scholarly journals Optimization of Hotel Financial Management Information System Based on Computational Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongmei Ma
Keyword(s):  
Financial Crisis ◽  
Response Time ◽  
Success Rate ◽  
Computational Intelligence ◽  
Financial Management ◽  
Hotel Industry ◽  
Support Vector ◽  
Hotel Management ◽  
Development Mode ◽  
Intelligent Optimization

Nowadays, the hotel management concept cannot keep pace with the times. Traditional concepts are often adopted to manage hotel financial personnel, for the hotel financial personnel cannot take timely and effective training. All these lead to the hotel financial staff designing the hotel’s related business without sufficient understanding of the hotel industry and judging and deciding if they do not master the hotel’s professional knowledge, which makes the participating projects unable to give correct and reasonable answers to the substantive problems of the hotel. This leads to the hotel management not going up; extensive management makes the hotel benefit not go up. Hotel intelligent technology can solve these problems and not only save manpower and material resources but also intelligently predict the financial crisis of hotels. In the context of the accelerated development of globalization and informatization, there are still many problems in the financial management process of my country’s hotel industry. Based on these questions, the article draws on foreign advanced experience, puts forward effective suggestions in financial management, and uses computational intelligence technology to design a centralized and intelligent financial management system. The research results show the following: (1) the financial crisis model is created by using the principle of support vector machine and logistic regression method, which greatly reduces the financial crisis of the enterprise. (2) The system can straightforwardly summarize the data for easy query. Taking three domestic hotels as an example, a comprehensive study has been carried out on the three aspects of pricing assessment risk, financial integration risk, and debt risk. In 2016, the financial leverage coefficient has been relatively high, the quick ratio has fluctuated greatly, and the interest protection coefficient has shown a downward trend. (3) The performance of the system is compared with traditional development mode, framework development mode, and intelligent optimization mode. The intelligent optimization system has the lowest response time and the highest success rate. The new system has reduced response time by about 57% compared with the original response time, and the access success rate has been greatly improved.

scholarly journals Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

2021 ◽  
Vol 18 (5) ◽  
pp. 2584
Author(s):  
Cheng-Chien Lai ◽  
Wei-Hsin Huang ◽  
Betty Chia-Chen Chang ◽  
Lee-Ching Hwang
Keyword(s):  
Machine Learning ◽  
Smoking Cessation ◽  
Success Rate ◽  
Prediction Models ◽  
Smoking Status ◽  
Medical Center ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Smoking Cessation Outcome

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

Reengineering in business functions and hotel controlling

1996 ◽  
Vol 2 (1) ◽  
pp. 113-120
Author(s):  
Vlado Galičić
Keyword(s):  
Business Process ◽  
Hotel Industry ◽  
Theory And Practice ◽  
Hotel Management ◽  
Modern Management ◽  
Business Results ◽  
Business Functions

Business reengineering as a new stream in philosophy in managerial theory and practice, deserves appropriate attention. Such request sounds extremely natural when an hotel industry is concerned. Employing positive aims in business reengineering and controlling, as a way of modern management in a hotel business process, hotel management achieves valuable possibilities to gain better business results.

Analysis and Management of COVID-19 Using Computational Intelligence Technologies

2021 ◽  
Vol 11 (6) ◽  
pp. 1642-1648
Author(s):  
Xiangmin Meng ◽  
Jie Zhang
Keyword(s):  
Computational Intelligence ◽  
Fuzzy System ◽  
World Economy ◽  
Support Vector ◽  
Medical Institutions ◽  
The World ◽  
Treatment Measures ◽  
Epidemic Prediction ◽  
Learning Machine ◽  
Intelligent Model

After the outbreak of COVID-19, the world economy and people’s health have been greatly challenged. What is the law of the spread of COVID-19, when will it reach its peak, and when will it be effectively controlled? These have all become major issues of common concern throughout China and the world. Based on this background, this article introduces a variety of classic computational intelligence technologies to predict the spread of COVID-19. Computational intelligence technology mainly includes support vector machine regression (SVR), Takagi-Sugeuo-Kang fuzzy system (TSK-FS), and extreme learning machine (ELM). Compare the predictions of the infection rate, mortality rate, and recovery rate of the COVID-19 epidemic in China by each intelligent model in 5 and 10 days, the effectiveness of the computational intelligence algorithm used in epidemic prediction is verified. Based on the prediction results, the patients are classified and managed. According to the time of illness, physical fitness and other factors, patients are divided into three categories: Severe, moderate, and mild. In the case of serious shortage of medical equipment and medical staff, auxiliary medical institutions take corresponding treatment measures for different patients.

Application of Computational Intelligence to Investigation of Defect Centers in Semi-Insulating Materials by Photoinduced Transient Spectroscopy

2007 ◽  
Vol 994 ◽  
Author(s):  
Pawel Kaminski ◽  
Stanislaw Jankowski ◽  
Roman Kozlowski ◽  
Janusz Bedkowski
Keyword(s):  
Computational Intelligence ◽  
Emission Rate ◽  
Thermal Emission ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Inverse Laplace Transform ◽  
Model Complexity ◽  
Support Vector ◽  
Defect Centers ◽  
Temperature Dependences

AbstractA computational intelligence algorithm has been applied to extracting trap parameters from the photocurrent relaxation waveforms recorded at the temperature range of 20-320 K for semi-insulating (SI) InP samples. Using the inverse Laplace transform procedure, the spectral surfaces, visualized in the three dimensional space as functions of temperature and emission rate, are calculated. The processes of thermal emission of charge carriers from defect centers manifest themselves as the sharp folds on the spectral surface. Using a set of Gaussian functions, the approximating surface is created and the ridgelines of the folds, giving the temperature dependences of the emission rate for the detected traps, are determined. The approximation is performed using the support vector machine (SVM) algorithm which allows for trading off between the model complexity and fitting accuracy. The new approach is exemplified by comparing the defect structure of SI InP wafers after annealing in iron phosphide and pure phosphorous atmospheres.

scholarly journals UPAYA MENINGKATKAN KEPUTUSAN PENGGUNAAN MEETING PACKAGE DI HOTEL BUMI ASIH JAYA BANDUNG MELALUI DEMAND BASED PRICING METHODS (Survei pada Tamu Wisatawan Bisnis Pengambil Keputusan Penggunaan Meeting Package Di Hotel Bumi Asih Jaya Bandung)

2016 ◽  
Vol 1 (2) ◽  
pp. 101
Author(s):  
Mohamad Fauzan ◽  
Heri Puspito Diyah Setiyorini
Keyword(s):  
Census Data ◽  
Hotel Industry ◽  
Tourism Industry ◽  
Purchase Decision ◽  
Hotel Management ◽  
Social Culture ◽  
Study Population ◽  
Parametric Statistical Test ◽  
Descriptive Survey ◽  
Positive Effect

Tourism is an industry thath as the potential to become an instrument of increasing foreign exchange earnings. The sector is evolving as it has become a necessary to travel along with the development of tourism social-culture undergoing changes. One of the tourism industry that is always growing increasingly is hospitality industry. Especially in hotel industry. Competitionin hotel industry in Indonesia, especially Bandungkeep growing fast. The key to success in the hotel management not only in terms of services but also from elements of the products and pricing. Those become an valuable asset it self and important in general. Hotel Bumi Asih Jaya Bandung is one of three stars hotel which always strive to provide variety of products through pricing approach. Demand-based pricing method pricing methods that focus on the customer's perspectiveis consistent with the pricing on the customer's perception of value to affect the decision of using meeting package. As the above background, the research conducted on the effects of demand-based pricing methods toeard to purchase decisionof meeting package. The purpose of this study was to determine how the demand-based pricing methods and purchase decision of meeting package at Hotel Bumi Asih Jaya Bandung, and to know how bigthe influence of demand-based pricing methods toward to purchase decision of meeting package. This study is descriptive and verifikatif, while the method is a descriptive survey and survey eksplanatory. Samples takenin this study population as many as 48 people. Sampling techniqueis carried out the census. Data processing is done using parametric statistical test which uses the formula path analysis via SPSS11.5 for Windows. The results showed that the dimensions of demand-based pricing methods that get the highest ratings on the dimensions of the buyer based pricing. While making use of sub-variables that get the highest ratings at the time of use. Demand based pricing methods that consist of buyers based pricing, psychologicalpricing, and negotiation has a positive effect amounting against the decisions of the use of meeting package at Hotel Bumi Asih Jaya Bandung, which means the better the demand-based pricing methods that have the higher the usage decision meetings package is formed.

scholarly journals Comparing Performances of CNN, BP, and SVM Algorithms for Differentiating Sweet Pepper Parts for Harvest Automation

2021 ◽  
Vol 11 (20) ◽  
pp. 9583
Author(s):  
Bongki Lee ◽  
Donghwan Kam ◽  
Yongjin Cho ◽  
Dae-Cheol Kim ◽  
Dong-Hoon Lee
Keyword(s):  
Success Rate ◽  
Sweet Pepper ◽  
Local Features ◽  
Bp Algorithm ◽  
Support Vector ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Plant Parts ◽  
Speeded Up Robust Features ◽  
Recognition Success

For harvest automation of sweet pepper, image recognition algorithms for differentiating each part of a sweet pepper plant were developed and performances of these algorithms were compared. An imaging system consisting of two cameras and six halogen lamps was built for sweet pepper image acquisition. For image analysis using the normalized difference vegetation index (NDVI), a band-pass filter in the range of 435 to 950 nm with a broad spectrum from visible light to infrared was used. K-means clustering and morphological skeletonization were used to classify sweet pepper parts to which the NDVI was applied. Scale-invariant feature transform (SIFT) and speeded-up robust features (SURFs) were used to figure out local features. Classification performances of a support vector machine (SVM) using the radial basis function kernel and backpropagation (BP) algorithm were compared to classify local SURFs of fruits, nodes, leaves, and suckers. Accuracies of the BP algorithm and the SVM for classifying local features were 95.96 and 63.75%, respectively. When the BP algorithm was used for classification of plant parts, the recognition success rate was 94.44% for fruits, 84.73% for nodes, 69.97% for leaves, and 84.34% for suckers. When CNN was used for classifying plant parts, the recognition success rate was 99.50% for fruits, 87.75% for nodes, 90.50% for leaves, and 87.25% for suckers.

Application of Computational Intelligence in Network Intrusion Detection

2018 ◽  
pp. 153-174
Author(s):  
Heba F. Eid
Keyword(s):  
Intrusion Detection ◽  
Computational Intelligence ◽  
Intrusion Detection System ◽  
Detection System ◽  
Effective Area ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Network Intrusion ◽  
New Research ◽  
Operational Issues

Intrusion detection system plays an important role in network security. However, network intrusion detection (NID) suffers from several problems, such as false positives, operational issues in high dimensional data, and the difficulty of detecting unknown threats. Most of the problems with intrusion detection are caused by improper implementation of the network intrusion detection system (NIDS). Over the past few years, computational intelligence (CI) has become an effective area in extending research capabilities. Thus, NIDS based upon CI is currently attracting considerable interest from the research community. The scope of this review will encompass the concept of NID and presents the core methods of CI, including support vector machine, hidden naïve Bayes, particle swarm optimization, genetic algorithm, and fuzzy logic. The findings of this review should provide useful insights into the application of different CI methods for NIDS over the literature, allowing to clearly define existing research challenges and progress, and to highlight promising new research directions.

A Computational Intelligence Approach to Supply Chain Demand Forecasting

2012 ◽  
pp. 1551-1565 ◽  
Author(s):  
Nicholas Ampazis
Keyword(s):  
Supply Chain ◽  
Computational Intelligence ◽  
Demand Forecasting ◽  
Business Environment ◽  
Chain Structure ◽  
Support Vector ◽  
Customer Demand ◽  
Public Data ◽  
Vector Machines ◽  
Supply Chain Structure

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.

Financial Crisis Modeling and Prediction with a Hilbert-EMD-Based SVM Approachs

2009 ◽  
pp. 286-299
Author(s):  
Lean Yu ◽  
Shouyang Wang ◽  
Kin Keung Lai
Keyword(s):  
Financial Crisis ◽  
Rare Event ◽  
Economic Indicator ◽  
Support Vector ◽  
Data Series ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Crisis Events ◽  
Financial Indicator ◽  
Economic Sustainable Development

Financial crisis is a kind of typical rare event, but it is harmful to economic sustainable development if occurs. In this chapter, a Hilbert-EMD-based intelligent learning approach is proposed to predict financial crisis events for early-warning purpose. In this approach a typical financial indicator currency exchange rate reflecting economic fluctuation condition is first chosen. Then the Hilbert-EMD algorithm is applied to the economic indicator series. With the aid of the Hilbert-EMD procedure, some intrinsic mode components (IMCs) of the data series with different scales can be obtained. Using these IMCs, a support vector machine (SVM) classification paradigm is used to predict the future financial crisis events based upon some historical data. For illustration purposes, two typical Asian countries including South Korea and Thailand suffered from the 1997-1998 disastrous financial crisis experience are selected to verify the effectiveness of the proposed Hilbert-EMD-based SVM methodology.

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