With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes.

The transmission of information, ideas, and thoughts requires communication, which is a crucial component of human contact. The utilization of Internet of Things (IoT) devices is a result of the advent of enormous volumes of messages delivered over the internet. The IoT botnet assault, which attempts to perform genuine, lucrative, and effective cybercrimes, is one of the most critical IoT dangers. To identify and prevent botnet assaults on connected computers, this study uses both quantitative and qualitative approaches. This study employs three basic machine learning (ML) techniques—random forest (RF), decision tree (DT), and generalized linear model (GLM)—and a stacking ensemble model to detect botnets in computer network traffic. The results reveled that random forest attained the best performance with a coefficient of determination (R²) of 0.9977, followed by decision tree with an R² of 0.9882, while GLM was the worst among the basic machine learning models with an R² of 0.9522. Almost all ML models achieved satisfactory performance, with an R² above 0.93. Overall, the stacking ensemble model obtained the best performance, with a root mean square error (RMSE) of 0.0084 m, a mean absolute error (MAE) of 0.0641 m, and an R² of 0.9997. Regarding the stacking ensemble model as compared with the single machine learning models, the R² of the stacking ensemble machine learning increased by 0.2% compared to the RF, 1.15% compared to the DT, and 3.75% compared to the GLM, while RMSE decreased by approximately 0.15% compared to the GLM, DT, and RF single machine learning techniques. Furthermore, this paper suggests best practices for preventing botnet attacks. Businesses should make major investments to combat botnets. This work contributes to knowledge by presenting a novel method for detecting botnet assaults using an artificial-intelligence-powered solution with real-time behavioral analysis. This study can assist companies, organizations, and government bodies in making informed decisions for a safer network that will increase productivity.

The analysis of the high volume of data spawned by web search engines on a daily basis allows scholars to scrutinize the relation between the user’s search preferences and impending facts. This study can be used in a variety of economics contexts. The purpose of this study is to determine whether it is possible to anticipate the unemployment rate by examining behavior. The method uses a cross-correlation technique to combine data from Google Trends with the World Bank's unemployment rate. The Autoregressive Integrated Moving Average (ARIMA), Autoregressive Integrated Moving Average with eXogenous variables (ARIMAX) and Vector Autoregression (VAR) models for unemployment rate prediction are fit using the analyzed data. The models were assessed with the various evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), median absolute error (MedAE), and maximum error (ME). The average outcome of the various evaluation metrics proved the significant performance of the models. The ARIMA (MSE = 0.26, RMSE = 0.38, MAE = 0.30, MAPE = 7.07, MedAE = 0.25, ME = 0.77), ARIMAX (MSE = 0.22, RMSE = 0.25, MAE = 0.29, MAPE = 6.94, MedAE = 0.25, ME = 0.75), and VAR (MSE = 0.09, RMSE = 0.09, MAE = 0.20, MAPE = 4.65, MedAE = 0.20, ME = 0.42) achieved significant error margins. The outcome demonstrates that Google Trends estimators improved error reduction across the board when compared to model without them.

A newly proposed or implemented government policy often encounters challenges. Ghanaian citizens have always look down negatively upon their government’s policies, hence those are rarely appreciated. This paper ponders over the Ghanaian government’s proposal of electronic levy on mobile money transactions which was announced in the 2022 budget on November 17, 2021. We have scrutinized this governmental policy through the ordinary citizen's perspective using lexicon-based sentiment analysis on Twitter data. Lexicons are collections of words that express specific emotions, and deals with interpreting emotions like happiness, frustration, anger, and sadness. Twitter, serving as a means for people to share their views, provides enormous user generated content, beneficial for research purposes. We collected e-levy specific Twitter data in five phases, namely; policy introduction, popularity, discussion, feeble, and debate phases. The policy introduction phase recorded the least volume of data containing 1400 tweets, among which our sentiment analyzer classifies 8.93% as positive, 89.29% as neutral, and 1.78% as negative. The debate phase recorded the highest amount of data containing 18.423 tweets, among which 24.43% tweets are classified as positive, 59.29% as neutral, and 16.28% as negative. An analysis on the entire data containing 38,771 tweets reports 25.50% positive, 59.02% neutral, and 15.48% negative tweets. Our study determines that people are not largely unhappy established by the stable positive sentiment percentage, however, there is a high neutral score in all the phases.

One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination ( R ² ), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy ( R ² varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL.

Background: Females benefit from ultrasound screening and diagnosis of breast cancer, and artificial intelligence has enabled the automatic identification of medical conditions on medical imaging. Methods: This study aimed to develop machine learning (ML) and deep learning (DL) models for the detection and classification of breast cancer in a breast ultrasound image (BUSI) and United States (US) ultrasound images datasets and to compare the models’ performance to previous studies. The ultrasound scans were collected from women between the ages of 25 and 75. The dataset contains 780 images with a resolution of 500 × 500 pixels. There were 133 normal images with no cancerous masses, 437 images with cancerous masses, and 210 images with benign masses among the 780 cancerous images in the BUSI dataset whiles the US ultrasound images includes 123 and 109 ultrasound images of malignant and benign breast tumors. Two traditional ML models, random forest (RF) and K-Nearest Neighbor (KNN), as well as a deep learning (DL) model using convolutional neural networks (CNN), were trained to classify breast masses as benign, malignant, or normal. Results: The CNN obtained an accuracy of 96.10%, the RF an accuracy of 61.46%, and the KNN an accuracy of 64.39% with the BUSI dataset. Standard evaluation measures were employed to assess the performance for benignancy, malignancy, and normality classification. Furthermore, the models’ area under the curve-receiver operating characteristics (AUC-ROC) are 0.99 by the CNN, 0.85 by the RF, and 0.65 by the KNN. Conclusions: The study’s findings revealed that DL surpasses conventional ML when it comes to training image datasets; hence, DL is suggested for breast cancer detection and classification. Furthermore, the resilience of the models used in this study overcomes data imbalance by allowing them to train both binary and multiclass datasets.