Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

IntroductionFinding the potential of groundwater resources is one of the basic principles in water resources management. The aim of this research is to determine the potential of groundwater using support vector machine learning (SVM) models as well as metaheuristic algorithms (hybrid support vector machine model and the bee metaheuristic optimization algorithm (SVM-BA) and hybrid model of the support vector machine and particle swarm optimization algorithm (SVM-PSO).Materials and methodsThe factors of elevation, slope, aspect, topographic humidity index, distance from stream, drainage density, distance from fault, lithology, topographic position index, land roughness index, relative slope position and flow convergence index were selected in Bojnurd region. Information on the location of 359 springs was received from the regional water company. Random division algorithm was used to divide training points (70%) and validation points (30%). Based on the removal sensitivity analysis, the importance and contribution of the input variables in determining the groundwater potential were determined. The accuracy of the models was evaluated in two stages of training and validation based on the receiver operating characteristic (ROC) curve method.ResultsThe evaluation of the accuracy of the models based on the evaluation criteria of the area under curve (AUC) showed that the prediction accuracy of the hybrid model of the support vector machine and the particle swarm optimization algorithm (SVM-PSO) is 0.945 more than other models (SVM: 0.918 and SVM-BA: 0.932). Based on the results of the superior model, the high potential class and the very high potential class accounted for 7.75% and 38.66% of the area respectively. Among the factors, relative slope position with 14.5%, distance from the fault with 13.4% and lithology with 12.3% were the most important in predicting groundwater potential.Discussion and ConclusionBased on the results of this research, the support vector machine model has a high performance, and two optimization algorithms, the bee metaheuristic and particle swarm optimization algorithm, strengthen the predictive power of the model. Also machine learning models can identify the relationship between the environmental factors and the water supply of the springs and determine their role by using the available data. The relative slope position factor was identified as the most important variable and the distance from the fault factor was considered as the second most important variable in the present study. The results of the research showed that the faults in the region play an important role in aquifer recharge, storage and flow of groundwater. The lithological factor was also introduced by the model as the third important variable in identifying the state of groundwater potential. In this research, by presenting the groundwater potential map, it is possible to plan and verify land use planning for the Bojnurd watershed.

In this study, the groundwater level of the Kabodarahang aquifer located in Hamadan Province, Iran, is simulated using MODFLOW, Extreme Learning Machine (ELM), and Wavelet-Extreme Learning Machine (WA-ELM) Models. The correlation coefficient and scatter index values for the MODFLOW model are calculated 0. 917 and 0. 0004, respectively. Then, by different input combination and using the stepwise selection, 10 different models are introduced for the ELM and WA-ELM models with different lags. By evaluating all activation functions of the ELM model, the sigmoid activation function predicts groundwater level values with more accuracy. Also, Daubechies2 is selected as the mother wavelet of the WA-ELM models. According to different numerical models results, the WA-ELM model is selected as the superior model in prediction of groundwater level. For the superior model, the correlation coefficient and Nash-Sutcliffe efficiency coefficient are calculated 0. 959 and 0. 915, respectively. These values for ELM model was respectively computed as 0. 828 and 0. 672.

There is an urgent need for precise and trustworthy models to forecast device behavior and evaluate vulnerabilities as a result of the Internet of Things' (IoT) explosive growth. By assessing the effectiveness of several machine learning algorithms logistic regression, decision trees, random forests, Naïve Bayes, and KNN on two popular IoT devices Alexa and Google Home Mini this study seeks to enhance IoT device behavior forecasting. Our results show that Naïve Bayes and random forest models are more accurate and efficient than other algorithms at predicting device behavior. These findings demonstrate how important algorithm selection is for maximizing the performance of IoT systems. The study also emphasizes the usefulness of precise device behavior prediction for practical uses such as industrial control systems, home automation, and medical monitoring. For example, accurate forecasts can improve decision-making in crucial situations, facilitate more seamless automation, and stop system failures. In addition to adding to the expanding corpus of research on IoT data analysis, this study establishes the foundation for the creation of increasingly sophisticated machine learning models that can manage the intricate and ever-changing nature of IoT ecosystems. Future studies should concentrate on increasing the dataset's diversity to encompass a wider range of IoT environments and devices and enhancing the model's adaptability to changing IoT environments.

Intrinsic motivation has garnered significant attention in recent years, empowering both living beings and robots to learn autonomously and cumulatively, even without extrinsic motivation (rewards from the environment). This concept, drawing inspiration from psychology and neuroscience, has opened up new avenues in artificial intelligence. Algorithmic architectures for intrinsic motivation facilitate exploration and the effective acquisition of motor skills in scenarios where environment rewards are sparse or absent. This is particularly relevant for many real-world problems, where large portions of the environment offer no explicit rewards. Consequently, intrinsic motivation holds not only theoretical significance for enhancing artificial intelligence algorithms, particularly in exploration tasks, but also practical implications for real-world or near-real-world applications. In this paper, we delve into the significance of intrinsic motivation, providing a brief overview of its origins in psychology. We then systematically categorize and examine research on intrinsic motivation in artificial intelligence. Additionally, we discuss the reinforcement learning method as a successful approach for incorporating intrinsic motivation. Finally, we explore the practical applications, limitations, and future intrinsic motivation research.