Yuwen Chen^{1, 2, 3, *}, Xiaolin Qin^{1, 3}, Lige Zhang^{1, 3}, Bin Yi⁴

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 495-510, 2020, DOI:10.32604/cmc.2020.011278

Abstract The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text… More >

Congcong Wang^{1, 2, 3}, Pengyu Liu^{1, 2, 3, *}, Kebin Jia^{1, 2, 3}, Xiaowei Jia⁴, Yaoyao Li^{1, 2, 3}

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2043-2055, 2020, DOI:10.32604/cmc.2020.010505

Abstract Weather phenomenon recognition plays an important role in the field of meteorology. Nowadays, weather radars and weathers sensor have been widely used for weather recognition. However, given the high cost in deploying and maintaining the devices, it is difficult to apply them to intensive weather phenomenon recognition. Moreover, advanced machine learning models such as Convolutional Neural Networks (CNNs) have shown a lot of promise in meteorology, but these models also require intensive computation and large memory, which make it difficult to use them in reality. In practice, lightweight models are often used to solve such problems. However, lightweight models often… More >

Muhammad Hasnain¹, Seung Ryul Jeong^{2, *}, Muhammad Fermi Pasha³, Imran Ghani⁴

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 729-752, 2020, DOI:10.32604/cmc.2020.010394

Abstract Performance anomaly detection is the process of identifying occurrences that do not conform to expected behavior or correlate with other incidents or events in time series data. Anomaly detection has been applied to areas such as fraud detection, intrusion detection systems, and network systems. In this paper, we propose an anomaly detection framework that uses dynamic features of quality of service that are collected in a simulated setup. Three variants of recurrent neural networks-SimpleRNN, long short term memory, and gated recurrent unit are evaluated. The results reveal that the proposed method effectively detects anomalies in web services with high accuracy.… More >

Amin Bemani¹, Alireza Baghban², Shahaboddin Shamshirband^{3, 4, *}, Amir Mosavi^{5, 6, 7}, Peter Csiba⁷, Annamaria R. Varkonyi-Koczy^{5, 7}

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1175-1204, 2020, DOI:10.32604/cmc.2020.07723

Abstract In the present work, a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide. Four different machine learning algorithms of radial basis function, multi-layer perceptron (MLP), artificial neural networks (ANN), least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are used to model the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and the dissociation constant of acid. To evaluate the proposed models, different graphical and statistical analyses, along with novel sensitivity analysis, are carried out.… More >

Shiqi Wang¹, Yimin Yang^{1, *}, Ruizhong Wei¹, Qingming Jonathan Wu²

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1081-1091, 2020, DOI:10.32604/cmc.2020.09648

Abstract Human motion recognition plays a crucial role in the video analysis framework. However, a given video may contain a variety of noises, such as an unstable background and redundant actions, that are completely different from the key actions. These noises pose a great challenge to human motion recognition. To solve this problem, we propose a new method based on the 3-Dimensional (3D) Bag of Visual Words (BoVW) framework. Our method includes two parts: The first part is the video action feature extractor, which can identify key actions by analyzing action features. In the video action encoder, by analyzing the action… More >

Zhong Liu, Xin’an Wang, Kuntao Lu, David Su

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1079-1079, 2020, DOI:10.32604/cmc.2020.04882

Abstract This article has no abstract. More >

Shiru Zhang¹, Zhiyao Liang^{1, *}, Jian Lin²

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 943-957, 2020, DOI:10.32604/cmc.2020.08800

Abstract Calculating the semantic similarity of two sentences is an extremely challenging problem. We propose a solution based on convolutional neural networks (CNN) using semantic and syntactic features of sentences. The similarity score between two sentences is computed as follows. First, given a sentence, two matrices are constructed accordingly, which are called the syntax model input matrix and the semantic model input matrix; one records some syntax features, and the other records some semantic features. By experimenting with different arrangements of representing the syntactic and semantic features of the sentences in the matrices, we adopt the most effective way of constructing… More >

Guangyong Yang¹, Jianqiu Zeng¹, Mengke Yang^{2, *}, Yifei Wei³, Xiangqing Wang³, Zulfiqar Hussain Pathan⁴

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 769-785, 2020, DOI:10.32604/cmc.2020.07528

Abstract A vast amount of information has been produced in recent years, which brings a huge challenge to information management. The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages. In this paper, we propose a nine-rectangle-grid information model according to the information value and privacy, and then present information use policies based on the rough set theory. Recurrent neural networks were employed to classify OTT messages. The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages, ending with a reliable trained classification model.… More >

Changrui Liu¹, Dengpan Ye^{1, *}, Yueyun Shang², Shunzhi Jiang¹, Shiyu Li¹, Yuan Mei¹, Liqiang Wang³

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1365-1386, 2020, DOI:10.32604/cmc.2020.07421

Abstract Image classifiers that based on Deep Neural Networks (DNNs) have been proved to be easily fooled by well-designed perturbations. Previous defense methods have the limitations of requiring expensive computation or reducing the accuracy of the image classifiers. In this paper, we propose a novel defense method which based on perceptual hash. Our main goal is to destroy the process of perturbations generation by comparing the similarities of images thus achieve the purpose of defense. To verify our idea, we defended against two main attack methods (a white-box attack and a black-box attack) in different DNN-based image classifiers and show that,… More >

Shang Li¹, Hongli Zhang^{1, *}, Lin Ye¹, Shen Su², Xiaoding Guo¹, Haining Yu^{1, 3}, Binxing Fang¹

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1217-1231, 2020, DOI:10.32604/cmc.2020.06787

Abstract The task of prison term prediction is to predict the term of penalty based on textual fact description for a certain type of criminal case. Recent advances in deep learning frameworks inspire us to propose a two-step method to address this problem. To obtain a better understanding and more specific representation of the legal texts, we summarize a judgment model according to relevant law articles and then apply it in the extraction of case feature from judgment documents. By formalizing prison term prediction as a regression problem, we adopt the linear regression model and the neural network model to train… More >