Crop Yield Prediction Using Supervised Machine Learning Algorithms

Rainfall plays a crucial role in agricultural practices, influencing the growth of essential crops and impacting economies reliant on agriculture. In Rajasthan, India, where agriculture is predominantly rainfed and dependent on unpredictable monsoon rains, accurate rainfall forecasting is vital for effective crop management, irrigation, optimizing agricultural productivity, managing water resources, and addressing drought conditions and. This study evaluates various machine learning models for predicting rainfall in East and West Rajasthan. The models assessed include Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), and K-Nearest Neighbours (KNN). These models were compared based on their accuracy measures, including Root Mean Squared Error (RMSE), Relative Root Squared Error (RRSE), and Relative Absolute Error (RAE). The results indicate that ANN and KNN perform poorly compared to SVR and RF suggesting their inadequacy for rainfall forecasting in water-scarce regions. The performance of SVR and RF are comparable as the difference is less. However SVR is identified as the most effective model for rainfall prediction in both subdivisions, as highlighted in the study. This study suggest that SVR and RF model should be includes in prediction of amount of rainfall.

Postoperative pain is a frequent clinical concern following endodontic treatment. This study aimed to develop and validate supervised machine learning models to predict the occurrence of postoperative pain in cases of irreversible pulpitis. A prospective sample of 354 patients aged 18 to 60 years undergoing standardised endodontic treatment was analysed. In the original randomised clinical trials from which the data were derived, each patient had only one eligible tooth included. Clinical variables included postoperative pain at 24 and 72 h, treated tooth group, occlusal reduction, photobiomodulation therapy, use of non-steroidal anti-inflammatory drugs (NSAIDs), sex and age. Eight supervised machine learning algorithms were trained to predict pain occurrence, including Logistic Regression, Support Vector Machine, Gradient Boosting, Random Forest, Decision Tree, K-Nearest Neighbours, AdaBoost and Multilayer Perceptron. The dataset was divided into training (70%) and testing (30%) sets using stratified sampling. Class imbalance in the training set, characterised by a lower proportion of cases with moderate or severe pain, was addressed using the Synthetic Minority Oversampling Technique. Hyperparameters were optimised through grid search combined with stratified five-fold cross validation. Model performance was evaluated using the area under the curve (AUC), accuracy, precision, recall and F1-score, with 95% confidence intervals estimated by bootstrapping. The predictive models achieved good discrimination of pain outcomes. Logistic Regression showed the best test performance at 24 h (AUC 0.74 [95% CI: 0.61 to 0.85], precision 0.81 [95% CI: 0.73 to 0.88]). At 72 h, the Support Vector Machine achieved the highest performance (AUC 0.81 [95% CI: 0.69 to 0.92], precision 0.88 [95% CI: 0.79 to 0.94]). Age and sex emerged as the most influential predictors across models. Supervised machine learning models demonstrated promising performance for predicting postoperative pain following endodontic treatment. Logistic Regression and Support Vector Machine algorithms presented the most consistent results, supporting their potential clinical application for personalised pain management.

This study presents a comparative analysis of machine learning models for predicting carbon monoxide (CO) emissions in automotive engines. Four models—Linear Regression, Decision Tree, Random Forest, and Support Vector Regression—were evaluated using a dataset of engine performance parameters and emission measurements. Among these, the Random Forest model demonstrated the highest predictive accuracy, achieving an R² score of 0.8965. Feature importance analysis identified nitrogen oxides (NOX), engine speed (RPM), and hydrocarbons (HC) as the most significant predictors of carbon monoxide emissions. Learning curve analysis provided insights into model generalization and highlighted potential limitations. The study underscores the value of data-driven approaches in optimizing engine design and controlling emissions. The findings contribute to the development of cleaner, more efficient vehicles, supporting sustainability efforts in the automotive industry. This research bridges data science and automotive engineering, offering a framework for advanced emission prediction and control that can be applied to other pollutants and engine types.

Due to the rapid growth of android applications and mobile users in this technological era, there is a large increase in cyber attacks through mobile phones. During Pandemic period, mobile malware attacks are one of the top most cyber attacks observed in android mobile users to steal the user personal credentials by intrusion of adware, spyware, banking malware, SMS malware, riskware, viruses, Trojan horse, worms, keylogger and many more. Machine learning methods are very useful and amicable to detect mobile malwares. Automation of the mobile malware detection is the need of the hour and it is imperative to identify the most suitable machine learning techniques. This book entitled “Dynamic Analysis based Mobile Malware Classification using Supervised Machine Learning Methods” investigates the evaluation of supervised machine learning algorithms that are applied to detect and classify the mobile malwares. A systematic method of evaluation of supervised machine learning model to detect the malware data points and to classify them into binary classification as malware or benign is essential. The purpose of evaluating the supervised machine learning algorithms is to identify the best supervised machine learning model for mobile malware detection with high efficacy rate.  All important performance measures like Precision, Recall, F1 score, R2 score, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error are applied and the entire experiments are conducted using benchmark dataset taken from kaggle community. Nine Supervised Machine Learning methods such as Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machine, Naïve Bayes, AdaBoost, Multi-layer Perceptron, Logistic Regression and Linear Discriminant Analysis are experimented and the results are discussed. +

Cardiovascular disease remains the most significant contributor to global mortality, highlighting the importance of early and precise risk assessment within preventive healthcare frameworks. Alongside the rapid growth of clinical data availability, machine learning approaches have increasingly been adopted to assist medical decision-making, particularly for interpreting complex and high-dimensional health information. This research investigates the predictive capability of six supervised machine learning models in determining the likelihood of cardiovascular disease incidence: Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosting. The Cleveland Heart Disease dataset from the UCI Machine Learning Repository served as the study's foundation. It includes 303 patient samples with a total of 76 recorded attributes. From this dataset, 14 clinically significant variables frequently reported in previous studies were selected for analysis. Considering the relatively small dataset size and the possibility of redundant or low-impact features, a feature selection approach was implemented to improve model robustness, minimize overfitting, and enhance interpretability. The data preparation process involved cleaning, normalization, feature selection, and division into datasets for testing and training. Metrics like accuracy, precision, recall, and F1-score were used to evaluate the model. The results of the experiment show that Random Forest and Logistic Regression models produced the highest predictive performance, followed by k-Nearest Neighbours and Support Vector Machine. These results indicate that supervised machine learning techniques, when supported by appropriate feature selection methods, are effective as decision-support tools for the early detection of cardiovascular disease.

Accurate real-time bus arrival information is essential for an efficient public transport network, as it significantly impacts passenger experience, system reliability, reduced waiting times, dwell times, and operational efficiency. In Sri Lanka's public transport system, current bus arrival time computations primarily rely on static data, neglecting real-time information and critical factors influencing travel times. This highlights the need to identify the unique variables affecting bus arrival times within the Sri Lankan context and to develop robust prediction models that account for these influences. While traditional methods such as Historical Average Models, Regression, Time Series Analysis, and Kalman Filtering have been used in previous research for short-term travel time predictions, Machine Learning (ML) approaches have proven to deliver superior accuracy. ML models are regarded as the most effective for heterogeneous, lane-less traffic conditions with varying traffic volumes, such as those found in Sri Lanka. ML techniques excel in processing large, high-quality datasets and provide accurate predictions by accounting for all relevant variables influencing travel times. Although research has been conducted on developing various basic ML models for travel time prediction, there is a noticeable gap in studies comparing these models to determine the most suitable one for the Sri Lankan context. A Long- Short Term Memory (LSTM) neural network is a deep learning model that is capable of handling long-term dependencies. In the context of bus travel time prediction, LSTMs can leverage historical traffic and travel data to capture temporal patterns and fluctuations that influence travel times. By evaluating LSTMs against basic machine learning models, this study seeks to explore the advantages of applying deep learning techniques to transportation forecasting, ultimately contributing to more accurate and efficient predictive systems in transit planning. The ML models selected in this study include two basic traditional models K- Nearest Neighbours (KNN) and Support Vector Regression (SVR) and four advanced models that utilize ensemble techniques and advanced optimization as Random Forest Regression (RFR), Ada Boost, XG Boost and Gradient Boosting Machine (GBM). The performance of these models was compared with the LSTM model to identify the gap in their accuracies.

To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms. The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-IV v2.0 (MIMIC-IV v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA). A total of 3 295 patients with septic shock were included, with 2 164 surviving and 1 131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2 307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95%CI) was 0.795-0.849], 0.823 (95%CI was 0.796-0.849), and 0.810 (95%CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95%CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95%CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score III (SAPS III), the highest WBC, acute physiology score III (APS III), the lowest Na+, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95%CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APS III score, SAPS III score, and sequential organ failure assessment (SOFA) score] with AUC (95%CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca2+, Hb, WBC, SAPS III score, APS III score, Na+, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

This paper compares the performance of five different supervised machine learning (SL) algorithms to identify the practical model for tracking copper using solution cathode glow discharge optical emission spectroscopy (SCGD-OES). A total of 4500 SCGD-OES spectra were collected, corresponding to 15 different copper concentrations ranging from 10.3 mg l−1 to 59.6 mg l−1, for training and validating five models: K-nearest neighbors (KNN), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and recurrent neural network (RNN). Then, performance of these models were evaluated based on accuracy, precision of predictions and training time. In terms of accuracy, the relative error (RE) was 3% for the CNN model and 4% for the ANN model. Conversely, the RE increased from 21% and 43% for the RF and RNN models to 103% for the KNN model due to the unpredictability of Cu concentration outside the training range. Regarding precision, the relative standard deviation (RSD) values were similar across all models, with the lowest RSD being 3% for the CNN model and the highest being 5% for the RF model. The training time indicated that the KNN and RF models provided the best learning speed, while the CNN model had the slowest learning speed, taking 5 times longer than the ANN model. The accuracy of the ANN and CNN models is in agreement with inductively coupled plasma-optical emission spectroscopy (ICP-OES), with a average difference not exceeding 6% when tracking six different Cu concentrations, both inside and outside the training range. Additionally, this research investigated the weight distribution in the hidden layers of the ANN, CNN, and RNN models and feature importances in the RF model to enhance understanding of the internal structure of models and assist analysts in selecting the practical model for tracking heavy metals based on SCGD-OES.

Cryptocurrency markets, particularly Bitcoin,are characterized by high volatility and complex non linear price movements, making trend prediction a significant challenge. The purpose of this research is to compare six different supervised machine learning models: Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), XGBoost, and Support Vector Machine (SVM), using a large dataset ofdaily changes in Bitcoin prices from September 2014 through January 2026 (24,863 records). The dataset is augmented by twelve different technical indicators (e.g. RSI, MACD, and multiple Simple Moving Averages (SMA)) that were created as input variables. As thefinancial data in this dataset is temporal in nature, time series cross-validation (Time Series Split) was used to evaluate the models in order to reduce the likelihood ofoverfitting due to random sample shuffling. Based on the experimental results, the Random Forest and XGBoost ensemble models are significantly better at predicting the price change for cryptocurrencies than the non-ensemble models, with the Random Forest model exhibiting an accuracy rate of 79.18% and AUC rate of 0.8675 for the validation folds, while the XGBoost model exhibited a 74.66% accuracy rate. This implies that advanced tree based ensemble models are capable of providing asignificant degree of prediction for cryptocurrency price trends if they are properly regularized and validated against financial market noise. Additionally, the predictive superiority of the ensemble models over the non-ensemble models was demonstrated statisticallythrough the use of McNemar's test and Point-Biserialcorrelation (p < 0.05). Keywords: Bitcoin, Machine Learning, Technical Analysis, Random Forest, XGBoost, Price Prediction, Financial Forecasting, Cryptocurrency, Supervised Machine Learning, Ensemble Learning, Time Series Cross-Validation, Feature Engineering.

BackgroundGiven the increasing healthcare costs, there is an interest in developing machine learning (ML) prediction models for estimating hospitalization charges. We use ML algorithms to predict hospitalization charges for patients undergoing transfemoral transcatheter aortic valve replacement (TF-TAVR) utilizing the National Inpatient Sample (NIS) database.MethodsPatients who underwent TF-TAVR from 2012 to 2016 were included in the study. The primary outcome was total hospitalization charges. Study dataset was divided into 80% training and 20% testing sets. We used following ML regression algorithms: random forest, gradient boosting, k-nearest neighbors (KNN), multi-layer perceptron and linear regression. ML algorithms were built for for 3 stages: Stage 1, including variables that were known pre-procedurally (prior to TF-TAVR); Stage 2, including variables that were known post-procedurally; Stage 3, including length of stay (LOS) in addition to the stage 2 variables.ResultsA total of 18,793 hospitalization for TF-TAVR were analyzed. The mean and median adjusted hospitalization charges were $220,725.2 ($137,675.1) and $187,212.0 ($137,971.0–264,824.8) respectively. Random forest regression algorithm outperformed other ML algorithms at all stages with higher R2 score and lower mean absolute error (MAE), root mean squared area (RMSE) and root mean squared logarithmic error (RMSLE) (Stage 1: MAE 79,979.11, R2 0.157; Stage 2: MAE 76,200.09, R2 0.256; Stage 3: MAE 69,350.09, R2 0.453). LOS was the most important predictor of hospitalization charges.ConclusionsWe built ML algorithms that predict hospitalization charges with good accuracy in patients undergoing TF-TAVR at different stages of hospitalization and that can be used by healthcare providers to better understand the drivers of charges.

This study aims to predict the US financial stock market through machine learning (ML) methods based on optimized feature selection algorithms. Two prediction models were compared: random forest (RF) and support vector regression (SVR). Seventeen variables are used to explain the movement of the S&P 500, NASDAQ, and DJIA indices. These variables are grouped into five categories: basic features, stock market variables, currencies, commodities, and technical indicators. This research work proceeds by applying a variable selection technique to identify the most relevant variables. The optimal set of selected variables was used for forecasting. The results obtained using SVR and RF after variable selection were compared with those obtained before selection. The outcomes of the comparison between these two Artificial Intelligence (AI) methods favor regression after variable selection. Findings show that the feature selection process has a large and significant impact on improving the prediction accuracy of the studied financial markets.

COVID-19 in Bangladesh: A Deeper Outlook into The Forecast with Prediction of Upcoming Per Day Cases Using Time Series.

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.

Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.

Comparative analysis of machine learning models for rainfall prediction