Ling-Jing Kao¹, Chih-Chou Chiu^1,*, Yu-Fan Lin², Heong Kam Weng¹

Computer Systems Science and Engineering, Vol.42, No.2, pp. 493-508, 2022, DOI:10.32604/csse.2022.022166

Abstract Inter-purchase time is a critical factor for predicting customer churn. Improving the prediction accuracy can exploit consumer’s preference and allow businesses to learn about product or pricing plan weak points, operation issues, as well as customer expectations to proactively reduce reasons for churn. Although remarkable progress has been made, classic statistical models are difficult to capture behavioral characteristics in transaction data because transaction data are dependent and short-, medium-, and long-term data are likely to interfere with each other sequentially. Different from literature, this study proposed a hybrid inter-purchase time prediction model for customers of on-line retailers. Moreover, the analysis… More >

Virginijus Baranauskas^1,*, Žydrūnas Jakas¹, Kastytis Kiprijonas Šarkauskas¹, Stanislovas Bartkevičius², Gintaras Dervinis¹, Alma Dervinienė³, Leonas Balaševičius¹, Vidas Raudonis¹, Renaldas Urniežius¹, Jolanta Repšytė¹

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1343-1356, 2022, DOI:10.32604/iasc.2022.022288

Abstract The research work presents a collision prediction method of mobile robots. The authors of the work use so-called, virtual cocoons to evaluate the collision criteria of two robots. The idea, mathematical representation of the calculations and experimental simulations are presented in the paper work. A virtual model of the industrial process with moving mobile robots was created. Obstacle avoidance was not solved here. The authors of the article were working on collision avoidance problem solving between moving robots. Theoretical approach presents mathematical calculations and dependences of path angles of mobile robots. Experimental simulations, using the software Centaurus CPN, based on… More >

Manish Agrawal¹, Piyush Kumar Shukla², Rajit Nair³, Anand Nayyar^4,5,*, Mehedi Masud⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 287-304, 2022, DOI:10.32604/cmc.2022.014637

Abstract Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature. The stock data is usually non-stationary, and attributes are non-correlative to each other. Several traditional Stock Technical Indicators (STIs) may incorrectly predict the stock market trends. To study the stock market characteristics using STIs and make efficient trading decisions, a robust model is built. This paper aims to build up an Evolutionary Deep Learning Model (EDLM) to identify stock trends’ prices by using STIs. The proposed model has implemented the Deep Learning (DL) model to establish the… More >

Wan Hyun Cho¹, Sang Kyoon Kim², Myung Hwan Na¹, In Seop Na^3,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4003-4024, 2021, DOI:10.32604/cmc.2021.018758

Abstract Fruit processing devices, that automatically detect the freshness and ripening stages of fruits are very important in precision agriculture. Recently, based on deep learning, many attempts have been made in computer image processing, to monitor the ripening stage of fruits. However, it is time-consuming to acquire images of the various ripening stages to be used for training, and it is difficult to measure the ripening stages of fruits accurately with a small number of images. In this paper, we propose a prediction system that can automatically determine the ripening stage of fruit by a combination of deep neural networks (DNNs)… More >

Shuxuan Xie, Zengchen Yu, Zhihan Lv^*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 489-522, 2021, DOI:10.32604/cmes.2021.016728

Abstract In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI’s research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some… More >

Xiaoli Li¹, Shuailing Zhou¹, Zixu An^2,*, Zhenlong Du¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3765-3780, 2021, DOI:10.32604/cmc.2021.012422

Abstract Rough set theory has been widely researched for time series prediction problems such as rainfall runoff. Accurate forecasting of rainfall runoff is a long standing but still mostly significant problem for water resource planning and management, reservoir and river regulation. Most research is focused on constructing the better model for improving prediction accuracy. In this paper, a rainfall runoff forecast model based on the variable-precision fuzzy neighborhood rough set (VPFNRS) is constructed to predict Watershed runoff value. Fuzzy neighborhood rough set define the fuzzy decision of a sample by using the concept of fuzzy neighborhood. The fuzzy neighborhood rough set… More >

Xueqing Wang¹, Shuang Liu^1,*, Lejun Zhang²

Computer Systems Science and Engineering, Vol.36, No.1, pp. 259-269, 2021, DOI:10.32604/csse.2021.014343

Abstract The cost of highway is affected by many factors. Its composition and calculation are complicated and have great ambiguity. Calculating the cost of highway according to the traditional highway engineering estimation method is a completely tedious task. Constructing a highway cost prediction model can forecast the value promptly and improve the accuracy of highway engineering cost. This work sorts out and collects 60 sets of measured data of highway engineering; establishes an expressway cost index system based on 10 factors, including main route mileage, roadbed width, roadbed earthwork, and number of bridges; and processes the data through principal component analysis… More >

Weifeng Ma^*, Jiao Lou, Caoting Ji, Laibin Ma

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 179-193, 2021, DOI:10.32604/cmc.2020.011969

Abstract Given the limitations of the community question answering (CQA) answer quality prediction method in measuring the semantic information of the answer text, this paper proposes an answer quality prediction model based on the question-answer joint learning (ACLSTM). The attention mechanism is used to obtain the dependency relationship between the Question-and-Answer (Q&A) pairs. Convolutional Neural Network (CNN) and Long Short-term Memory Network (LSTM) are used to extract semantic features of Q&A pairs and calculate their matching degree. Besides, answer semantic representation is combined with other effective extended features as the input representation of the fully connected layer. Compared with other quality… More >

Xijun Zhang^*, Qirui Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 95-109, 2020, DOI:10.32604/cmes.2020.011013

Abstract According to the time series characteristics of the trajectory history data, we predicted and analyzed the traffic flow. This paper proposed a LSTMXGBoost model based urban road short-term traffic flow prediction in order to analyze and solve the problems of periodicity, stationary and abnormality of time series. It can improve the traffic flow prediction effect, achieve efficient traffic guidance and traffic control. The model combined the characteristics of LSTM (Long Short-Term Memory) network and XGBoost (Extreme Gradient Boosting) algorithms. First, we used the LSTM model that increases dropout layer to train the data set after preprocessing. Second, we replaced the… More >

Yuqing Yang^{1, 3}, Peng Fu^{2, *}, Xiaojiang Yang^{1, 4}, Hong Hong⁵, Dequn Zhou¹

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2413-2423, 2020, DOI:10.32604/cmc.2020.011881

Abstract Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better… More >