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.

Model compression and acceleration are attracting increasing attentions due to the demand for embedded devices and mobile applications. Research on efficient convolutional neural networks (CNNs) aims at removing feature redundancy by decomposing or optimizing the convolutional calculation. In this work, feature redundancy is assumed to exist among channels in CNN architectures, which provides some leeway to boost calculation efficiency. Aiming at channel compression, a novel convolutional construction named compact convolution is proposed to embrace the progress in spatial convolution, channel grouping and pooling operation. Specifically, the depth-wise separable convolution and the point-wise interchannel operation are utilized to efficiently extract features. Different from the existing channel compression method which usually introduces considerable learnable weights, the proposed compact convolution can reduce feature redundancy with no extra parameters. With the point-wise interchannel operation, compact convolutions implicitly squeeze the channel dimension of feature maps. To explore the rules on reducing channel redundancy in neural networks, the comparison is made among different point-wise interchannel operations. Moreover, compact convolutions are extended to tackle with multiple tasks, such as acoustic scene classification, sound event detection and image classification. The extensive experiments demonstrate that our compact convolution not only exhibits high effectiveness in several multimedia tasks, but also can be efficiently implemented by benefitting from parallel computation.

In this brief, a customized and pipelined hardware implementation of the quasi-Newton (QN) method on field-programmable gate array (FPGA) is proposed for fast artificial neural networks onsite training, targeting at the embedded applications. The architecture is scalable to cope with different neural network sizes while it supports batch-mode training. Experimental results demonstrate the superior performance and power efficiency of the proposed implementation over CPU, graphics processing unit, and FPGA QN implementations.

Feature selection plays an important role in data mining and pattern recognition. However, most existing feature selection methods suffer from stagnation in local optimal and/or high computational cost. In general, feature selection process can be considered as a global optimization problem. The swarm intelligence (SI) algorithm can effectively find the optimal feature subset due to its global search ability. But it usually consumes very long running time when dealing with large datasets. In this paper, feature selection is transformed into a global optimization problem, which provides a fast and efficient method based on swarm intelligence algorithm. First, we propose a global optimization framework for filter-based feature selection and its mathematical model. Furthermore, to solve feature selection problem for acoustic defect detection, we combine the Shuffled Frog Leaping Algorithm (SFLA) with an improved minimumredundancy maximum-relevancy (ImRMR), named SFLA-ImRMR. In the experiments, a back propagation neural network (BPNN) is employed to evaluate the classification performance of the selected feature subset on the test sets of acoustic defect detection. The results show that SFLA-ImRMR achieved similar performance to the other algorithms within the shortest time.