Predicted Number of Pregnant Women in Aichi Prefecture, Japan: Estimation by Machine Learning Database Construction for Disaster Preparation

Aichi prefecture, Japan is predicted to be hit by Mega-earthquake. Aichi Prefectural Association of Midwives has been making efforts to improve disaster preparedness for pregnant women. This project aims to acquire area data of pregnant women for simulated studies of rescue activities. Number of women in census survey areas in Nagoya City was acquired from nationwide data of pregnant women by machine learning (Cascade-Correlation Learning Architecture). Quite high correlation coefficients between actual data and estimation data were observed. Rescue simulations have been carried out based on the data acquired by this study.

A New Cascade-Correlation Growing Deep Learning Neural Network Algorithm

In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used to solve the actual problem by artificial neural networks as a new architecture and supervised learning algorithm. This process optimizes the architectures of the network which intends to accelerate the learning process and produce better performance in generalization. Many researchers have to date proposed several growing algorithms to optimize the feedforward neural network architectures. The proposed algorithm has been tested on various medical data sets. The results prove that the proposed algorithm is a better method to evaluate the accuracy and flexibility resulting from it.

Аналіз існуючих варіантів класифікації хворих на серцево-судинними захворюваннями за допомогою нейронними мережами

Робота присвячена аналізу інформаційних технологій хронобіологічного моніторингу кардіосистем, розробці систему підтримки прийняття рішень для лікаря-дослідника на базі методів класифікації з використанням нейронних мереж таких як імовірностна неронна мережа PNN (Probabilistic Neural Networks), багатошаровий персептрон MLP NN (Multi-Layer Perceptron), каскадно-кореляційна мережа CasCor (Cascade Correlation). У результаті отримано наступне: найкращим класифікатором є нейромережа каскадної кореляції з 85-88% точністю класифікації. Найгіршим класифікатором стала ймовірнісна нейронна мережа, оскільки точність цього алгоритму залежить від розміру набору даних.

Applying Cascade-Correlation Neural Networks to In-Fill Gaps in Mediterranean Daily Flow Data Series

The analyses of water resources availability and impacts are based on the study over time of meteorological and hydrological data trends. In order to perform those analyses properly, long records of continuous and reliable data are needed, but they are seldom available. Lack of records as in gaps or discontinuities in data series and quality issues are two of the main problems more often found in databases used for climate studies and water resources management. Flow data series from gauging stations are not an exception. Over the last 20 years, forecasting models based on artificial neural networks (ANNs) have been increasingly applied in many fields of natural resources, including hydrology. This paper discusses results obtained on the application of cascade-correlation ANN models to predict daily water flow using Julian day and rainfall data provided by nearby weather stations in the Ebro river watershed (Northeast Spain). Five unaltered gauging stations showing a rainfall-dominated hydrological regime were selected for the study. Daily flow and weather data series covered 30 years to encompass the high variability of Mediterranean environments. Models were then applied to the in-filling of existing gaps under different conditions related to the characteristics of the gaps (6 scenarios). Results showed that when short periods before and after the gap are considered, this is a useful approach, although no general rule applied to all stations and gaps investigated. Models for low-water-flow periods provided better results (r = 0.76–0.8).

INDOOR POSITIONING BASED-ON IMAGES AIDED BY ARTIFICIAL NEURAL NETWORKS

<p><strong>Abstract.</strong> Indoor positioning has attracted much attention in recent years due to the trend of Internet of Things (IoT), which is capable of providing numerous applications such as personal tracking, vehicle locator, and Location-Based Service (LBS). To put LBS into practice, positioning and navigation are one of the necessary techniques. Using the smartphone to process indoor positioning also become more and more usual. The most common algorithm in the inertial navigation system is Pedestrian Dead Reckoning (PDR), utilizing sensors built-in the smartphones to conquer the strait of GNSS-denied environment. However, for the purpose of eliminating the error accumulated with time, PDR combining with other algorithms, for instance, updating some geospatial information steadily is a better way to solve this problem. Therefore, this research proposes the imaged based aided algorithm. Moreover, in this study, a novel Artificial Neural Networks (ANN) embedded the system is proposed. The self-designed georeferenced markers and the indoor floor plan will be produced by an Indoor Mobile Mapping System (IMMS) in advance. This research proposed using Cascade-Correlation neural Network (CCN) to estimate the distance between the marker and the smartphone camera. The accuracy using this method can achieve to 0.27 meter. As if at least three coordinates and the distance can be obtained simultaneously, the position of the user can be calculated by the trilateration method. From the experiment, the accuracy of the positioning is about 0.5 meter. This way seems to have the high potential to bring into play on the real-time indoor positioning.</p>