Xingxing Xiao¹, Yang^1,*, Rui Li², Weiming Zhang³

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 101-115, 2019, DOI:10.32604/cmc.2019.05293

Abstract The medical industry develops rapidly as science and technology advance. People benefit from medical resource sharing, but suffer from privacy leaks at the same time. In order to protect patients’ privacy and improve quality of medical images, a novel reversible data hiding (RDH) scheme based on lesion extraction and with contrast enhancement is proposed. Furthermore, the proposed scheme can enhance the contrast of medial image's lesion area directly and embed high-capacity privacy data reversibly. Different from previous segmentation methods, this scheme first adopts distance regularized level set evolution (DRLSE) to extract lesion and targets at… More >

Jiwei Zhang¹, Yueying Li², Shaozhang Niu^1,*, Zhiyi Cao¹, Xinyi Wang¹

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 287-303, 2019, DOI:10.32604/cmc.2019.05353

Abstract With the rapid development of image editing techniques, the image splicing behavior, typically for those that involve copying a portion from one original image into another targeted image, has become one of the most prevalent challenges in our society. The existing algorithms relying on hand-crafted features can be used to detect image splicing but unfortunately lack precise location information of the tampered region. On the basis of changing the classifications of fully convolutional network (FCN), here we proposed an improved FCN that enables locating the spliced region. Specifically, we first insert the original images into… More >

Chengzhang Zhu^{1, 2}, Wentao Zhao^{2, *}, Qian Li¹, Pan Li², Qiaobo Da³

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 317-333, 2019, DOI:10.32604/cmc.2019.05677

Abstract Detecting collaborative fraudsters who manipulate opinions in social media is becoming extremely important in order to provide reliable information, in which, however, the diversity in different groups of collaborative fraudsters presents a significant challenge to existing collaborative fraudsters detection methods. These methods often detect collaborative fraudsters as the largest group of users who have the strongest relation with each other in the social media, consequently overlooking the other groups of fraudsters that are with strong user relation yet small group size. This paper introduces a novel network embedding-based framework NEST and its instance BEST to… More >

Assia Maamar^1,*, Khelifa Benahmed²

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 15-39, 2019, DOI:10.32604/cmc.2019.06497

Abstract Recently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we… More >

Muhammad Shoaib Arif^1,*, Ali Raza¹, Muhammad Rafiq², Mairaj Bibi³, Rabia Fayyaz³, Mehvish Naz³, Umer Javed⁴

CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 787-804, 2019, DOI:10.32604/cmc.2019.04655

Abstract In this paper, a reliable stochastic numerical analysis for typhoid fever incorporating with protection against infection has been considered. We have compared the solutions of stochastic and deterministic typhoid fever model. It has been shown that the stochastic typhoid fever model is more realistic as compared to the deterministic typhoid fever model. The effect of threshold number T* hold in stochastic typhoid fever model. The proposed framework of the stochastic non-standard finite difference scheme (SNSFD) preserves all dynamical properties like positivity, bounded-ness and dynamical consistency defined by Mickens, R. E. The stochastic numerical simulation of More >

Sultan Noman Qasem^1,2, Amar Nazar³, Attia Qamar⁴, Shahaboddin Shamshirband^5,6,*, Ahmad Karim⁴

CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 713-727, 2019, DOI:10.32604/cmc.2019.05617

Abstract Brain tumor is one of the most dangerous disease that causes due to uncontrollable and abnormal cell partition. In this paper, we have used MRI brain scan in comparison with CT brain scan as it is less harmful to detect brain tumor. We considered watershed segmentation technique for brain tumor detection. The proposed methodology is divided as follows: pre-processing, computing foreground applying watershed, extract and supply features to machine learning algorithms. Consequently, this study is tested on big data set of images and we achieved acceptable accuracy from K-NN classification algorithm in detection of brain More >

Demin Gao^{1, 2, *}, Shuo Zhang¹, Fuquan Zhang¹, Xijian Fan¹, Jinchi Zhang^1,∗

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 649-667, 2019, DOI:10.32604/cmc.2019.05195

Abstract For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data More >

Zhe Liu¹, Charlie Maere^1,*, Yuqing Song¹

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 669-686, 2019, DOI:10.32604/cmc.2019.04590

Abstract Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing. We propose a novel approach of automatically identifying region of interest in Computed Tomography Image (CT) images based on temporal and spatial data . Our method is a 3 stages approach, 1) We extract organ features from the CT images by adopting the Hounsfield filter. 2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’ area and automatically detect a seed point. 3) We use a novel approach to More >

Pingshui Wang^1,*, Zecheng Wang¹, Tao Chen^1,2, Qinjuan Ma¹

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 533-546, 2019, DOI:10.32604/cmc.2019.05570

Abstract With the rapid development of the new generation of information technology, the analysis of mobile social network big data is getting deeper and deeper. At the same time, the risk of privacy disclosure in social network is also very obvious. In this paper, we summarize the main access control model in mobile social network, analyze their contribution and point out their disadvantages. On this basis, a practical privacy policy is defined through authorization model supporting personalized privacy preferences. Experiments have been conducted on synthetic data sets. The result shows that the proposed privacy protecting model More >

Daniel Rivera Ruiz^1,*, Alisha Sawant¹

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 389-396, 2019, DOI:10.32604/cmc.2019.06433

Abstract In this paper we aim to identify certain social factors that influence, and thus can be used to predict, the occurrence of crimes. The factors under consideration for this analytic are social demographics such as age, sex, poverty, etc., train ridership, traffic density and the number of business licenses per community area in Chicago, IL. A factor will be considered pertinent if there is high correlation between it and the number of crimes of a particular type in that community area. More >