, and limitations. It can be inferred from reviewing articles that the hybridization of the hybrid concept, as a recent advancement in time series forecasting, can significantly improve traditional hybrid models' accuracy. Furthermore, each category's research gaps and some future research directions are identified in this paper. [ABSTRACT FROM AUTHOR]

Groundwater level fluctuations are one of the main components of the hydrogeological cycle and one of the required variables for many water resources operation models. The numerical models can estimate groundwater level (GWL) based on extensive statistics and information and using complex equations in any area. But one of the most important challenges in analyzing and predicting groundwater depletion in water management is the lack of reliable and complete data. For this reason, the use of artificial intelligence models with high predictive accuracy and due to the need for less data is inevitable. In recent years, the use of different numerical models has been noticed as an efficient solution. These models are able to estimate groundwater levels in any region based on extensive statistics and information and also various field experiments such as pumping tests, geophysics, soil and land use maps, topography and slope data, different boundary conditions and complex equations. In the current research, first, by using available statistics, information and maps, the groundwater level fluctuations of the Sonqor plain are simulated by the GMS model, and the accuracy of the model is evaluated in two stages of calibration and validation. Then, due to the need for much less data volume in artificial intelligence-based methods, the GA-ANN and ICA-ANN hybrid methods and the ELM and ORELM models are utilized. The results display that the output of the ORELM model has the best fit with observed data with a correlation coefficient equal to 0.96, and it also has the best and closest scatter points around the 45 degrees line, and in this sense, it is considered as the most accurate model. To ensure the correct selection of the best model, the Taylor diagram is also used. The results demonstrate that the closest point to the reference point is related to the ORELM method. Therefore, to predict the groundwater level in the whole plain, instead of using the complex GMS model with a very large volume of data and also the very time-consuming process of calibration and verification, the ORELM model can be used with confidence. This approach greatly helps researchers to predict groundwater level variations in dry and wet years using artificial intelligence with high accuracy instead of numerical models with complex and time-consuming structures. [ABSTRACT FROM AUTHOR]

We present a spatial hybrid discrete–continuum modelling framework for the interaction dynamics between tumour cells and cytotoxic T cells, which play a pivotal role in the immune response against tumours. In this framework, tumour cells and T cells are modelled as individual agents while chemokines that drive the chemotactic movement of T cells towards the tumour are modelled as a continuum. We formally derive the continuum counterpart of this model, which is given by a coupled system that comprises an integro-differential equation for the density of tumour cells, a partial differential equation for the density of T cells and a partial differential equation for the concentration of chemokines. We report on computational results of the hybrid model and show that there is an excellent quantitative agreement between them and numerical solutions of the corresponding continuum model. These results shed light on the mechanisms that underlie the emergence of different levels of infiltration of T cells into the tumour and elucidate how T-cell infiltration shapes anti-tumour immune response. Moreover, to present a proof of concept for the idea that, exploiting the computational efficiency of the continuum model, extensive numerical simulations could be carried out, we investigate the impact of T-cell infiltration on the response of tumour cells to different types of anti-cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Existence of several challenges and high cost in the development of monitoring infrastructure have become major reasons for data sparsity by statutory government agencies tasked to study pollution exposure in urban areas. As an effort to mitigate this problem, the recent usage of satellite aerosol optical depth data along with the usage of learning algorithms have become popular in recent times. This paper presents a novel four-staged approach using different machine learning, deep learning and statistical methods to develop a spatio-temporal hybrid model for temporal forecasting using data from existing stations along with satellite aerosol optical depth data for spatial interpolation. Experiments conducted on real-world data belonging to the cities of Kolkata, Bengaluru and Mumbai show that a consistent pattern is not followed in all the cities in all stages except in spatial interpolation where Random Forest Regression is found to surpass all other models used. While a long short-term memory network (LSTM Auto-Encoder) when employed in temporal forecasting inside the hybrid method outperforms others in Mumbai, a random forest regression-based method and a multi-layer perceptron-based method outperform others similarly in Kolkata and Bengaluru, respectively. [ABSTRACT FROM AUTHOR]

PM2.5 has a significant negative impact on human health and atmospheric quality, and accurate prediction of its concentration is necessary. When using common point prediction models for PM2.5 concentration prediction, the influence of various uncertainties on PM2.5 concentrations makes the prediction results suffer from poor accuracy. To address this issue, this paper proposes the quantile regression neural network (QRNN) model based on the least absolute shrinkage and selection operator (LASSO), combined with kernel density estimation (KDE) for probabilistic density prediction of PM2.5 concentrations. The model uses LASSO regression to select the influential factors, and then the quartiles of daily PM2.5 concentrations calculated by the QRNN model are imported into the KDE model to obtain the probability density predictions of PM2.5 concentrations. In the paper, empirical analyses are carried out with the cities of Beijing and Jinan in China as well as six other datasets, and the prediction performance of the model is assessed by using evaluation criteria in both point prediction and interval prediction. The simulation reveals that the predictive performance of the LASSO-QRNN-KDE model is well, and the model is not only effective in filtering high-dimensional data, but also has a higher accuracy compared to common research models. In addition, the model is able to describe the uncertainty of PM2.5 concentration fluctuations and carry more information on the variation of PM2.5 concentrations, which can provide a novel and excellent PM2.5 concentration prediction tool for relevant policy makers. [ABSTRACT FROM AUTHOR]

Estimation of reservoir inflow is of particular importance in optimal planning and management of water resources, proper allocation of water to consumption sectors, hydrological studies, etc. This study aimed to estimate monthly inflow (Q) to the Maroon Dam reservoir located in Iran utilizing climatic data such as minimum, maximum, and mean air temperatures (Tmin, Tmax, T), reservoir evaporation (E), and rainfall (R). The impact of any of the mentioned variables was analyzed by the entropy-based pre-processing technique. The results of the pre-processing showed that the rainfall is the most important parameter affecting the reservoir inflow. Therefore, three types of input patterns were taken into consideration consisting the antecedent Q-based, antecedent R-based, and combined antecedent Q and R-based input combinations. To estimate the monthly reservoir inflow, a random forest (RF) was firstly employed as the standalone model. Then, two different types of hybrid models were proposed via coupling the RF on complete ensemble empirical mode decomposition (CEEMD) and wavelet analysis (W) in order to implement the coupled CEEMD-RF and W-RF models. It is worthwhile to mentioning that six mother wavelets were used in developing the hybrid W-RF models. Four error metrics including root mean square error (RMSE), mean absolute error (MAE), Kling-Gupta efficiency (KGE), and Willmott index (WI) were used to assess the accuracy of implemented models. The attained results indicated the superiority of proposed hybrid models over the classic RF for estimating the monthly reservoir inflow. The most precise model during the test phase was W-RF(3) utilizing the Sym(2) as the mother wavelet under a lagged Q-based pattern with error measures of RMSE = 15.011 m3/s, MAE = 10.439 m3/s, KGE = 0.832, WI = 0.773. [ABSTRACT FROM AUTHOR]