Vol.61, No.1, 2019-Table of Contents
  • Examining the Impacts of Key Influencers on Community Development
  • Abstract In this research, we aim to identify and investigate the impacts of key influencers on community formations and developments. We assess the impacts of key influencers by analyzing the activities and structure of the social media presence of a local community. Results of our analysis show that key influencers play important roles in connecting the community, transferring information, and improving overall sentiment of the community members. Our findings suggest that community practitioners can apply social network analysis to identify value-added influencers and discover strategies for improving the community and keeping leadership roles. More
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  • Parkinson’s Disease Detection Using Biogeography-Based Optimization
  • Abstract In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The… More
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  • A Correlation Coefficient Approach for Evaluation of Stiffness Degradation of Beams Under Moving Load
  • Abstract This paper presents a new approach using correlation and cross-correlation coefficients to evaluate the stiffness degradation of beams under moving load. The theoretical study of identifying defects by vibration methods showed that the traditional methods derived from the vibration measurement data have not met the needs of the actual issues. We show that the correlation coefficients allow us to evaluate the degree and the effectiveness of the defects on beams. At the same time, the cross-correlation model is the basis for determining the relative position of defects. The results of this study are experimentally conducted to confirm the relationship between… More
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  • Design of Working Model of Steering, Accelerating and Braking Control for Autonomous Parking Vehicle
  • Abstract Now a days, the number of vehicles especially cars are increased day by day and the people expect sophistication with safety and they wish automation for the perfection by reducing their effort and to prevent damage from collision of the vehicle. Parking the vehicle has always been a big task for the drivers that lead to problems such as traffic, congestion, accident, pollution etc. In order to overcome the parking problem, an automatic steering, braking and accelerating system is proposed to park a vehicle in a stipulated area and also to enhance the parking in a safety and secured way.… More
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  • Modeling and Predicting of News Popularity in Social Media Sources
  • Abstract The popularity of news, which conveys newsworthy events which occur during day to people, is substantially important for the spectator or audience. People interact with news website and share news links or their opinions. This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources. These techniques consist of basically two phrases: a) the training data is sent as input to the classifier algorithm, b) the performance of pre-learned algorithm is tested on the testing data. And so, a knowledge discovery from the data is performed. In this context, firstly, twelve datasets… More
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  • Smart Security Framework for Educational Institutions Using Internet of Things (IoT)
  • Abstract Educational institutions are soft targets for the terrorist with massive and defenseless people. In the recent past, numbers of such attacks have been executed around the world. Conducting research, in order to provide a secure environment to the educational institutions is a challenging task. This effort is motivated by recent assaults, made at Army Public School Peshawar, following another attack at Charsada University, Khyber Pukhtun Khwa, Pakistan and also the Santa Fe High School Texas, USA massacre. This study uses the basic technologies of edge computing, cloud computing and IoT to design a smart emergency alarm system framework. IoT is… More
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  • Improved Logistic Regression Algorithm Based on Kernel Density Estimation for Multi-Classification with Non-Equilibrium Samples
  • Abstract Logistic regression is often used to solve linear binary classification problems such as machine vision, speech recognition, and handwriting recognition. However, it usually fails to solve certain nonlinear multi-classification problem, such as problem with non-equilibrium samples. Many scholars have proposed some methods, such as neural network, least square support vector machine, AdaBoost meta-algorithm, etc. These methods essentially belong to machine learning categories. In this work, based on the probability theory and statistical principle, we propose an improved logistic regression algorithm based on kernel density estimation for solving nonlinear multi-classification. We have compared our approach with other methods using non-equilibrium samples,… More
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  • Designing and Optimization of Fuzzy Sliding Mode Controller for Nonlinear Systems
  • Abstract For enhancing the control effectiveness, we firstly design a fuzzy logic based sliding mode controller (FSMC) for nonlinear crane systems. On basis of overhead crane dynamic characteristic, the sliding mode function with regard to trolley position and payload angle. Additionally, in order to eliminate the chattering problem of sliding mode control, the fuzzy logic theory is adopted to soften the control performance. Moreover, aiming at the FSMC parameter setting problem, a DE algorithm based optimization scheme is proposed for enhancing the control performance. Finally, by implementing the computer simulation, the DE based FSMC can effectively tackle the overhead crane sway… More
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  • Privacy-Aware Service Subscription in People-Centric Sensing: A Combinatorial Auction Approach
  • Abstract With the emergence of ambient sensing technologies which combine mobile crowdsensing and Internet of Things, large amount of people-centric data can be obtained and utilized to build people-centric services. Note that the service quality is highly related to the privacy level of the data. In this paper, we investigate the problem of privacy-aware service subscription in people-centric sensing. An efficient resource allocation framework using a combinatorial auction (CA) model is provided. Specifically, the resource allocation problem that maximizes the social welfare in view of varying requirements of multiple users is formulated, and it is solved by a proposed computationally tractable… More
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  • Development of an Ultrasonic Nomogram for Preoperative Prediction of Castleman Disease Pathological Type
  • Abstract An ultrasonic nomogram was developed for preoperative prediction of Castleman disease (CD) pathological type (hyaline vascular (HV) or plasma cell (PC) variant) to improve the understanding and diagnostic accuracy of ultrasound for this disease. Fifty cases of CD confirmed by pathology were gathered from January 2012 to October 2018 from three hospitals. A grayscale ultrasound image of each patient was collected and processed. First, the region of interest of each gray ultrasound image was manually segmented using a process that was guided and calibrated by radiologists who have been engaged in imaging diagnosis for more than 5 years. In addition,… More
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  • Multi-Label Learning Based on Transfer Learning and Label Correlation
  • Abstract In recent years, multi-label learning has received a lot of attention. However, most of the existing methods only consider global label correlation or local label correlation. In fact, on the one hand, both global and local label correlations can appear in real-world situation at same time. On the other hand, we should not be limited to pairwise labels while ignoring the high-order label correlation. In this paper, we propose a novel and effective method called GLLCBN for multi-label learning. Firstly, we obtain the global label correlation by exploiting label semantic similarity. Then, we analyze the pairwise labels in the label… More
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  • Joint Spectrum Partition and Performance Analysis of Full-Duplex D2D Communications in Multi-Tier Wireless Networks
  • Abstract Full-duplex (FD) has been recognized as a promising technology for future 5G networks to improve the spectrum efficiency. However, the biggest practical impediments of realizing full-duplex communications are the presence of self-interference, especially in complex cellular networks. With the current development of self-interference cancellation techniques, full-duplex has been considered to be more suitable for device-to-device (D2D) and small cell communications which have small transmission range and low transmit power. In this paper, we consider the full-duplex D2D communications in multi-tier wireless networks and present an analytical model which jointly considers mode selection, resource allocation, and power control. Specifically, we consider… More
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  • Credit Card Fraud Detection Based on Machine Learning
  • Abstract In recent years, the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit. Credit card transactions take a salient role in nowadays’ online transactions for its obvious advantages including discounts and earning credit card points. So credit card fraudulence has become a target of concern. In order to deal with the situation, credit card fraud detection based on machine learning is been studied recently. Yet, it is difficult to detect fraudulent transactions due to data imbalance (normal and fraudulent transactions), for which Smote algorithm is proposed in order to resolve data imbalance. The assessment of… More
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  • Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration
  • Abstract Word sense disambiguation (WSD) is a fundamental but significant task in natural language processing, which directly affects the performance of upper applications. However, WSD is very challenging due to the problem of knowledge bottleneck, i.e., it is hard to acquire abundant disambiguation knowledge, especially in Chinese. To solve this problem, this paper proposes a graph-based Chinese WSD method with multi-knowledge integration. Particularly, a graph model combining various Chinese and English knowledge resources by word sense mapping is designed. Firstly, the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet. Then, English word similarity… More
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  • Readability Assessment of Textbooks in Low Resource Languages
  • Abstract Readability is a fundamental problem in textbooks assessment. For low re-sources languages (LRL), however, little investigation has been done on the readability of textbook. In this paper, we proposed a readability assessment method for Tibetan textbook (a low resource language). We extract features based on the information that are gotten by Tibetan segmentation and named entity recognition. Then, we calculate the correlation of different features using Pearson Correlation Coefficient and select some feature sets to design the readability formula. Fit detection, F test and T test are applied on these selected features to generate a new readability assessment formula. Experiment… More
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  • Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data
  • Abstract Monitoring, understanding and predicting Origin-destination (OD) flows in a city is an important problem for city planning and human activity. Taxi-GPS traces, acted as one kind of typical crowd sensed data, it can be used to mine the semantics of OD flows. In this paper, we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China. The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows. Then based on a novel complex network model, a semantics mining method of OD flows… More
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  • Fast Scene Reconstruction Based on Improved SLAM
  • Abstract Simultaneous location and mapping (SLAM) plays the crucial role in VR/AR application, autonomous robotics navigation, UAV remote control, etc. The traditional SLAM is not good at handle the data acquired by camera with fast movement or severe jittering, and the efficiency need to be improved. The paper proposes an improved SLAM algorithm, which mainly improves the real-time performance of classical SLAM algorithm, applies KDtree for efficient organizing feature points, and accelerates the feature points correspondence building. Moreover, the background map reconstruction thread is optimized, the SLAM parallel computation ability is increased. The color images experiments demonstrate that the improved SLAM… More
  •   Views:730       Downloads:393        Download PDF
  • An Intrusion Detection Algorithm Based on Feature Graph
  • Abstract With the development of Information technology and the popularization of Internet, whenever and wherever possible, people can connect to the Internet optionally. Meanwhile, the security of network traffic is threatened by various of online malicious behaviors. The aim of an intrusion detection system (IDS) is to detect the network behaviors which are diverse and malicious. Since a conventional firewall cannot detect most of the malicious behaviors, such as malicious network traffic or computer abuse, some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches. However, there are very few… More
  •   Views:342       Downloads:277        Download PDF
  • Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection
  • Abstract Due to the widespread use of the Internet, customer information is vulnerable to computer systems attack, which brings urgent need for the intrusion detection technology. Recently, network intrusion detection has been one of the most important technologies in network security detection. The accuracy of network intrusion detection has reached higher accuracy so far. However, these methods have very low efficiency in network intrusion detection, even the most popular SOM neural network method. In this paper, an efficient and fast network intrusion detection method was proposed. Firstly, the fundamental of the two different methods are introduced respectively. Then, the self-organizing feature… More
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  • Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks
  • Abstract Words are the most indispensable information in human life. It is very important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level moral information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, great achievements have been made in text information detection and recognition. However, when dealing with text characters in natural scene images, there are still some limitations in the detection and recognition of natural scene images. Because natural scene image has more interference and complexity than… More
  •   Views:367       Downloads:258        Download PDF
  • Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks
  • Abstract With the rapid development of the semantic web and the ever-growing size of uncertain data, representing and reasoning uncertain information has become a great challenge for the semantic web application developers. In this paper, we present a novel reasoning framework based on the representation of fuzzy PR-OWL. Firstly, the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning, incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory, and introduces fuzzy PR-OWL, an Ontology language based on OWL2. Fuzzy PR-OWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation. Secondly,… More
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  • A Novel Improved Bat Algorithm in UAV Path Planning
  • Abstract Path planning algorithm is the key point to UAV path planning scenario. Many traditional path planning methods still suffer from low convergence rate and insufficient robustness. In this paper, three main methods are contributed to solving these problems. First, the improved artificial potential field (APF) method is adopted to accelerate the convergence process of the bat’s position update. Second, the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm. Third chaos strategy is proposed to avoid falling into a local optimum. Compared with standard APF and chaos strategy in UAV path planning scenarios, the… More
  •   Views:444       Downloads:288        Download PDF
  • Bus Priority Control for Dynamic Exclusive Bus Lane
  • Abstract One problem with the existing dynamic exclusive bus lane strategies is that bus signal priority strategies with multi-phase priority request at intersections are not adequately considered. The principle of bus signal priority level was designed based on the isolated multi-phase structure principle consideration of the bus signal priority, and a new priority approach for the dynamic exclusive bus lane was proposed. Two types of priority strategies, green extension and red truncation, were proposed for current phase and next phase buses, respectively. The control parameters including minimum green time, green extension time, maximum green time and bus arrival time are calculated.… More
  •   Views:355       Downloads:270        Download PDF
  • A Robust Zero-Watermarking Based on SIFT-DCT for Medical Images in the Encrypted Domain
  • Abstract Remote medical diagnosis can be realized by using the Internet, but when transmitting medical images of patients through the Internet, personal information of patients may be leaked. Aim at the security of medical information system and the protection of medical images, a novel robust zero-watermarking based on SIFT-DCT (Scale Invariant Feature Transform-Discrete Cosine Transform) for medical images in the encrypted domain is proposed. Firstly, the original medical image is encrypted in transform domain based on Logistic chaotic sequence to enhance the concealment of original medical images. Then, the SIFT-DCT is used to extract the feature sequences of encrypted medical images.… More
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  • A Compensation Controller Based on a Nonlinear Wavelet Neural Network for Continuous Material Processing Operations
  • Abstract Continuous material processing operations like printing and textiles manufacturing are conducted under highly variable conditions due to changes in the environment and/or in the materials being processed. As such, the processing parameters require robust real-time adjustment appropriate to the conditions of a nonlinear system. This paper addresses this issue by presenting a hybrid feedforward-feedback nonlinear model predictive controller for continuous material processing operations. The adaptive feedback control strategy of the controller augments the standard feedforward control to ensure improved robustness and compensation for environmental disturbances and/or parameter uncertainties. Thus, the controller can reduce the need for manual adjustments. The controller… More
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  • A Physical Layer Algorithm for Estimation of Number of Tags in UHF RFID Anti-Collision Design
  • Abstract A priori knowledge of the number of tags is crucial for anti-collision protocols in slotted UHF RFID systems. The number of tags is used to decide optimal frame length in dynamic frame slotted ALOHA (DFSA) and to adjust access probability in random access protocols. Conventional researches estimate the number of tags in MAC layer based on statistics of empty slots, collided slots and successful slots. Usually, a collision detection algorithm is employed to determine types of time slots. Only three types are distinguished because of lack of ability to detect the number of tags in single time slot. In this… More
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  • Multiple Kernel Clustering Based on Self-Weighted Local Kernel Alignment
  • Abstract Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample. However, we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance, while local kernel alignment on different sample actually has different contribution to clustering performance. Therefore this assumption could have a negative effective on clustering performance. To solve this issue, we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment, which can learn a proper weight to clustering performance for each local kernel alignment. Specifically,… More
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  • A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter
  • Abstract In the paper, we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment. In general, the different slices for different task scenarios exist in the same edge layer synchronously. A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity. In the condition, the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment. Based on the slicing and container concept, we propose the resource allocation scheme named Two-Dimension allocation and… More
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