Mobile Information Systems
 Journal metrics
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Acceptance rate43%
Submission to final decision48 days
Acceptance to publication25 days
CiteScore2.300
Journal Citation Indicator-
Impact Factor-

The Inhibition of Institutional Investor Clique on the Tunneling Behavior

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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.

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Research Article

Remote Sensing Image Change Detection Network Based on Twin High-Resolution Representation

With the increase of spatial resolution of remote sensing images, features of feature imaging become more and more complex, and the change detection methods based on techniques such as texture representation and local semantics are difficult to meet the demand. Most change detection methods usually focus on extracting semantic features and ignore the importance of high-resolution shallow information and fine-grained features, which often lead to uncertainty in edge detection and small target detection. For single-input networks when two temporal images are connected, the shallow layer of the network cannot provide the information of the individual original image to the deep layer features to help reconstruct the image, and therefore, the change detection results may be missing in detail and feature compactness. For this purpose, a twins context aggregation network (TCANet) is proposed to perform change detection on remote sensing images. In order to reduce the loss of spatial accuracy of remote sensing images and maintain high-resolution representation, we introduce HRNet as our backbone network to initially extract the features of interest. Our proposed context aggregation module (CAM) can amplify the convolutional neural network receptive field to obtain more detailed contextual information without significantly increasing the computational effort. The side output embedding module (SOEM) is proposed to improve the accuracy of small volume target change detection as well as to shorten the training process and speed up the detection while ensuring the performance. The method has experimented on the publicly available CDD dataset, the SYSU-CD dataset, and a challenging DSIFN dataset. With significant improvements in precision, recall, F1 score, and overall accuracy, the method outperforms the five methods mentioned in the literature.

Research Article

Design of Personalized News Recommendation System Based on an Improved User Collaborative Filtering Algorithm

To solve the problem of information overload in the field of news, this paper designs and implements a feasible news recommendation system, where the front-end web page is made by Django framework, whose performance is optimized by bootstrap and jquery, while in the back-end design, the original user similarity calculation method is improved by adding the time attenuation factor, and a news recommendation model based on user collaborative filtering (CF) algorithm is proposed. Experimental results show that the proposed algorithm achieves highest recall, accuracy, and F1 score ratio compared with other algorithms, which indicate that the proposed algorithm has better performance.

Research Article

Unmanned Aerial Vehicle and Geospatial Analysis in Smart Irrigation and Crop Monitoring on IoT Platform

The geospatial analysis provides high potential for modeling, understanding, and visualizing artificial and natural ecosystems, utilizing big data analytics and the Internet of things as a pervasive sensing infrastructure. Precision agriculture, weed control, fertilizer distribution, and field management benefit from unmanned ariel vehicles (UAVs). Reduced production costs and improved crop quality are some of the benefits of using this method. Smart farming denotes geographical data utilization to identify field variability, guarantee optimal inputs, and enhance a farm’s output. Hence, in this paper, an IoT-assisted Smart Farming Framework (IoT-SFF) with big data analytics has been proposed using geospatial analysis. The use of wireless sensors in IoT devices and communication methods in agricultural applications is thoroughly examined. IoT sensors are available for particular agriculture applications, such as crop status, soil preparation, insect, pest detection, and irrigation scheduled. It is now possible to view our regions in various ways and make accurate agrotechnological decisions, thanks to a computer-generated geographic information system (GIS) for crop irrigation and monitoring. Analytical and monitoring processes that yield timely and accurate decision-making add value to big data, which is a key component for intelligently managing and operating farms. Still, it is constrained by both technical and socioeconomic variables. The simulation findings show that the proposed IoT-SFF model improves the crop yield ratio by 92.4%, prediction ratio by 97.7%, accuracy ratio by 94.5%, the average error by 38.3%, and low-cost rate by 34.4%.

Research Article

Deconstruction of Related Technologies of Ground Image Processing Based on High-Resolution Satellite Remote Sensing Images

The Earth observation system heavily relies on sophisticated remotely sensed satellites, an important means to obtain global high-precision geospatial products and an important strategic area for the world’s major scientific and technological powers to develop. Although China’s satellites currently have real-time or quasi-real-time observations with a high temporal resolution, there are still a lot of gaps between their positioning accuracy and the world’s advanced level. This essay aims to study an efficient ground image processing technology and apply it to high-resolution satellite remote sensing images. The convolutional neural network is an efficient deep learning method for image recognition and feature extraction. In this essay, people use a convolutional neural network (CNN) to identify ground images, use a support vector machine (SVM) to classify and summarize images, and then use a Kalman filter for noise reduction, so as to obtain sophisticated remotely sensed images. In the experiment, 100 satellite remote sensing images in the GeoImageDB database were selected for the simulation test, the images were divided into 5 types, and their recognition accuracy, classification accuracy, image signal-to-noise ratio, and resolution were analyzed. The results show that the accuracy of CNN’s recognition of different types of images is up to about 94%, and the lowest is about 85%. The accuracy of the SVM for image classification is above 80%, and the highest is about 95%. The SNR of the image after noise reduction is basically above 6.5, and some even reach above 8.0. The resolution of the image is basically above 800ppi, and the highest even reaches an ultra-high resolution of 1400ppi. Overall, the processed images are of high quality. This shows that this essay uses CNN for image recognition and then uses an SVM for classification, and finally, the method of denoising the image has certain feasibility and has achieved good results through experiments.

Research Article

Preserving Resource Handiness and Exigency-Based Migration Algorithm (PRH-EM) for Energy Efficient Federated Cloud Management Systems

On-demand computing ability and efficient service delivery are the major benefits of cloud systems. The limitation in resource availability in single data centers causes the extraction of additional resources from the cloud providers group. The federation scheme dynamically increases resource availability in response to service requests. The dynamic increase in resource count leads to excessive energy consumption, maximum cost, and carbon footprints emission. Hence, the reduction of resources is the major requirement to construct the optimized cloud source models for profit maximization without considering energy mix and CO2. This paper proposes the novel migration method to reduce carbon emissions and energy consumption. The initial stage in the proposed work is the categorization of data centers based on the MIPS and cost prior to job allocation offers scalable and efficient services and resources to the cloud user. Then, the job with the maximum size is allotted to the VM only if its capacity is less than the cumulative capacity of data centers. A novel migration based on overutilized and underutilized levels provides the services to the user even if the particular VM fails. The proposed work offers efficient maintenance of resource availability and maximizes the profit of the cloud providers associated with the federated cloud environment. The comparative analysis of the proposed algorithm with the existing methods regarding the response time, accuracy, profit, carbon emission, and energy consumption assures the effectiveness in a confederated cloud environment.

Research Article

Display Design Model Based on the Internet of Things Prototype System

The display design of the Internet of Things prototype system is the top priority of the design of the display space, and it is also a problem to be studied in the future development of the display space. However, the current research on the display design direction of IoT prototype system is not deep enough. This article mainly studies the display design model based on the Internet of Things prototype system. This article mainly uses the related technology of IoT heterogeneous protocol to analyze the structure of the IoT prototype system and proposes to use the heterogeneous network to speculate the future development trend of the IoT. In the aspect of display design, applications such as network information retrieval and data mining at the level of missions are accelerated in depth so that the arrangement of multiple factors is more reasonable. Finally, the data conversion function is used to design each port in the IoT accessor architecture. The protocol is adapted and converted, and the parsed protocol is uniformly encapsulated and uniformly transmitted on the platform. The model in this article integrates various heterogeneous detection networks, realizes the standardized operation of top-level applications, and completes the decoupling of top-level applications. It is encapsulated in a unified protocol and meets the standard operation requirements of web applications. The experimental data in this article show that the frequency of the audio signal designed by the system under normal working conditions is mostly concentrated at about 180 Hz, and the highest frequency value is less than 800 Hz; when the system has a partial discharge failure, the frequency of the audio signal is mostly concentrated between 1400 Hz and 1800 Hz. The experimental results of this article show that the prototype system of the Internet of Things can identify and judge the state of the display design model. Applying it in practice can effectively solve practical problems such as large-scale data storage of the Internet of Things.

Mobile Information Systems
 Journal metrics
See full report
Acceptance rate43%
Submission to final decision48 days
Acceptance to publication25 days
CiteScore2.300
Journal Citation Indicator-
Impact Factor-
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.