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JournalISSN: 1574-017X

Mobile Information Systems 

IOS Press
About: Mobile Information Systems is an academic journal published by IOS Press. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 1574-017X. It is also open access. Over the lifetime, 3314 publications have been published receiving 15897 citations. The journal is also known as: Mobile information systems.


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Journal ArticleDOI
TL;DR: The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently and can easily identify and classify people with heart disease from healthy people.
Abstract: Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. The performance of the proposed system has been validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.

336 citations

Journal ArticleDOI
TL;DR: A distributed system for collecting radio fingerprints by mobile devices with the Android operating system enables volunteers to create radio-maps and update them continuously and focuses on the improvement of its results with the aid of a new Bluetooth Low Energy technology.
Abstract: The paper describes basic principles of a radio-based indoor localization and focuses on the improvement of its results with the aid of a new Bluetooth Low Energy technology. The advantage of this technology lies in its support by contemporary mobile devices, especially by smartphones and tablets. We have implemented a distributed system for collecting radio fingerprints by mobile devices with the Android operating system. This system enables volunteers to create radio-maps and update them continuously. New Bluetooth Low Energy transmitters (Apple uses its “iBeacon” brand name for these devices) have been installed on the floor of the building in addition to existing WiFi access points. The localization of stationary objects based on WiFi, Bluetooth Low Energy, and their combination has been evaluated using the data measured during the experiment in the building. Several configurations of the transmitters’ arrangement, several ways of combination of the data from both technologies, and other parameters influencing the accuracy of the stationary localization have been tested.

224 citations

Journal ArticleDOI
TL;DR: From 2013 through 2022, $14.4 trillion of value (net profit) will be “up for grabs” for private-sector businesses globally — driven by the Internet of Everything.
Abstract: In other words, from 2013 through 2022, $14.4 trillion of value (net profit) will be “up for grabs” for private-sector businesses globally — driven by the Internet of Everything (IoE). IoE will both create new value and redistribute (migrate) value, based on how well companies take advantage of the opportunities that IoE presents. Those that harness IoE best will reap this value in either of two ways:

132 citations

Journal ArticleDOI
TL;DR: Dense blocks that are proposed in DenseNets are introduced into MobileNet in order to further reduce the number of network parameters and improve the classification accuracy, and two Dense-MobileNet models are designed.
Abstract: As a lightweight deep neural network, MobileNet has fewer parameters and higher classification accuracy. In order to further reduce the number of network parameters and improve the classification accuracy, dense blocks that are proposed in DenseNets are introduced into MobileNet. In Dense-MobileNet models, convolution layers with the same size of input feature maps in MobileNet models are taken as dense blocks, and dense connections are carried out within the dense blocks. The new network structure can make full use of the output feature maps generated by the previous convolution layers in dense blocks, so as to generate a large number of feature maps with fewer convolution cores and repeatedly use the features. By setting a small growth rate, the network further reduces the parameters and the computation cost. Two Dense-MobileNet models, Dense1-MobileNet and Dense2-MobileNet, are designed. Experiments show that Dense2-MobileNet can achieve higher recognition accuracy than MobileNet, while only with fewer parameters and computation cost.

130 citations

Journal ArticleDOI
TL;DR: The blockchain technology is utilized to build the first incentive mechanism of nodes as per data storage for wireless sensor networks (WSNs) and adopts the provable data possession to replace the proof of work in original bitcoins to carry out the mining and storage of new data blocks.
Abstract: In this paper, the blockchain technology is utilized to build the first incentive mechanism of nodes as per data storage for wireless sensor networks (WSNs) In our system, the nodes storing the data are rewarded with digital money The more the data stored by the node, the more the reward it achieves Moreover, two blockchains are constructed One is utilized to store data of each node and another is to control the access of data In addition, our proposal adopts the provable data possession to replace the proof of work (PoW) in original bitcoins to carry out the mining and storage of new data blocks, which greatly reduces the computing power comparing to the PoW mechanism Furthermore, the preserving hash functions are used to compare the stored data and the new data block The new data can be stored in the node which is closest to the existing data, and only the different subblocks are stored Thus, it can greatly save the storage space of network nodes

123 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202382
20221,827
2021497
2020115
201989
2018129