Learning efficiency analysis has evolved immensely in the aftermath of the COVID-19 epidemic. Technological methods for online learning and big data in the cloud have been applied to develop various curriculums; however, an analysis of learners' performance may only be done at a similar personal background group classification level. Therefore, this study presents a machine learning model for identifying cognitive performance under the principles and characteristics of the Internet of Behavior (IoB) and human brainwaves, which is more reliable than traditional data analysis. The proposed methodology can precisely classify learners according to their current cognitive performance of the brain through simple classification and IoB analysis that can immediately enhance class manipulation.

Event-related potential (ERP) is a distinctive pattern of brain activity that is elicited by the brain’s sensitivity and cognition whereas P300 evoked potential changes in cognitive functions. Since P300 wave is a cognitive response across multiple brain channels correlated between the measured electroencephalogram (EEG) and deviant stimulus in a specific period, it requires a suitable signal processing application for interpretation. Moreover, multiple steps of data processing under neuroscience criteria make the P300 reflection difficult to analyze by common methods. Therefore, this study proposes the processing model for brainwave applications based on P300 peak signal detection in multiple brain channels. This study applies 64 channels ERP datasets throughout bandpass filter in fast Fourier transform (FFT) with the specific ranges of signal processing while ERP averaging is applied as a feature extraction method. Furthermore, the experimental metadata is applied with the filtered P300 peak signals in channel classification via a machine learning method, the Decision Tree. The experimental results indicate the accurate mental reflection of P300 evoked potential in different brain channels with high classification accuracy relying on the contrast condition throughout the original data source averaged across the individual electrodes.

Relaxation, which means the absence of tension and stress, is important for health, as extended periods of tension may lead to illness. This study detects induced relaxation through Electroencephalography (EEG) for real-time monitoring of brain activity. The Multivariate Empirical Mode Decomposition with Dynamic Phase-Synchronized Hilbert-Huang Transform (MEMD-DPS-HHT) algorithm was utilized to extract dynamic brain wave patterns from EEG data of participants exposed to blended essential oil. Preprocessing included artifact removal using Independent Component Analysis (ICA), bandpass filtering within the 4-12 Hz range, and normalization. The EEG data underwent decomposition into five Intrinsic Mode Functions (IMFs) through the application of MEMD. The average frequency of IMF1 (8.56 Hz) and IMF2 (4.91 Hz) are within the designated frequency range. Dynamic phase synchronization and instantaneous frequency analysis employed adaptive time-frequency-energy representation. Analysis of phase synchronization across channels indicated a maximum coherence of 0.87. The dynamic Hilbert spectra and relaxation indices, pre- and post-stimulation, showed an average maximum of 0.16, aligning with paired t-test results (p ≥ 0.05), suggesting no significant difference in the Theta and Alpha bands (TA band) frequencies and stimulation. A two-way repeated measures ANOVA indicated p = 1, implying no significant effect of stimulation on the TA band. The results demonstrate the ability of MEMD-DPS-HHT to analyze cross-channel, multi-dimensional, dynamic EEG data associated with relaxation. The average relaxation index from this dataset was rather low, suggesting that the impact of this particular blended essential oil stimulation may differ among individuals.

Normally, organizations have to estimate the workload relying on limited resources. An appropriate estimation method can improve workforce optimization. In the software house, workload categorization and estimation can be acquired from the information technology management. Nevertheless, there are several factors such as work priority and specific goals that affect the workload level. General workload management spends a long time and decreases task management quality. Therefore, this study applies machine learning, Naïve Bayes, to estimate the workload automatically. This classification method increases the accuracy of workload estimation, along with reducing the time consumption for the whole system.

Solar-powered pumping systems using series pumps are commonly applied in the delivery of water to remote agricultural regions, particularly in hilly tropical terrain. The synchronization of these pumps typically depends on reliable communication; however, dense vegetation, elevation changes, and weather conditions often disrupt signals. To address these limitations, a fully decentralized, communication-free control system is proposed. Each pumping station operates independently while maintaining synchronized operation through emulated neighbor sensing. The system applies a discrete-time control algorithm with virtual sensing that estimates neighboring pump statuses. Each station consists of a solar photovoltaic (PV) array, variable-speed drive, variable inlet valve, reserve tank, and local control unit. The controller adjusts the valve positions and pump power based on real-time water level measurements and virtual neighbor sensing. The simulation results across four scenarios, including clear sky, cloudy conditions, temporary outage, and varied irradiance, demonstrated steady-state operation with no water overflow or shortage and a steady-state error less than 4% for 3 m³ transfer. The error decreased as the average power increased. The proposed method maintained system functionality under simulated power outage and variable irradiance, confirming its suitability for remote agricultural areas where communication infrastructure is limited.