Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication for Securing IoT

Wireless Sensor Networks (WSNs) is the innovative technology that covers wide range of application that possesses high potential merits such as long-term operation, unmonitored network access, data transmission, and low implementation cost. In this context, Internet of Things (IoT) have evolved as an exciting paradigm with the rapid advancement of cellular mobile networks, near field communications and cloud computing. WSNs potentially interacts with the IoT devices based on the sensing features of web devices and communication technologies in sensors. At this juncture, IoT need to facilitate huge amount of data aggregation with security and disseminate it to the reliable path to make it reach the required base station. In this paper, Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication Mechanism (UAIPCA-BSO) is proposed for  Securing data and estimate the optimal path through which it can be forwarded in the IoT environment. In specific, Unity Attractors Inspired Programmable Cellular Automata is adopted for guaranteeing security during the data transmission process. It also aids in determining the optimal path of data transmission based on the merits of Barnacles Swarm Optimization Algorithm (BSOA), such that data is made to reach the base station at the required destination in time. The simulation results of UAIPCA-BSO confirmed minimized end-to-end delay , accuracy and time taken for malicious node detection, compared to the baseline approaches used for comparison

Estimation of Flores Sea Aftershock Rupture Data Based on AI

The earthquake catalog notes that there have been earthquakes with Mw > 7 that hit the Flores area, three of which occurred in the Flores Sea in 1992, 2015, and 2021. Revealed that the seismic activity of Eastern Indonesia is thought to be influenced by the isolated thrust fault segment of the island of Flores and the island of Wetar. The study of the rising fault segment on Flores Island and Wetar Island helps in further understanding the fault behavior, earthquake pattern, and seismic risk in the Flores Sea region. In earthquakes with giant magneto, an aftershock can occur due to the interaction of ground movements. This research analyzes and compares the data from the evaluation of the classification algorithm and the regression algorithm. The initial stages of this research include requesting IRIS DMC Web Service data. The data is then subjected to a cleaning process to obtain the expected feature extraction. The next stage is to perform the clustering process. This stage is carried out to label dependent data by adding new features as data clusters. The following procedure divides the validation value, which consists of training and test data. The estimation results show that the classification algorithm's evaluation value is better than that of the regression algorithm. The evaluation value of several algorithms indicates this, with an accuracy rate between 80% and 100%

Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced‑concrete deep beams

This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm, called “optimized support vector machines with adaptive ensemble weight- ing” (OSVM-AEW), to predict the shear capacity of reinforced-concrete (RC) deep beams. This ensemble learning-based system combines two supervised learning models—the support vector machine (SVM) and least-squares support vector machine (LS-SVM)—with the SOS optimization algorithm as the optimizer. In OSVM-AEW, SOS is integrated to simulta- neously select the optimal parameters of SVM and LS-SVM, and control the coordination process of the learning outputs. Experimental results show that OSVM-AEW achieves the greatest evaluation criteria for coefficient of correlation (0.9620), coefficient of determination (0.9254), mean absolute error (0.3854 MPa), mean absolute percentage error (7.68%), and root- mean-squared error (0.5265 MPa). This paper demonstrates the successful application of OSVM-AEW as an efficient tool for helping structural engineers in the RC deep beams design process

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process

Elaboración de una metodología de trabajo para el tratamiento y la predicción de series temporales de consumo de agua potable

[ES] El presente trabajo consiste en la elaboración de una metodología para el análisis de una serie temporal de caudal de agua potable en un sector hidráulico de tipo domiciliario de una ciudad de la provincia de Valencia (España). Esta metodología aborda la reconstrucción de la serie temporal mediante la imputación de valores faltantes, la corrección de valores anómalos y la predicción del consumo a corto plazo mediante el uso de técnicas de machine learning y deep learning. La investigación llevada a cabo propone una metodología novedosa, puesto que en la literatura científica relacionada con este ámbito no se ha abordado el problema del tratamiento de este tipo de series temporales de manera integral. La metodología desarrollada, por lo tanto, pretende ser la semilla de un sistema de ayuda para la toma de decisiones que permita decidir, para cada tipo de serie temporal de caudal de agua potable o similares, cuál es la estrategia idónea que debe seguir el analista para optimizar la predicción del consumo en un sector hidráulico, y por ende, la operación del propio sistema de distribución asociado.[EN] The following research consists in the elaboration of a methodology for the analysis of a time series of drinking water flow rate in a domestic hydraulic sector of a city in the province of Valencia (Spain). This methodology deals with the reconstruction of the time series through the imputation of missing values, the correction of outliers and the forecasting of short-term consumption using machine learning and deep learning techniques. The conducted research proposes a novel methodology since the treatment of this kind of time series has not been addressed in a comprehensive way in the scientific literature related to this field. The developed methodology, aims to be the seed of a decision support system that allows to decide, for each kind of time series of drinking water flow rate or similar, which is the ideal strategy to be followed by the analyst to optimize the forecast of the flow rate in a hydraulic sector, and therefore, the operation of the associated distribution system.Morer, FE. (2020). Elaboración de una metodología de trabajo para el tratamiento y la predicción de series temporales de consumo de agua potable. Universitat Politècnica de València. http://hdl.handle.net/10251/159161TFG