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The significance of intracellular recording in neurophysiology is emphasized in this article, with considering the functions of neurons, particularly the role of first spike latency in response to external stimuli. The study employs advanced machine learning techniques to predict first spike latency from whole cell patch recording data. Experiments were conducted on Control (Salin) and Experiment (Harmaline) groups, generating a dataset for developing predictive models. Because the dataset has a limited number of samples, we utilized models that are effective with small datasets. Among different groups of regression models (linear, ensemble, and tree models), the ensemble models, specifically the LGB method, can achieve better performance. The results demonstrate accurate prediction of first spike latency, with an average mean squared error of 0.0002 and mean absolute error of 0.01 in 10-fold cross-validation. The research suggests the potential of machine learning in forecasting the first spike latency, allowing reliable estimation without the need for extensive animal testing. This intelligent predictive system facilitates efficient analysis of first spike latency changes in both healthy and unhealthy brain cells, streamlining experimentation and providing more detailed insights into the captured signals.

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Nowadays, telemedicine can provide remote clinical services for the elderly, using smart devices like embedded sensors, via real-time communication with the healthcare provider. In particular, inertial measurement sensors such as accelerometers embedded in smartphones can provide sensory data fusion for human activities. Thus, the technology of Human Activity Recognition can be applied to handle such data. In recent studies, the three-dimensional axis has been used to detect human activities. Since most changes in individual activities occur in the x- and y-axis, the label of each activity is determined using a new two-dimensional Hidden Markov Mode based on these two axes. To evaluate the proposed method, we use the WISDM dataset which is based on an accelerometer. The proposed strategy is compared to General Model and User-Adaptive Model. The results indicate that the proposed model is more accurate than the others.

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There is a growing trend toward the use of interaction network methods and algorithms, including community-based detection methods, in various fields of science. The approach is already used in many applications, for example, in social sciences and health informatics to analyze behavioral patterns during the COVID-19 pandemic, protein-protein networks in biological sciences, agricultural science, economy, and so forth. This paper attempts to build interaction networks based on high-entropy alloy (HEA) descriptors in order to discover HEA communities with similar functionality. In addition, these communities could be leveraged to discover new alloys not yet included in the data set without any experimental laboratory effort. This research has been carried out using two community detection algorithms, the Louvain algorithm and the enhanced particle swarm optimization (PSO) algorithm. The data set, which is used in this paper, includes 90 HEAs and 6 descriptors. The results reveal 13 alloy communities, and the accuracy of the results is validated by the modularity. The experimental results show that the method with the PSO-based community detection algorithm can achieve alloy communities with an average accuracy improvement of 0.26 compared to the Louvain algorithm. Furthermore, some characteristics of HEAs, for example, their phase composition, could be predicted by the extracted communities. Also, the HEA phase composition has been predicted by the proposed method and achieved about 93% precision.