Yibo Zhao^1,2

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 835-845, 2019, DOI:10.31209/2019.100000087

Abstract In this paper, a new model is proposed to predict the permeability of porous media. This model introduces the Euler-Poincaré Characteristic (Euler Number), a parameter that reflects the connectivity of porous media. Using fractal and percolation theory, we establish a permeability model as a function of critical radius, porosity and Euler number. In order to relate the result to the Euler number, we introduce the Connectivity Function to calculate the critical aperture in the percolation theory, then calculate the percolation threshold value, and establish the relationship between the percolation threshold and the Euler number. The validity of the model is… More >

Ahsan Hussain, Bettahally N. Keshavamurthy, Ravi Prasad K. Jagannath

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 473-486, 2019, DOI:10.31209/2018.11007092

Abstract Prediction space of distinct check-in locations in Location-Based Social Networks is a challenge. In this paper, a thorough analysis of Foursquare Check-ins is done. Based on previous check-in sequences, next location of social-users is accurately predicted using multinomial-Hidden Markov Model (mHMM) with Steady-State probabilities. This information benefits security-agencies in tracking suspects and restaurant-owners to predict their customers’ arrivals at different venues on given days. Higher accuracy and Steady-State venuepopularities obtained for location-prediction using the proposed method, outperform various other baseline methods. More >

Belayat Hossain^a, Takatoshi Morooka^b, Makiko Okuno^b, Manabu Nii^a, Shinichi Yoshiya^b, Syoji Kobashi^a

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 >

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 >

Nana Zhang¹, Kun Zhu¹, Shi Ying^{1, *}, Xu Wang²

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 279-308, 2020, DOI:10.32604/cmc.2020.011001

Abstract Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—Stacked Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes (defective or non-defective). (2) we… More >

Muhammad Adnan Khan^{1, *}, Sagheer Abbas², Ayesha Atta^{2, 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 >

Bingjie Yan¹, Jun Wang¹, Zhen Zhang², Xiangyan Tang^{1, *}, Yize Zhou¹, Guopeng Zheng¹, Qi Zou¹, Yao Lu¹, Boyi Liu³, Wenxuan Tu⁴, Neal Xiong⁵

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1473-1490, 2020, DOI:10.32604/cmc.2020.011317

Abstract New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM… More >

Bolun Chen^{1, 2}, Guochang Zhu^{1, *}, Min Ji¹, Yongtao Yu¹, Jianyang Zhao¹, Wei Liu³

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1039-1049, 2020, DOI:10.32604/cmc.2020.010583

Abstract Air quality prediction is an important part of environmental governance. The accuracy of the air quality prediction also affects the planning of people’s outdoor activities. How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention, pollution control and pollution early warning. In this paper, we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants. Firstly, the data of air pollutants in different cities are collected by a sliding… More >

Hui Li¹, Cailin Shi^{2, 3}, Xin Liu^{2, 3}, Aziguli Wulamu^{2, 3, *}, Alan Yang⁴

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 987-1004, 2020, DOI:10.32604/cmc.2020.09812

Abstract The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid. The three-phase unbalanced is closely related to power planning and load distribution. When the unbalance occurs, the safe operation of the electrical equipment will be seriously jeopardized. This paper proposes a Hierarchical Temporal Memory (HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding, the spatial pooler for frequency pattern learning, the temporal pooler for pattern sequence learning, and the sparse distributed representations classifier for unbalance prediction. Following the feasibility of spatialtemporal streaming data analysis, we adopted… More >

Jinyuan Li¹, Hao Li¹, Guorong Cui¹, Yan Kang^{1, *}, Yang Hu¹, Yingnan Zhou²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 925-940, 2020, DOI:10.32604/cmc.2020.09903

Abstract With continuous urbanization, cities are undergoing a sharp expansion within the regional space. Due to the high cost, the prediction of regional traffic flow is more difficult to extend to entire urban areas. To address this challenging problem, we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet, which predicts traffic flow of surrounding areas based on inflow and outflow information in central area. The method is data-driven, and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional matrix. We introduce adversarial training to improve performance… More >