DPU-ALDOSKI dataset of Monitoring and Controlling distributed far distances energy consumed system based on Internet of Things.

This article presents the created dataset obtained from implementation of a proposed distributed energy management system based on the Internet of Things (IoT). The proposed approach was implemented and evaluated at two distinct institutions, namely Duhok and Shekhan, which are geographically distant from each other. The proposed system comprises two primary phases, namely Monitoring and Controlling, which are implemented using ESP32 microcontrollers. The power usage indicators, namely Voltage, current, and Frequency, have been subjected to meticulous monitoring and regulation. Three principal methodologies are employed for each institution, namely: comprehensive monitoring of all metrics for the entire institution, comprehensive monitoring of all metrics for the uncontrolled laboratory, and monitoring and regulation of all metrics for the controlled laboratory. The technology in question continuously collects data, leading to the creation of a substantial energy management database. The data collection process involved the utilization of server-side protocols, namely Message Query Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP).

Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce.

Increasing demands for information and the rapid growth of big data have dramatically increased the amount of textual data. In order to obtain useful text information, the classification of texts is considered an imperative task. Accordingly, this article will describe the development of a hybrid optimization algorithm for classifying text. Here, pre-processing was done using the stemming process and stop word removal. Additionally, we performed the extraction of imperative features and the selection of optimal features using the Tanimoto similarity, which estimates the similarity between features and selects the relevant features with higher feature selection accuracy. Following that, a deep residual network trained by the Adam algorithm was utilized for dynamic text classification. Dynamic learning was performed using the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). These processes are carried out under the MapReduce framework. Our analysis revealed that the proposed RIWO-based deep residual network outperformed other techniques with the highest true positive rate (TPR) of 85%, true negative rate (TNR) of 94%, and accuracy of 88.7%.