Road crashes are a growing concern of governments and are rising to become one of the leading preventable causes of death, especially in developing countries. The ubiquitous presence of smartphones provides a new platform on which to implement sensor networks and driver-assistance systems, as well as other intelligent transportation system (ITS) applications. In this study, existing approaches of using smartphones for ITS applications are analysed and compared. Particular focus is placed on vehicle-based monitoring systems, such as driving behaviour and style recognition, accident detection and road condition monitoring systems. Further opportunities for use of smartphones in ITS systems are highlighted, and remaining challenges in this emerging field of research are identified.

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Survey of smartphone-based sensing in vehicles for intelligent transportation system applications

There has been a large amount of work on smart classrooms spanning over a wide range of research areas including information communication technology, machine learning, sensor networks, mobile computing, and hardware. Consequently, there have been several disparate reviews on various aspects of smart classrooms. Such piecemeal development is not sufficient for a pragmatic smart classroom solution. This article complements the literature by providing a consolidated review of interdisciplinary works under a common nomenclature and taxonomy. This multi-field review has exposed new research opportunities and challenges that need to be addressed for the synergistic integration of interdisciplinary works.

How Smart Are Smart Classrooms? A Review of Smart Classroom Technologies

A growing body of literature has been devoted to harnessing the crowdsourcing power of social media by extracting knowledge from the huge amounts of information available online. This paper discusses how social media data can be used indirectly and with minimal cost to extract travel attributes such as trip purpose and activity location. As a result, the capacity of Twitter data in complementing other sources of transport related data such as household travel surveys or traffic count data is examined. Further, a detailed discussion is provided on how short term travellers, such as tourists, can be identified using Twitter data and how their travel pattern can be analysed. Having appropriate information about tourists/visitors -- such as the places they visit, their origin and the pattern of their movements at their destination -- is of great importance to urban planners. The available profile information of users and self-reported geo-location data on Twitter are used to identify tourists visiting Sydney as well as also those Sydney residents who made a trip outside Sydney. The presented data and analysis enable us to understand and track tourists' movements in cities for better urban planning. The results of this paper open up avenues for travel demand modellers to explore the possibility of using big data (in this case Twitter data) to model short distance (day-to-day or activity based) and long distance (vacation) trips.

Utilising Location Based Social Media in Travel Survey Methods: bringing Twitter data into the play

Smart campus is based on the Internet of Things (IoT) technology, and its purpose is to achieve the intelligent management and service on campus. Through the IoT, the mutual blending of teachers and students, learning facilities, and information generated by facilities is realized. This paper introduces and analyzes the current research status of smart campus and the difficulties in integrating various kinds of service data in smart campus. After analyzing various kinds of situational data, this paper analyzes and designs smart campus service discovery algorithm and perceptual data fusion algorithm on the basis of previous research on the situational awareness system framework for smart campus. Compared with the traditional campus platform, smart campus can manage perceptual data more efficiently, which brings convenience to campus service and management.

Situational Awareness System in the Smart Campus

Purpose – The purpose of this paper is to make a unified approach in identifying the issues affecting usage intention of mobile-based payment services. The work aims to analyze the reduced factors from data obtained from a survey to highlight the influencers of usage intention mobile-based payment in an integrated manner by incorporating the technical characteristics, technology-specific characteristics, user-specific characteristics and task-specific characteristics. Design/methodology/approach – A nationwide primary survey was conducted using validated questionnaire requesting response for 11 factors obtained from published literature. In all, 196 respondents participated in the survey in India. Valid responses were analyzed using Growing Hierarchical Self-Organizing Map (GHSOM) model. The interactive GHSOM application was applied to automatically determine the filtering rules for clustering. Findings – The hierarchical structure of clusters as obtained by applying GHSOM is mainly influenced by factors ...

Examining mobile based payment services adoption issues: A new approach using hierarchical clustering and self-organizing maps

Restricted Boltzmann Machine (RBM) is a generative stochastic energy-based model of artificial neural network for unsupervised learning. Recently, RBM is well known to be a pre-training method of Deep Learning. In addition to visible and hidden neurons, the structure of RBM has a number of parameters such as the weights between neurons and the coefficients for them. Therefore, we may meet some difficulties to determine an optimal network structure to analyze big data. In order to evade the problem, we investigated the variance of parameters to find an optimal structure during learning. For the reason, we should check the variance of parameters to cause the fluctuation for energy function in RBM model. In this paper, we propose the adaptive learning method of RBM that can discover an optimal number of hidden neurons according to the training situation by applying the neuron generation and annihilation algorithm. In this method, a new hidden neuron is generated if the energy function is not still converged and the variance of the parameters is large. Moreover, the inactivated hidden neuron will be annihilated if the neuron does not affect the learning situation. The experimental results for some benchmark data sets were discussed in this paper.

An adaptive learning method of Restricted Boltzmann Machine by neuron generation and annihilation algorithm

An Adaptive Learning Method of Restricted Boltzmann Machine by Neuron Generation and Annihilation Algorithm.

Mobile Phone based Participatory Sensing (MPPS) systems involve a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. We have developed the tourist subjective data collection system with Android smartphone. The tourist can tweet the information of sightseeing spots by using the application. The application can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM and C4.5.

A Generation Method of Filtering Rules of Twitter Via Smartphone Based Participatory Sensing System for Tourist by Interactive GHSOM and C4.5

Near Field Communication (NFC) standard covers communication protocols and data exchange formats. NFC technology is one of radio-frequency identification (RFID) standards. In Japan, Felica card is a popular way to identify the unique ID. We developed the attendance management system (AMS) as the Android application which works in the smart tablet with NFC. Generally, the AMS in the university is fixed to the wall and each student touches or slides his/her own card to the dedicated equipment. Because a teacher can use his/her own smart tablet and/or smartphone, the attendance records are viewed anytime and anywhere. Moreover, we developed the collecting system between PC and some tablets by using Android beam. Any personal data are encrypted and the file can be transferred over the NFC Bluetooth Handover between PC Linux and smart tablet. By the mining of the collected records, early discovery for chronic non-attenders are extracted in educational affairs section. In this paper, a registration system on the cloud campus system by using the personal smartphone with NFC is developed. The system enables to introduce the university courses that are open to the general public.

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Shin Kamada

Papers

An adaptive learning method of Restricted Boltzmann Machine by neuron generation and annihilation algorithm

An Adaptive Learning Method of Restricted Boltzmann Machine by Neuron Generation and Annihilation Algorithm.

A Generation Method of Filtering Rules of Twitter Via Smartphone Based Participatory Sensing System for Tourist by Interactive GHSOM and C4.5

Registration system of cloud campus by using android smart tablet

A generation method of filtering rules of Twitter via smartphone based Participatory Sensing system for tourist by interactive GHSOM and C4.5