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Introduction: The Indian Himalayas' susceptibility to landslides, particularly as a location where climate change effects may be event catalysts, necessitates the development of dependable landslide susceptibility maps (LSM). Method: This study diverges from traditional binary classification models, framing LSM as a positive-unlabeled learning problem. This approach acknowledges that regions without recorded landslides are not necessarily at low risk but could simply have not experienced landslides yet. The study utilizes novel positive-unlabeled learning-enhanced algorithms—Random Forest, K-Nearest Neighbor, and Decision Tree—to create LSM for Chamoli district, India. Eleven causative factors for landslides are identified, including elevation, aspect, slope, geology, geomorphology, distance to lineament, lithology, NDVI, distance to river, distance to road and residential land use. To address spatial correlation biases, instead of randomly splitting the dataset, the study adopts spatial splitting to get the training and testing datasets. Conclusion: The study reveals that positive-unlabeled learning substantially improves the Area Under Curve and recall, leading to a more conservative LSM compared to binary classification methods. Analysis shows that the southern region of Chamoli exhibits high recall but lower accuracy, suggesting a latent high landslide susceptibility despite a lack of historical landslides in this region. The study also quantifies the impact of human activity on landslide risk, indicating an elevated threat to life and the local economy, especially in Chamoli's southwestern areas. [ABSTRACT FROM AUTHOR]

The fundamental method for pre-processing speech or text data that enables computers to comprehend human language is known as natural language processing. Numerous models have been developed to date to pre-process data in the English language; however, the Hindi language does not support these models. India's national tongue is Hindi. In order to help the locals, the authors of this study used supervised learning methods like Linear Regression, SVM, and Naive Bayes algorithm to investigate a dataset of complicated terms in the Hindi language. Additionally, a sophisticated Hindi word classification model is suggested employing several methods based on the forecasts as well as collective learning strategies like Random Forest, Adaboost, and Decision Tree. Depending on how well the user's language is understood, the suggested model will assist in simplifying Hindi text. Authors attempt to classify the uncharted dataset using deep learning algorithms like Bi-LSTM and GRU approaches in further processing. [ABSTRACT FROM AUTHOR]

Background: The role of artificial intelligence and radiomics in prediction model development in cancer has been increasing every passing day. Cervical cancer is the 4th most common cancer in women worldwide, contributing to 6.5% of all cancer types. The treatment outcome of cervical cancer patients varies and individualized prediction of disease outcome is of paramount importance. Purpose: The purpose of this study is to develop and validate the digital signature for 5-year overall survival prediction in cervical cancer using robust CT radiomic and clinical features. Materials and Methods: Pretreatment clinical features and CT radiomic features of 68 patients, who were treated with chemoradiation therapy in our hospital, were used in this study. Radiomic features were extracted using an in-house developed python script and pyradiomic package. Clinical features were selected by the recursive feature elimination technique. Whereas radiomic feature selection was performed using a multi-step process i.e., step-1: only robust radiomic features were selected based on our previous study, step-2: a hierarchical clustering was performed to eliminate feature redundancy, and step-3: recursive feature elimination was performed to select the best features for prediction model development. Four machine algorithms i.e., Logistic regression (LR), Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC), were used to develop 24 models (six models using each algorithm) using clinical, radiomic and combined features. Models were compared based on the prediction score in the internal validation. Results: The average prediction accuracy was found to be 0.65 (95% CI: 0.60– 0.70), 0.72 (95% CI: 0.63–0.81), and 0.77 (95% CI: 0.72–0.82) for clinical, radiomic, and combined models developed using four prediction algorithms respectively. The average prediction accuracy was found to be 0.69 (95% CI: 0.62–0.76), 0.79 (95% CI: 0.72–0.86), 0.71 (95% CI: 0.62–0.80), and 0.72 (95% CI: 0.66–0.78) for LR, RF, SVC and GBC models developed on three datasets respectively. Conclusion: Our study shows the promising predictive performance of a robust radiomic signature to predict 5-year overall survival in cervical cancer patients. [ABSTRACT FROM AUTHOR]

Accurate forecasting of daily evapotranspiration (ET) is essential for enhancing real-time irrigation scheduling and informed decision-making in water resources allocation. This study investigates the intricate relationships between meteorological variables and evapotranspiration (ET) to enhance the accuracy of ET estimation models. Robust correlations were identified, emphasizing the significance of net radiation (Rn) in predicting ET. The study explores three distinct scenarios, incorporating different combinations of weather variables as input. The first scenario incorporates all weather variables, including date and time, as inputs for model development. The second scenario utilizes only Rn as input to predict ET values. In the third and final scenario, all weather variables, along with date and time, are employed as inputs for comprehensive model development. The multivariate linear regression (MLR) model demonstrated exceptional performance when exclusively using Rn, achieving an impressive R2 value of 0.99 in both calibration and validation phases. However, limitations were observed when Rn was excluded, highlighting the necessity of a comprehensive set of input data. Penalized regression models, including ridge regression, LASSO, and ELNET, exhibited improved performance with the inclusion of Rn, supporting the importance of this variable in refining ET estimates. Machine learning models displayed remarkable performance, with most achieving R2 values exceeding 0.95 in scenarios involving extensive input data. The Support Vector Regression (SVR) model faced challenges, indicating potential overfitting in certain scenarios. In scenarios with limited input data, machine learning models exhibited varying performance, with the Random Forest (RF) model emerging as the most robust model with R2 value of 0.99 and 0.84 during the calibration and validation, respectively. [ABSTRACT FROM AUTHOR]

• Proposed a Hybrid Model for Paddy Crop prediction based on phenological parameters. • CNN-LSTM attained RMSE value of 0.219 t/ha compared to other existing methods. • Random Forest method achieved highest accuracy of 96.6% in paddy classification. Rice is one of the predominant food sources to fulfill the dietary requirements of well-being in India. Therefore, accurate and timely paddy crop yield prediction is crucial to ensure the food security of the country. In this direction, the present study proposed a hybrid deep-learning method based on Conv-1D and LSTM layers using the classification-derived phenological with meteorological parameters for paddy crop yield prediction. The paddy crop classification has been conducted using high-resolution (10 m) multispectral imagery based on GPS coordinates collected during the paddy field visits to extract the phenological parameters for input to the prediction model. In this context, the efficiency of Random Forest, Naïve Bayes, SVM, and Gradient Tree boost classifiers was assessed. Furthermore, we have also analyzed the accuracy of Landsat-8, Sentinel-1 GRD, and Sentinel-2 satellite imagery in paddy crop classification based on area estimation. The Statistical Abstract of Haryana was utilized to validate the paddy crop area estimation and yield prediction. The classification outcomes showed that the Random Forest method attained the highest accuracy of 96.6 % compared to other GEE-based classifiers. The proposed Hybrid Deep learning approach achieved an RMSE value of 0.219 t/ha compared to CNN, LSTM, CNN-Bi-LSTM, and Regression techniques for crop yield prediction. The study conclusion highlighted that the sentinel-2 satellite imagery performed well and found that the proposed hybrid approach provided an alternative for paddy crop yield prediction. [ABSTRACT FROM AUTHOR]

The historical prevalence of banking crises and their profound impact on global economies underscores the imperative for policy makers to refine their crisis forecasting frameworks. Against this backdrop, the present study endeavors to predict potential banking crises in India by leveraging a spectrum of artificial intelligence and machine learning techniques (AI-ML). These techniques encompass logistic regression, random forest, naïve Bayes, gradient boosting, support vector machine, neural networks, K-nearest neighbors, and decision trees. Initially, a banking fragility index was constructed utilizing monthly banking data spanning 2002 to 2023, demarcating the periods of crisis and stability. Subsequently, an extensive array of early warning indicators (EWIs) encompassing asset prices, macroeconomic factors, external influences, and credit-related variables were employed to forecast crisis periods. Our findings reveal that AI-ML models exhibit reasonable accuracy in predicting banking crises. Moreover, advanced model performance metrics highlight neural networks and random forest models as particularly effective in crisis prediction, surpassing other methodologies. Notably, among the EWIs, variables related to credit, interest rates, and liquidity emerge as possessing relatively higher information value in discerning fragilities within the Indian banking system. Importantly, the methodological framework presented herein can be extrapolated for banking crisis prediction in other economies. [ABSTRACT FROM AUTHOR]

Mining is one of the most daunting occupations gain the sector since it entails risk at any point in the operation. In its operation, the main focus is on slope stability. To avoid slope failures, work should be performed in line with both the regulations and the safety criteria. Slope stability is essential in mining activities owing to slope failure putting productivity and safety at risk. Prediction of slope failure is difficult because of the complexity of traditional engineering techniques. Through study, recent technologies have helped mining companies predict slope problems quickly and effectively. In this current research, an ensemble of machine learning intelligence algorithms was used to estimate and assess the Factor of Safety (FOS). In Ostapal Chromicte Mine, India, 79 experimental and failure slope occurrences were tracked to gather in-the-moment field data. The available data were split into training and testing sets at random to build algorithms. The five influenced factors such as the unit weight, the friction angle, the cohesiveness, the mining depth, as well as the slope angle used as input variables to estimate the FOS. Selected machine learning techniques such as Multiple Linear Regression (MLR), Decision Tree, Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and ensemble hybrid model combining eXtreme Gradient Boosting and Random Forest (XGBoost-RF) were developed to evaluate the FOS. The validity and efficiency of created models can be evaluated using standard evaluation parameters such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD). The most precise model to assess the FOS across all models was discovered to be the XGBOOST-RF ensemble model, which had a high R² of 0.931, MSE of 0.009, NRMSE of 0.069, MAD of 0.037, MAPE of 3.581 and an RMSE of 0.098. [ABSTRACT FROM AUTHOR]

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Language identification has become a critical challenge in NLP, particularly in multilingual countries like India. This study addresses the identification of closely related Indo-Aryan languages, proposing a robust deep-learning ensemble model with data augmentation. Pre-trained models (Indic BERT, Indic Transformer, DevBERT, L3Cube-HindBERT) and various embedding techniques (word2vec, Indic transformer, Keras embedding) are explored with LSTM, Bi-LSTM, GRU, Bi-GRU, and CNN-Bi-LSTM deep learning models. Keras embedding performs better as compared to other embedding methods. The four best deep learning models are selected, and ensemble methods (soft voting, hard voting, stacking with meta models Random Forest and SVM classifier) are employed. When utilizing data augmentation with the training data and fitting the models Bi-LSTM, GRU, Bi-GRU, and CNN-Bi-LSTM with Keras embedding, the best performance, a 93% F1-score, was achieved by their soft voting classifier as compared to the other models. The performance of the proposed model was compared with the existing baseline models on the same dataset. For the comparison of model performance, the macro F1 score, accuracy, recall, and precision were used. [ABSTRACT FROM AUTHOR]

Sugarcane is one of the leading commercial crops grown in India. The prevailing weather during the various crop-growth stages significantly impacts sugarcane productivity and the quality of its juice. The objective of this study was to predict the yield of sugarcane during different growth periods using machine learning techniques viz., random forest (RF), support vector machine (SVM), stepwise multiple linear regression (SMLR) and artificial neural networks (ANN). The performance of different yield forecasting models was assessed based on the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (nRMSE) and model efficiency (EF). Among the models, ANN model was able to predict the yield at different growth stages with higher R2 and lower nRMSE during both calibration and validation. The performance of models across the forecasts was ranked based on the model efficiency as ANN > RF > SVM > SMLR. This study demonstrated that the ANN model can be used for reliable yield forecasting of sugarcane at different growth stages. [ABSTRACT FROM AUTHOR]

Gully erosion is a severe environmental issue that poses threats to agriculture, human safety, habitats, infrastructure, and soil integrity. Selecting the right machine learning model is vital for accurate Gully Erosion Susceptibility Mapping (GESM) due to varying environmental hazard performance. This study employed a comparative analysis of two machine learning techniques, Random Forest (RF) and Extreme Gradient Boost (XGBoost), to develop a highly precise GESM for the Silabati watershed (India). The analysis incorporated 24 controlling factors and examined a dataset of 460 sample points, with equal representation of gullies and non-gullies. Variance Inflation Factors (VIF) and Information Gain Ratio (IGR) techniques were applied to assess multicollinearity test among the controlling factors. Lithology, elevation, distance from the road, LULC, geomorphology, rainfall, drainage density, and coarse fragments emerged as crucial factors in determining GESM. Statistical tests, including the Kappa index, Root Mean Square Error (RMSE), Accuracy (ACC), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Receiver Operating Characteristic (ROC), were employed to evaluate the RF and XGB models on training and testing data. Both models demonstrated strong performance, with the XGBoost and RF models achieving ROC values of 84.1% and 83.2%, respectively. The applied quantile classification method resulted in the creation of five distinct GESMs, categorized as very high (VH), high (H), moderate (M), low (L), and very low (VL). We found that the very high GESM areas of the watershed were 4.10% in the XGBoost model and 4.61%. in the RF model. Out of the 26 sub-watersheds, the results have identified five sub-watersheds as highly prioritized for sustainable management. Therefore, the present study provides an accurate gully erosion identification using advanced models, offering valuable insights for policymakers to proper management implementation. [ABSTRACT FROM AUTHOR]

The purpose of this study is to verify whether the transportation infrastructure investment carried out by the Asian Infrastructure Investment Bank (AIIB) has promoted the economic development of its recipient countries. Since the establishment of the AIIB, its investments in infrastructure development, aimed at promoting economic growth in Asian developing countries, have garnered considerable attention. This study selects India, the largest recipient country of the AIIB, as the research object and chooses the Gujarat Road Project as the research case, since it is a completed infrastructure construction investment project in the transportation field. This paper provides an overview of the project's operation and summarizes key factors in the project's implementation. In the data analysis section, the per capita GDP is selected as the explained variable to measure economic development, and the LASSO regression method is used to select several variables that affect economic development. Moreover, the random forest model is used to obtain the causal relationship between road construction and the per capita GDP from 2001 to 2022. The results indicate that road construction in India has a significant positive effect on per capita GDP growth, the Gujarat Road Project supported by the AIIB also has a positive effect on per capita GDP growth, and this effect is stronger than that at the national level. The main contribution of this work is the validation of the investment strategy of the AIIB and the quantification of the economic contribution of AIIB investment projects to the local area. [ABSTRACT FROM AUTHOR]

Water bodies classification using remote sensing and deep learning techniques plays a pivotal role in the effective management of water resources. This study aims to address the challenge of accurate water body detection and classification, which is essential for understanding their distribution and characteristics, ultimately informing water usage and conservation efforts. Current methodologies predominantly rely on Support Vector Machines (SVM) and pixel-based approaches, resulting in suboptimal accuracy. In response, this paper proposes an ensemble model that combines the U-Net neural network and the Random Forest algorithm for enhanced water body detection and classification. The research commences by obtaining highresolution satellite images with a resolution of 0.5 m. The UNet model is employed to segment water bodies, and contour analysis is subsequently applied to extract shape features. The Random Forest Classifier is then utilized to classify the segmented water bodies into distinct categories, including rivers, ponds, lakes, canals, and other water bodies. Following the U-Net segmentation, the rasterized segments are converted into vector format. These vector data are leveraged to update Geographic Information System (GIS) maps, contributing to more accurate cartographic representations. The proposed approach is rigorously evaluated using a dataset from urban areas in Kolkata, West Bengal, India. The achieved accuracy rate stands at 67.01%. [ABSTRACT FROM AUTHOR]

According to researches, there are around 188 million unemployed people around the globe. We may find many job vacancies on job portals and across the internet to help the job seekers. India alone has more than a hundred job portals. One major issue people face here is that the job seekers are not sure if the employer is real or fake. Most of these portals do not have a system that could check if the employer, posting a job is real or fake. Scammers are making use of this opportunity to post fake job offers which might look genuine to the job seekers applying for it. This way the poor job seekers might lose a large amount of money and time. A best possible solution for this problem would be that the job portal itself being able to identify if the job being posted is real or fake. This paper suggests using a machine learning model to achieve this goal. The idea here is to use natural language processing to understand and analyze the job posting and then making use of a machine learning model to predict if the job posting is real or fake. The first step is to import a dataset which has real life real and fake job posting. In this project, Employment Scam Aegean Dataset provided by University of Aegean Laboratory of Information and Communication system Security is being used. This dataset contains 18000 samples containing real life job postings. Various text cleaning techniques like lemmatization, stop words removal and special characters and punctuation removal is done on the data. Once the text data is processed, various algorithms like Random Forest, Linear SVC, Gradient Boosting Classifier, Gaussian naïve Bayes classifier and XGB classifier is used to test the performance of the model. The best two algorithms with respect to the percentage of accuracy with which the models could classify real and fake job posting was taken into consideration. Random Forest and Linear SVC could give accuracy close to 98%. Both of these algorithms were tuned using GridSearchCV, a library function which is a part of sklearn's model selection package. After tuning, the performance of both these algorithms increased and Linear SVC gave a better accuracy score of 99%. Hence Linear SVC is being used in this project for predicting real and fake job posting on a job portal.. [ABSTRACT FROM AUTHOR]

Agriculture has a big impact on society because it is essential for a large percentage of our food. The issue of hunger is getting worse because of a growing population in many nations, resulting in food shortages or insufficiencies. To meet the world's food needs, it is ever more crucial to provide crop protection, conduct detailed land surveys, and predict crop yields. To calculate the estimated number of crops that are produced in a year, this research focuses on the use of machine learning techniques to predict crop yield and recommend crops with the highest yield and profitability in the Northeast region of India. The crop market's fluctuations in prices may be controlled with the aid of this information. To estimate agricultural crop yields, this study accurately evaluates a range of machine learning regression models, such as Linear Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost (eXtreme Gradient Boosting), and AdaBoost. With a 0.98 R2 score for the XGBoost and 0.96 for the Random Forest, they performed better than the other models. By evaluating crop yields along with their corresponding market prices and costs, we have determined their profitability. As a result, we have provided recommendations for the top five profitable crops in India’s Northeastern region. [ABSTRACT FROM AUTHOR]

Objectives: Recent advancement in understanding neurological disorders has revealed the involvement of dysbiosis of the gut microbiota in the pathophysiology of Parkinson's disease (PD). We sequenced microbial DNA using fecal samples collected from PD cases and healthy controls (HCs) to evaluate the role of gut microbiota. Methods: Full-length bacterial 16S rRNA gene sequencing of fecal samples was performed using amplified polymerase chain reaction (PCR) products on the GridION Nanopore sequencer. Sequenced data were analyzed using web-based tools BugSeq and MicrobiomeAnalyst. Results: We found that certain bacterial families like Clostridia UCG 014, Cristensenellaceae, and Oscillospiraceae are higher in abundance, and Lachinospiracea, Coriobacteriaceae and genera associated with short-chain fatty acid production, Faecalibacterium, Fusicatenibacter, Roseburia and Blautia, are lower in abundance among PD cases when compared with the HC. Genus Akkermansia, Dialister, Bacteroides, and Lachnospiraceae NK4A136 group positively correlated with constipation in PD. Conclusion: Observations from this study support the other global research on the PD gut microbiome background and provide fresh insight into the gut microbial composition of PD patients from a south Indian population. We report a higher abundance of Clostridia UCG 014 group, previously not linked to PD. [ABSTRACT FROM AUTHOR]

India is the world's second-largest exporter of agricultural products. Agriculture is India's most populated sector of the economy which plays a key part in the country's overall socio-economic fabric. Crop yield prediction is crucial yet desirable for every country to ensure food security. Machine learning can be a very important decision support tool for the prediction of crop yield which can help in preparing for the future in a much more substantial way. Many studies have been conducted for crop yield prediction using multi-source data, still, the predictions of the crop of the Rajasthan region in India have not been investigated. In the present study well-known machine learning techniques, decision tree, random forest, lasso regression, and support vector regression have been implemented for crop yield prediction using multi-source data. Machine learning techniques were implemented on data set that consists of Vegetation Indices, extracted from remote sensing data and Weather data. Random forest attained the more accurate prediction with the coefficient of determination (R2) of 0.77, root mean square error of 0.39 t/ha and mean absolute error of 0.28 t/ha. The research obtained a good estimation of crop yield prior to the harvest. The crop yield is impacted by the change in environmental factors, this research would assist the farmers to have a prior estimation of the yield. The study also reveals that gradually adding more data to the model increases the overall prediction rate. [ABSTRACT FROM AUTHOR]

The growing concerns surrounding water supply, driven by factors such as population growth and industrialization, have highlighted the need for accurate estimation of streamflow at the river basin level. To achieve this, rainfall-runoff models are widely employed as valuable tools in watershed management. For this specific study, two modelling approaches were employed: the Soil and Water Assessment Tool (SWAT) model and a set of eight artificial intelligence (AI) models. The AI models consisted of seven data-driven approaches, namely k-nearest neighbour regression, support vector regression, linear regression, artificial neural networks, random forest regression, XGBoost, and Histogram-based Gradient Boost regression. Additionally, a deep learning model known as Long Short-Term Memory (LSTM) was also utilized. The study focused on monthly streamflow modelling in the Murredu River basin, with a calibration period from 1999 to 2003 and a validation period from 2004 to 2005, spanning a total of 7 years from 1999 to 2005. The results indicated that all nine models were generally suitable for simulating the rainfall-runoff process, with the LSTM model demonstrating exceptional performance in both the calibration (R2 is 0.97 and NSE is 0.96) and validation (R2 is 0.97 and NSE is 0.92) periods. Its high coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) values indicated its superior ability to accurately model the rainfall-runoff relationship. While the other models also produced satisfactory results, the findings suggest that selecting the most efficient model, such as the LSTM model, could significantly contribute to the effective management and planning of sustainable water resources in the Murredu watershed. [ABSTRACT FROM AUTHOR]

Climate change significantly impacts the global hydrological cycle, leading to pronounced shifts in hydroclimatic extremes such as increased duration, occurrence, and intensity. Despite these significant changes, our understanding of hydroclimatic risks and hydrological resilience remains limited, particularly at the catchment scale in peninsular India. This study aims to address this gap by examining hydroclimatic extremes and resilience in 54 peninsular catchments from 1988 to 2011. We initially assess extreme precipitation and discharge indices and estimate design return levels using non-stationary Generalized Extreme Value (GEV) models that use global climate modes (ENSO, IOD, and AMO) as covariates. Further, hydrological resilience is evaluated using a convex model that inputs simulated discharge from the best hydrological model among SVM, RVM, random forest, and a conceptual model (abcd). Our analysis shows that the spatial patterns of mean extreme precipitation indices (R1 and R5) mostly resemble with extreme discharge indices (Q1 and Q5). Additionally, all extreme indices, including R1, Q1, R5, and Q5, demonstrate non-stationary behavior, indicating the substantial influence of global climate modes on extreme precipitation and flooding across the catchments. Our results indicate that the random forest model outperforms the others. Furthermore, we find that 68.52% of the catchments exhibit low to moderate hydrological resilience. Our findings emphasize the importance of understanding hydroclimatic risks and catchment resilience for accurate climate change impact predictions and effective adaptation strategies. [ABSTRACT FROM AUTHOR]

In a survey conducted in the year 2020, we came to know that India's around 50% of population includes young people of the age group of 25 and students. Guiding this young mass in the right way and strengthening their future is a huge responsibility put over the head of the elder citizens of India such as their parents teachers and professors. This paper aims to build a model that can predict the students' competency level and the risk factors or the fields where he needs to put their effort to improve themselves, and this model also helps the parents, professors and Educational institutes to know about their children's and students in which zone they stand, are they ready to compete with others. This analysis is done by using different ML bifurcation algorithms. Also we aim to find the best classifier which can emerge with the highest predicting accuracy among all other classifiers to the above-said problem. The accuracy of 88.5% is achieved through the proposed machine learning algorithm for particular education datasets which have been taken into consideration. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is undeniably an infectious condition that stems from the Mycobacterium tuberculosis bacterium. While it primarily impacts the lungs, it can also harm other body regions if not addressed promptly. TB control in India faces several challenges, including high population density, inadequate healthcare infrastructure in certain regions, limited awareness, stigma associated with the disease, and the emergence of drug-resistant strains. These challenges require ongoing efforts to improve diagnosis, treatment, and prevention strategies. To address the burden of TB, recent advancements in machine learning technologies employed to efficiently detect this disease at an early stage. A completely automated diagnostic system can accurately predict the presence of TB, effectively preventing prolonged lung infections and related diseases caused by TB. In this research, we employed augmentation techniques with HOG (Histogram Oriented Gradients) to extract features from chest X-ray images of TB. These features were then utilized in a Random Forest Classifier to train the proposed model of RF-HOG for classifying Tuberculosis and Normal cases from the image. This proposed method holds promise for diagnosing TB in chest X-ray images and can be beneficial for medical analysts. [ABSTRACT FROM AUTHOR]

Numerous applications in agriculture, climate, ecology, hydrology, and the environment are severely constrained by the lack of detailed information on soil texture. The purpose of this study was to predict soil particle-size fractions (PSF) in the Ri-Bhoi district of Meghalaya state, India, using a random forest model (RF). For the modeling of soil particle-size fractions, we employed 95 soil profiles (456 depth-wise layers) gathered from a recent national land resource inventory as well as currently accessible environmental variables. Sand, silt, and clay content were predicted using the Random Forest model at varied depths of 0–5, 5–15, 30–60, 60–100, and 100–200 cm. Our results showed the R2 for sand was found to be 0.30 (0–5 cm), 0.28 (5–15 cm), and 0.21 (15–30 cm). For the sand, silt, and clay fractions, respectively, the concordance correlation coefficient (CCC) was found to be greater in the 0–30 cm, 0–60 cm, and 0–15 cm depths. When there is a reasonably close monitoring of the coverage probability with a confidence level along the 1:1 line, prediction interval coverage probability (PICP) gives a decent indicator of what to anticipate. The most crucial variables for the prediction of sand and silt were channel network base level (CNBL) and LS-Factor, whereas Min Temperature of Coldest Month (°C) (BIO6) was discovered for clay prediction. For all three soil texture fractions, the range between the 5% lower and 95% higher prediction bounds was large, indicating that the existing spatial predictions may be improved. The maps of soil texture were significantly more precise, and they accurately depicted the spatial variations of particle-size fractions. Additionally, there is still a need to investigate novel methodologies for extensive digital soil mapping, which will be very advantageous for many international initiatives. [ABSTRACT FROM AUTHOR]

Different soil characteristics in different parts of India affect agriculture growth. Crop growth and crop production are significantly impacted by healthy soil. Soil enzymes mediate almost all biochemical reactions in the soil. Understanding the biological processes of soil carbon and nitrogen cycling requires defining the significance of prospective elements at the play of soil enzymes and evaluating their activities. A combination of Multiple Linear Regression (MLR), Random Forest (RF) models, and Artificial Neural Networks (ANN) was employed in this study to assess soil enzyme activity, including amylase and urease activity, soil physical properties, such as sand, silt, clay, and soil chemical properties, including organic matter (SOM), nitrogen (N), phosphorus (P), soil organic carbon (SOC), pH, and fertility level. Compared to other methods for estimating soil phosphatase, cellulose, and urease activity, the RF model significantly outperforms the MLR model. In addition, due to its ability to manage dynamic and hierarchical relationships between enzyme activities, the RF model outperforms other models in evaluating soil enzyme activity. This study collected 3972 soil samples from 25 villages in the Bhandara district of Maharashtra, India, with chemical, physical, and biological parameters. Overall, 99% accuracy was achieved for cellulase enzyme activity and 94% for N-acetyl-glucosaminidase enzyme activity using the Random Forest model. Crops have been suggested based on the best performance accuracy algorithms and evaluation performance metrics. [ABSTRACT FROM AUTHOR]

The rapidly evolving high-throughput sequencing (HTS) technologies generate voluminous genomic and metagenomic sequences, which can help classify the microbial communities with high accuracy in many ecosystems. Conventionally, the rule-based binning techniques are used to classify the contigs or scaffolds based on either sequence composition or sequence similarity. However, the accurate classification of the microbial communities remains a major challenge due to massive data volumes at hand as well as a requirement of efficient binning methods and classification algorithms. Therefore, we attempted here to implement iterative K-Means clustering for the initial binning of metagenomics sequences and applied various machine learning algorithms (MLAs) to classify the newly identified unknown microbes. The cluster annotation was achieved through the BLAST program of NCBI, which resulted in the grouping of assembled scaffolds into five classes, i.e., bacteria, archaea, eukaryota, viruses and others. The annotated cluster sequences were used to train machine learning algorithms (MLAs) to develop prediction models to classify unknown metagenomic sequences. In this study, we used metagenomic datasets of samples collected from the Ganga (Kanpur and Farakka) and the Yamuna (Delhi) rivers in India for clustering and training the MLA models. Further, the performance of MLAs was evaluated by 10-fold cross validation. The results revealed that the developed model based on the Random Forest had a superior performance compared to the other considered learning algorithms. The proposed method can be used for annotating the metagenomic scaffolds/contigs being complementary to existing methods of metagenomic data analysis. An offline predictor source code with the best prediction model is available at (https://github.com/Nalinikanta7/metagenomics). [ABSTRACT FROM AUTHOR]

Rainfall forecasting is critical for the economy, but it has proven difficult due to the uncertainties, complexities, and interdependencies that exist in climatic systems. An efficient rainfall forecasting model will be beneficial in implementing suitable measures against natural disasters such as floods and landslides. In this paper, a novel hybrid model of empirical mode decomposition (EMD) and random forest (RF) was developed to enhance the accuracy of annual rainfall prediction. The EMD technique was utilized to decompose the rainfall signal into six intrinsic mode functions (IMFs) to extract underlying patterns, while the RF algorithm was employed to make predictions based on the IMFs. The hybrid RF–IMF model was trained and tested using a dataset of annual rainfall in Kerala from 1871 to 2020, and its performance was compared to traditional models such as RF regression and the autoregressive moving average (ARMA) model. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination or R-squared (R2) were used to compare the performances of these three models. Model evaluation metrics show that the RF–IMF model outperformed both the RF model and ARMA model. [ABSTRACT FROM AUTHOR]

Publicly available EO datasets offer new possibilities to generate biodiversity information at the community composition level, an essential biodiversity variable, beyond forest type. We demonstrated the potential of Sentinel-2, GEDI LiDAR canopy height and ALOS-DEM in discriminating and classifying tropical tree communities in the Western Himalayas, India. For this, tree communities were first identified based on the ordination of field data and subsequently classified using satellite data applying machine learning, i.e. random forest (RF). From the three forest types in the study area, eight distinct tree communities were identified for which classification accuracy increased from single date (75.17%) to multi-date images (85.33%) and further by applying feature selection (88.17%). Whereas the best classification accuracy of 94.66% was achieved when canopy height and topographic variables were also considered. The findings suggest that RF is suitable for mapping tree communities by combining Sentinel-2 with GEDI and DEM parameters. [ABSTRACT FROM AUTHOR]

This study aims to estimate and analyse extreme climate indices such as standardised precipitation index (SPI) coupled with enviro-met (air pollutants and meteorological) parameters over the Vidarbha region from 1980 to 2019. Seasonal SPI, also known as the draught index, is derived from rainfall data using the R language. An attempt is made to determine the best combination of enviro-met on SPI using the random forest (RF) models. The study region is divided into four zones to assess the microclimatic impact on the forecast model. Three sets of data combinations, viz., meteorological and air pollution parameters, are applied for SPI prediction using RF. The autoregressive integrated moving average (ARIMA) model is also used for a future scenario projection. It is observed from the projection results that the drought severity is enhancing with time. The drought severity scale from 1980 to 1989 is found to be between − 1 and 1, but the scale increases from 1990 to 2019 (− 3). From 1990 to 2019, SPI's negative (−) scale has become more prominent in all Vidarbha regions. These trends are indicative of drought severity and will have a significant impact on both life and property. [ABSTRACT FROM AUTHOR]

Predicting student dropout is a challenging problem in the education sector. This is due to an imbalance in student dropout data, mainly because the number of registered students is always higher than the number of dropout students. Developing a model without taking the data imbalance issue into account may lead to an ungeneralized model. In this study, different data balancing techniques were applied to improve prediction accuracy in the minority class while maintaining a satisfactory overall classification performance. Random Over Sampling, Random Under Sampling, Synthetic Minority Over Sampling, SMOTE with Edited Nearest Neighbor and SMOTE with Tomek links were tested, along with three popular classification models: Logistic Regression, Random Forest, and Multi-Layer Perceptron. Publicly accessible datasets from Tanzania and India were used to evaluate the effectiveness of balancing techniques and prediction models. The results indicate that SMOTE with Edited Nearest Neighbor achieved the best classification performance on the 10-fold holdout sample. Furthermore, Logistic Regression correctly classified the largest number of dropout students (57348 for the Uwezo dataset and 13430 for the India dataset) using the confusion matrix as the evaluation matrix. The applications of these models allow for the precise prediction of at-risk students and the reduction of dropout rates. [ABSTRACT FROM AUTHOR]

Mismanagement of fresh water is a primary concern that negatively impacts agricultural productivity. Judicious use of water in agriculture is possible by estimating the optimal requirement. The present practice of estimating crop water requirements is using reference evapotranspiration (ET0) values, which is considered a standard method. Hence, predicting ET0 is vital in allocating and managing available resources. In this study, different machine learning (ML) algorithms, namely random forests (RF), extreme gradient boosting (XGB), and light gradient boosting (LGB), were optimized using the naturally inspired grey wolf optimizer (GWO) viz. GWORF, GWOXGB, and GWOLGB. The daily meteorological data of 10 locations falling under humid and sub-humid regions of India for different cross-validation stages were employed, using eighteen input scenarios. Besides, different empirical models were also compared with the ML models. The hybrid ML models were found superior in accurately predicting at all the stations than the conventional and empirical models. The reduction in the root mean square error (RMSE) from 0.919 to 0.812 mm/day in the humid region and 1.253 mm/day to 1.154 mm/day in the sub-humid region was seen in the least accurate model using the hyperparameter tuning. The RF models have improved their accuracies substantially using the GWO optimizer than LGB and XGB models. [ABSTRACT FROM AUTHOR]

Climate change poses a major threat to global food security. Agricultural systems that rely on monsoon rainfall are especially vulnerable to changes in climate variability. This paper uses machine learning to deepen understanding of how monsoon variability impacts agricultural productivity. We demonstrate that random forest modelling is effective in representing rice production variability in response to monsoon weather variability. Our random forest modelling found monsoon weather predictors explain similar levels of detrended anomaly variation in both rice yield (33%) and area harvested (35%). The role of weather in explaining harvested rice area highlights that production area changes are an important pathway through which weather extremes impact agricultural productivity, which may exacerbate losses that occur through changes in per-area yields. We find that downwelling shortwave radiation flux is the most important weather variable in explaining variation in yield anomalies, with proportion of area under irrigation being the most important predictor overall. Machine learning modelling is capable of representing crop-climate variability in monsoonal agriculture and reveals additional information compared to traditional parametric models. For example, non-linear yield and area responses of irrigation, monsoon onset and season length all match biophysical expectations. Overall, we find that random forest modelling can reveal complex non-linearities and interactions between climate and rice production variability. [ABSTRACT FROM AUTHOR]

Landslides are a significant issue in India due to the country's varied topography, heavy monsoon rains, and deforestation, which contribute to soil instability and increased landslide risk. These natural disasters can cause damage to infrastructure and loss of life. In light of the ongoing problem of landslides in India, this research paper aims to address the need for effective landslide prediction strategies. Through the findings of this research study, a novel approach has been presented for predicting landslide occurrences in India, which will aid in reducing the impact of these events on infrastructure, communities, and lives. The work that has been carried out using data and information based in India has shown to have a low accuracy level. As a result, the model created using this information is not deemed to be very reliable. This study focuses on predicting landslides in India using machine learning models such as (Xtreme Gradient Boost) XGboost, random forest, and AdaBoost. Previous research on landslide prediction in India had not been widely done or had not achieved acceptable accuracy levels. This study aims to address this gap in knowledge and improve the predictability of landslides in India. The research is based on a database of different areas in India and aims to increase awareness and save lives and resources by predicting landslides with 91% accuracy. [ABSTRACT FROM AUTHOR]

Pigeon pea is the second most important grain legume in India, primarily grown under rainfed conditions. Any changes in agro-climatic conditions will have a profound influence on the productivity of pigeon pea (Cajanus cajan) yield and, as a result, the total pulse production of the country. In this context, weather-based crop yield prediction will enable farmers, decision-makers, and administrators in dealing with hardships. The current study examines the application of the stepwise linear regression method, supervised machine learning algorithms (support vector machines (SVM) and random forest (RF)), shrinkage regression approaches (least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)), and artificial neural network (ANN) model for pigeon pea yield prediction using long-term weather data. Among the approaches, ANN resulted in a higher coefficient of determination (R2 = 0.88–0.99), model efficiency (0.88–1.00) with subsequent lower normalised root mean square error (nRMSE) during calibration (1.13–12.55%), and validation (0.33–21.20%) over others. The temperature alone or its interaction with other weather parameters was identified as the most influencing variables in the study area. The Pearson correlation coefficients were also determined for the observed and predicted yield. Those values also showed ANN as the best model with correlation values ranging from 0.939 to 0.999 followed by RF (0.955–0.982) and LASSO (0.880–0.982). However, all the approaches adopted in the study were outperformed the statistical method, i.e. stepwise linear regression with lower error values and higher model efficiency. Thus, these approaches can be effectively used for precise yield prediction of pigeon pea over different districts of Karnataka in India. [ABSTRACT FROM AUTHOR]

In 2014, the State of Telangana in southern India began repairing and restoring more than 46,000 irrigation water tanks (artificial reservoirs) under the Mission Kakatiya project with an investment in excess of USD 2 billion. In this study, we attempted to map the temporal changes that have occurred in cropland areas and water bodies as a result of the project, using remote sensing imagery and applying land use/land cover (LULC) mapping algorithms. We used 16-day time series data from Landsat 8 to study the spatial distribution of changes in water bodies and cropland areas over the 2013–18 period. Ground survey information was used to assess the pixel-based accuracy of the Landsat-derived data. The areas served by these tanks were identified on the basis of training data and Random Forest algorithms using Google Earth Engine. Our spatial analysis revealed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. We observed a 20% increase in total tank area in 2017–18 and total cropland and irrigated area expansion of the order of 0.6 M ha and 0.2 M ha, respectively. A comparison of ground survey data and four LULC classes derived from Landsat temporal imagery showed an overall accuracy of 87%, significantly correlated with national agriculture statistics. Periodic monitoring based on remote sensing has proved to be an effective method of capturing LULC changes resulting from the Mission Kakatiya interventions. Higher-resolution satellite data can further improve the accuracy of estimates. [ABSTRACT FROM AUTHOR]

Human life sustained for decades due to the availability of basic needs, and freshwater is one of them. However, groundwater quality is constantly under pressure. This can be attributed to anthropogenic activities not limited to urban areas but to rural zones. Machine learning methods like linear discriminant analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbour (KNN), Support Vector Machines (SVM) and, Random Forest (RF) models were used to analyse groundwater quality variables. The mean accuracy of each classifier was calculated, and the obtained mean accuracies were 77.5% (LDA), 87% (CART), 96% (KNN), 93.5% (SVM) and 96% (RF). RF and KNN models were selected as optimal models with higher accuracy. This study made it apparent that machine learning algorithms can estimate and predict water quality variables with significant accuracy. In this study, the observations and variables were compared with the water quality index and drinking water limits provided by the Bureau of Indian Standards. The water quality index for each observation was calculated. If at least four variables have a higher value than prescribed limits, it was assigned a value of 1; if more than four variables reported higher values, it was assigned a value of 2. [ABSTRACT FROM AUTHOR]

In the realm of data analytics, machine learning (ML) is one of the most successful techniques for making predictions. The ability of ML algorithms has also been studied in various aspects of land surface temperature (LST) besides its retrieval. The few investigations on LST retrieval using ML algorithms suggested that it may potentially obtain the LST values incorporating relevant variables of land surface parameters; however, the variables and ML models used differ, and so do their accuracies. The accuracy of the model is affected by the variable's contribution, its quality and quantity, and the fulfilment of each technique's assumptions. Hence this study provides a wide range of LST indicators to be employed for LST retrieval using a widely used ML algorithm, random forest. The ML algorithm framework for LST prediction is illustrated with significant spectral indices and terrain parameters across the highly industrialised district of Jharkhand, India. With the exception of one (aspect) variable, the analysis shows that all 20 variables that were included as independent factors were significant and equally contributed to the model. The model built with all the variables including the aspect of the terrain obtained an RMSE of 1.13 degree Celsius and R2 of 0.48. However, after the removal of aspect, the model obtained an R2 of 0.89 and RMSE of 0.74 °C. The performance of the model on consecutive removal of lesser significant variables are evaluated and the study made clear how crucial it is to consider several environmental or land-use factors that could be pertinent to LST. [ABSTRACT FROM AUTHOR]

The soil–environmental relationship identified and standardised over the years has expedited the growth of digital soil-mapping techniques; hence, various machine learning algorithms are involved in predicting soil attributes. Therefore, comparing the different machine learning algorithms is essential to provide insights into the performance of the different algorithms in predicting soil information for Indian landscapes. In this study, we compared a suite of six machine learning algorithms to predict quantitative (Cubist, decision tree, k-NN, multiple linear regression, random forest, support vector regression) and qualitative (C5.0, k-NN, multinomial logistic regression, naïve Bayes, random forest, support vector machine) soil information separately at a regional level. The soil information, including the quantitative (pH, OC, and CEC) and qualitative (order, suborder, and great group) attributes, were extracted from the legacy soil maps using stratified random sampling procedures. A total of 4479 soil observations sampled were non-spatially partitioned and intersected with 39 environmental covariate parameters. The predicted maps depicted the complex soil–environmental relationships for the study area at a 30 m spatial resolution. The comparison was facilitated based on the evaluation metrics derived from the test datasets and visual interpretations of the predicted maps. Permutation feature importance analysis was utilised as the model-agnostic interpretation tool to determine the contribution of the covariate parameters to the model's calibration. The R2 values for the pH, OC, and CEC ranged from 0.19 to 0.38; 0.04 to 0.13; and 0.14 to 0.40, whereas the RMSE values ranged from 0.75 to 0.86; 0.25 to 0.26; and 8.84 to 10.49, respectively. Irrespective of the algorithms, the overall accuracy percentages for the soil order, suborder, and great group class ranged from 31 to 67; 26 to 65; and 27 to 65, respectively. The tree-based ensemble random forest and rule-based tree models' (Cubist and C5.0) algorithms efficiently predicted the soil properties spatially. However, the efficiency of the other models can be substantially increased by advocating additional parameterisation measures. The range and scale of the quantitative soil attributes, in addition to the sampling frequency and design, greatly influenced the model's output. The comprehensive comparison of the algorithms can be utilised to support model selection and mapping at a varied scale. The derived digital soil maps will help farmers and policy makers to adopt precision information for making decisions at the farm level leading to productivity enhancements through the optimal use of nutrients and the sustainability of the agricultural ecosystem, ensuring food security. [ABSTRACT FROM AUTHOR]

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Movements in the India VIX are an important gauge of how the market's risk perception shifts from day to day. This research attempts to forecast movements one day ahead of the India VIX using logistic regression and 11 ensemble learning classifiers. The period of study is from April 2009 to March 2021. To achieve the stated task, classifiers were trained and validated with 90% of the given sample, considering two-fold time-series cross-validation for hyper-tuning. Optimised models were then predicted on an unseen test dataset, representing 10% of the given sample. The results showed that optimal models performed well, and their accuracy scores were similar, with minor variations ranging from 63.33% to 67.67%. The stacking classifier achieved the highest accuracy. Furthermore, CatBoost, Light Gradient Boosted Machine (LightGBM), Extreme Gradient Boosting (XGBoost), voting, stacking, bagging and Random Forest classifiers are the best models with statistically similar performances. Among them, CatBoost, LightGBM, XGBoost and Random Forest classifiers can be recommended for forecasting day-to-day movements of the India VIX because of their inherently optimised structure. This finding is very useful for anticipating risk in the Indian stock market. [ABSTRACT FROM AUTHOR]

Movements in the volatility index of the Indian economy are influenced by global volatility indices (fear index). This study evaluates the influence of various global implied volatility indices in forecasting the day-to-day binary movements in the implied volatility index of India, denoted by the symbol 'India VIX'. Historical daily data from 18 September, 2009, to 2 December, 2021, was acquired, and the target labels were created from changes in the India VIX. A set of classifiers, consisting of Logistic Regression, Random Forest and Extreme Gradient Boosting (XG Boost), were applied to rank the feature variables according to their importance. This study revealed that India's VIX was impacted most by the previous day's changes in the closing value of the US implied volatility indices, except for the Chicago Board Options Exchange (CBOE) Eurocurrency volatility index. Additionally, the Eurozone implied volatility index was also important. However, the implied volatility indices of Australian Hang Seng and Japan were the least important. This study's outcomes help Indian traders in creating a watch list of important volatility indices. [ABSTRACT FROM AUTHOR]

For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India's climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates. [ABSTRACT FROM AUTHOR]

We develop an enhanced accident occurrence prediction model which depends on the heterogeneous ensemble learning to tackle the topic of a accident period prediction in the early stages of the tragedy using millions of the traffic accident information's from the India. In order to start with, we concentrate on the early stages of development of accidents and choose few useful data from five categories: location, the traffic, climate, objects, and the time field. Further, we implement data cleansing, processing of outlier, and the missing value of processing to raise the quality of the data. Data mining methods can support in foreseeing the factors that are influential in concern to make severe damages. The research has significant factors that are closely connected through the severity of accidents on thruways are identified by Random Forest. Top elements influencing unintentional seriousness include temperature, distance, wind Chills, moisture, direction of wind and visibility. The main aim of this research work is to give a architecture to anticipate road crashes gathering data from the social media handles and the open access data, by implementing a ensembled Deep Learning Model. After which the result shows decent outcomes as a resort to the problem and fulfills the objective of prediction model based on algorithms and deep Learning models. [ABSTRACT FROM AUTHOR]

Polycystic Ovarian Syndrome (PCOS) is a poorly understood, under-diagnosed, and under-treated condition, with prevalence ranging from 2.2 percent to 26 percent worldwide. A prevalent endocrine disorder in women of childbearing age, PCOS is a syndrome that results in the development of ovarian cysts and may ultimately cause infertility. Oily skin, darker acne scars, weight gain, hypertension, and irregular menstrual cycles are a few of the prevalent symptoms. According to these symptoms, we have taken the PCOS dataset from Kaggle which contains 541 records and 43 attributes including patient number and target class. This target class contains the labels as 1 for PCOS affected and 0 for normal women. This data was collected from 10 nearest hospital from Kerala in India. Among 43 features, we selected 21 essential features based on gynecologist suggestions and recommending a proper diet. This work focuses on the prevention of both PCOS patients and normal women by recommending diet through the construction of rule set. We transform the existing target label into a new multi-label target class that includes the dietary class by using this rule set. This system recommends a PCOS diet based on clinical data of the patients using machine learning techniques such as K-Nearest Neighbor, Decision Tree, Random Forest classifier, and Multi-Layer Perceptron. Based on a variety of evaluation indicators, the findings are analyzed, and the performance of the algorithms is validated. This type of analysis is useful for early prevention and safe recovery of PCOS patients by recommending nutrition diets. [ABSTRACT FROM AUTHOR]

One of the most crucial stages in the building of a Machine Learning (ML) model is the evaluation and analysis of classifier model performance. The agricultural sector is the economic backbone of India and needs extensions to provide solutions to the problems faced by the farmers. This paper presents agriculture soil health analysis using Machine Learning approaches for best model and tool selection and also bibliometric analysis to identify different sources and author's keywords for finding the area of focus for proposed work. Models are built on SK-Learn, KNIME, WEKA and Rapid Miner tools using different ML algorithms. Nave Bayes, Random Forest (RF), Decision Tree (DT), Ensemble learning (EL), and k-Nearest Neighbor (KNN) are used to analyze soil data on these tools. Results show that Decision Tree model outperforms other algorithms, followed by RF algorithm which is a set of multiple Decision tree algorithms and SK-Learn tool gives better accuracy followed by WEKA tool then KNIME tool. Maximum accuracy obtained by Decision Tree algorithm is 98.40% using SK-Learn followed by KNIME tool with 73.07%, Maximum accuracy obtained by Naïve Bayes algorithm is 69.50% using SKLearn followed by KNIME tool with 68.14%, maximum accuracy obtained by Random Forest algorithm is 85.00% using SK-Learn followed by 73.06% using WEKA tool, maximum accuracy obtained by Ensemble algorithm is 89.00% using SK-Learn followed by 73.06% using WEKA tool and for KNN it is 95.50% using SK-Learn followed by 71.85% using WEKA tool. [ABSTRACT FROM AUTHOR]

The ability to predict global horizontal irradiance (GHI) in the wake of changing weather conditions is becoming increasingly important in driving economic growth in the renewable energy industry. The choice of relevant meteorological variables for predicting GHI is crucial because it influences the prediction accuracy due to various geographical conditions. To fulfill this aim, this proposed study identifies a subset of the relevant input variables for the prediction of GHI by applying two methods Feature Combination (FC) and Feature Selection (FS). The results reveal that the most significant input variables for predicting GHI are Solar Zenith Angle, Dew Point, Diffuse Horizontal irradiance, Direct Normal Irradiance, and Wind speed obtained by the FS method. The predictive performance of the selected features is evaluated by feeding them into six different types of Machine Learning (ML) regressor algorithms such as Multiple Linear regressor (MLR), Decision Tree (DT), Random Forest (RF), Gradient Boost (GB), Light Gradient Boost Machine (LGBM) and Extra Tree (ET). The performances of models are evaluated by using statistical measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). ET regressor gives the best prediction performance among all the six models. It is observed that the percentage reduction in error for the FS method as compared to the FC method in terms of the statistical indicators MAE, RMSE, and MAPE are 8.5%, 7.72%, and 25.84% respectively. It shows that the FS method gives better predictive performance than the FC method for the estimation of GHI. [ABSTRACT FROM AUTHOR]

Background: Large-scale hunting and various anthropogenic pressures in the recent past have pushed the Asiatic caracal (Caracal caracal schmitzi), an elusive medium-sized and locally threatened felid species towards local extinction in India. Though widely distributed historically, it has been sparsely reported from several regions of central and northern states in India till twentieth century. Later, the species distribution became confined only to the states of Rajasthan, Gujarat and Madhya Pradesh, which have had reported sightings in the twenty-first century. In order to highlight the potentially suitable habitats for Asiatic caracals in India, we targeted forth-filtering of the spatial model ensemble by creating and utilizing the validated and spatially thinned species presence information (n = 69) and related ecological variables (aridity, NDVI, precipitation seasonality, temperature seasonality, terrain ruggedness), filtered with anthropological variable (nightlight). Results: Out of eight spatial prediction models, the two most parsimonious models, Random Forest (AUC 0.91) and MaxEnt (AUC 0.89) were weighted and ensembled. The ensemble model indicated several clustered habitats, covering 1207.83 km2 areas in Kachchh (Gujarat), Aravalli mountains (Rajasthan), Malwa plateau (Rajasthan and Madhya Pradesh), and Bundelkhand region (Madhya Pradesh) as potentially suitable habitats for caracals. Output probabilities of pixels were further regressed with converted vegetation height data within selected highly potential habitats, i.e., Ranthambore Kuno Landscape (RKL) (suitability ~ 0.44 + 0.03(vegetation height) **, R2 = 0.27). The regression model inferred a significant positive relation between vegetation height and habitat suitability, hence the lowest ordinal class out of three classes of converted vegetation height was masked out from the RKL, which yielded in an area of 567 km2 as potentially highly suitable habitats for caracals, which can be further proposed as survey areas and conservation priority areas for caracals. Conclusion: The study charts out the small pockets of landscape in and around dryland protected areas, suitable for caracal in the Indian context, which need attention for landscape conservation. [ABSTRACT FROM AUTHOR]

We use a rich set of transaction data from a large retailer in India and a dataset on bribe payments to train random forest and XGBoost models using empirical measures guided by Benford's Law, a commonly used tool in forensic analytics. We evaluate the performance around the 2016 Indian Demonetization, which affects the distribution of legal tender notes in India, and find that models using only pre-2016 data or post-2016 data for both training and testing data had F1 score ranges around 90%, suggesting that these models and Benford's law criteria contain meaningful information for detecting bribe payments. However, the performance for models trained in one regime and tested in another falls dramatically to less than 10%, highlighting the role of the institutional setting when using financial data analytics in an environment subject to regime shifts. [ABSTRACT FROM AUTHOR]

Background: The number of people experiencing functional limitations due to health conditions (capacity) is expected to increase in low and middle-income countries as populations age and rates of non-communicable disease rise. This trend could raise the prevalence and levels of disability worldwide. Understanding the demographic and environmental factors associated with disability can inform the design of policy interventions to make societies more accessible and inclusive for all.Methods: Approximately 2,500-3,000 participants in each of India, Laos, and Tajikistan responded to the Gallup World Poll and the World Health Organization's Brief Model Disability Survey through face-to-face interviews. For each country, random forest regression was performed to explore the associations of demographic and environmental factors with disability while controlling for capacity. Using the variable importance measures generated by the random forest models, linear regression models were built in a stepwise manner for each country to predict disability level based on these contextual factors.Results: Capacity was strongly associated with disability in all three countries. Most of the variance in disability was explained by minimally adjusted linear models that included only capacity, sex, and age. Inclusion of additional demographic factors and environmental factors explained slightly more of the variance in disability score. Across all three countries, the level of basic infrastructure, public services, and financial stability were moderately associated with disability. Age, sex, employment status, the use of assistive technologies, and other factors had associations with disability that were highly variable across countries.Conclusion: While capacity was the main determinant of disability, individual demographic and environmental factors were associated with disability in a country-specific manner while controlling for the effects of capacity. [ABSTRACT FROM AUTHOR]

Multi-Model Ensembles (MMEs) are used for improving the performance of GCM simulations. This study evaluates the performance of MMEs of precipitation, maximum temperature and minimum temperature over a tropical river basin in India developed by various techniques like arithmetic mean, Multiple Linear Regression (MLR), Support Vector Machine (SVM), Extra Tree Regressor (ETR), Random Forest (RF) and long short-term memory (LSTM). The 21 General Circulation Models (GCMs) from National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and 13 GCMs of Coupled Model Inter-comparison Project, Phase 6 (CMIP6) are used for this purpose. The results of the study reveal that the application of a LSTM model for ensembling performs significantly better than models in the case of precipitation with a coefficient of determination (R2) value of 0.9. In case of temperature, all the machine learning (ML) methods showed equally good performance, with RF and LSTM performing consistently well in all the cases of temperature with R2 value ranging from 0.82 to 0.93. Hence, based on this study RF and LSTM methods are recommended for creation of MMEs in the basin. In general, all ML approaches performed better than mean ensemble approach. [ABSTRACT FROM AUTHOR]

This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. However, machine learning models do not consider the time dependency in the COVID-19 data series. Here, dynamic information has been taken into account to alleviate this limitation by introducing lagged measurements in constructing the investigated machine learning models. Additionally, we assessed the contribution of the incorporated features to the COVID-19 prediction using the Random Forest algorithm. Results reveal that significant improvement can be obtained using the proposed dynamic machine learning models. In addition, the results highlighted the superior performance of the dynamic GPR compared to the other models (i.e., Support vector regression, Boosted trees, Bagged trees, Decision tree, Random Forest, and XGBoost) by achieving an averaged mean absolute percentage error of around 0.1%. Finally, we provided the confidence level of the predicted results based on the dynamic GPR model and showed that the predictions are within the 95% confidence interval. This study presents a promising shallow and simple approach for predicting COVID-19 spread. [ABSTRACT FROM AUTHOR]

This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E20) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model ( R M S E t e s t i n g = 0.002 , R t e s t i n g 2 = 0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume. [ABSTRACT FROM AUTHOR]