Liu Zhang^1,*, Yi-Fei Chen^1,2, Zi-Quan Pei¹, Jia-Wei Yuan², Nai-Qiao Tang¹

Journal on Artificial Intelligence, Vol.4, No.1, pp. 15-26, 2022, DOI:10.32604/jai.2022.027730

Abstract Analyzing and predicting the learning behavior data of students in blended teaching can provide reference basis for teaching. Aiming at weak generalization ability of existing algorithm models in performance prediction, a BP neural network is introduced to classify and predict the grades of students in the blended teaching. L2 regularization term is added to construct the BP neural network model in order to reduce the risk of overfitting. Combined with Pearson coefficient, effective feature data are selected as the validation dataset of the model by mining the data of Chao-Xing platform. The performance of common machine learning algorithms and the… More >

Yanhua Lu¹, Xuehui Gong^2,*, Andrew Byron Kipnis³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5481-5497, 2022, DOI:10.32604/cmc.2022.027089

Abstract Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University, the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation (BP) neural network to solve nonlinear problems and have the ability of global approximation and generalization. By analyzing the influence of different uses, different building surfaces and different energy-saving schemes on the change of building energy consumption, the grey correlation method is used to determine the main influencing factors affecting each building energy consumption, including uses, building surfaces and energy-saving schemes, which are used as… More >

Xiaorui Zhang^1,2,*, Xun Sun¹, Wei Sun², Tong Xu¹, Pengpai Wang¹, Sunil Kumar Jha³

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1041-1053, 2022, DOI:10.32604/iasc.2022.016543

Abstract This paper proposes a soft tissue grasping deformation model, where BP neural network optimized by the genetic algorithm is used to realize the real-time and accurate interaction of soft tissue grasping during virtual surgery. In the model, the soft tissue epidermis is divided into meshes, and the meshes generate displacements under the action of tension. The relationship between the tension and displacement of the mesh is determined by the proposed cylindrical spiral spring model. The optimized BP neural network is trained based on the sample data of the mesh point and vertical tension, so as to obtain the force and… More >

Baiqing Zhou¹, Na Li^1,*, Duan Lu¹, Jinyue Xia²

Computer Systems Science and Engineering, Vol.41, No.1, pp. 127-140, 2022, DOI:10.32604/csse.2022.017750

Abstract With the rapid development of the economy, China’s environment has been damaged severely, which has attracted much attention from scholars and the local government. The concept of green development has been an underlying trend since 2012, and it is of great significance to explore the relationship between economic growth and environmental quality. Huzhou is a prefecture-level city under the jurisdiction of Zhejiang Province, and it is one of the 27 cities in the central area of the Yangtze River Delta. In recent years, this city develops well not only in economic development but also in maintaining a green environment. In… More >

Xiaona Zhang^1,*, Jie Feng², Zhiguo Yu¹, Zhen Hong³, Xinge Yun¹

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3871-3888, 2021, DOI:10.32604/cmc.2021.020057

Abstract The existence of soil macropores is a common phenomenon. Due to the existence of soil macropores, the amount of solute loss carried by water is deeply modified, which affects watershed hydrologic response. In this study, a new improved BP (Back Propagation) neural network method, using Levenberg–Marquand training algorithm, was used to analyze the solute loss on slopes taking into account the soil macropores. The rainfall intensity, duration, the slope, the characteristic scale of macropores and the adsorption coefficient of ions, are used as the variables of network input layer. The network middle layer is used as hidden layer, the number… More >

Yifu Sheng¹, Jianjun Zhang^1,*, Wenwu Tan¹, Jiang Wu¹, Haijun Lin¹, Guang Sun², Peng Guo³

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4043-4059, 2021, DOI:10.32604/cmc.2021.019900

Abstract There are many influencing factors of fiscal revenue, and traditional forecasting methods cannot handle the feature dimensions well, which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend. The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso. It can reduce the dimensionality of the original data, make separate predictions for each explanatory variable, and then use neural networks to make multivariate predictions, thereby making up for the… More >

Tao Li¹, Ning Peng Li¹, Qi Qian¹, Wen Duo Xu¹, Yong Jun Ren^2,*, Jin Yue Xia³

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 741-755, 2021, DOI:10.32604/iasc.2021.018496

Abstract In this paper, the inversion method of atmospheric temperature and humidity profiles via ground-based microwave radiometer is studied. Using the three-layer BP neural network inversion algorithm, four BP neural network models (temperature and humidity models with and without cloud information) are established using L-band radiosonde data obtained from the Atmospheric Exploration base of the China Meteorological Administration from July 2018 to June 2019. Microwave radiometer level 1 data and cloud radar data from July to September 2019 are used to evaluate the model. The four models are compared with the measured sounding data, and the inversion accuracy and the influence… More >

Zhenzhou Wang¹, Yinuo Ma¹, Pingping Yu^1,*, Ning Cao², Heiner Dintera³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3431-3446, 2021, DOI:10.32604/cmc.2021.017729

Abstract Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes. The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience, which have problems such as inaccurate recognition time, time-consuming and energy sapping. Therefore, this paper proposes a neural network-based method for predicting the flowering phase of pear tree. Firstly, based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019, three principal components (the temperature factor, weather factor, and humidity factor) with high correlation coefficient with the flowering phase… More >

Yunzuo Zhang^*, Yi Li, Wei Guo, Lei Huo, Jiayu Zhang, Kaina Guo

Journal of Cyber Security, Vol.3, No.1, pp. 45-54, 2021, DOI:10.32604/jcs.2021.017071

Abstract In the field of educational examination, automatic marking technology plays an essential role in improving the efficiency of marking and liberating the labor force. At present, the implementation of the policy of expanding erolments has caused a serious decline in the teacher-student ratio in colleges and universities. The traditional marking system based on Optical Mark Reader technology can no longer meet the requirements of liberating the labor force of teachers in small and medium-sized examinations. With the development of image processing and artificial neural network technology, the recognition of handwritten character in the field of pattern recognition has attracted the… More >

Qiong Wang^*, Xiaokan Wang

Journal on Internet of Things, Vol.2, No.2, pp. 75-80, 2020, DOI:10.32604/jiot.2020.010226

Abstract The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model, because the heating furnace for heating treatment with the big inertia, the pure time delay and nonlinear time-varying. Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting (Z-N) method. A heating furnace for the object was simulated with MATLAB, simulation results show that the control system has the quicker response characteristic, the better… More >