Mohammed Algabri^a,c, Mohamed Abdelkader Bencherif^c, Mansour Alsulaiman^b,c, Ghulam Muhammad^b, Mohamed Amine Mekhtiche^c

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 267-274, 2018, DOI:10.1080/10798587.2017.1278961

Abstract A method that uses fuzzy logic to classify two simple speech features for the automatic classification of voiced and unvoiced phonemes is proposed. In addition, two variants, in which soft computing techniques are used to enhance the performance of fuzzy logic by tuning the parameters of the membership functions, are also presented. The three methods, manually constructed fuzzy logic (VUFL), fuzzy logic optimized with genetic algorithm (VUFL-GA), and fuzzy logic with optimized particle swarm optimization (VUFL-PSO), are implemented and then evaluated using the TIMIT speech corpus. Performance is evaluated using the TIMIT database in both More >

Jie Liu^a,b,c,d, Xiaodong Fu^d, Jin Liu^a,b,c, Yunchuan Sun^a,e

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 73-80, 2018, DOI:10.1080/10798587.2016.1267244

Abstract Reviews are contents written by users to express opinions on products or services. The information contained in reviews is valuable to users who are going to make decisions on products or services. However, there are numbers of reviews for popular products, and the quality of reviews is not always good. It’s necessary to pick out reviews, which are in high quality from numbers of reviews to assist user in making decision. In this paper, we collected 21,501 reviews flagged as good from 499,253 products on JD.com. We observed the level of users is an important More >

Albert C. Hergenroeder¹, Douglas S. Moodie², Daniel J. Penny², Constance M. Wiemann¹, Blanca Sanchez-Fournier¹, Lauren K. Moore², Jane Head³

Congenital Heart Disease, Vol.13, No.4, pp. 548-553, 2018, DOI:10.1111/chd.12604

Abstract Objective: To describe changes in functional status between the last pediatric and first adult congenital heart disease (CHD) clinic visits in patients with moderate to severe CHD after implementing a healthcare transition (HCT) planning program.
Design: Quasi-experimental design. Patients were followed prospectively following the implementation of the intervention; Control patients transitioned from the Pediatric CHD Clinic into Adult CHD Clinic before the intervention.
Setting: Texas Children’s Hospital (TCH).
Patients: Sixteen to 25-year-olds, cognitively normal, English speaking patients with moderate to severe CHD who transitioned from the Pediatric to the Adult CHD clinic.
Interventions: An EMR-based transition planning tool (TPT) was… More >

Yang Yang^1,*, Jinbao Duan¹, Haitao Yu¹, Zhipeng Gao¹, Xuesong Qiu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 555-575, 2018, DOI:10.31614/cmes.2018.04249

Abstract The development of deep learning has revolutionized image recognition technology. How to design faster and more accurate image classification algorithms has become our research interests. In this paper, we propose a new algorithm called stochastic depth networks with deep energy model (SADIE), and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics. First, the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training. Then in the backpropagation process, the energy function is designed More >

Qidi Wu¹, Yibing Li¹, Yun Lin^1,*, Ruolin Zhou²

CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 91-105, 2018, DOI:10.3970/cmc.2018.02771

Abstract The conventional sparse representation-based image classification usually codes the samples independently, which will ignore the correlation information existed in the data. Hence, if we can explore the correlation information hidden in the data, the classification result will be improved significantly. To this end, in this paper, a novel weighted supervised spare coding method is proposed to address the image classification problem. The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation. And then, it introduced the extracted structural information to a novel weighted sparse representation model More >

Rui Wang¹, Miaomiao Shen^1,*, Yanping Li¹, Samuel Gomes²

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 25-48, 2018, DOI:10.32604/cmc.2018.02408

Abstract Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on More >

Xuefeng Xi¹, Victor S. Sheng^1,2,*, Binqi Sun², Lei Wang¹, Fuyuan Hu¹

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 185-198, 2018, DOI:10.3970/cmc.2018.03694

Abstract Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It has received relatively small attention from the Machine Learning community. However, multi-target regression exists in many real-world applications. In this paper we conduct extensive experiments to investigate the performance of three representative multi-target regression learning algorithms (i.e. Multi-Target Stacking (MTS), Random Linear Target Combination (RLTC), and Multi-Objective Random Forest (MORF)), comparing the baseline single-target learning. Our experimental results show that all three multi-target regression learning algorithms do improve the performance of the single-target More >

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 >