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  • Open Access

    ARTICLE

    Surgical Outcome Prediction in Total Knee Arthroplasty Using Machine Learning

    Belayat Hossaina, Takatoshi Morookab, Makiko Okunob, Manabu Niia, Shinichi Yoshiyab, Syoji Kobashia

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 105-115, 2019, DOI:10.31209/2018.100000034

    Abstract This work aimed to predict postoperative knee functions of a new patient prior to total knee arthroplasty (TKA) surgery using machine learning, because such prediction is essential for surgical planning and for patients to better understand the TKA outcome. However, the main difficulty is to determine the relationships among individual varieties of preoperative and postoperative knee kinematics. The problem was solved by constructing predictive models from the knee kinematics data of 35 osteoarthritis patients, operated by posterior stabilized implant, based on generalized linear regression (GLR) analysis. Two prediction methods (without and with principal component analysis followed by GLR) along with… More >

  • Open Access

    ARTICLE

    Quantum Generative Model with Variable-Depth Circuit

    Yiming Huang1, *, Hang Lei1, Xiaoyu Li1, *, Qingsheng Zhu2, Wanghao Ren3, Xusheng Liu2, 4

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 445-458, 2020, DOI:10.32604/cmc.2020.010390

    Abstract In recent years, an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm. The hybrid quantum-classical framework, which is constructed by a variational quantum circuit (VQC) and an optimizer, plays a key role in the latest quantum machine learning studies. Nevertheless, in these hybridframework-based quantum machine learning models, the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems. There are also few studies focused on comparing the performance of quantum generative models with different loss functions. In this… More >

  • Open Access

    ARTICLE

    Applying Stack Bidirectional LSTM Model to Intrusion Detection

    Ziyong Ran1, Desheng Zheng1, *, Yanling Lai1, Lulu Tian2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 309-320, 2020, DOI:10.32604/cmc.2020.010102

    Abstract Nowadays, Internet has become an indispensable part of daily life and is used in many fields. Due to the large amount of Internet traffic, computers are subject to various security threats, which may cause serious economic losses and even endanger national security. It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data. As machine learning technology matures, deep learning is widely used in various industries. Combining deep learning with network security and intrusion detection is the current trend. In this paper, the problem of data… More >

  • Open Access

    ARTICLE

    Network-Aided Intelligent Traffic Steering in 5G Mobile Networks

    Dae-Young Kim1, Seokhoon Kim2, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 243-261, 2020, DOI:10.32604/cmc.2020.011253

    Abstract Recently, the fifth generation (5G) of mobile networks has been deployed and various ranges of mobile services have been provided. The 5G mobile network supports improved mobile broadband, ultra-low latency and densely deployed massive devices. It allows multiple radio access technologies and interworks them for services. 5G mobile systems employ traffic steering techniques to efficiently use multiple radio access technologies. However, conventional traffic steering techniques do not consider dynamic network conditions efficiently. In this paper, we propose a network aided traffic steering technique in 5G mobile network architecture. 5G mobile systems monitor network conditions and learn with network data. Through… More >

  • Open Access

    ARTICLE

    Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

    Muhammad Adnan Khan1, *, Sagheer Abbas2, Ayesha Atta2, 3, Allah Ditta4, Hani Alquhayz5, Muhammad Farhan Khan6, Atta-ur-Rahman7, Rizwan Ali Naqvi8

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 139-151, 2020, DOI:10.32604/cmc.2020.011416

    Abstract The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases. The most fatal of these is the issue of heart disease that cannot be detected from a naked eye, and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate, body temperature, and blood pressure. The real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner, followed by prescribing appropriate treatments and keeping prescription errors to a minimum. In developing countries, the domain of healthcare is progressing day by day using… More >

  • Open Access

    ARTICLE

    Applying Feature-Weighted Gradient Decent K-Nearest Neighbor to Select Promising Projects for Scientific Funding

    Chuqing Zhang1, Jiangyuan Yao2, *, Guangwu Hu3, Thomas Schøtt4

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1741-1753, 2020, DOI:10.32604/cmc.2020.010306

    Abstract Due to its outstanding ability in processing large quantity and high-dimensional data, machine learning models have been used in many cases, such as pattern recognition, classification, spam filtering, data mining and forecasting. As an outstanding machine learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations, yet in selecting qualified applicants for winning a funding is almost new. The major problem lies in how to accurately determine the importance of attributes. In this paper, we propose a Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify funding applicants in to two types: approved ones or not approved ones.… More >

  • Open Access

    ARTICLE

    Machine Learning and Classical Forecasting Methods Based Decision Support Systems for COVID-19

    Ramazan Ünlü1, Ersin Namlı2, *

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1383-1399, 2020, DOI:10.32604/cmc.2020.011335

    Abstract From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, Prophet, and Long-Short Term Memory… More >

  • Open Access

    ARTICLE

    Data Driven Modelling of Coronavirus Spread in Spain

    G. N. Baltas1, *, F. A. Prieto1, M. Frantzi2, C. R. Garcia-Alonso1, P. Rodriguez1, 3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1343-1357, 2020, DOI:10.32604/cmc.2020.011243

    Abstract During the late months of last year, a novel coronavirus was detected in Hubei, China. The virus, since then, has spread all across the globe forcing Word Health Organization (WHO) to declare COVID-19 outbreak a pandemic. In Spain, the virus started infecting the country slowly until rapid growth of infected people occurred in Madrid, Barcelona and other major cities. The government in an attempt to stop the rapssid spread of the virus and ensure that health system will not reach its capacity, implement strict measures by putting the entire country in quarantine. The duration of these measures, depends on the… More >

  • Open Access

    ARTICLE

    Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique

    Suhas S. Aralikatti1, K. N. Ravikumar1, Hemantha Kumar1,*, H. Shivananda Nayaka1, V. Sugumaran2

    Structural Durability & Health Monitoring, Vol.14, No.2, pp. 127-145, 2020, DOI:10.32604/sdhm.2020.07595

    Abstract The state of cutting tool determines the quality of surface produced on the machined parts. A faulty tool produces poor surface, inaccurate geometry and non-economic production. Thus, it is necessary to monitor tool condition for a machining process to have superior quality and economic production. In the present study, fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique. Cutting force and vibration signals were acquired to monitor tool condition during machining. A set of four tooling conditions namely healthy, worn flank, broken insert and extended tool overhang have been considered… More >

  • Open Access

    ARTICLE

    Quantum Generative Adversarial Network: A Survey

    Tong Li1, Shibin Zhang1, *, Jinyue Xia2

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 401-438, 2020, DOI:10.32604/cmc.2020.010551

    Abstract Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren… More >

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