Yuyu Chen¹, Bangxu Yin², Hongjie He², Shu Yan², Fan Chen^2,*, Hengming Tai³

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 299-312, 2018, DOI:10.3970/cmc.2018.03179

Abstract To improve the security and quality of decrypted images, this work proposes a reversible data hiding in encrypted image based on iterative recovery. The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR (XOR), which improves the security of encrypted images. And then, a pixel-type-mark generation method based on block-compression is designed to reduce the extra burden of key management and transfer. At last, an iterative recovery strategy is proposed to optimize the marked decrypted image, which allows the original image to be obtained only using the encryption key. The proposed More >

Yuhong Zhang^1,*, Qinqin Wang¹, Yuling Li¹, Xindong Wu²

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 285-297, 2018, DOI:10.3970/cmc.2018.02604

Abstract Recently, the effectiveness of neural networks, especially convolutional neural networks, has been validated in the field of natural language processing, in which, sentiment classification for online reviews is an important and challenging task. Existing convolutional neural networks extract important features of sentences without local features or the feature sequence. Thus, these models do not perform well, especially for transition sentences. To this end, we propose a Piecewise Pooling Convolutional Neural Network (PPCNN) for sentiment classification. Firstly, with a sentence presented by word vectors, convolution operation is introduced to obtain the convolution feature map vectors. Secondly, More >

Shengqun Fang¹, Zhiping Cai^1,*, Wencheng Sun¹, Anfeng Liu², Fang Liu³, Zhiyao Liang⁴, Guoyan Wang⁵

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 419-433, 2018, DOI:10.3970/cmc.2018.02289

Abstract By using efficient and timely medical diagnostic decision making, clinicians can positively impact the quality and cost of medical care. However, the high similarity of clinical manifestations between diseases and the limitation of clinicians’ knowledge both bring much difficulty to decision making in diagnosis. Therefore, building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain. In this paper, we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease… More >

Ya Tu¹, Yun Lin¹, Jin Wang^2,3,*, Jeong-Uk Kim⁴

CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 243-254, 2018, DOI:10.3970/cmc.2018.01755

Abstract Deep Learning (DL) is such a powerful tool that we have seen tremendous success in areas such as Computer Vision, Speech Recognition, and Natural Language Pro-cessing. Since Automated Modulation Classification (AMC) is an important part in Cognitive Radio Networks, we try to explore its potential in solving signal modula-tion recognition problem. It cannot be overlooked that DL model is a complex mod-el, thus making them prone to over-fitting. DL model requires many training data to combat with over-fitting, but adding high quality labels to training data manually is not always cheap and accessible, especially in More >

Athanasios Koutsoukis¹, Xavier Halna du Fretay², Patrick Dupouy³, Phalla Ou⁴, Jean-Pierre Laissy⁴, Jean-Michel Juliard⁵, Fabien Hyafil⁶, Pierre Aubry⁵

Congenital Heart Disease, Vol.12, No.6, pp. 726-732, 2017, DOI:10.1111/chd.12504

Abstract Objective: The diagnosis of anomalous connections of the coronary arteries (ANOCOR) requires an appropriate identification for the management of the patients involved. We studied the observer variability in the description and classification of ANOCOR between a nonexpert group of physicians and a group of expert physicians, using the ANOCOR cohort.
Patients and design: Consecutive patients identified by 71 referring cardiologists were included in the ANOCOR cohort. Anomalous connection was diagnosed by invasive and/or computed tomography coronary angiography. Angiographic images were reviewed by an angiographic committee with experience in this field. Both investigators and angiographic committee filled out… More >

Alamelu Manghai T.M.¹, Jegadeeshwaran R², Sugumaran V.³

Structural Durability & Health Monitoring, Vol.11, No.1, pp. 43-67, 2017, DOI:10.3970/sdhm.2017.012.043

Abstract Diagnosis is the recognition of the nature and cause of a certain phenomenon. It is generally used to determine cause and effect of a problem. Machine fault diagnosis is a field of finding faults arising in machines. To identify the most probable faults leading to failure, many methods are used for data collection, including vibration monitoring, thermal imaging, oil particle analysis, etc. Then these data are processed using methods like spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform, high-resolution spectral analysis, waveform analysis, etc., The results of this analysis are used in a root More >

Ravikumar Gurusamy¹, Dr Vijayan Subramaniam²

CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 91-108, 2017, DOI:10.3970/cmc.2017.053.091

Abstract A new method for the denoising, extraction and tumor detection on MRI images is presented in this paper. MRI images help physicians study and diagnose diseases or tumors present in the brain. This work is focused towards helping the radiologist and physician to have a second opinion on the diagnosis. The ambiguity of Magnetic Resonance (MR) image features is solved in a simpler manner. The MRI image acquired from the machine is subjected to analysis in the work. The real-time data is used for the analysis. Basic preprocessing is performed using various filters for noise More >

Gautum Agarwal, David Buethe, Christopher Russell, Adam Luchey, Julio M. Pow-Sang

Canadian Journal of Urology, Vol.23, No.2, pp. 8215-8219, 2016

Abstract Introduction: Up to 50% of patients will have disease reclassification while on active surveillance (AS) for their prostate cancer. Determining which patients will have reclassification that will impact their survival is difficult. We investigated clinicopathologic factors associated with disease reclassification and differences in both overall and metastasis free survival between those treated and those remaining on AS.
Materials and methods: We performed a retrospective review of patients who were enrolled in an AS protocol between 1994 and 2000. Inclusion criteria for AS were: < cT2a disease, PSA < 10 ng/mL, < 50% of single core involvement, and… More >

Liming Yang¹, Yongping Gao², Qun Sun³

CMES-Computer Modeling in Engineering & Sciences, Vol.104, No.6, pp. 493-506, 2015, DOI:10.3970/cmes.2015.104.493

Abstract Minimax probability machine (MPM) has been recently proposed and shown its advantage in pattern recognition. In this paper, we present a new minimax probabilistic approach (MPA),which can provide an explicit lower bound on prediction accuracy. Applying the Chebyshev-Cantelli inequality, the MPA is posed as a second order cone program formulation and solved effectively. Following that, this method is exploited directly to recognize the purity of hybrid seeds using near-infrared spectroscopic data. Experimental results in different spectral regions show that the proposed MPA is competitive with the existing minimax probability machine and support vector machine in More >

Ricardo A. Rendon¹, Ross J. Mason¹, Susan Kirkland², Joseph G. Lawen¹, Mohamed Abdolell³

Canadian Journal of Urology, Vol.21, No.4, pp. 7379-7384, 2014

Abstract Introduction: To develop a classification tree for the preoperative prediction of benign versus malignant disease in patients with small renal masses.
Materials and methods: This is a retrospective study including 395 consecutive patients who underwent surgical treatment for a renal mass < 5 cm in maximum diameter between July 1st 2001 and June 30th 2010. A classification tree to predict the risk of having a benign renal mass preoperatively was developed using recursive partitioning analysis for repeated measures outcomes. Age, sex, volume on preoperative imaging, tumor location (central/peripheral), degree of endophytic component (1%–100%), and tumor axis position… More >