Neural networks application for water distribution demand-driven decision support system

doi:10.20474/jater-4.4.3

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…It takes into account the stochastic nature of process disturbances and measurement noise. SVM [9,10,11] has been proposed in time series forecasting. By integrating multiple sources of information, previous works proved that support vector machine can accurately predict bus arrival times.…”

Section: Literature Reviewmentioning

confidence: 99%

The bus arrival time prediction using LSTM neural network and location analysis

2020

J. adv. tec. eng. res.

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The accurate bus arrival time information is crucial to passengers for reducing waiting times at the bus stop and improve the attractiveness of public transport. GPS-equipped buses can be considered as mobile sensors showing traf ic lows on road surfaces. In this paper, we present an approach that predicts bus arrival time using historical bus GPS information and real-time situation on the road. In this study, we divide bus arrival time into bus dwelling time at bus stops and bus travel time between stations and predict each of them separately. The clustering approach used to predict the travel time between stations, and then for each cluster, we apply LSTM NN to predict walking time between stations. The latency at each bus stop we evaluate by historical dwelling time and using location analysis to ind the importance of the bus stop as a point of interest during prediction time. The study is trained and tested on GPS data collected from 1200 buses in a period of 3 months. According to tests results our method show small mean absolute error for buses that not far from departure station. The outcomes of this work can be used as an additional information for bus passengers to know possible bus coming time and to estimate possible travel time in bus journey. The method for arrival time prediction proposed in this research has several advantages. It considers historical bus travel time information, real time information, bus dwelling time, riding time, traf ic lights and city facilities.

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Section: Literature Reviewmentioning

confidence: 99%

The bus arrival time prediction using LSTM neural network and location analysis

2020

J. adv. tec. eng. res.

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“…An Eye Assessment is a technique aimed at understanding the intentions and interests of the user [6]. Gaze assessment technology focuses on the link between data image and gaze direction [7,8]. The direction of the line of sight is assessed based on the specific characteristics of the eye (for example pupil and corneal reflexes) drawn from the eye area, image data gathered by one or more cameras [9].…”

Section: Basic Conceptsmentioning

confidence: 99%

Research on Eye Pupil Location and Eye Tracking by Designing a Fully Conventional Neural Network (FCNN)

Raza¹,

Wang²,

Xue³

et al. 2020

Int. j. technol. eng. stud.

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Eye-tracking has become a significant tool in various areas, incorporating interaction of humans with a computer, computer vision, psychology, and medical diagnostics. Various protocols have been utilized to track the gaze. Although, some may not be accurate in the real world, while others may need explicit custom calibration, which can cause problems. Few of these techniques are associated with low image grades and varying lighting situations. The latest success and popularity of deep studying has dramatically increased the effectiveness of eye-tracking. The convenience of huge datasets additionally improves the function of deep learning-depend techniques. This technical paper introduces the latest deep studying-depend gaze assessment technology with a concentration on Fully Convolution Neural Networks (FCNN). This technical paper also gives an overview of other machine-based eye assessment methods. This research objective to enable the research population to generate significant and useful horizons that can improve the structure and growth of better, more effective deep learning depends on eye-tracking methods. This paper also gives information on different pertained models, network structures, and open origin datasets to help train deep learning models.

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“…Although excluding unnecessary links or information might be one of good website require-ments, it is not always true from the user's point of view. We already proposed in [7,8] website evaluation system by combining some of website indexes and user evaluation indexes by applying user's perspective information quality measures, [9,10,11]. In that research, we tried to construct a formula describing some of information quality measures as a formula with link or term based indexes.…”

Section: Introductionmentioning

confidence: 99%

Website evaluation using cluster structures

Nagata¹

2019

J. adv. tec. eng. res.

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Since the early in 1990's when frequent commercial use of the Internet starts, research on websites has been actively conducted both by academic researcher and by website practitioners. Three broad categories of the research are Web content mining, Web structure mining, and Web usage mining. Apart from those, there are some research on coloring, placement technique of images, texts, and links on each page. In this paper, we focus on the difference between two cluster structures of website, one induced from link-based property and the other from term-based property. The link-based property is stable until a new link is added, but the term-based one varies depending on the items for searching. We propose an evaluation method for website by comparing the structures of clusters resulted from these properties. As the clustering method, here we adopt kernel k-means method, and compare partial clusters derived from term-based property depending on the given sequence of particular terms to de inite partial clusters from link-based property. In order to distinguish them, we try to adopt spectral analysis.

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Neural networks application for water distribution demand-driven decision support system

Cited by 3 publications

References 11 publications

The bus arrival time prediction using LSTM neural network and location analysis

The bus arrival time prediction using LSTM neural network and location analysis

Research on Eye Pupil Location and Eye Tracking by Designing a Fully Conventional Neural Network (FCNN)

Website evaluation using cluster structures

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