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  • Open Access


    Impact of Financial Technology on Regional Green Finance

    Zheng Liu1, Juanjuan Song1, Hui Wu2,*, Xiaomin Gu2, Yuanjun Zhao3, Xiaoguang Yue4, Lihua Shi1

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 391-401, 2021, DOI:10.32604/csse.2021.014527

    Abstract Finance is the core of modern economy, and a strong country cannot do without the support of financial system. With the rapid development of economy and society, the traditional financial services can not support the increasingly large and complex economic system. As a brand-new format, financial technology can help the financial industry to restructure and upgrade. At the same time, as an international consensus, green development is the only way for China to achieve sustainable development. Therefore, it is of great practical significance to study the impact of finance on the regional development of green finance. Based on the essence… More >

  • Open Access


    Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN

    Huizhi Gou1,2,*, Yuncai Ning1

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 803-822, 2021, DOI:10.32604/cmes.2021.015922

    Abstract Accurate photovoltaic (PV) power prediction can effectively help the power sector to make rational energy planning and dispatching decisions, promote PV consumption, make full use of renewable energy and alleviate energy problems. To address this research objective, this paper proposes a prediction model based on kernel principal component analysis (KPCA), modified cuckoo search algorithm (MCS) and deep convolutional neural networks (DCNN). Firstly, KPCA is utilized to reduce the dimension of the feature, which aims to reduce the redundant input vectors. Then using MCS to optimize the parameters of DCNN. Finally, the photovoltaic power forecasting method of KPCA-MCS-DCNN is established. In… More >

  • Open Access


    Research on Forecasting Flowering Phase of Pear Tree Based on Neural Network

    Zhenzhou Wang1, Yinuo Ma1, Pingping Yu1,*, 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 >

  • Open Access


    A Learning-based Static Malware Detection System with Integrated Feature

    Zhiguo Chen1,*, Xiaorui Zhang1,2, Sungryul Kim3

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 891-908, 2021, DOI:10.32604/iasc.2021.016933

    Abstract The rapid growth of malware poses a significant threat to the security of computer systems. Analysts now need to examine thousands of malware samples daily. It has become a challenging task to determine whether a program is a benign program or malware. Making accurate decisions about the program is crucial for anti-malware products. Precise malware detection techniques have become a popular issue in computer security. Traditional malware detection uses signature-based strategies, which are the most widespread method used in commercial anti-malware software. This method works well against known malware but cannot detect new malware. To overcome the deficiency of the… More >

  • Open Access


    Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

    Samreen Naeem1, Wali Khan Mashwani2,*, Aqib Ali1,3, M. Irfan Uddin4, Marwan Mahmoud5, Farrukh Jamal6, Christophe Chesneau7

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3451-3461, 2021, DOI:10.32604/cmc.2021.015872

    Abstract This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter (called tweets). A dataset of the exchange rates between the United States Dollar (USD) and the Pakistani Rupee (PKR) was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words. The dataset was collected in raw form, and was subjected to natural language processing by way of data preprocessing. Response variable labeling was then applied to the standardized dataset, where the response variables were… More >

  • Open Access


    A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout (Salmo Letnica)

    Sunil Kr. Jha1,*, Ivan Uzunov2, Xiaorui Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 991-1009, 2021, DOI:10.32604/cmes.2021.011821

    Abstract Ohrid trout (Salamo letnica) is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia (FYROM). The growth of Ohrid trout was examined in a controlled environment for a certain period, thereafter released into the lake to grow their natural population. The external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their growth. The data mining methods-based computational model can be used for fast, accurate, reliable, automatic, and improved growth monitoring procedures and classification of Ohrid trout. With this motivation, a combined approach of principal component… More >

  • Open Access


    Anthocyanin Profiles in Grape Berry Skins of Different Species of Wine Grapes in Shanxi, China

    Wei Tan1, Mingxiu Xu1, Siqi Xie1, Yan Zhang1, Shuai Wu1, Qinyan Zou1, Qifeng Zhao2, Qingliang Li3,*

    Phyton-International Journal of Experimental Botany, Vol.90, No.2, pp. 553-570, 2021, DOI:10.32604/phyton.2021.014082

    Abstract To understand the anthocyanin characteristics of wine grape varieties, the anthocyanin composition and content of 31 wine grape varieties were analyzed to explore the use of anthocyanins as chemical fingerprints to distinguish varieties. Results showed that a total of 21 anthocyanins were detected in the skins, including cyanidin, delphinidin, petunidin, peonidin and malvidin 3-monoglucosides (or 3,5-diglucosides) along with the corresponding acetyl and p-coumaroyl derivatives. The highest and lowest total amount of anthocyanins were detected in ‘Ruby Cabernet’ and ‘Muscat Rouge’, respectively. In the 21 Vitis vinifera grapes, there were 3~11 monoglucoside anthocyanins detected, however, there were 4 to 9 monoglucoside… More >

  • Open Access


    A New Population Initialization of Particle Swarm Optimization Method Based on PCA for Feature Selection

    Shichao Wang, Yu Xue*, Weiwei Jia

    Journal on Big Data, Vol.3, No.1, pp. 1-9, 2021, DOI:10.32604/jbd.2021.010364

    Abstract In many fields such as signal processing, machine learning, pattern recognition and data mining, it is common practice to process datasets containing huge numbers of features. In such cases, Feature Selection (FS) is often involved. Meanwhile, owing to their excellent global search ability, evolutionary computation techniques have been widely employed to the FS. So, as a powerful global search method and calculation fast than other EC algorithms, PSO can solve features selection problems well. However, when facing a large number of feature selection, the efficiency of PSO drops significantly. Therefore, plenty of works have been done to improve this situation.… More >

  • Open Access


    Highway Cost Prediction Based on LSSVM Optimized by Intial Parameters

    Xueqing Wang1, Shuang Liu1,*, 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 >

  • Open Access


    Evaluation of Industry Eco-Industrialization: Case Study of Shaanxi, China

    Shuru Liu1, Ping He1, Jiqiang Dang2

    Computer Systems Science and Engineering, Vol.33, No.5, pp. 389-395, 2018, DOI:10.32604/csse.2018.33.389

    Abstract The rapid development of industry brings great pressure on resource and environment, forcing people to explore the road of ecological development. In China, the level of regional industry ecological development differs from each other due to different economic level of regions. This paper establishes ecoindustrialization index system from aspects of resource consumption, pollution emission, pollution abatement, economic and social development to reveal the connotation and characteristics of industry eco-industrialization, taking Shaanxi Province as an example, and adopts the principal component analysis to evaluate the industry eco-industrialization level of 2001-2015 in Shaanxi. The study shows that the level of industry eco-industrialization… More >

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