Abstract
Big data plays a serious role within the business for creating higher predictions over business information that is collected from the real world. Finance is that the new sector wherever the big data technologies like Hadoop, NoSQL are creating its mark in predictions from financial data by the analysts. It’s a lot of fascinating within the stock exchange choices which might predict on a lot of profits of stock exchange. For this stock exchange analysis each regular information and historical information of specific stock exchange are needed for creating predictions. There are varied techniques used for analyzing the unstructured information like stock exchange reviews (day-to-day information) and historical statistic of economic information severally. This paper involves discussion regarding the strategies that square measure used for analyzing each varieties of information.
Keywords: Big data, prediction, finance, stock market, business intelligence
A large number of techniques has been developed so far to tell the diversity of machine learning. Machine learning is categorized into supervised, unsupervised and reinforcement learning .Every instance in given data-set used by Machine learning algorithms is represented same set of features .On basis of label of instances it is divided into category. In this review paper our main focus is on Supervised, unsupervised learning techniques and its performance parameters.
AI TESTING: ENSURING A GOOD DATA SPLIT BETWEEN DATA SETS (TRAINING AND TEST) ...ijsc
Artificial Intelligence and Machine Learning have been around for a long time. In recent years, there has been a surge in popularity for applications integrating AI and ML technology. As with traditional development, software testing is a critical component of a successful AI/ML application. The development methodology used in AI/ML contrasts significantly from traditional development. In light of these distinctions, various software testing challenges arise. The emphasis of this paper is on the challenge of effectively splitting the data into training and testing data sets. By applying a k-Means clustering strategy to the data set followed by a decision tree, we can significantly increase the likelihood of the training data set to represent the domain of the full dataset and thus avoid training a model that is likely to fail because it has only learned a subset of the full data domain.
PREDICTING BANKRUPTCY USING MACHINE LEARNING ALGORITHMSIJCI JOURNAL
This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global
problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies.
A Clustering Method for Weak Signals to Support Anticipative IntelligenceCSCJournals
Organizations need appropriate anticipative information to support their decision making process. Contrarily to some strategic information analyses that help managers to establish patterns using past information, anticipative intelligence is intended to help managers to act based on the analysis of pieces of information that indicate some sort of trend that may become true in the future. One example of this kind of information is known as a weak signal, which is a short text related to a specific domain. In this work, pairs of weak signals, written in Portuguese, are compared to each other so that similarities can be identified and correlated weak signals can be clustered together. The idea is that the analysis of the resulting similar groups may lead to the formulation of a hypothesis that can support the decision making process. The proposed technique consists of two main steps: preprocessing the set of weak signals and clustering. The proposed method was evaluated on a database of bio-energy weak signals. The main innovations of this work are: (i) the application of a computational methodology from the literature for analyzing anticipative information; and (ii) the adaptation of data mining techniques to implement this methodology in a software product.
A large number of techniques has been developed so far to tell the diversity of machine learning. Machine learning is categorized into supervised, unsupervised and reinforcement learning .Every instance in given data-set used by Machine learning algorithms is represented same set of features .On basis of label of instances it is divided into category. In this review paper our main focus is on Supervised, unsupervised learning techniques and its performance parameters.
AI TESTING: ENSURING A GOOD DATA SPLIT BETWEEN DATA SETS (TRAINING AND TEST) ...ijsc
Artificial Intelligence and Machine Learning have been around for a long time. In recent years, there has been a surge in popularity for applications integrating AI and ML technology. As with traditional development, software testing is a critical component of a successful AI/ML application. The development methodology used in AI/ML contrasts significantly from traditional development. In light of these distinctions, various software testing challenges arise. The emphasis of this paper is on the challenge of effectively splitting the data into training and testing data sets. By applying a k-Means clustering strategy to the data set followed by a decision tree, we can significantly increase the likelihood of the training data set to represent the domain of the full dataset and thus avoid training a model that is likely to fail because it has only learned a subset of the full data domain.
PREDICTING BANKRUPTCY USING MACHINE LEARNING ALGORITHMSIJCI JOURNAL
This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global
problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies.
A Clustering Method for Weak Signals to Support Anticipative IntelligenceCSCJournals
Organizations need appropriate anticipative information to support their decision making process. Contrarily to some strategic information analyses that help managers to establish patterns using past information, anticipative intelligence is intended to help managers to act based on the analysis of pieces of information that indicate some sort of trend that may become true in the future. One example of this kind of information is known as a weak signal, which is a short text related to a specific domain. In this work, pairs of weak signals, written in Portuguese, are compared to each other so that similarities can be identified and correlated weak signals can be clustered together. The idea is that the analysis of the resulting similar groups may lead to the formulation of a hypothesis that can support the decision making process. The proposed technique consists of two main steps: preprocessing the set of weak signals and clustering. The proposed method was evaluated on a database of bio-energy weak signals. The main innovations of this work are: (i) the application of a computational methodology from the literature for analyzing anticipative information; and (ii) the adaptation of data mining techniques to implement this methodology in a software product.
Understanding the Applicability of Linear & Non-Linear Models Using a Case-Ba...ijaia
This paper uses a case based study – “product sales estimation” on real-time data to help us understand
the applicability of linear and non-linear models in machine learning and data mining. A systematic
approach has been used here to address the given problem statement of sales estimation for a particular set
of products in multiple categories by applying both linear and non-linear machine learning techniques on
a data set of selected features from the original data set. Feature selection is a process that reduces the
dimensionality of the data set by excluding those features which contribute minimal to the prediction of the
dependent variable. The next step in this process is training the model that is done using multiple
techniques from linear & non-linear domains, one of the best ones in their respective areas. Data Remodeling
has then been done to extract new features from the data set by changing the structure of the
dataset & the performance of the models is checked again. Data Remodeling often plays a very crucial and
important role in boosting classifier accuracies by changing the properties of the given dataset. We then try
to explore and analyze the various reasons due to which one model performs better than the other & hence
try and develop an understanding about the applicability of linear & non-linear machine learning models.
The target mentioned above being our primary goal, we also aim to find the classifier with the best possible
accuracy for product sales estimation in the given scenario.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Development of Decision Support System for Ordering Goods using Fuzzy Tsukamoto IJECEIAES
The determination of a number of items in the right number is very essential for a company, but in actual practice, it is not trivial task. There are many factors that influence them such as inventory and sales levels. If a number of the ordering goods is too slight or too much, it will effect in the fulfillment of consumer demand. One of the ways that can be used to predict a number of ordering goods is a Fuzzy Inference System (FIS) using Tsukamoto fuzzy logic method. Three variables that are used in this study, namely sales, inventory, and ordering or purchasing. The sales input variables are divided into 3 categories, namely down, constant, and rise. Then, inventory input variebles are divided into 3 categories, namely a slight, moderate and many, likewise ordering input variables also consists in 3 categories, namely less, constant, and increase. The next step is the combination of rules from all events, then performing inference and defuzzifikasi to find average centered. To prove the applied method against manual calculations are then implemented in the developed system. The results of the system calculation do not much different with the calculation results that are done by manually. This is proven by information in the table of Mean Squared Error (MSE) with error results of under 1. So, without prejudice to accuracy in the calculation, the system can be used to save time in determining the amount of the ordering goods. The proposed method can help for research object, in this case is retail company to determine a number of ordering goods.
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
This white paper, written for non-technical audiences, discusses the scope and applicability of the IEEE (Institute of Electrical and Electronics Engineers) 1366 standard methodology. It focuses on solutions available to improve utility indices and Network Operations Center (NOC) metrics. The purpose is to provide an educational and informational white paper and not overload the audience with statistical formulas and complex details.
One of the goals for this white paper is to illustrate the statistical concept and provide awareness. Another goal is to be able to summarize concepts and its applications to facilitate socializing with a wider audience such as Critical Infrastructure, Network Operations Centers (NOCs), and the Sustainability program.
PROVIDING A METHOD FOR DETERMINING THE INDEX OF CUSTOMER CHURN IN INDUSTRYIJITCA Journal
Churn customer, one of the most important issues in customer relationship management and marketing is especially in industries such as telecommunications, the financial and insurance. In recent decades much
research has been done in this area. In this research, the index set for the reasons set reason churn customers for our customers is of particular importance. In this study we are intended to provide a formula for the index churn customers, the better to understand the reasons for customers to provide churn. Therefore, in order to evaluate the formula provided through six Classification methods (Decision tree QUEST, Decision tree C5.0, Decision tree CHAID, Decision trees CART, Bayesian network, Neural network) to evaluate the formula will be involved with individual indicators
A Magnified Application of Deficient Data Using Bolzano Classifierjournal ijrtem
Abstract: Deficient and Inconsistent knowledge are a pervasive and lasting problem in heavy information sets. However, basic accusation is attractive often used to impute missing data, whereas multiple imputation generates right value to replace. Consequently, variety of machine learning (ML) techniques are developed to reprocess the incomplete information. It is estimated multiple imputation of missing information in large datasets and the performance of MI focuses on several unsupervised ML algorithms like mean, median, standard deviation and Supervised ML techniques for probabilistic algorithm like NBI classifier. Such research is carried out employing a comprehensive range of databases, for which missing cases are first filled in by various sets of plausible values to create multiple completed datasets, then standard complete- data operations are applied to each completed dataset, and finally the multiple sets of results combine to generate a single inference. The main aim of this report is to offer general guidelines for selection of suitable data imputation algorithms based on characteristics of the data. Implementing Bolzano Weierstrass theorem in machine learning techniques to assess the functioning of every sequence of rational and irrational number has a monotonic subsequence and specify every sequence always has a finite boundary. For estimate imputation of missing data, the standard machine learning repository dataset has been applied. Tentative effects manifest the proposed approach to have good accuracy and the accuracy measured in terms of percent. Keywords: Bolzano Classifier, Maximum Likelihood, NBI classifier, posterior probability, predictor probability, prior probability, Supervised ML, Unsupervised ML.
10.sentiment analysis of customer product reviews using machine learniVenkat Projects
10.sentiment analysis of customer product reviews using machine learning In this project author is detecting sentiments from amazon reviews by using various machine learning algorithms such as SVM, Decision Tree and Naïve Bayes. In all 3 algorithms SVM is giving better accuracy and to train this algorithms author has used AMAZON reviews dataset and this dataset is saved inside ‘Amazon_Reviews_dataset’ folder. Below screen shot show example reviews from dataset
Understanding the Applicability of Linear & Non-Linear Models Using a Case-Ba...ijaia
This paper uses a case based study – “product sales estimation” on real-time data to help us understand
the applicability of linear and non-linear models in machine learning and data mining. A systematic
approach has been used here to address the given problem statement of sales estimation for a particular set
of products in multiple categories by applying both linear and non-linear machine learning techniques on
a data set of selected features from the original data set. Feature selection is a process that reduces the
dimensionality of the data set by excluding those features which contribute minimal to the prediction of the
dependent variable. The next step in this process is training the model that is done using multiple
techniques from linear & non-linear domains, one of the best ones in their respective areas. Data Remodeling
has then been done to extract new features from the data set by changing the structure of the
dataset & the performance of the models is checked again. Data Remodeling often plays a very crucial and
important role in boosting classifier accuracies by changing the properties of the given dataset. We then try
to explore and analyze the various reasons due to which one model performs better than the other & hence
try and develop an understanding about the applicability of linear & non-linear machine learning models.
The target mentioned above being our primary goal, we also aim to find the classifier with the best possible
accuracy for product sales estimation in the given scenario.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Development of Decision Support System for Ordering Goods using Fuzzy Tsukamoto IJECEIAES
The determination of a number of items in the right number is very essential for a company, but in actual practice, it is not trivial task. There are many factors that influence them such as inventory and sales levels. If a number of the ordering goods is too slight or too much, it will effect in the fulfillment of consumer demand. One of the ways that can be used to predict a number of ordering goods is a Fuzzy Inference System (FIS) using Tsukamoto fuzzy logic method. Three variables that are used in this study, namely sales, inventory, and ordering or purchasing. The sales input variables are divided into 3 categories, namely down, constant, and rise. Then, inventory input variebles are divided into 3 categories, namely a slight, moderate and many, likewise ordering input variables also consists in 3 categories, namely less, constant, and increase. The next step is the combination of rules from all events, then performing inference and defuzzifikasi to find average centered. To prove the applied method against manual calculations are then implemented in the developed system. The results of the system calculation do not much different with the calculation results that are done by manually. This is proven by information in the table of Mean Squared Error (MSE) with error results of under 1. So, without prejudice to accuracy in the calculation, the system can be used to save time in determining the amount of the ordering goods. The proposed method can help for research object, in this case is retail company to determine a number of ordering goods.
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
This white paper, written for non-technical audiences, discusses the scope and applicability of the IEEE (Institute of Electrical and Electronics Engineers) 1366 standard methodology. It focuses on solutions available to improve utility indices and Network Operations Center (NOC) metrics. The purpose is to provide an educational and informational white paper and not overload the audience with statistical formulas and complex details.
One of the goals for this white paper is to illustrate the statistical concept and provide awareness. Another goal is to be able to summarize concepts and its applications to facilitate socializing with a wider audience such as Critical Infrastructure, Network Operations Centers (NOCs), and the Sustainability program.
PROVIDING A METHOD FOR DETERMINING THE INDEX OF CUSTOMER CHURN IN INDUSTRYIJITCA Journal
Churn customer, one of the most important issues in customer relationship management and marketing is especially in industries such as telecommunications, the financial and insurance. In recent decades much
research has been done in this area. In this research, the index set for the reasons set reason churn customers for our customers is of particular importance. In this study we are intended to provide a formula for the index churn customers, the better to understand the reasons for customers to provide churn. Therefore, in order to evaluate the formula provided through six Classification methods (Decision tree QUEST, Decision tree C5.0, Decision tree CHAID, Decision trees CART, Bayesian network, Neural network) to evaluate the formula will be involved with individual indicators
A Magnified Application of Deficient Data Using Bolzano Classifierjournal ijrtem
Abstract: Deficient and Inconsistent knowledge are a pervasive and lasting problem in heavy information sets. However, basic accusation is attractive often used to impute missing data, whereas multiple imputation generates right value to replace. Consequently, variety of machine learning (ML) techniques are developed to reprocess the incomplete information. It is estimated multiple imputation of missing information in large datasets and the performance of MI focuses on several unsupervised ML algorithms like mean, median, standard deviation and Supervised ML techniques for probabilistic algorithm like NBI classifier. Such research is carried out employing a comprehensive range of databases, for which missing cases are first filled in by various sets of plausible values to create multiple completed datasets, then standard complete- data operations are applied to each completed dataset, and finally the multiple sets of results combine to generate a single inference. The main aim of this report is to offer general guidelines for selection of suitable data imputation algorithms based on characteristics of the data. Implementing Bolzano Weierstrass theorem in machine learning techniques to assess the functioning of every sequence of rational and irrational number has a monotonic subsequence and specify every sequence always has a finite boundary. For estimate imputation of missing data, the standard machine learning repository dataset has been applied. Tentative effects manifest the proposed approach to have good accuracy and the accuracy measured in terms of percent. Keywords: Bolzano Classifier, Maximum Likelihood, NBI classifier, posterior probability, predictor probability, prior probability, Supervised ML, Unsupervised ML.
10.sentiment analysis of customer product reviews using machine learniVenkat Projects
10.sentiment analysis of customer product reviews using machine learning In this project author is detecting sentiments from amazon reviews by using various machine learning algorithms such as SVM, Decision Tree and Naïve Bayes. In all 3 algorithms SVM is giving better accuracy and to train this algorithms author has used AMAZON reviews dataset and this dataset is saved inside ‘Amazon_Reviews_dataset’ folder. Below screen shot show example reviews from dataset
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Measuring effort for modifying software package as reusable package using pac...eSAT Journals
Abstract In any engineering field the data associated with knowledge is important one for taking decisions for solving problems in the current system development. The specification mining can give support for analyzing collected data to help the project management team to fulfill their responsibilities. In this paper ‘Package Specification Mining’ is designed by using packages’ reusability quality factor. It supports to give effort required for modifying the package to be reusable package for using those packages in new software development. This methodology may reduce the risks in various domains of software engineering. Keywords: Specification Mining, Reusability, Effort Estimation, Coupling, Project Management
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Similar to Big data analytics in financial market (20)
Mechanical properties of hybrid fiber reinforced concrete for pavementseSAT Journals
Abstract
The effect of addition of mono fibers and hybrid fibers on the mechanical properties of concrete mixture is studied in the present
investigation. Steel fibers of 1% and polypropylene fibers 0.036% were added individually to the concrete mixture as mono fibers and
then they were added together to form a hybrid fiber reinforced concrete. Mechanical properties such as compressive, split tensile and
flexural strength were determined. The results show that hybrid fibers improve the compressive strength marginally as compared to
mono fibers. Whereas, hybridization improves split tensile strength and flexural strength noticeably.
Keywords:-Hybridization, mono fibers, steel fiber, polypropylene fiber, Improvement in mechanical properties.
Material management in construction – a case studyeSAT Journals
Abstract
The objective of the present study is to understand about all the problems occurring in the company because of improper application
of material management. In construction project operation, often there is a project cost variance in terms of the material, equipments,
manpower, subcontractor, overhead cost, and general condition. Material is the main component in construction projects. Therefore,
if the material management is not properly managed it will create a project cost variance. Project cost can be controlled by taking
corrective actions towards the cost variance. Therefore a methodology is used to diagnose and evaluate the procurement process
involved in material management and launch a continuous improvement was developed and applied. A thorough study was carried
out along with study of cases, surveys and interviews to professionals involved in this area. As a result, a methodology for diagnosis
and improvement was proposed and tested in selected projects. The results obtained show that the main problem of procurement is
related to schedule delays and lack of specified quality for the project. To prevent this situation it is often necessary to dedicate
important resources like money, personnel, time, etc. To monitor and control the process. A great potential for improvement was
detected if state of the art technologies such as, electronic mail, electronic data interchange (EDI), and analysis were applied to the
procurement process. These helped to eliminate the root causes for many types of problems that were detected.
Managing drought short term strategies in semi arid regions a case studyeSAT Journals
Abstract
Drought management needs multidisciplinary action. Interdisciplinary efforts among the experts in various fields of the droughts
prone areas are helpful to achieve tangible and permanent solution for this recurring problem. The Gulbarga district having the total
area around 16, 240 sq.km, and accounts 8.45 per cent of the Karnataka state area. The district has been situated with latitude 17º 19'
60" North and longitude of 76 º 49' 60" east. The district is situated entirely on the Deccan plateau positioned at a height of 300 to
750 m above MSL. Sub-tropical, semi-arid type is one among the drought prone districts of Karnataka State. The drought
management is very important for a district like Gulbarga. In this paper various short term strategies are discussed to mitigate the
drought condition in the district.
Keywords: Drought, South-West monsoon, Semi-Arid, Rainfall, Strategies etc.
Life cycle cost analysis of overlay for an urban road in bangaloreeSAT Journals
Abstract
Pavements are subjected to severe condition of stresses and weathering effects from the day they are constructed and opened to traffic
mainly due to its fatigue behavior and environmental effects. Therefore, pavement rehabilitation is one of the most important
components of entire road systems. This paper highlights the design of concrete pavement with added mono fibers like polypropylene,
steel and hybrid fibres for a widened portion of existing concrete pavement and various overlay alternatives for an existing
bituminous pavement in an urban road in Bangalore. Along with this, Life cycle cost analyses at these sections are done by Net
Present Value (NPV) method to identify the most feasible option. The results show that though the initial cost of construction of
concrete overlay is high, over a period of time it prove to be better than the bituminous overlay considering the whole life cycle cost.
The economic analysis also indicates that, out of the three fibre options, hybrid reinforced concrete would be economical without
compromising the performance of the pavement.
Keywords: - Fatigue, Life cycle cost analysis, Net Present Value method, Overlay, Rehabilitation
Laboratory studies of dense bituminous mixes ii with reclaimed asphalt materialseSAT Journals
Abstract
The issue of growing demand on our nation’s roadways over that past couple of decades, decreasing budgetary funds, and the need to
provide a safe, efficient, and cost effective roadway system has led to a dramatic increase in the need to rehabilitate our existing
pavements and the issue of building sustainable road infrastructure in India. With these emergency of the mentioned needs and this
are today’s burning issue and has become the purpose of the study.
In the present study, the samples of existing bituminous layer materials were collected from NH-48(Devahalli to Hassan) site.The
mixtures were designed by Marshall Method as per Asphalt institute (MS-II) at 20% and 30% Reclaimed Asphalt Pavement (RAP).
RAP material was blended with virgin aggregate such that all specimens tested for the, Dense Bituminous Macadam-II (DBM-II)
gradation as per Ministry of Roads, Transport, and Highways (MoRT&H) and cost analysis were carried out to know the economics.
Laboratory results and analysis showed the use of recycled materials showed significant variability in Marshall Stability, and the
variability increased with the increase in RAP content. The saving can be realized from utilization of recycled materials as per the
methodology, the reduction in the total cost is 19%, 30%, comparing with the virgin mixes.
Keywords: Reclaimed Asphalt Pavement, Marshall Stability, MS-II, Dense Bituminous Macadam-II
Laboratory investigation of expansive soil stabilized with natural inorganic ...eSAT Journals
Abstract
Soil stabilization has proven to be one of the oldest techniques to improve the soil properties. Literature review conducted revealed
that uses of natural inorganic stabilizers are found to be one of the best options for soil stabilization. In this regard an attempt has
been made to evaluate the influence of RBI-81 stabilizer on properties of black cotton soil through laboratory investigations. Black
cotton soil with varying percentages of RBI-81 viz., 0, 0.5, 1, 1.5, 2, and 2.5 percent were studied for moisture density relationships
and strength behaviour of soils. Also the effect of curing period was evaluated as literature review clearly emphasized the strength
gain of soils stabilized with RBI-81 over a period of time. The results obtained shows that the unconfined compressive strength of
specimens treated with RBI-81 increased approximately by 250% for a curing period of 28 days as compared to virgin soil. Further
the CBR value improved approximately by 400%. The studies indicated an increasing trend for soil strength behaviour with
increasing percentage of RBI-81 suggesting its potential applications in soil stabilization.
Influence of reinforcement on the behavior of hollow concrete block masonry p...eSAT Journals
Abstract
Reinforced masonry was developed to exploit the strength potential of masonry and to solve its lack of tensile strength. Experimental
and analytical studies have been carried out to investigate the effect of reinforcement on the behavior of hollow concrete block
masonry prisms under compression and to predict ultimate failure compressive strength. In the numerical program, three dimensional
non-linear finite elements (FE) model based on the micro-modeling approach is developed for both unreinforced and reinforced
masonry prisms using ANSYS (14.5). The proposed FE model uses multi-linear stress-strain relationships to model the non-linear
behavior of hollow concrete block, mortar, and grout. Willam-Warnke’s five parameter failure theory has been adopted to model the
failure of masonry materials. The comparison of the numerical and experimental results indicates that the FE models can successfully
capture the highly nonlinear behavior of the physical specimens and accurately predict their strength and failure mechanisms.
Keywords: Structural masonry, Hollow concrete block prism, grout, Compression failure, Finite element method,
Numerical modeling.
Influence of compaction energy on soil stabilized with chemical stabilizereSAT Journals
Abstract
Increase in traffic along with heavier magnitude of wheel loads cause rapid deterioration in pavements. There is a need to improve
density, strength of soil subgrade and other pavement layers. In this study an attempt is made to improve the properties of locally
available loamy soil using twin approaches viz., i) increasing the compaction of soil and ii) treating the soil with chemical stabilizer.
Laboratory studies are carried out on both untreated and treated soil samples compacted by different compaction efforts. Studies
show that increase in compaction effort results in increase in density of soil. However in soil treated with chemical stabilizer, rate of
increase in density is not significant. The soil treated with chemical stabilizer exhibits improvement in both strength and performance
properties.
Keywords: compaction, density, subgradestabilization, resilient modulus
Geographical information system (gis) for water resources managementeSAT Journals
Abstract
Water resources projects are inherited with overlapping and at times conflicting objectives. These projects are often of varied sizes
ranging from major projects with command areas of millions of hectares to very small projects implemented at the local level. Thus,
in all these projects there is seldom proper coordination which is essential for ensuring collective sustainability.
Integrated watershed development and management is the accepted answer but in turn requires a comprehensive framework that can
enable planning process involving all the stakeholders at different levels and scales is compulsory. Such a unified hydrological
framework is essential to evaluate the cause and effect of all the proposed actions within the drainage basins.
The present paper describes a hydrological framework developed in the form of a Hydrologic Information System (HIS) which is
intended to meet the specific information needs of the various line departments of a typical State connected with water related aspects.
The HIS consist of a hydrologic information database coupled with tools for collating primary and secondary data and tools for
analyzing and visualizing the data and information. The HIS also incorporates hydrological model base for indirect assessment of
various entities of water balance in space and time. The framework would be maintained and updated to reflect fully the most
accurate ground truth data and the infrastructure requirements for planning and management.
Keywords: Hydrological Information System (HIS); WebGIS; Data Model; Web Mapping Services
Forest type mapping of bidar forest division, karnataka using geoinformatics ...eSAT Journals
Abstract
The study demonstrate the potentiality of satellite remote sensing technique for the generation of baseline information on forest types
including tree plantation details in Bidar forest division, Karnataka covering an area of 5814.60Sq.Kms. The Total Area of Bidar
forest division is 5814Sq.Kms analysis of the satellite data in the study area reveals that about 84% of the total area is Covered by
crop land, 1.778% of the area is covered by dry deciduous forest, 1.38 % of mixed plantation, which is very threatening to the
environmental stability of the forest, future plantation site has been mapped. With the use of latest Geo-informatics technology proper
and exact condition of the trees can be observed and necessary precautions can be taken for future plantation works in an appropriate
manner
Keywords:-RS, GIS, GPS, Forest Type, Tree Plantation
Factors influencing compressive strength of geopolymer concreteeSAT Journals
Abstract
To study effects of several factors on the properties of fly ash based geopolymer concrete on the compressive strength and also the
cost comparison with the normal concrete. The test variables were molarities of sodium hydroxide(NaOH) 8M,14M and 16M, ratio of
NaOH to sodium silicate (Na2SiO3) 1, 1.5, 2 and 2.5, alkaline liquid to fly ash ratio 0.35 and 0.40 and replacement of water in
Na2SiO3 solution by 10%, 20% and 30% were used in the present study. The test results indicated that the highest compressive
strength 54 MPa was observed for 16M of NaOH, ratio of NaOH to Na2SiO3 2.5 and alkaline liquid to fly ash ratio of 0.35. Lowest
compressive strength of 27 MPa was observed for 8M of NaOH, ratio of NaOH to Na2SiO3 is 1 and alkaline liquid to fly ash ratio of
0.40. Alkaline liquid to fly ash ratio of 0.35, water replacement of 10% and 30% for 8 and 16 molarity of NaOH and has resulted in
compressive strength of 36 MPa and 20 MPa respectively. Superplasticiser dosage of 2 % by weight of fly ash has given higher
strength in all cases.
Keywords: compressive strength, alkaline liquid, fly ash
Experimental investigation on circular hollow steel columns in filled with li...eSAT Journals
Abstract
Composite Circular hollow Steel tubes with and without GFRP infill for three different grades of Light weight concrete are tested for
ultimate load capacity and axial shortening , under Cyclic loading. Steel tubes are compared for different lengths, cross sections and
thickness. Specimens were tested separately after adopting Taguchi’s L9 (Latin Squares) Orthogonal array in order to save the initial
experimental cost on number of specimens and experimental duration. Analysis was carried out using ANN (Artificial Neural
Network) technique with the assistance of Mini Tab- a statistical soft tool. Comparison for predicted, experimental & ANN output is
obtained from linear regression plots. From this research study, it can be concluded that *Cross sectional area of steel tube has most
significant effect on ultimate load carrying capacity, *as length of steel tube increased- load carrying capacity decreased & *ANN
modeling predicted acceptable results. Thus ANN tool can be utilized for predicting ultimate load carrying capacity for composite
columns.
Keywords: Light weight concrete, GFRP, Artificial Neural Network, Linear Regression, Back propagation, orthogonal
Array, Latin Squares
Experimental behavior of circular hsscfrc filled steel tubular columns under ...eSAT Journals
Abstract
This paper presents an outlook on experimental behavior and a comparison with predicted formula on the behaviour of circular
concentrically loaded self-consolidating fibre reinforced concrete filled steel tube columns (HSSCFRC). Forty-five specimens were
tested. The main parameters varied in the tests are: (1) percentage of fiber (2) tube diameter or width to wall thickness ratio (D/t
from 15 to 25) (3) L/d ratio from 2.97 to 7.04 the results from these predictions were compared with the experimental data. The
experimental results) were also validated in this study.
Keywords: Self-compacting concrete; Concrete-filled steel tube; axial load behavior; Ultimate capacity.
Evaluation of punching shear in flat slabseSAT Journals
Abstract
Flat-slab construction has been widely used in construction today because of many advantages that it offers. The basic philosophy in
the design of flat slab is to consider only gravity forces; this method ignores the effect of punching shear due to unbalanced moments
at the slab column junction which is critical. An attempt has been made to generate generalized design sheets which accounts both
punching shear due to gravity loads and unbalanced moments for cases (a) interior column; (b) edge column (bending perpendicular
to shorter edge); (c) edge column (bending parallel to shorter edge); (d) corner column. These design sheets are prepared as per
codal provisions of IS 456-2000. These design sheets will be helpful in calculating the shear reinforcement to be provided at the
critical section which is ignored in many design offices. Apart from its usefulness in evaluating punching shear and the necessary
shear reinforcement, the design sheets developed will enable the designer to fix the depth of flat slab during the initial phase of the
design.
Keywords: Flat slabs, punching shear, unbalanced moment.
Evaluation of performance of intake tower dam for recent earthquake in indiaeSAT Journals
Abstract
Intake towers are typically tall, hollow, reinforced concrete structures and form entrance to reservoir outlet works. A parametric
study on dynamic behavior of circular cylindrical towers can be carried out to study the effect of depth of submergence, wall thickness
and slenderness ratio, and also effect on tower considering dynamic analysis for time history function of different soil condition and
by Goyal and Chopra accounting interaction effects of added hydrodynamic mass of surrounding and inside water in intake tower of
dam
Key words: Hydrodynamic mass, Depth of submergence, Reservoir, Time history analysis,
Evaluation of operational efficiency of urban road network using travel time ...eSAT Journals
Abstract
Efficiency of the road network system is analyzed by travel time reliability measures. The study overlooks on an important measure of
travel time reliability and prioritizing Tiruchirappalli road network. Traffic volume and travel time were collected using license plate
matching method. Travel time measures were estimated from average travel time and 95th travel time. Effect of non-motorized vehicle
on efficiency of road system was evaluated. Relation between buffer time index and traffic volume was created. Travel time model has
been developed and travel time measure was validated. Then service quality of road sections in network were graded based on
travel time reliability measures.
Keywords: Buffer Time Index (BTI); Average Travel Time (ATT); Travel Time Reliability (TTR); Buffer Time (BT).
Estimation of surface runoff in nallur amanikere watershed using scs cn methodeSAT Journals
Abstract
The development of watershed aims at productive utilization of all the available natural resources in the entire area extending from
ridge line to stream outlet. The per capita availability of land for cultivation has been decreasing over the years. Therefore, water and
the related land resources must be developed, utilized and managed in an integrated and comprehensive manner. Remote sensing and
GIS techniques are being increasingly used for planning, management and development of natural resources. The study area, Nallur
Amanikere watershed geographically lies between 110 38’ and 110 52’ N latitude and 760 30’ and 760 50’ E longitude with an area of
415.68 Sq. km. The thematic layers such as land use/land cover and soil maps were derived from remotely sensed data and overlayed
through ArcGIS software to assign the curve number on polygon wise. The daily rainfall data of six rain gauge stations in and around
the watershed (2001-2011) was used to estimate the daily runoff from the watershed using Soil Conservation Service - Curve Number
(SCS-CN) method. The runoff estimated from the SCS-CN model was then used to know the variation of runoff potential with different
land use/land cover and with different soil conditions.
Keywords: Watershed, Nallur watershed, Surface runoff, Rainfall-Runoff, SCS-CN, Remote Sensing, GIS.
Estimation of morphometric parameters and runoff using rs & gis techniqueseSAT Journals
Abstract
Land and water are the two vital natural resources, the optimal management of these resources with minimum adverse environmental
impact are essential not only for sustainable development but also for human survival. Satellite remote sensing with geographic
information system has a pragmatic approach to map and generate spatial input layers of predicting response behavior and yield of
watershed. Hence, in the present study an attempt has been made to understand the hydrological process of the catchment at the
watershed level by drawing the inferences from moprhometric analysis and runoff. The study area chosen for the present study is
Yagachi catchment situated in Chickamaglur and Hassan district lies geographically at a longitude 75⁰52’08.77”E and
13⁰10’50.77”N latitude. It covers an area of 559.493 Sq.km. Morphometric analysis is carried out to estimate morphometric
parameters at Micro-watershed to understand the hydrological response of the catchment at the Micro-watershed level. Daily runoff
is estimated using USDA SCS curve number model for a period of 10 years from 2001 to 2010. The rainfall runoff relationship of the
study shows there is a positive correlation.
Keywords: morphometric analysis, runoff, remote sensing and GIS, SCS - method
-
Effect of variation of plastic hinge length on the results of non linear anal...eSAT Journals
Abstract The nonlinear Static procedure also well known as pushover analysis is method where in monotonically increasing loads are applied to the structure till the structure is unable to resist any further load. It is a popular tool for seismic performance evaluation of existing and new structures. In literature lot of research has been carried out on conventional pushover analysis and after knowing deficiency efforts have been made to improve it. But actual test results to verify the analytically obtained pushover results are rarely available. It has been found that some amount of variation is always expected to exist in seismic demand prediction of pushover analysis. Initial study is carried out by considering user defined hinge properties and default hinge length. Attempt is being made to assess the variation of pushover analysis results by considering user defined hinge properties and various hinge length formulations available in literature and results compared with experimentally obtained results based on test carried out on a G+2 storied RCC framed structure. For the present study two geometric models viz bare frame and rigid frame model is considered and it is found that the results of pushover analysis are very sensitive to geometric model and hinge length adopted. Keywords: Pushover analysis, Base shear, Displacement, hinge length, moment curvature analysis
Effect of use of recycled materials on indirect tensile strength of asphalt c...eSAT Journals
Abstract
Depletion of natural resources and aggregate quarries for the road construction is a serious problem to procure materials. Hence
recycling or reuse of material is beneficial. On emphasizing development in sustainable construction in the present era, recycling of
asphalt pavements is one of the effective and proven rehabilitation processes. For the laboratory investigations reclaimed asphalt
pavement (RAP) from NH-4 and crumb rubber modified binder (CRMB-55) was used. Foundry waste was used as a replacement to
conventional filler. Laboratory tests were conducted on asphalt concrete mixes with 30, 40, 50, and 60 percent replacement with RAP.
These test results were compared with conventional mixes and asphalt concrete mixes with complete binder extracted RAP
aggregates. Mix design was carried out by Marshall Method. The Marshall Tests indicated highest stability values for asphalt
concrete (AC) mixes with 60% RAP. The optimum binder content (OBC) decreased with increased in RAP in AC mixes. The Indirect
Tensile Strength (ITS) for AC mixes with RAP also was found to be higher when compared to conventional AC mixes at 300C.
Keywords: Reclaimed asphalt pavement, Foundry waste, Recycling, Marshall Stability, Indirect tensile strength.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
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• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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/
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
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 02 | Feb-2015, Available @ http://www.ijret.org 422
BIG DATA ANALYTICS IN FINANCIAL MARKET
Kavitha S1
, Raja Vadhana P2
, Nivi A N3
1
Department of Computer Science and Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu,
India
2
Department of Computer Science and Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu,
India
3
Department of Computer Science and Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu,
India
Abstract
Big data plays a serious role within the business for creating higher predictions over business information that is collected from
the real world. Finance is that the new sector wherever the big data technologies like Hadoop, NoSQL are creating its mark in
predictions from financial data by the analysts. It’s a lot of fascinating within the stock exchange choices which might predict on a
lot of profits of stock exchange. For this stock exchange analysis each regular information and historical information of specific
stock exchange are needed for creating predictions. There are varied techniques used for analyzing the unstructured information
like stock exchange reviews (day-to-day information) and historical statistic of economic information severally. This paper
involves discussion regarding the strategies that square measure used for analyzing each varieties of information.
Keywords: Big data, prediction, finance, stock market, business intelligence
--------------------------------------------------------------------***----------------------------------------------------------------------
1. INTRODUCTION
Big data means a lot in financial services in the
transformation of the organization services, profits, etc. It is
more promising for financial analysts and also the investors
for their services and investments. The large information
gathered over stock message boards is being large assets for
nearly 71 percent of organizations and it also uses the
historical time series data for accurate predictions of the
stock market. It also gains new insights of financial
organizations as well as investors. Big data technologies
create a value for these types of data in financial market.
Once the data are collected from message board
(unstructured) they have to be classified based on the users
sentiment on data in order to predict the correct results by
integrating the historical data. The historical data is analyzed
by financial volatility models. This paper deals with the
models that can be used with predictive analytics of big data
in financial market for better predictions.
1.1 Sentiment Analysis
Sentiment analysis is the process of identifying user’s view
from their reviews or feedback from social media. These
reviews are only unstructured and it is handled by the tools
of big data which implements the machine learning
algorithm for analyzing sentiments. The Sentiment Analysis
is focused when it comes to big data analytics because of the
reviews of customers who are majorly involved in the
benefit of the organization. The sentiment of the reviews is
based on the subjectivity f the content. For example the
phrase like “not bad” means “good”, the words directly give
positive feedback are “good”, “amazing”, etc. the negative
feedbacks include “poor”, “not good”, etc., These are the
features to be extracted from each and every statement of
the review and classified as polarity represented in figure 1.
It is mainly used in fields like marketing and advertisement
to improve the quality and profit of the product by various
organizations. In financial analysis it is the reviews on stock
exchange dash board from the investors or analysts or by
any organizations.
Fig -1 Sentiment Analysis
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Volume: 04 Issue: 02 | Feb-2015, Available @ http://www.ijret.org 423
1.2 Financial Volatility
Even though online reviews are analyzed using sentiment
analysis methods the analysis of financial volatility using
traditional volatility methods is important to make accurate
predictions on stock or other financial data. The veracity
will be there in the mere analysis of online reviews and
hence it requires historical data analysis for producing better
result than complete uncertainty in data. In finance,
volatility could be a measure for variation of worth of a
financial mechanism over a period of time. Historic
volatility springs from statistic of past market costs. An
implied volatility springs from the value of a market listed
spinoff (in explicit, an option). The symbol σ is employed
for volatility, and corresponds to std. deviation, that
shouldn't be confused with the equally named variance, that
is instead the square, σ2. In stock market, the shares may go
up and down day by day which is not consistent is said to be
the volatility. The historic data can be structured but it holds
a large amount of data that could be integrated with reviews.
2. SENTIMENT ANALYSIS
Sentiment analysis is the classification of sentiment features
from the real life data such as comments posted on review
boards. It plays a major role in big data analytics to provide
predictive results with the machine learning algorithms. The
sentiment is categorized into positive, negative and neutral
[1]. There are two main tasks (1) product features are
identified from the comments of reviewers and (2) the
comments are classified as positive or negative. These are
very challenging tasks. The classification can be done with
document level, sentence level, phrase level, etc., using
algorithms such as machine learning technologies such as
supervised and unsupervised algorithms [2].
2.1 Unsupervised Machine Learning
Unsupervised learning is that of tries to search out the
hidden structure in untagged knowledge. Because the
examples provided to the learner are untagged, there's no
error or reward signal to gauge a possible resolution. The
unsupervised learning is distinguished from supervised
learning and reinforcement learning when there is no error.
Unsupervised learning traditionally uses the lexicon based
approach for sentiment classification [3]. These methods
uses sentiment lexicon to identify entire document’s
sentiment polarity [4] - [6].
2.1.1 Lexicon Based Approach:
It is the sentiment based on each word and phrase. It is
identified by Turney [7] as semantic orientation of the
reviews. Later the lexicon based approach is used for
sentiment extraction. A lexicon based approach is a practical
and easy approach for Sentiment Analysis of data without a
requirement for training. A Lexicon based approach is good
on how the lexicon is used. A lexicon-based approach is
mainly projected to perform task using opinion bearing
words (or simply opinion words).Opinion words are the
commonly used words to express positive or negative
opinions (or sentiments), For example, “good”, “bad”,
“poor” and “excellent”. The count of positive and negative
opinion words is used to determine the feature of the
product in each sentence of the review. When the positive
opinion words are more than the negative opinion words,
then final conclusion on the feature will be positive or
otherwise negative [8].
2.2 Supervised Machine Learning:
Supervised machine learning uses a trained label set to
classify the sentiment on data. The training corpus is used
for learning new classification of data [9]. A set of training
examples will be there for training data. Each example
consists of an input object (vector) and a desired output
value (supervisory signal).A literature study shows often
SVM yields higher accuracy results than other techniques
[2].
2.2.1 Naïve Bayes Classifier:
Naïve Bayes classification method is simple method and
comparatively produces good results with sensible accuracy.
It uses a bag of words for classifying the subjectivity of the
content. It is based on Naïve Bayes rule assuming
conditional independence which is a main drawback of this
classification [10]. For classifying a document d and class c
using Bayes theorem is given by Eq. 1 and Eq. 2,
dp
p(c)
c
d
p
d
c
p
(1)
Naïve Bayes Classifier,
d
c
argmaxcPc* (2)
2.2.2 Support Vector Machine:
SVM is a classification and regression model used for data
analysis. It constructs a set of hyper planes used for linear
classification and regression shown in figure 2. It uses
kernel mapping for non linear classification [11] which is
used for erroneous classification. It produces more accurate
results on both classification and regression than other
machine learning techniques [2].
3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 02 | Feb-2015, Available @ http://www.ijret.org 424
Fig -2 SVM Linear Classifier
3. FINANCIAL VOLATILITY
Financial volatility is predictable and deals with the
historical data which is a time series model. These large data
is handled with some classic methods of financial volatility
to determine the future. It is very important for financial
investments of investors [12]. Volatility is the change in
prices of assets during particular period of time. It is
measured with the standard deviation continuously with the
specific period of time [13]. Gaussian process is the widely
used process for this measure but the efficient method
ARCH was first introduce by Engle [14]. Later it is
extended to GARCH which is more persistent even other
models are derived from it for the stock market volatility.
3.1 ARCH
Autoregressive Conditional Heteroscedasticity (ARCH) is
introduced by R.F Engle[14] for the calculation of financial
volatility. ARCH is a regression model to find the maximum
likelihood estimation b the calculation of mean and variance
V using Eq. 3.
σ
2
V (3)
ARCH model uses conditional variance and the variance
function can be generally expressed as in Eq. 4.
α)h( yyyh p,t2,...,t1,tt
(4)
Where y is the variance, p is the order of ARCH process and
is a vector of unknown parameters
3.2 GARCH
Generalized Autoregressive Conditional Heteroscedasticity
(GARCH) is the derived process of ARCH to allow the
lagged conditional variance which is not allowed in ARCH
process. This is also a learning mechanism which can be
used for measuring stock volatility for efficient results than
ARCH process [15]. In GARCH (p,q) process p,q is the
order of process and GARCH (1,1) is more efficient and
useful. It is given by Eq. 5.
00,0,, βααhβααh 1101t1
2
1t10t
(5)
Where and are vector of unknown parameters
4. SVR BASED ON GARCH MODEL
SVR based on GARCH model is a review over using SVM
and GARCH model for predictive analysis which is a part of
big data predictive analytics which can predict the future
with the day to day information and historical data of
finance. The following review gives the results on these
models showing the better predictions can be made for
future in the application of finance.
4.1 Desheng Dash Wu, Lijuan Zheng, and David L.
Olson [16]:
A popular financial website Sina Finance is used to collect
the financial reviews. This data also identified the
communication between investors are during opening and
closing time of stock market. From this data the sentiments
are classified using SVM where the chi square test is used to
4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 02 | Feb-2015, Available @ http://www.ijret.org 425
compare the sentiment analysis techniques. The significance
shows the accuracy that SVM gives better result than Naïve
Bayes classifier. For the same period of time Stock index
values (historical) data are downloaded to calculate the
financial volatility using GARCH model. The volatility is
calculated at both individual and industry level. GARCH-
SVM predicts better volatility trend at individual stocks
which keeps the parameters in order of GARCH terms. It is
good for individual level than industry level because of the
effect on stock features over sentiment of stock forum. This
model works better for small organizations than larger
organizations.
4.2 Fernando Perez-Cruz, Julio A Afonso
Rodriguez and Javier Giner [17]:
In general, standard ML procedure is used to evaluate the
parameters of the GARCH model. Here SVM is used to
evaluate the parameters of GARCH model. In ML
estimation the fit should be Gaussian distribution to produce
best fit or else it provides more error terms. But SVM does
not rely on previous data and also it tries to produce the best
fit.SVM also does not produce best fit on Gaussian
distribution because of pdf model. Here SVM is used as
linear distribution not as nonlinear distribution which can be
used in future. With linear distribution SVM evaluates the
GARCH parameters to produce best fit.
4.3 Peter R. Hansen and Asger Lunde [18]:
In order to find the ability of volatility models in forecasting
conditional variance, a large number of models are
compared using the DM-$ exchange rates and IBM stock
returns. The volatility models are compared with its
parameters like realized variance using first R observations
by considering null hypothesis as the benchmark model.
Nearly 330 ARCH models are compared but everything is
outperformed by GARCH (1,1) model in terms of exchange
rates. SPA test is used as the significance test to estimate the
GARCH (1,1) model to make conclusion on selecting the
volatility model. It is proven that in terms of IBM returns
GARCH (1,1) is inferior but in terms of exchange rates, no
other model can beat GARCH(1,1) model.
4.4 Robert P.Schumaker and Hsinchun Chen [19]:
A predictive machine learning technique is used to examine
the financial news articles using textual representations. The
two data sources , company generated and independently
generated data sources are used. SVM is used to predict the
stock specific variables and also for discrete numeric
prediction. Here a new model called AZFinText system is
designed for identifying the performance of future stock
profit by closeness measure, accuracy and trading. Textual
representation is good while using proper nouns and SVM is
used to learn the change in share prices and adjust it due to
the change of price severity. SVM produce better results
than other machine learning techniques.
4.5 Jun Hua Zhao, Zhao Yang Dong, Zhao Xu, Kit
Po Wang [20]:
The forecasting of electricity price is estimated using SVM.
The estimation is done for electricity price series and its
interval. SVM is the best regression technique used for
forecasting price value and here non liner heteroscedastic
model is required where GARCH model is linear and hence
new Nonlinear Conditional Heteroscedastic Forecasting
(NHCF) is introduced to evaluate the time changing
variance. The electricity time series is estimated with
Australian National Electricity Market data. Non linear
SVM function is used to estimate the parameters of NHCF
model and to find the maximum likelihood. The experiments
are conducted to compare the GARCH and NHCH models
where NHCF is good for non linear distribution like
deregulating electricity market.
4.6 Reinaldo C. Garcia, Javier Contreras, Marco
van Akkeren, and João Batista C. Garcia [21]:
The forecasting of electricity price is estimated with
GARCH model. In historical time series analysis of data, the
present and future values of the data is estimated using
maximum likelihood. For non linear networks, selection of
model is done by comparing ARIMA and GARCH model
with time varying variance. Statistical significance and
hypothesis tests are carried out to verify GARCH
parameters such as auto correlation and residuals. The
GARCH methodology is used to estimate the electricity
prices for Spain and California electricity markets. The
result proves that GARCH model outperforms the
generalized time series model ARIMA. The extreme ability
of the volatility model is also tested to prove that GARCH
model can be used for non linear distribution like
deregulating electricity market.
4.7 Altaf Hossain, Mohammed Nasser [22]:
The ARMA-GARCH, SVM, Relevance Vector Machine
(RVM), Recurrent SVM (RSVM) are applied for estimating
the financial volatility. The comparison of GARCH and
ARMA-GARCH is made with RRVM and RSVM. For this
evaluation India’s Bombay Stock Exchange (BSE) SENSEX
Index data and Japan’s stock market NIKKEI225 is used.
The prices of stock index are collected from Yahoo Finance.
The collected data should be transformed log returns for the
analysis. The models SVM and RVM are applied to
GARCH for comparing GARCH and ARMA-GARCH
model to forecast stock market volatility. DD, MSE, MAE
and R2
are used for evaluation. The conclusion states that
ARMA-GARCH is better than pure GARCH model,
relevance vector produced by RRVM is smaller than RSVM
but both RRVM and RSVM performs equally. In terms of
DS and MSE RRVM is better than RSVM whereas in terms
of MAE, RSVM is better than RRVM. The result also
proven RRVM is better than RSVM for forecasting stock
market volatility.
5. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 02 | Feb-2015, Available @ http://www.ijret.org 426
4.8 Robert Engle [23]:
The usefulness of ARCH and GARCH models are described
in financial forecasting. The standard deviation is calculated
to find the usefulness of both the models. The weights for
forecasting the variance is determined by the ARCH model.
GARCH model also estimate the weights but it never
decline it to complete zero. It is easy and simple form to
estimate by conditional variances. Both the models are used
in various applications but successful especially in financial
forecasting due to the risk measurement. These models and
their derivatives provide the storage to analyze and test the
pricing and its change over time.
4.9 Pichhang Ou, Hengshan Wang [24]:
For classification and regression, RVM is used in
forecasting financial time series. RVM uses Bayesian
approach which is identical to SVM. Here GARCH(1,1) is
used with RVM to forecast Shanghai composite Index. This
GARCH-RVM is compared with other machine learning
techniques such as recurrent Least Square SVM and RSVM
and also basic GARCH(1,1) model. The predictive
capability of RVM is better than LSSVM and SVM because
of its larger memory to rely on previous series of
information.
4.10Wei Sun, Jian-Chang Lu, Ming Meng [25]:
SVM is used to forecast market clearing price (MCP) of
electricity market. For this analysis nonlinear SVM function
called kernel SVM is used. It is compared with RBF kernel
of BP neural network forecasting to estimate its efficiency to
predict MCP. In this analysis past price is not included
except some influencing factors of electricity price. On
comparison it is proved that SVM has lesser errors and
better performance on generalization than BP neural
network model.
5. CONCLUSION
In this paper we discussed about how the big data analytics
especially predictive analytics is influenced in the financial
market mainly in the stock exchange with its emerging
technologies. The data analytics is also the main focused
area on big data analytics from the volume, variety of data
collected over the internet with its velocity but the value of
data results in applications where the data could give some
meaning. For its value it is analyzed to predict the future
with predictive analytics of big data and veracity or
uncertainty is something that always remains in any type of
analysis. So the historical data is used here with some
traditional techniques to give optimized predictions. For all
these analysis some techniques and methods are
unconditionally required to complete the required process.
We mainly focused on such methods that are used to predict
the future of the stock market using methods and algorithms
for both day to day information and historical data. Among
the entire methods the study shows that SVM algorithm is
majorly used for sentimental classification because of its
accurate results compared to other machine learning
algorithms. Similarly in the financial volatility GARCH is
the widely used method for the historical or time series data
analysis. The integration of SVM and GARCH model
largely helps for the analysts of stock market and also for
the investors to predict the stocks for more profit.
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BIOGRAPHIES
Kavitha S is currently pursuing her
Masters in Computer Science and
Engineering in Dr. N. G. P. Institute of
Technology, Anna University,
Coimbatore, Tamil Nadu, India. She
was a Software Trainer in NIIT Pvt
Ltd. in the year 2011. She worked as a
Computer Instructor in Kendriya Vidyalaya, Coimbatore for
elementary board for the year 2013. She is specialized in
.Net Framework and other areas of interest are Data Mining
and Cloud Computing.
kvth.sgm@gmail.com
Raja Vadhana P is currently pursuing
her Masters under the department of
Computer Science and Engineering in
Dr. N. G. P. Institute of Technology,
Anna University, Coimbatore, Tamil
Nadu, India. She was a Software
Engineer and Associate Consultant in
HCL Technologies Ltd. from 2008 to 2011. She worked as
an Associate Consultant with Larsen and Toubro Infotech
Ltd. under Enterprise Application Integration for the year
2011. She is specialized in BFSI domain and has extensive
experience in Service Oriented Architecture technologies
like TIBCO, ORACLE BPM and IBM BPM.
Nivi A N is currently pursuing her
Masters in Computer Science and
Engineering in Dr. N. G. P. Institute
of Technology, Anna University,
Coimbatore, Tamil Nadu, India. Her
areas of interest are Data Mining and
Cloud Computing.