Travel agencies should be able to judge the market demand for tourism to develop sales plans accordingly.
However, many travel agencies lack the ability to judge the market demand for tourism, and thus make
risky business decisions. Based on the above, this study applied the Artificial Neural Network combined
with the Genetic Algorithm (GA) to establish a prediction model of air ticket sales revenue. GA was used to
determine the optimum number of input and hidden nodes of a feedforward neural network. The empirical
results suggested that the mean absolute relative error(MARE) of the proposed hybrid model’s predicted
value of air ticket sales revenue and the actual value was 10.51%and the correlation coefficient was 0.913.
The proposed model had good predictive capability and could provide travel agency operators with
reliable and highly efficient analysis data.
TOURISM DEMAND FORECASTING MODEL USING NEURAL NETWORKijcsit
Travel agencies should be able to judge the market demand for tourism to develop sales plans accordingly.However, many travel agencies lack the ability to judge the market demand for tourism, and thus make risky business decisions. Based on the above, this study applied the Artificial Neural Network combined with the Genetic Algorithm (GA) to establish a prediction model of air ticket sales revenue. GA was used to
determine the optimum number of input and hidden nodes of a feedforward neural network. The empirical results suggested that the mean absolute relative error(MARE) of the proposed hybrid model’s predicted value of air ticket sales revenue and the actual value was 10.51%and the correlation coefficient was 0.913. The proposed model had good predictive capability and could provide travel agency operators with reliable and highly efficient analysis data.
Stock market prediction using Twitter sentiment analysisjournal ijrtem
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
The Stock Market is known for its volatile and unstable nature. A particular stock could be thriving in one
period and declining in the next. Stock traders make money from buying equity when they are at their
lowest and selling when they are at their highest. The logical question would be: "What Causes Stock
Prices To Change?". At the most fundamental level, the answer to this would be the demand and supply.
In reality, there are many theories as to why stock prices fluctuate, but there is no generic theory that
explains all, simply because not all stocks are identical, and one theory that may apply for today, may not
necessarily apply for tomorrow. This paper covers various approaches taken to attempt to predict the
stock market without extensive prior knowledge or experience in the subject area, highlighting the
advantages and limitations of the different techniques such as regression and classification. We formulate
both short term and long term predictions. Through experimentation we achieve 81% accuracy for future
trend direction using classification, 0.0117 RMSE for next day price and 0.0613 RMSE for next day
change in price using regression techniques. The results obtained in this paper are achieved using only
historic prices and technical indicators. Various methods, tools and evaluation techniques will be
assessed throughout the course of this paper, the result of this contributes as to which techniques will be
selected and enhanced in the final artefact of a stock prediction model. Further work will be conducted
utilising deep learning techniques to approach the problem. This paper will serve as a preliminary guide
to researchers wishing to expose themselves to this area.
TOURISM DEMAND FORECASTING MODEL USING NEURAL NETWORKijcsit
Travel agencies should be able to judge the market demand for tourism to develop sales plans accordingly.However, many travel agencies lack the ability to judge the market demand for tourism, and thus make risky business decisions. Based on the above, this study applied the Artificial Neural Network combined with the Genetic Algorithm (GA) to establish a prediction model of air ticket sales revenue. GA was used to
determine the optimum number of input and hidden nodes of a feedforward neural network. The empirical results suggested that the mean absolute relative error(MARE) of the proposed hybrid model’s predicted value of air ticket sales revenue and the actual value was 10.51%and the correlation coefficient was 0.913. The proposed model had good predictive capability and could provide travel agency operators with reliable and highly efficient analysis data.
Stock market prediction using Twitter sentiment analysisjournal ijrtem
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
The Stock Market is known for its volatile and unstable nature. A particular stock could be thriving in one
period and declining in the next. Stock traders make money from buying equity when they are at their
lowest and selling when they are at their highest. The logical question would be: "What Causes Stock
Prices To Change?". At the most fundamental level, the answer to this would be the demand and supply.
In reality, there are many theories as to why stock prices fluctuate, but there is no generic theory that
explains all, simply because not all stocks are identical, and one theory that may apply for today, may not
necessarily apply for tomorrow. This paper covers various approaches taken to attempt to predict the
stock market without extensive prior knowledge or experience in the subject area, highlighting the
advantages and limitations of the different techniques such as regression and classification. We formulate
both short term and long term predictions. Through experimentation we achieve 81% accuracy for future
trend direction using classification, 0.0117 RMSE for next day price and 0.0613 RMSE for next day
change in price using regression techniques. The results obtained in this paper are achieved using only
historic prices and technical indicators. Various methods, tools and evaluation techniques will be
assessed throughout the course of this paper, the result of this contributes as to which techniques will be
selected and enhanced in the final artefact of a stock prediction model. Further work will be conducted
utilising deep learning techniques to approach the problem. This paper will serve as a preliminary guide
to researchers wishing to expose themselves to this area.
STOCK MARKET PREDICTION USING MACHINE LEARNING METHODSIAEME Publication
Stock price forecasting is a popular and important topic in financial and academic
studies. Share market is an volatile place for predicting since there are no significant
rules to estimate or predict the price of a share in the share market. Many methods
like technical analysis, fundamental analysis, time series analysis and statistical
analysis etc. are used to predict the price in tie share market but none of these
methods are proved as a consistently acceptable prediction tool. In this paper, we
implemented a Random Forest approach to predict stock market prices. Random
Forests are very effectively implemented in forecasting stock prices, returns, and stock
modeling. We outline the design of the Random Forest with its salient features and
customizable parameters. We focus on a certain group of parameters with a relatively
significant impact on the share price of a company. With the help of sentiment
analysis, we found the polarity score of the new article and that helped in forecasting
accurate result. Although share market can never be predicted with hundred per-cent
accuracy due to its vague domain, this paper aims at proving the efficiency of Random
forest for forecasting the stock prices
COMPARISON OF BANKRUPTCY PREDICTION MODELS WITH PUBLIC RECORDS AND FIRMOGRAPHICScscpconf
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to
study the impacts of public records and firmographics and predict the bankruptcy in a 12-
month-ahead period with using different classification models and adding values to traditionally
used financial ratios. Univariate analysis shows the statistical association and significance of
public records and firmographics indicators with the bankruptcy. Further, seven statistical
models and machine learning methods were developed, including Logistic Regression, Decision
Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and
Neural Network. The performance of models were evaluated and compared based on
classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset.
Moreover, an experiment was set up to show the importance of oversampling for rare event
prediction. The result also shows that Bayesian Network is comparatively more robust than
other models without oversampling.
Nowadays during increasingly developed technology of the World Wide Web and Internet, the data is becoming extremely rich. With the application of data recognition process, the information extracted from data has become the most important part in some areas of society, management field, finance and markets, etc. It is necessary to develop the valid method to understand the knowledge of the data. Whether you are looking for good investments or are into stock trading, stock prediction or forecast plays the most crucial role in determining where to put in the money or which stock to be acquired or sold.
Now knowledge pre-processing, model and reasoning issues, power metrics, quality
issues, post-processing of discovered structures, visualization, and on-line change is best challenge.
In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay
Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the
markets[9]. The motivation for the development of neural network technology stemmed from the
desire to develop an artificial system that could perform “intelligent" tasks similar to those performed
by the human brain. Artificial Neural Networks are being counted as the wave of the future in
computing. They are indeed self-learning mechanisms which don’t require the traditional skills of a
programmer. Back propagation is one of the approaches to implement concept of neural networks.
Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer
is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most
often used as training algorithm in current neural network applications. In this paper, we apply data
mining technology to stock market in order to research the trend of price; it aims to predict the future
trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in
current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to
predict the stock market by establishing a three-tier structure of the neural network, namely input
layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast
accuracy
Corporate bankruptcy prediction using Deep learning techniquesShantanu Deshpande
Corporate Bankruptcy prediction using Recurrent neural networks – Aim is to build a recurrent neural network-based model to predict whether company will become bankrupt or not using financial ratios of Polish companies.
Methodologies & Tools: CRISP-DM, SMOTE-ENN, GA Algorithm, LSTM network (type of RNN)
The usage of Neural network s has determined a variegated area of packages in the present world. This has caused the
improvement of various fashions for economic markets and funding. This paper represents the idea the way to predict share
market fee the use of artificial Neural community with a given enter parameters of share marketplace. The proportion
marketplace is dynamic in nature approach to expect percentage fee could be very complex method by using trendy prediction
or computation method. Its predominant motive is that there is no linear relationship between market parameters and target last
price. Since there is no linear relationship between input patterns and corresponding output patterns, so use of neural network is
a desire of hobby for share market prediction.
The use of genetic algorithm, clustering and feature selection techniques in ...IJMIT JOURNAL
Decision tree modelling, as one of data mining techniques, is used for credit scoring of bank customers.
The main problem is the construction of decision trees that could classify customers optimally. This study
presents a new hybrid mining approach in the design of an effective and appropriate credit scoring model.
It is based on genetic algorithm for credit scoring of bank customers in order to offer credit facilities to
each class of customers. Genetic algorithm can help banks in credit scoring of customers by selecting
appropriate features and building optimum decision trees. The new proposed hybrid classification model is
established based on a combination of clustering, feature selection, decision trees, and genetic algorithm
techniques. We used clustering and feature selection techniques to pre-process the input samples to
construct the decision trees in the credit scoring model. The proposed hybrid model choices and combines
the best decision trees based on the optimality criteria. It constructs the final decision tree for credit
scoring of customers. Using one credit dataset, results confirm that the classification accuracy of the
proposed hybrid classification model is more than almost the entire classification models that have been
compared in this paper. Furthermore, the number of leaves and the size of the constructed decision tree
(i.e. complexity) are less, compared with other decision tree models. In this work, one financial dataset was
chosen for experiments, including Bank Mellat credit dataset.
Prediction of Default Customer in Banking Sector using Artificial Neural Networkrahulmonikasharma
The aim of this article is to present perdition and risk accuracy analysis of default customer in the banking sector. The neural network is a learning model inspired by biological neuron it is used to estimate and predict that can depend on a large number of inputs. The bank customer dataset from UCI repository, used for data analysis method to extract informative data set from a large volume of the dataset. This dataset is used in the neural network for training data and testing data. In a training of data, the data set is iterated till the desired output. This training data is cross check with test data. This paper focuses on predicting default customer by using deep learning neural network (DNN) algorithm.
IMPROVED TURNOVER PREDICTION OF SHARES USING HYBRID FEATURE SELECTIONIJDKP
Predicting the total turnover of a company in the most unstable stock market and trade conditions has
always proved to be a costly affair causing rise and fall of several trades. Data mining is a well-known
sphere of Computer Science that aims at extracting meaningful information from large databases. However,
despite the existence of many algorithms for the purpose of predicting future trends, their efficiency is
questionable as their predictions suffer from a high error rate. The objective of this paper is to investigate
and rate the performance of classifiers based on the features selected by Hybrid Feature Selection. The
authorized dataset for predicting the turnover was taken fromwww.bsc.com and included the stock market
values of various companies over the past 10 years. The algorithms were investigated using the Weka tool.
The Hybrid feature selection (HFS) algorithm, was run on this dataset to extract the important and
influential features for classification. With these extracted features, the Total Turnover of the company was
predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression.
This prediction mechanism was implemented to predict the turnover of a company on an everyday basis
and hence could help navigate through dubious stock markets trades. An accuracy rate of was achieved by
the above prediction process. Moreover, the importance of the stock market attributes through Incremental
Feature Selection (IFS) was established as well.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A novel hybrid deep learning approach for tourism demand forecasting IJECEIAES
This paper proposes a new hybrid deep learning framework that combines search query data, autoencoders (AE) and stacked long-short term memory (staked LSTM) to enhance the accuracy of tourism demand prediction. We use data from Google Trends as an additional variable with the monthly tourist arrivals to Marrakech, Morocco. The AE is applied as a feature extraction procedure to dimension reduction, to extract valuable information and to mine the nonlinear information incorporated in data. The extracted features are fed into stacked LSTM to predict tourist arrivals. Experiments carried out to analyze performance in forecast results of proposed method compared to individual models, and different principal component analysis (PCA) based and AE based hybrid models. The experimental results show that the proposed framework outperforms other models.
This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days
using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies
and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and
Ethereum Dominance (ETH.D) in
adaily timeframe. The methods used to teach the data are hybrid and
backpropagation algorithms, as well as grid partition, subtractive clustering
, and Fuzzy C-means
clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed
in this paper has been compared with different inputs and neural network models in terms of statistical
evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time
STOCK MARKET PREDICTION USING MACHINE LEARNING METHODSIAEME Publication
Stock price forecasting is a popular and important topic in financial and academic
studies. Share market is an volatile place for predicting since there are no significant
rules to estimate or predict the price of a share in the share market. Many methods
like technical analysis, fundamental analysis, time series analysis and statistical
analysis etc. are used to predict the price in tie share market but none of these
methods are proved as a consistently acceptable prediction tool. In this paper, we
implemented a Random Forest approach to predict stock market prices. Random
Forests are very effectively implemented in forecasting stock prices, returns, and stock
modeling. We outline the design of the Random Forest with its salient features and
customizable parameters. We focus on a certain group of parameters with a relatively
significant impact on the share price of a company. With the help of sentiment
analysis, we found the polarity score of the new article and that helped in forecasting
accurate result. Although share market can never be predicted with hundred per-cent
accuracy due to its vague domain, this paper aims at proving the efficiency of Random
forest for forecasting the stock prices
COMPARISON OF BANKRUPTCY PREDICTION MODELS WITH PUBLIC RECORDS AND FIRMOGRAPHICScscpconf
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to
study the impacts of public records and firmographics and predict the bankruptcy in a 12-
month-ahead period with using different classification models and adding values to traditionally
used financial ratios. Univariate analysis shows the statistical association and significance of
public records and firmographics indicators with the bankruptcy. Further, seven statistical
models and machine learning methods were developed, including Logistic Regression, Decision
Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and
Neural Network. The performance of models were evaluated and compared based on
classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset.
Moreover, an experiment was set up to show the importance of oversampling for rare event
prediction. The result also shows that Bayesian Network is comparatively more robust than
other models without oversampling.
Nowadays during increasingly developed technology of the World Wide Web and Internet, the data is becoming extremely rich. With the application of data recognition process, the information extracted from data has become the most important part in some areas of society, management field, finance and markets, etc. It is necessary to develop the valid method to understand the knowledge of the data. Whether you are looking for good investments or are into stock trading, stock prediction or forecast plays the most crucial role in determining where to put in the money or which stock to be acquired or sold.
Now knowledge pre-processing, model and reasoning issues, power metrics, quality
issues, post-processing of discovered structures, visualization, and on-line change is best challenge.
In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay
Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the
markets[9]. The motivation for the development of neural network technology stemmed from the
desire to develop an artificial system that could perform “intelligent" tasks similar to those performed
by the human brain. Artificial Neural Networks are being counted as the wave of the future in
computing. They are indeed self-learning mechanisms which don’t require the traditional skills of a
programmer. Back propagation is one of the approaches to implement concept of neural networks.
Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer
is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most
often used as training algorithm in current neural network applications. In this paper, we apply data
mining technology to stock market in order to research the trend of price; it aims to predict the future
trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in
current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to
predict the stock market by establishing a three-tier structure of the neural network, namely input
layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast
accuracy
Corporate bankruptcy prediction using Deep learning techniquesShantanu Deshpande
Corporate Bankruptcy prediction using Recurrent neural networks – Aim is to build a recurrent neural network-based model to predict whether company will become bankrupt or not using financial ratios of Polish companies.
Methodologies & Tools: CRISP-DM, SMOTE-ENN, GA Algorithm, LSTM network (type of RNN)
The usage of Neural network s has determined a variegated area of packages in the present world. This has caused the
improvement of various fashions for economic markets and funding. This paper represents the idea the way to predict share
market fee the use of artificial Neural community with a given enter parameters of share marketplace. The proportion
marketplace is dynamic in nature approach to expect percentage fee could be very complex method by using trendy prediction
or computation method. Its predominant motive is that there is no linear relationship between market parameters and target last
price. Since there is no linear relationship between input patterns and corresponding output patterns, so use of neural network is
a desire of hobby for share market prediction.
The use of genetic algorithm, clustering and feature selection techniques in ...IJMIT JOURNAL
Decision tree modelling, as one of data mining techniques, is used for credit scoring of bank customers.
The main problem is the construction of decision trees that could classify customers optimally. This study
presents a new hybrid mining approach in the design of an effective and appropriate credit scoring model.
It is based on genetic algorithm for credit scoring of bank customers in order to offer credit facilities to
each class of customers. Genetic algorithm can help banks in credit scoring of customers by selecting
appropriate features and building optimum decision trees. The new proposed hybrid classification model is
established based on a combination of clustering, feature selection, decision trees, and genetic algorithm
techniques. We used clustering and feature selection techniques to pre-process the input samples to
construct the decision trees in the credit scoring model. The proposed hybrid model choices and combines
the best decision trees based on the optimality criteria. It constructs the final decision tree for credit
scoring of customers. Using one credit dataset, results confirm that the classification accuracy of the
proposed hybrid classification model is more than almost the entire classification models that have been
compared in this paper. Furthermore, the number of leaves and the size of the constructed decision tree
(i.e. complexity) are less, compared with other decision tree models. In this work, one financial dataset was
chosen for experiments, including Bank Mellat credit dataset.
Prediction of Default Customer in Banking Sector using Artificial Neural Networkrahulmonikasharma
The aim of this article is to present perdition and risk accuracy analysis of default customer in the banking sector. The neural network is a learning model inspired by biological neuron it is used to estimate and predict that can depend on a large number of inputs. The bank customer dataset from UCI repository, used for data analysis method to extract informative data set from a large volume of the dataset. This dataset is used in the neural network for training data and testing data. In a training of data, the data set is iterated till the desired output. This training data is cross check with test data. This paper focuses on predicting default customer by using deep learning neural network (DNN) algorithm.
IMPROVED TURNOVER PREDICTION OF SHARES USING HYBRID FEATURE SELECTIONIJDKP
Predicting the total turnover of a company in the most unstable stock market and trade conditions has
always proved to be a costly affair causing rise and fall of several trades. Data mining is a well-known
sphere of Computer Science that aims at extracting meaningful information from large databases. However,
despite the existence of many algorithms for the purpose of predicting future trends, their efficiency is
questionable as their predictions suffer from a high error rate. The objective of this paper is to investigate
and rate the performance of classifiers based on the features selected by Hybrid Feature Selection. The
authorized dataset for predicting the turnover was taken fromwww.bsc.com and included the stock market
values of various companies over the past 10 years. The algorithms were investigated using the Weka tool.
The Hybrid feature selection (HFS) algorithm, was run on this dataset to extract the important and
influential features for classification. With these extracted features, the Total Turnover of the company was
predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression.
This prediction mechanism was implemented to predict the turnover of a company on an everyday basis
and hence could help navigate through dubious stock markets trades. An accuracy rate of was achieved by
the above prediction process. Moreover, the importance of the stock market attributes through Incremental
Feature Selection (IFS) was established as well.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A novel hybrid deep learning approach for tourism demand forecasting IJECEIAES
This paper proposes a new hybrid deep learning framework that combines search query data, autoencoders (AE) and stacked long-short term memory (staked LSTM) to enhance the accuracy of tourism demand prediction. We use data from Google Trends as an additional variable with the monthly tourist arrivals to Marrakech, Morocco. The AE is applied as a feature extraction procedure to dimension reduction, to extract valuable information and to mine the nonlinear information incorporated in data. The extracted features are fed into stacked LSTM to predict tourist arrivals. Experiments carried out to analyze performance in forecast results of proposed method compared to individual models, and different principal component analysis (PCA) based and AE based hybrid models. The experimental results show that the proposed framework outperforms other models.
This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days
using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies
and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and
Ethereum Dominance (ETH.D) in
adaily timeframe. The methods used to teach the data are hybrid and
backpropagation algorithms, as well as grid partition, subtractive clustering
, and Fuzzy C-means
clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed
in this paper has been compared with different inputs and neural network models in terms of statistical
evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time
Project report on Share Market applicationKRISHNA PANDEY
This is the proposal document for AVS Group of Technology service offering in the website design and development and custom web application development space. The document details our understanding of the brief, the objectives of the services suite, the methodology, and deliverable and commercials.
Predicting reaction based on customer's transaction using machine learning a...IJECEIAES
Banking advertisements are important because they help target specific customers on subscribing to their packages or other deals by giving their current customers more fixed-term deposit offers. This is done through promotional advertisements on the Internet or media pages, and this task is the responsibility of the shopping department. In order to build a relationship with them, offer them the best deals, and be appropriate for the client with the company's assurance to recover these deposits, many banks or telecommunications firms store the data of their customers. The Portuguese bank increases its sales by establishing a relationship with its customers. This study proposes creating a prediction model using machine learning algorithms, to see how the customer reacts to subscribe to those fixed-term deposits or offers made with the aid of their past record. This classification is binary, i.e., the prediction of whether or not a customer will embrace these offers. Four classifiers that include k-nearest neighbor (k-NN) algorithm, decision tree, naive Bayes, and support vector machines (SVM) were used, and the best result was obtained from the classifier decision tree with an accuracy of 91% and the other classifier SVM with an accuracy of 89%.
Now knowledge pre-processing, model and reasoning issues, power metrics, quality
issues, post-processing of discovered structures, isualization, and on-line change is best challenge.
In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay
Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the
markets[9]. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform “intelligent" tasks similar to those performed by the human brain. Artificial Neural Networks are being counted as the wave of the future in computing. They are indeed self-learning mechanisms which don’t require the traditional skills of a
programmer. Back propagation is one of the approaches to implement concept of neural networks. Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most often used as training algorithm in current neural network applications. In this paper, we apply data
mining technology to stock market in order to research the trend of price; it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast accuracy.
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
Cloud Computing, being one of the most recent innovative developments of the IT world, has been
instrumental not just to the success of SMEs but, through their productivity and innovative contribution to
the economy, has even made a remarkable contribution to the economic growth of the United States. To
this end, the study focuses on how cloud computing technology has impacted economic growth through
SMEs in the United States. Relevant literature connected to the variables of interest in this study was
reviewed, and secondary data was generated and utilized in the analysis section of this paper. The findings
of this paper revealed that there have been meaningful contributions that the usage of virtualization has
made in the commercial dealings of small firms in the United States, and this has also been reflected in the
economic growth of the country. This paper further revealed that as important as cloud-based software is,
some SMEs are still skeptical about how it can help improve their business and increase their bottom line
and hence have failed to adopt it. Apart from the SMEs, some notable large firms in different industries,
including information and educational services, have adopted cloud computing technology and hence
contributed to the economic growth of the United States. Lastly, findings from our inferential statistics
revealed that no discernible change has occurred in innovation between small and big businesses in the
adoption of cloud computing. Both categories of businesses adopt cloud computing in the same way, and
their contribution to the American economy has no significant difference in the usage of virtualization.
Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in
recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized
application of MIMO technology within WSNs. This approach operates effectively, especially in
challenging and resource-constrained environments. By facilitating collaboration among sensor nodes,
Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments.
Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring,
industrial automation, and healthcare applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication. IJCSIT publishes original research papers and review papers, as well as auxiliary material such as: research papers, case studies, technical reports etc.
With growing, Car parking increases with the number of car users. With the increased use of smartphones
and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking
Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This
gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are
utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the
server and are recovered by the mobile application which offers many options attractively and with no cost
to users and lets the user check reservation details. With IoT technology, the smart parking system can be
connected wirelessly to easily track available locations.
Welcome to AIRCC's International Journal of Computer Science and Information Technology (IJCSIT), your gateway to the latest advancements in the dynamic fields of Computer Science and Information Systems.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language
Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such
systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different
questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper
is the hierarchal representation of the system's basic grammar skills as domain knowledge. That
representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
determine the student learning level for each lesson as a long-term model. The results of the evaluation
showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This research aims to further understanding in the field of continuous authentication using behavioural
biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing
Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed
machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and
Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust
model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch
dynamics can effectively distinguish users. However, further studies are needed to make it viable option
for authentication systems. You can access our dataset at the following
link:https://github.com/AuthenTech2023/authentech-repo
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
context of text understanding. We begin by describing the architecture and training process of GPT-3, and
provide an overview of its impressive performance across a wide range of natural language processing
tasks, such as language translation, question-answering, and text completion. Throughout this research
project, a summarizing tool was also created to help us retrieve content from any types of document,
specifically IELTS (0) Reading Test data in this project. We also aimed to improve the accuracy of the
summarizing, as well as question-answering capabilities of GPT-3 (0) via long text
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
The security of Electric Vehicle (EV) charging has gained momentum after the increase in the EV adoption
in the past few years. Mobile applications have been integrated into EV charging systems that mainly use a
cloud-based platform to host their services and data. Like many complex systems, cloud systems are
susceptible to cyberattacks if proper measures are not taken by the organization to secure them. In this
paper, we explore the security of key components in the EV charging infrastructure, including the mobile
application and its cloud service. We conducted an experiment that initiated a Man in the Middle attack
between an EV app and its cloud services. Our results showed that it is possible to launch attacks against
the connected infrastructure by taking advantage of vulnerabilities that may have substantial economic and
operational ramifications on the EV charging ecosystem. We conclude by providing mitigation suggestions
and future research directions.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This paper describes the outcome of an attempt to implement the same transitive closure (TC) algorithm
for Apache MapReduce running on different Apache Hadoop distributions. Apache MapReduce is a
software framework used with Apache Hadoop, which has become the de facto standard platform for
processing and storing large amounts of data in a distributed computing environment. The research
presented here focuses on the variations observed among the results of an efficient iterative transitive
closure algorithm when run against different distributed environments. The results from these comparisons
were validated against the benchmark results from OYSTER, an open source Entity Resolution system. The
experiment results highlighted the inconsistencies that can occur when using the same codebase with
different implementations of Map Reduce.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Laundry management system project report.pdfKamal Acharya
Laundry firms currently use a manual system for the management and maintenance of critical information. The current system requires numerous paper forms, with data stores spread throughout the laundry management infrastructure. Often information is incomplete or does not follow management standards. Records are often lost in transit during computation requiring a comprehensive auditing process to ensure that no vital information is lost. Multiple copies of the same information exist in the laundry firm data and may lead to inconsistencies in data in various data stores.
A significant part of the operation of any laundry firm involves the acquisition, management and timely retrieval of great volumes of information. This information typically involves; customer personal information and clothing records history, user information, price of delivery and received date, users scheduling as regards customers details and dealings in service rendered, also our products package waiting list. All of this information must be managed in an efficient and cost wise fashion so that the organization resources may be effectively utilized.
We present the design and implementation of a laundry database management system (LBMS) used in a laundry establishment. Laundry firms are usually faced with difficulties in keeping detailed records of customers clothing; this little problem as seen to most laundry firms is highly discouraging as customers are filled with disappointments, arising from issues such as customer clothes mix-ups and untimely retrieval of clothes. The aim of this application is to determine the number of clothes collected, in relation to their owners, as this also helps the users fix a date for the collection of their clothes. Also customer’s information is secured, as a specific id is allocated per registration to avoid contrasting information.
Fruit shop management system project report.pdfKamal Acharya
The export maintenance system is a fully featured application that can help we manage fruit delivery business and achieve more control and information at a very low cost of total ownership.
A fruit export maintains automatically monitors purchase, sales, supplier information. The system includes receiving fruit from the different supplier. Customer order is placed in the system, based on the order fruit has been sales to the customer.
The report contains the details about product, purchase, sales, stock, and invoice. The main objective of this project is to computerize the company activities and to provide details about the production process at the fruit export maintenance system.
The demand of fresh fruit fruits and processed food items in international and domestic market has shown a decent increase. This estimation is creating a necessity for growing more and more fruit fruits to cater the growing demand of domestic & international market.
The customers effectively and hence help for establishing good relation between customer and fruit shop organization. It contains various customized modules for effectively maintaining fruit and stock information accurately and safely.
When the fruits are sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting fruits for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
The proposed project is developed to manage the fruit shop in the fruits for shop. The first module is the login. The admin should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Online blood donation management system project.pdfKamal Acharya
Blood Donation Management System is a web database application that enables the public to make online session reservation, to view nationwide blood donation events online and at the same time provides centralized donor and blood stock database. This application is developed
by using ASP.NET technology from Visual Studio with the MySQL 5.0 as the database management system. The methodology used to develop this system as a whole is Object Oriented Analysis and Design; whilst, the database for BDMS is developed by following the steps in Database Life Cycle. The targeted users for this application are the public who is eligible to donate blood ,'system moderator, administrator from National Blood Center and the staffs who are working in the blood banks of the participating hospitals. The main objective of the development of this application is to overcome the problems that exist in the current system, which are the lack of facilities for online session reservation and online advertising on the nationwide blood donation events, and also decentralized donor and blood stock database. Besides, extra features in the system such as security protection by using password, generating reports, reminders of blood stock shortage and workflow tracking can even enhance the efficiency of the management in the blood banks. The final result of this project is the development of web database application, which is the BDMS.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Natalia Rutkowska - BIM School Course in Krakówbim.edu.pl
Teaching effects after 128 hours of Building Information Modeling course in Cracow, Poland. Natalia works in Revit, Navisworks and Dynamo for BIM Coordination position. More https://bim.edu.pl or https://bimedu.eu
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Q.1 A single plate clutch with both sides of the plate effective is required to transmit 25 kW at 1600 r.p.m. The outer diameter of the plate is limited to 300 mm and the intensity of pressure between the plates not to exceed 0.07N / m * m ^ 2 Assuming uniform wear and coefficient of friction 0.3, find the inner diameter of the plates and the axial force necessary to engage the clutch.
Q.2 A multiple disc clutch has radial width of the friction material as 1/5th of the maximum radius. The coefficient of friction is 0.25. Find the total number of discs required to transmit 60 kW at 3000 r.p.m. The maximum diameter of the clutch is 250 mm and the axial force is limited to 600 N. Also find the mean unit pressure on each contact surface.
Q.3 A cone clutch is to be designed to transmit 7.5 kW at 900 r.p.m. The cone has a face angle of 12°. The width of the face is half of the mean radius and the normal pressure between the contact faces is not to exceed 0.09 N/mm². Assuming uniform wear and the coefficient of friction between the contact faces as 0.2, find the main dimensions of the clutch and the axial force required to engage the clutch.
Q.4 A cone clutch is mounted on a shaft which transmits power at 225 r.p.m. The small diameter of the cone is 230 mm, the cone face is 50 mm and the cone face makes an angle of 15 deg with the horizontal. Determine the axial force necessary to engage the clutch to transmit 4.5 kW if the coefficient of friction of the contact surfaces is 0.25. What is the maximum pressure on the contact surfaces assuming uniform wear?
Q.5 A soft surface cone clutch transmits a torque of 200 N-m at 1250 r.p.m. The larger diameter of the clutch is 350 mm. The cone pitch angle is 7.5 deg and the face width is 65 mm. If the coefficient of friction is 0.2. find:
1. the axial force required to transmit the torque:
2. the axial force required to engage the clutch;
3. the average normal pressure on the contact surfaces when the maximum torque is being transmitted; and
4. the maximum normal pressure assuming uniform wear.
Q.6 A single block brake, as shown in Fig. 1. has the drum diameter 250 mm. The angle of contact is 90° and the coefficient of friction between the drum and the lining is 0.35. If the torque transmitted by the brake is 70 N-m, find the force P required to operate the brake. Q.7 The layout and dimensions of a double shoe brake is shown in Fig. 2. The diameter of the
brake drum is 300 mm and the contact angle for each shoe is 90°. If the coefficient of friction for the brake lining and the drum is 0.4, find the spring force necessary to transmit a torque of 30 N-m. Also determine the width of the brake shoes, if the bearing pressure on the lining material is not to exceed 0.28N / m * m ^ 2
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Hall booking system project report .pdfKamal Acharya
PHP and MySQL project on Hall Booking System is a web based project and it has been developed in PHP and MySQL and we can manage Payment, Booking, Inventory, Booking Dates, Customers and Hall from this project.
The main objective to develop Hall Booking System PHP, MySQL, JAVA SCRIPT and BOOTSRAP Project is to overcome the manual errors and make a computerized system.
In this project, there are various type of modules available to manage Customers, Booking, Payment. We can also generate reports for Booking, Payment, Booking Dates, Hall. Here the Payment module manage all the operations of Payment, Booking module can manage Booking, Inventory module is normally developed for managing Inventory, Booking Dates module manages Booking Dates operations, Customers module has been implemented to manage Customers.
In this project all the modules like Payment, Booking Dates, Booking are tightly coupled and we can track the information easily. Ifyou are looking for Free Hall Booking System Project in PHP and MySQL then you can visit our free projects section.
We can easily get the list of wedding halls & lawns in Nagpur. Also we have detailed contact information for some particular hall. But we cannot get the availability about hall. So background behind this web portal is that it gives the area wise listing of wedding halls & lawns with the detailed information of individual and also display for particular date the hall is available or not. Just dial is the system in which we can only find the name of Hall and Lawns in city. In just dial we cannot find Halls in specific area. This system cannot show all information about any Hall. This system is not able to book the Halls online.
The A Web Based Hall Booking Management System is designed to overcome the disadvantage of previous system.We can easily get the list of Wedding Halls. But we cannot get the availability about Hall. So background behind this web portal is that it gives the area wise listing of Wedding Halls with the detailed information of individual and also display for particular date the Hall is available or not. This is a special type of web portal to easily get the information of all Wedding Halls in Nagpur which display separate calendar for separate Hall. For particular date the Hall. We can availability of Hall as well as Lawns detailed information about individuals Hall in our web portal . It provides all facilities to clients with lowest cost and lowest maintenance problems.
Tourism demand forecasting model using neural network vol 9 no 2 april 2017
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
DOI:10.5121/ijcsit.2017.9202 19
TOURISM DEMAND FORECASTING MODEL USING
NEURAL NETWORK
Han-Chen Huang and Cheng-I Hou
Department of Tourism and M.I.C.E., Chung Hua University, Taiwan
ABSTRACT
Travel agencies should be able to judge the market demand for tourism to develop sales plans accordingly.
However, many travel agencies lack the ability to judge the market demand for tourism, and thus make
risky business decisions. Based on the above, this study applied the Artificial Neural Network combined
with the Genetic Algorithm (GA) to establish a prediction model of air ticket sales revenue. GA was used to
determine the optimum number of input and hidden nodes of a feedforward neural network. The empirical
results suggested that the mean absolute relative error(MARE) of the proposed hybrid model’s predicted
value of air ticket sales revenue and the actual value was 10.51%and the correlation coefficient was 0.913.
The proposed model had good predictive capability and could provide travel agency operators with
reliable and highly efficient analysis data.
KEYWORDS
Artificial neural network;Genetic algorithm;Air ticket sales;Prediction model
1. INTRODUCTION
There are nearly 3,500 travel agencies competing in the overseas tourism market in Taiwan, with
a value of about USD21billion per year [1]. In a competitive environment, travel agencies must
be able to accurately predict the market demand to make the right operational decisions. However,
tourism is not an essentialcomponent of people’s lives, and tourism can be directly affected by
economic downturns. Travel agency operators should have good predictive capability formarket
demand and excellent financial management capability; otherwise, it will bedifficultto survive in
a competitive market [2].
Among the numerous businesses of travel agencies, air ticket salesarean extremely important
source of revenue. If atravel agency can accurately predict the market demand for air tickets, it
can purchase a sufficient amount of air ticketsat alow cost to have the opportunity to get higher
sales profits. In addition, it can reduce the cost accumulation during the purchase process or order
loss due to lack of air tickets[3,4].
Studies inthe past have developed many sales prediction models, such as qualitative methods(the
Delphi method, market research, and the group opinion method), the sequence analysis method
(exponential smoothing, autoregressive models, moving average models, etc.) andeconometric
methods (discussing the relationship with external economic variables and using statistical
theoretical method to measure or test the relationships in between some variables to provide the
basis for analysis). However, these models have a number of limitations[5]. In recent years, the
prevalent prediction method has beenartificial intelligence. Among the various types of artificial
intelligence prediction models, neural networkshavebeen confirmed as a very effective tool[6].
Therefore, this study applied anArtificial Neural Network (ANN) combined with the Genetic
Algorithm(GA) to establish a prediction model forair ticket sales revenue. The findings of this
study couldprovide the industry with a more reliable and efficient reference in practical operation.
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
20
2. LITERATURE REVIEW
2.1. Travel Agencies
Travel agenciesare the mediators of tourism product suppliers and customers, as they are
responsible for planning and making arrangements for tours in order to win profits[2]. In
accordance with Taiwan’s Statute for the Development of Tourism[7] Article 2 states that a travel
enterprise is: “also referred to as travel agency, a profit-taking enterprise licensed by the central
administrative authority to provide tourists with arranged travel schedules, board and lodging,
tour guides, and to purchase transportation tickets and apply for travel documents and visas on
tourists’ behalf, as well as to provide related services for remuneration.”
There are nearly3,500 travel agencies competing in the overseas tourism market in Taiwan, with
a value of about USD21 billion per year[1]. In such a competitive environment, travel agencies
must be able to more efficiently and accurately predict market demand to make the right
operational decisions. If a travel agency can understand the market demand earlier than its
competitors, purchase products, mobilize manpower and adjust business operational directions in
advance, it can surely win in a fiercely competitive environment.
2.2. Prediction Methods
Stynes[8] categorized prediction methods into four types: 1) the Delphi technique; 2) time series
or trend extension models; 3) structural models; and 4) system or simulation models. The
commonly-used prediction methods proposed in other related studies [9-16] include trend
analysis, cause analysis, judgment analysis, survey analysis, and artificial neural networks,etc.
2.2.1. Trend Analysis
The trend analysis method is used to predict the trend changes in future sales of an enterprise
according to the historical sales data by using certain calculation methods. Such a method is
suitable for enterprises with relatively stable product sales. It mainly includes the simple moving
average method, the moving average method, the weighted moving average method, the
exponential smoothing method and the seasonal prediction method.
2.2.2. Cause Analysis
Various factors in economic activities are often interrelated, mutually influential,and form a
certain corresponding relationship among each other. Product sales in general are affected by
various factors. The cause analysis method is used to find the function relationship of various
related factors that may affect product sales and sales volume, as well as to predict future sales
according to such a causality relationship. Such a method often requires the establishment of
prediction mathematical model, and thus it is often known as the regression analysis method. It
commonly includes simple regression analysis and multiple regression analysis.
2.2.3. Judgment Analysis
As a qualitative analysis method, judgment analysis is mainly based on the analysis of future
market changes according to the experience of management personnel, personnel with sales
experience, or other experts, in order to determine the sales trends of certain products in a certain
period of time
.
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
21
2.2.4. Survey Analysis
The survey analysis method is used to predict the sales trends of the product of an enterprise by
investigating the demand and supply of a certain product and the consumption orientation of
consumers. The survey contents may include product surveys, customer surveys, surveys on
economic development trends and industry surveys, etc.
2.2.5Artificial Neural Network
An artificial neural network (ANN) is a mathematical model imitating the structure and function
of a biological neural network. The neural network performs calculations using a large amount of
artificial neurons. In most cases, ANN can change the internal structure according to external
information as an adaptive system. ANNs are a modeling tool for non-linear statistical data, and
they are commonly used for the modeling of complex relationships between input and output or
data exploration [17].
ANN construction is generated by the inspiration of biological neural networks. ANN can have
human-like simple determination capability and judgment, which is advantageous to formal logic
reasoning.A common multilayer feedforward network consists of three parts (Figure 1)[18-20]:
• The input layer, in which numerous neurons receive a large amount of input
information.
• The output layer, in which information is transmitted and analyzed in neuron links to
form the output results.
• The hidden layer, which is a layer with numerous neurons and links in between the
input and the output layers. It can consist of multiple layers but is customarily one
layer only. There is no recognized number of neurons in the hidden layer; however,
when the number of neurons is larger, the non-linearity will be more significant and
the robustness of the neural network will be more significant.
Figure 1. BPNN network architecture[18-20]
The Back Propagation Neural Network (BPNN) is the most representative and commonly used
ANN[18]. BPNN applies the steepest descent method to adjust the parameters of the network and
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
22
obtain a more accurate solution by iteration computation. BPNN has high-speed computing
power, a fast recall speed, high learning accuracy, and fault tolerance, and thus it has been widely
applied in different fields[18-20].
3. RESEARCH METHOD
3.1. Back Propagation Neural Network
BPNN is a supervised learning algorithm consisting of ANNs.A BPNN is the combination of
multilayer perceptrons (MLP) and error back propagation (EBP). The computation process can be
divided into the learning process and the recall process[21].
3.1.1. Learning process
Step1: Set the network architecture parameters and learning parameters
Step2: Randomly generate the weight matrix and bias vector initial value
Step 3: Input the training examples, including the input values (X1,X2,X3) and target output
values (T1,T2,T3)
Step4: Compute and infer the output values (Y1,Y2,Y3)
(1) Hidden layer (H1,H2,H3)(Eq.1 and 2)
∑ −
=
i
k
i
ik
k x
W
net θ (1)
)
exp(
1
1
k
k
net
H
−
+
= (2)
(2) Output layer(Y1,Y2,Y3) (Eq.3 and 4)
∑ −
=
ki
j
k
kj
j h
W
net θ (3)
)
exp(
1
1
j
j
net
Y
−
+
= (4)
Step5: Compute the gap δ(Eq.5 and 6)
(1)Hidden layer
)
1
(
)
( j
k
j
kj
j
k H
h
W −
⋅
⋅
⋅
= ∑δ
δ (5)
(2) Output layer
)
1
(
)
( j
j
j
j
j Y
Y
Y
T −
⋅
⋅
−
=
δ (6)
Step6: Compute weight revision and bias revision (Eq.7~10)
(1) Hidden layer
)
1
(
)
( −
∆
⋅
+
=
∆ n
W
x
n
W ik
i
k
jk α
ηδ (7)
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
23
)
1
(
)
( −
∆
⋅
+
−
=
∆ n
n kj
k
k θ
α
ηδ
θ (8)
(2) Output layer
)
1
(
)
( −
∆
⋅
+
=
∆ n
W
H
n
W kj
k
j
kj α
ηδ (9)
)
1
(
)
( −
∆
⋅
+
−
=
∆ n
n j
j
j θ
α
ηδ
θ (10)
Step7: Update the weight and bias(Eq.11~14)
(1) Hidden layer
jk
jk
jk W
W
W ∆
+
= (11)
k
k
k θ
θ
θ ∆
+
= (12)
(2) Output layer
kj
kj
kj W
W
W ∆
+
= (13)
j
j
j θ
θ
θ ∆
+
= (14)
Step8: Repeat Step 3- Step 7 until convergence (no significant change in error or
implementation of certain times of learning cycles).
3.1.2. Recall process
Step 1: Set network parameters
Step 2: Read in theweight matrix and bias vector
Step 3: Input the unknown data vector(X1,X2,X3)
Step 4: Compute and infer the output vector(y1,y2,y3)
(1) Hidden layer output values (H1,H2,H3) (Eq.15 and 16)
∑ −
=
i
k
i
ik
k X
W
net θ (15)
)
exp(
1
1
k
k
net
H
−
+
= (16)
(2)Compute and infer the output values (Y1,Y2,Y3) (Eq.17 and 18)
∑ −
=
k
j
k
kj
j H
W
net θ (17)
)
exp(
1
1
j
j
net
Y
−
+
= (18)
3.2. Forecast Model Variables
According to the relevant literature [13-16,22-26], this study used theNTD/USD exchange rate,
the number of people traveling abroad from Taiwan each month, the international oil price, the
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
24
Taiwan stock market weighted index, Taiwan’s monthly monitor indicator, Taiwan’s monthly
composite leading index, Taiwan’s monthly composite coincident index, and W travel
agency’s monthly air ticket sales(T-1~T-18)( Table 1) as the input variablesto predict W travel
agency’s air ticket sales revenue in Month T. The selected data were the monthly data of the
period from January 2003 to December 2015. This study randomly selected70%as the training
Data,15% as the cross validation data, and 15% as the testing data.GA improves the performance
of ANNs by selecting the optimum input features of the neural network. This study used different
operators for selection and crossover operations (Table 2)[26-30].
Table1. Forecast model variables
Variable Unit
Input
NTD/USD exchange rate (T-1 month) NTD/USD
Number of people traveling abroad from Taiwan each month
(T-1 month)
Number of people
International oil price (T-1 month) USD/Barrel
Taiwan stock market weighted index (T-1 month) Point
Taiwan’s monthly monitor indicator (T-1 month) Score
Taiwan’s monthly composite leading index (T-1 month) Point
Taiwan’s monthly composite coincident index (T-1 month) Point
W Travel Agency’s air ticket sales revenue(T-1 month to
T-18 month)
NTD
Output Air ticket sales revenue (T month) NTD
Table 2. Description of different operators for select and crossover operations in GA[26-30]
Operation Operator Description
Select
Best Selects the best chromosome.
Random Randomly selects a chromosome from the population.
Tournament
The winner of each tournament (the one with the best
fitness) is selected for crossover.
Top percent (15)
Randomly selects a chromosome from the top 15 percent
of the population.
Roulette
The chance of a chromosome getting selected is
proportional to its fitness.
Crossover
Arithmetic
Linearly combines two parent chromosome vectors to
produce two new offspring.
Heuristic
Use the fitness values of the two parent chromosomes to
determine the direction of the search.
Uniform
Decides (with some probability – known as the mixing
ratio) which parent will contribute each of the gene values
in the offspring chromosomes.
One point
Randomly selects a crossover point within a chromosome,
interchanges the two parent chromosomes at this point to
produce two new offspring.
Two point
Randomly selects two crossover points within a
chromosome, interchanges the two parent chromosomes
between these points to produce two new offspring.
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
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3.3. Architecture Design and Model Training
ANN’s input activation function uses the hyperbolic tangent, the output error function uses the
sum-of-squares andthe output activation function uses logistic. GA improves the performance of
ANNs by selecting the optimum hidden nodes of the neural network. This study used different
operators for selection and crossover operations (Table 2)[26-30].
Training Algorithm: Quick Propagation Algorithm, Training Algorithm’s Parameters is Quick
Propagation Coefficient = 1.75, Learning Rate=0.1. The overtraining control and weights
randomization methodwere used to increase the model accuracy (Figure 2).
4. EMPIRICAL RESULTS
The correlation (r) and Mean Absolute Relative Error (MARE) were adopted as indicators for
evaluating the model.
• Correlation (r): As r approaches 1, the model predicted value and actual value correlation
level becomes higher.
• MARE(Eq. 19): The smaller the value, the smaller the error between the forecast value
and the actual value:
%
100
'
1
1
⋅
−
= ∑
n
i
i
i
Y
Y
Y
n
MARE (19)
where n is the number of the forecasting periods, Yi is the actual value for the i period, and
i
Y' is the forecast value for the i period.
Figure 2. Training options
The optimal network architecture is 12-142-1(Figure 3 and Table 3). The input layer had 12
neurons, the hidden layer had 142 neurons, and the output layer had one neuron. The actual value
and model output value distribution are shown in figure 4. It can be learnt from the figure that the
model output value was largely distributed along both sides of the diagonal line
(Output/Target=1), indicating the model had good predictive capability. The trends of the actual
value and model output valueare shown in Figure 5. It can be learnt from the figure that the
established air ticket sales revenueprediction modelhad a good capability to reflect the change in
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
26
sales of air tickets. The prediction results of the model are shown in Table 4. The mean absolute
relative error (MARE) was10.51%, the correlation coefficient was 0.913, and the model had the
capability of accurately predicting the air ticket sales revenue.
Figure 3. Best network architecture search results
Table 3. Performance of the Proposed Model in Prediction
Select operator Crossover operator Number of inputs Number of hidden nodes MARE Correlation
Best
Arithmetic 10 24 0.1113 0.913
Heuristic 7 22 0.1301 0.876
Uniform 11 53 0.1471 0.919
One point 9 90 0.1149 0.876
Two point 5 75 0.1281 0.900
Random
Arithmetic 4 127 0.1215 0.916
Heuristic 7 86 0.1426 0.923
Uniform 4 8 0.1457 0.928
One point 8 127 0.1270 0.898
Two point 7 142 0.1051 0.913
Tournament
Arithmetic 5 54 0.1284 0.876
Heuristic 8 136 0.1337 0.890
Uniform 13 149 0.1251 0.897
One point 13 34 0.1539 0.925
Two point 10 72 0.1607 0.936
Top percent (15)
Arithmetic 4 17 0.1406 0.920
Heuristic 9 20 0.1447 0.923
Uniform 11 65 0.1296 0.923
One point 12 64 0.1647 0.915
Two point 7 24 0.1093 0.934
Roulette
Arithmetic 4 23 0.1532 0.916
heuristic 13 58 0.1421 0.878
Uniform 6 5 0.1136 0.875
One point 12 143 0.1211 0.918
Two point 5 104 0.1451 0.913
9. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
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Figure 4. Scatter plot of actual value and model output value
Figure 5. Time series of actual vs. predicted
5. CONCLUSION
This study used theBack Propagation Neural Network and Genetic algorithm (GA) to establish a
travel agency air ticket sales revenue prediction model. GA was used to determine the optimum
number of input and hidden nodes of a feedforward neural network. The empirical results
suggested that the proposed prediction modelhad the capability to accurately predict air ticket
sales revenue and reflect the change in air ticket sales. The MARE of the model was only 10.51%,
and the correlation coefficient was up to 0.913., whenusing the proposed prediction model,travel
agency operators can predict the future demand for air tickets and purchase sufficient air tickets at
a lower cost to win more profits. It could reduce the loss caused by excessive purchaseor
customer loss caused by lack of stock.
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