Mobile Information Systems
 Journal metrics
Acceptance rate37%
Submission to final decision102 days
Acceptance to publication42 days
CiteScore4.100
Journal Citation Indicator0.370
Impact Factor1.802

Article of the Year 2020

A Novel Image Classification Approach via Dense-MobileNet Models

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 Journal profile

Mobile Information Systems publishes original research articles as well as review articles that report the theory and/or application of new ideas and concepts in the field of mobile information systems.

 Editor spotlight

Chief Editor Dr Alessandro Bazzi is based at the University of Bologna, Italy. His current research is focused on wireless technologies applied to automated and connected vehicles.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Research Article

Research on the New Model of Aerobics Physical Education under the Background of Artificial Intelligence Era

This study aims to investigate and analyze the use of a new model of teaching physical education in higher learning institutions. Most traditional methods entail the instructor-based approach, which might not be appropriate to derive all the benefits and restore sanity in college and university students’ health status. A new model is thus imminent that will be able to place the student at the center of the entire activity. Self-motivation is the most critical intrapersonal skill needed to ensure continual improvement. Developing a model that will oversee the development of self-motivation is thus essential. In this study, future research was conducted based on the previous literature on the best model for PE. The acquired data were then presented graphically, discussions were derived, and conclusions were ensued.

Research Article

A Network That Balances Accuracy and Efficiency for Lane Detection

In the automatic lane-keeping system (ALKS), the vehicle must stably and accurately detect the boundary of its current lane for precise positioning. At present, the detection accuracy of the lane algorithm based on deep learning has a greater leap than that of the traditional algorithm, and it can achieve better recognition results for corners and occlusion situations. However, mainstream algorithms are difficult to balance between accuracy and efficiency. In response to this situation, we propose a single-step method that directly outputs lane shape model parameters. This method uses MobileNet v2 and spatial CNN (SCNN) to construct a network to quickly extract lane features and learn global context information. Then, through depth polynomial regression, a polynomial representing each lane mark in the image is output. Finally, the proposed method was verified in the TuSimple dataset. Compared with existing algorithms, it achieves a balance between accuracy and efficiency. Experiments show that the recognition accuracy and detection speed of our method in the same environment have reached the level of mainstream algorithms, and an effective balance has been achieved between the two.

Research Article

Institutional Teaching Innovation under the Demand of Internet + PE

In order to study the effectiveness of student-based teaching methods, this study uses open-ended and closed-ended questionnaires to collect qualitative results and studies by collecting different types of data, including quantitative data of the body mass index. The results show that the biggest difference from the traditional teaching strategy is that the internet teaching model can help to improve personal communication ability and better develop interpersonal relationship. At the same time, through social interaction in e-learning, learners can cultivate their ability to find and solve problems, collect, analyze, and use information, and learn to share and cooperate. The research of the project can provide some reference ideas and theoretical basis for follow-up research.

Research Article

Detection of Tuberculosis Disease Using Image Processing Technique

Machine learning is a branch of computing that studies the design of algorithms with the ability to “learn.” A subfield would be deep learning, which is a series of techniques that make use of deep artificial neural networks, that is, with more than one hidden layer, to computationally imitate the structure and functioning of the human organ and related diseases. The analysis of health interest images with deep learning is not limited to clinical diagnostic use. It can also, for example, facilitate surveillance of disease-carrying objects. There are other examples of recent efforts to use deep learning as a tool for diagnostic use. Chest X-rays are one approach to identify tuberculosis; by analysing the X-ray, you can spot any abnormalities. A method for detecting the presence of tuberculosis in medical X-ray imaging is provided in this paper. Three different classification methods were used to evaluate the method: support vector machines, logistic regression, and nearest neighbors. Cross-validation and the formation of training and test sets were the two classification scenarios used. The acquired results allow us to assess the method’s practicality.

Research Article

Feature Optimization of Exhaled Breath Signals Based on Pearson-BPSO

Feature optimization, which is the theme of this paper, is actually the selective selection of the variables on the input side at the time of making a predictive kind of model. However, an improved feature optimization algorithm for breath signal based on the Pearson-BPSO was proposed and applied to distinguish hepatocellular carcinoma by electronic nose (eNose) in the paper. First, the multidimensional features of the breath curves of hepatocellular carcinoma patients and healthy controls in the training samples were extracted; then, the features with less relevance to the classification were removed according to the Pearson correlation coefficient; next, the fitness function was constructed based on K-Nearest Neighbor (KNN) classification error and feature dimension, and the feature optimization transformation matrix was obtained based on BPSO. Furthermore, the transformation matrix was applied to optimize the test sample’s features. Finally, the performance of the optimization algorithm was evaluated by the classifier. The experiment results have shown that the Pearson-BPSO algorithm could effectively improve the classification performance compared with BPSO and PCA optimization methods. The accuracy of SVM and RF classifier was 86.03% and 90%, respectively, and the sensitivity and specificity were about 90% and 80%. Consequently, the application of Pearson-BPSO feature optimization algorithm will help improve the accuracy of hepatocellular carcinoma detection by eNose and promote the clinical application of intelligent detection.

Research Article

Modulation and Signal Detection for Diffusive-Drift Molecular Communication with a Mobile Receiver

Molecular communication (MC), which allows nanomachines to communicate with each other by using chemical molecules, is considered to be a promising method for communications in liquid environment. Available works on MC mainly focus on modulation and signal detection schemes for MC systems with fixed nanomachines, i.e., fixed molecular communication (FMC) systems. However, the more complex systems with mobile nanomachines (i.e., mobile molecular communication (MMC) systems) have been largely unexplored. This paper considers a MMC system with a fixed transmitter and a mobile receiver communicating over diffusive-drift channels of a limited boundary. We first propose a new modulation scheme to address the issue of misalignment in the signal detection of MMC systems by adopting three types of molecules in the signal modulation and modulating the transmitted signals into blocks with equal length to avoid the transferring of a signal error in the current block on the signal detection in other blocks. We then propose a new signal detection scheme of the MMC systems by calculating the distance between the transmitter and the receiver based on a distance prediction method and detecting signals at the receiver based on the decided adaptive concentration threshold in each time interval. To verify the efficiency of our proposed scheme, we then conducted extensive simulations by the Monte Carlo simulation, and comparisons are also made among our proposed schemes, a well-known fixed threshold signal detection scheme, the CATD scheme, the PAD scheme, and a low complexity signal detection scheme for MMC systems in terms of the BER (bit error rate). Results show that our proposed schemes can outperform these schemes regarding the BER.

Mobile Information Systems
 Journal metrics
Acceptance rate37%
Submission to final decision102 days
Acceptance to publication42 days
CiteScore4.100
Journal Citation Indicator0.370
Impact Factor1.802
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.