Prediction in the stock market is very challenging in these days. Large datasets available from Twitter micro blogging platform and widely available stock market records. Machine learning was employ to conduct sentiment analysis of data and to estimate for future stock prices. The relation between sentiments and the stock value is to be determined. A comparative study of these algorithms Multiple linear Regression, Support Vector Machine and Artificial Neural Network are done. Thin Thin Swe | Phyu Phyu | Sandar Pa Pa Thein "Predition Model for Stock Price on Big Data Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26660.pdfPaper URL: https://www.ijtsrd.com/computer-science/database/26660/predition-model-for-stock-price-on-big-data-analytics/thin-thin-swe
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
The objective of this project is to discuss the importance of Machine Learning in different sectors and how does it solve the problems in the Marketing Analytics field. We have discussed Marketing Segmentation, Advertisement, and Fraud detection in our project. We used different Machine Learning algorithms and used R and Python library to predict and solve these problems. After making models and running test data on those models we got following results:
• We trained a Decision tree and Random Forest classifier model which has 73% accuracy to predict whether a person will be a defaulter or not based on credit history, income, job type, dependents etc.
• We segmented the Social networking profiles based on the likes and dislikes of a person using K-Means Clustering.
• We made a predictive model of the messages a customer receives and determined whether a message will be a Spam or not a spam with an accuracy of 97%. We used Naïve Bayes classifier for this model.
STOCK TREND PREDICTION USING NEWS SENTIMENT ANALYSISijcsit
Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research
has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such
as financial news articles about a company and predicting its future stock trend with news sentiment
classification. Assuming that news articles have impact on stock market, this is an attempt to study
relationship between news and stock trend. To show this, we created three different classification models
which depict polarity of news articles being positive or negative. Observations show that RF and SVM
perform well in all types of testing. Naïve Bayes gives good result but not compared to the other two.
Experiments are conducted to evaluate various aspects of the proposed model and encouraging results are
obtained in all of the experiments. The accuracy of the prediction model is more than 80% and in
comparison with news random labelling with 50% of accuracy; the model has increased the accuracy by
30%.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
The objective of this project is to discuss the importance of Machine Learning in different sectors and how does it solve the problems in the Marketing Analytics field. We have discussed Marketing Segmentation, Advertisement, and Fraud detection in our project. We used different Machine Learning algorithms and used R and Python library to predict and solve these problems. After making models and running test data on those models we got following results:
• We trained a Decision tree and Random Forest classifier model which has 73% accuracy to predict whether a person will be a defaulter or not based on credit history, income, job type, dependents etc.
• We segmented the Social networking profiles based on the likes and dislikes of a person using K-Means Clustering.
• We made a predictive model of the messages a customer receives and determined whether a message will be a Spam or not a spam with an accuracy of 97%. We used Naïve Bayes classifier for this model.
STOCK TREND PREDICTION USING NEWS SENTIMENT ANALYSISijcsit
Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research
has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such
as financial news articles about a company and predicting its future stock trend with news sentiment
classification. Assuming that news articles have impact on stock market, this is an attempt to study
relationship between news and stock trend. To show this, we created three different classification models
which depict polarity of news articles being positive or negative. Observations show that RF and SVM
perform well in all types of testing. Naïve Bayes gives good result but not compared to the other two.
Experiments are conducted to evaluate various aspects of the proposed model and encouraging results are
obtained in all of the experiments. The accuracy of the prediction model is more than 80% and in
comparison with news random labelling with 50% of accuracy; the model has increased the accuracy by
30%.
Big data is a term that describes a large or complex
data volume. That data volume can be processes using traditional
data processing software or techniques that are insufficient to deal
with them. But big data is often noisy, heterogeneous, irrelevant
and untrustworthy. As the speed of information growth exceeds
Moore’s Law at the beginning of this new century, excessive data
is making great troubles to human beings. However this data with
special attributes can’t be managed and processed by the current
traditional software system, which become a real problem. In this
paper was discussed some big data challenges and problems that
are faced by organizations. These challenges may relate
heterogeneity, scale, timelines, privacy and human collaboration.
Survey method was used as a theoretical solution framework.
Survey method consists of a questionnaires report. Questionnaires
report consists of all challenges and problems faced by
organizations. After knowing the problem and challenges of
organizations, a solution was given to organization to solve big
data challenges.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Businesses, governments, and the research community can all derive value from the massive amounts of digital data they collect. Analyzing big-data application projects by governments offers guidance for follower countries for their own future big-data initiatives. Decision making in government usually takes much longer and is conducted through consultation and mutual consent of a large number of diverse actors, including officials, interest groups, and ordinary citizens. Governments deal not only with general issues of big-data integration from multiple sources and in different formats and cost but also with some special challenges. The biggest is collecting data, governments have difficulty, as the data not only comes from multiple channels but from different sources. Most governments operating or planning big-data projects need to take a step-by-step approach for setting the right goals and realistic expectations. Success depends on their ability to integrate and analyze information, develop supporting systems, and support decision making through analytics. Pravin Dattu Gangad | Pranay Pravin Gaikwad"Use of Big Data in Government Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15676.pdf http://www.ijtsrd.com/computer-science/database/15676/use-of-big-data-in-government-sector/pravin-dattu-gangad
This slide is about real time analytics of Big Data. It explains about Big Data and Analytics. How to deal with them.
see more at - http://bigdataconcept.blogspot.in/2016/03/real-time-analytics-of-big-data.html
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Knowledge Graphs and their central role in big data processing: Past, Present...Amit Sheth
Keynote at CODS-COMAD 2020, Hyderabad, India, 06 Jan 2020: https://cods-comad.in/keynotes.html
Abstract : Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox. Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques. Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.
the influence of machine language and data science in the emerging worldijtsrd
The study describes the machine learning language with respect to big data sciences. The process of machine learning has evolved to have grown significantly to progress in information science. This progress has led to conquer different domains and are capable of solving myriad problems and upgrading the applicative properties. Hence, the present study is drafted to highlight the importance of machine learning process and language. Anitha. S "The Influence of Machine Language and Data Science in the Emerging World" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31907.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31907/the-influence-of-machine-language-and-data-science-in-the-emerging-world/anitha-s
Big data is a domain which is growing rapidly in every sector. In just a few years, big data has impacted industries and activities from marketing to law enforcement. It is often regarded by optimists as a revolution that will change for the better how we live, think, and work. Big data has a great impact on the economy since it helps companies, organizations, and governments to efficiently manage large volumes of data. This paper addresses the impact of big data in the economic field and provides some applications of big data in economy. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Big Data in Economics: An Introduction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49839.pdf Paper URL: https://www.ijtsrd.com/economics/other/49839/big-data-in-economics-an-introduction/matthew-n-o-sadiku
Deriving Business Value from Big Data using Sentiment analysisCTRM Center
‘Big Data’ are two small words that are widely used to describe the massive growth in data of all forms and that hold; the promise of delivering huge potential business impact. The question is, how?
Today, and increasingly in the future, businesses are surrounded by masses of data and raw information. Some of this data is very relevant but much of it is not. Further, most of that data is unstructured in the form of email, documents, images and different types of social media, blogs, and so on. Unstructured data is notoriously difficult to access and query, it is scattered across many different locations and formats, and it requires some form of preprocessing before it can be analyzed and used. Yet, it is this unstructured type data that is primarily exploding in quantity, representing around 80 per cent of the annual growth of data and doubling in quantity every two years.
Big data is a term that describes a large or complex
data volume. That data volume can be processes using traditional
data processing software or techniques that are insufficient to deal
with them. But big data is often noisy, heterogeneous, irrelevant
and untrustworthy. As the speed of information growth exceeds
Moore’s Law at the beginning of this new century, excessive data
is making great troubles to human beings. However this data with
special attributes can’t be managed and processed by the current
traditional software system, which become a real problem. In this
paper was discussed some big data challenges and problems that
are faced by organizations. These challenges may relate
heterogeneity, scale, timelines, privacy and human collaboration.
Survey method was used as a theoretical solution framework.
Survey method consists of a questionnaires report. Questionnaires
report consists of all challenges and problems faced by
organizations. After knowing the problem and challenges of
organizations, a solution was given to organization to solve big
data challenges.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Businesses, governments, and the research community can all derive value from the massive amounts of digital data they collect. Analyzing big-data application projects by governments offers guidance for follower countries for their own future big-data initiatives. Decision making in government usually takes much longer and is conducted through consultation and mutual consent of a large number of diverse actors, including officials, interest groups, and ordinary citizens. Governments deal not only with general issues of big-data integration from multiple sources and in different formats and cost but also with some special challenges. The biggest is collecting data, governments have difficulty, as the data not only comes from multiple channels but from different sources. Most governments operating or planning big-data projects need to take a step-by-step approach for setting the right goals and realistic expectations. Success depends on their ability to integrate and analyze information, develop supporting systems, and support decision making through analytics. Pravin Dattu Gangad | Pranay Pravin Gaikwad"Use of Big Data in Government Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15676.pdf http://www.ijtsrd.com/computer-science/database/15676/use-of-big-data-in-government-sector/pravin-dattu-gangad
This slide is about real time analytics of Big Data. It explains about Big Data and Analytics. How to deal with them.
see more at - http://bigdataconcept.blogspot.in/2016/03/real-time-analytics-of-big-data.html
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Knowledge Graphs and their central role in big data processing: Past, Present...Amit Sheth
Keynote at CODS-COMAD 2020, Hyderabad, India, 06 Jan 2020: https://cods-comad.in/keynotes.html
Abstract : Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox. Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques. Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.
the influence of machine language and data science in the emerging worldijtsrd
The study describes the machine learning language with respect to big data sciences. The process of machine learning has evolved to have grown significantly to progress in information science. This progress has led to conquer different domains and are capable of solving myriad problems and upgrading the applicative properties. Hence, the present study is drafted to highlight the importance of machine learning process and language. Anitha. S "The Influence of Machine Language and Data Science in the Emerging World" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31907.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31907/the-influence-of-machine-language-and-data-science-in-the-emerging-world/anitha-s
Big data is a domain which is growing rapidly in every sector. In just a few years, big data has impacted industries and activities from marketing to law enforcement. It is often regarded by optimists as a revolution that will change for the better how we live, think, and work. Big data has a great impact on the economy since it helps companies, organizations, and governments to efficiently manage large volumes of data. This paper addresses the impact of big data in the economic field and provides some applications of big data in economy. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Big Data in Economics: An Introduction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49839.pdf Paper URL: https://www.ijtsrd.com/economics/other/49839/big-data-in-economics-an-introduction/matthew-n-o-sadiku
Deriving Business Value from Big Data using Sentiment analysisCTRM Center
‘Big Data’ are two small words that are widely used to describe the massive growth in data of all forms and that hold; the promise of delivering huge potential business impact. The question is, how?
Today, and increasingly in the future, businesses are surrounded by masses of data and raw information. Some of this data is very relevant but much of it is not. Further, most of that data is unstructured in the form of email, documents, images and different types of social media, blogs, and so on. Unstructured data is notoriously difficult to access and query, it is scattered across many different locations and formats, and it requires some form of preprocessing before it can be analyzed and used. Yet, it is this unstructured type data that is primarily exploding in quantity, representing around 80 per cent of the annual growth of data and doubling in quantity every two years.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Learning Resources Week 2
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.
· Chapter 2, “The Organization and Graphic Presentation Data” (pp. 27-74)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
· Chapter 5, “Charts and Graphs”
· Chapter 11, “Editing Output”
Walden University Writing Center. (n.d.). General guidance on data displays. Retrieved from http://waldenwritingcenter.blogspot.com/2013/02/general-guidance-on-data-displays.html
Use this website to guide you as you provide appropriate APA formatting and citations for data displays.
Laureate Education (Producer). (2016j). Visual displays of data [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 9 minutes.
In this media program, Dr. Matt Jones discusses frequency distributions. Focus on how his explanation might support your analysis in this week’s Assignment. (video attached separately)
sustainability
Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018
����������
�������
Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development of information and communication
technology has made collection and analysis of big data essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA). However, many of these studies are
not linked to BI, as companies do not understand and utilize the concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data,
and BDA to show that they are not separate methods but an integrated decision support system.
Second, we explore how businesses use big data and BDA practically in conjunction with BI through
a case study of sorting and logistics processing of a typical courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from
actual application. Our findings may enable companies to achieve management efficiency by utilizing
big data through efficient BI without investing in additional infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.
Keywords: business application; big data; big data analysis; business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and cont.
Learning Resources Week 2
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.
· Chapter 2, “The Organization and Graphic Presentation Data” (pp. 27-74)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
· Chapter 5, “Charts and Graphs”
· Chapter 11, “Editing Output”
Walden University Writing Center. (n.d.). General guidance on data displays. Retrieved from http://waldenwritingcenter.blogspot.com/2013/02/general-guidance-on-data-displays.html
Use this website to guide you as you provide appropriate APA formatting and citations for data displays.
Laureate Education (Producer). (2016j). Visual displays of data [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 9 minutes.
In this media program, Dr. Matt Jones discusses frequency distributions. Focus on how his explanation might support your analysis in this week’s Assignment. (video attached separately)
sustainability
Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018
����������
�������
Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development of information and communication
technology has made collection and analysis of big data essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA). However, many of these studies are
not linked to BI, as companies do not understand and utilize the concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data,
and BDA to show that they are not separate methods but an integrated decision support system.
Second, we explore how businesses use big data and BDA practically in conjunction with BI through
a case study of sorting and logistics processing of a typical courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from
actual application. Our findings may enable companies to achieve management efficiency by utilizing
big data through efficient BI without investing in additional infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.
Keywords: business application; big data; big data analysis; business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and cont.
How ‘Big Data’ Can Create Significant Impact on Enterprises? Part I: Findings...IJERA Editor
Big data is the latest buzz word in the BI domain, and is increasingly gaining traction amongst enterprises. The prospect of gaining highly targeted business and market insight from unmanageable and unstructured data sets is creating huge adoption potential for such solutions. The scope of big data moves beyond conventional enterprise databases to more open environments, covering new sources of information typically relating to various social networking sites, wikis and blogs. Moreover, advancements in communications and M2M technologies are also contributing to the massive availability of big data
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data, accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters, automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions of individuals around the world to contribute to the amount of data available. In addition, the extremely increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security & Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources along with their characteristics in each domain. Later, it presents the highly productive and competitive big data applications with innovative big data technologies. Subsequently, the study showcases the impact of big data on each domain to capture value addition in its services. Finally, the study put forwards many more research opportunities as all these domains differ in their complexity and development in the usage of big data analytics
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The
advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,
automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In
fact, as they go about their business and interact with individuals, they are producing an incredible amount
of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data
and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation
factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics
in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security &
Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources
along with their characteristics in each domain. Later, it presents the highly productive and competitive big
data applications with innovative big data technologies. Subsequently, the study showcases the impact of
big data on each domain to capture value addition in its services. Finally, the study put forwards many
more research opportunities as all these domains differ in their complexity and development in the usage of
big data analytics.
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
Model Attribute Check Company Auto PropertyCeline George
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
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Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
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at Integral University, Lucknow, 06.06.2024
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Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2. International Journal of Trend in Scientific Research and Development (IJTSRD)
@ IJTSRD | Unique Paper ID – IJTSRD26660
A. Data Collection and Processing
The Yahoo finance data contain BSE data and NSE data. The
financial index of BSE is SENSEX and for NSE is NIFTY. This
finance data contain many attributes of the stock market.
Some of the attributes are opening value, closing value,
adjacent close value, min value,maxvalue,volume,dividend,
date and time of the day. The key attributes are selected for
stock market prediction in this work. The collected data are
processed by normalization and used as the input data. For
monthly prediction and daily prediction historicaldata from
Yahoo finance is used.
B. Multiple Linear Regression
Regression is a data mining task of predicting thestock price
by implementing a model based on one or
This technique is a compact method of mathematical
representation. It represents the relationship between the
response parameter and the input parameter in a given
prediction model. Prediction of theoutcomey fromtheinput
parameter x of the form
Y = β0 + β1 x1 + β2 x2… + βk xk
y – Outcome, β0 – Intercept, β1, β2, βk – Partial Regression
Co-efficient, x1, x2, xk – Input Parameters.
The least square value is determined by the current market
price y from the PE ratio and mean of the input parameters
y = a + bx
C. Support Vector Machine
In machine learning, Support vector machine is supervised
learning models with associated learning algorithms to
identify the data. This process includes theclassificationand
regression methods to analyse the prediction. It implements
the margins adoption to predict the market price. The
support vector means the closest hyper-plane and it is equal
in distance are identified. The value of support vector y is
calculated using the two attribute case .
y = w0 + w1 x1 + w2 x2
y – Outcome, w0, w1, w2 – three weights to be
learned by SVM model, x1, x2 – input parameters
D. Artificial Neural Network
The artificial neural network is deep learning at
hidden layer neuron. Artificial Intelligence
used to train the prediction model to identify the trends in a
complicated environment. The input value is assigned with
different weights in the input layer. The output of the input
layer is fed into the hidden layer neuron as the input. T
summation function is evaluated in the hidden layer. It
learns every prediction at each node. If any hidden layer
neuron is not completed, then it is fed into the summation
function.
h = ∑ wi.xi
h – Summation function, ∑wi – Weight of theinput
value.
The output of the hidden layer is given as input to theoutput
layer where the sigmoid function O is determined using the
summation function h.
O = 1 / 1+e (-h)
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com
26660 | Volume – 3 | Issue – 5 | July - August 2019
The Yahoo finance data contain BSE data and NSE data. The
financial index of BSE is SENSEX and for NSE is NIFTY. This
finance data contain many attributes of the stock market.
e of the attributes are opening value, closing value,
adjacent close value, min value,maxvalue,volume,dividend,
date and time of the day. The key attributes are selected for
stock market prediction in this work. The collected data are
alization and used as the input data. For
monthly prediction and daily prediction historicaldata from
Regression is a data mining task of predicting thestock price
by implementing a model based on one or more attributes.
This technique is a compact method of mathematical
representation. It represents the relationship between the
response parameter and the input parameter in a given
prediction model. Prediction of theoutcomey fromtheinput
(1)
Partial Regression
The least square value is determined by the current market
the input parameters
(2)
In machine learning, Support vector machine is supervised
learning models with associated learning algorithms to
identify the data. This process includes theclassificationand
hods to analyse the prediction. It implements
the margins adoption to predict the market price. The
plane and it is equal
in distance are identified. The value of support vector y is
(3)
three weights to be
input parameters
The artificial neural network is deep learning at
hidden layer neuron. Artificial Intelligence classifiers are
used to train the prediction model to identify the trends in a
complicated environment. The input value is assigned with
different weights in the input layer. The output of the input
layer is fed into the hidden layer neuron as the input. The
summation function is evaluated in the hidden layer. It
learns every prediction at each node. If any hidden layer
neuron is not completed, then it is fed into the summation
(4)
Weight of theinput,xi–Input
The output of the hidden layer is given as input to theoutput
layer where the sigmoid function O is determined using the
(5)
Figure2: Process of ANN
E. Sentiment Analysis
The expression of each individual varies and it is analysedin
social media data. The polarity of each word is evaluated.
The polarity can be neutral, positive and negative. The
correlation between the news and actual price is
determined. The Sentiment val
For each word in a row do the Sentiment Calculation.
Sent_Word (word,dictionary)
Sent_Word : compare the word with dictionary.
S = Sent_Word (7)
S – Calculates the value of sentiment anlysis of the data.
III. discussion of result
As it can be observed from presented results,
stock prices depend strongly on
their preparation methods and number of appearing
messages per time interval. Predictions conducted with
models trained with datasets with messages containing
company stock symbol performs better. It can be explained
by the fact that these messages refertostock market.Tweets
with company name may just transfer information, which
does not affect financial results.
Another important factor is a choice of preparation of
training set. Two methods were used. One of the methods
was a manual labeling sentiment value of messages. This
method allows to more accurately label training data but is
not effective for creating large training sets. The other
method was applying SentiWordNet, which is a lexical
resource for sentiment opinion mining.
It enabled to create bigger training datasets, which resulted
in building more accurate models. Last factor that is
important for prediction is number of appearing messages
per time interval. Although model trainedwithdatasetswith
company name were not accurateincomparisontotheother
datasets, there is bigger number of tweets per time interval.
It allowed performing prediction f
which were not possible for dataset with messages
containing company stock symbol. Described methods can
be also used with other stock predictions procedures in
order to maintain higher accuracy. It is also important to
note that stock prediction methods are not able to predict
sudden events called ‘black swans’ [7] .
IV. conclusions
The purpose of this study is to compare the performance of
the three prediction algorithms Multiple Linear Regression,
Support Vector Machine, Artificia
stock market. The Multiple Linear Regression
www.ijtsrd.com eISSN: 2456-6470
August 2019 Page 1445
2: Process of ANN
The expression of each individual varies and it is analysedin
social media data. The polarity of each word is evaluated.
The polarity can be neutral, positive and negative. The
correlation between the news and actual price is
determined. The Sentiment value in a row S is initial zero.
For each word in a row do the Sentiment Calculation.
Sent_Word (word,dictionary) (6)
Sent_Word : compare the word with dictionary.
Calculates the value of sentiment anlysis of the data.
discussion of result
As it can be observed from presented results, predictions of
stock prices depend strongly on choice of training dataset,
methods and number of appearing
time interval. Predictions conducted with
trained with datasets with messages containing
company stock symbol performs better. It can be explained
by the fact that these messages refertostock market.Tweets
with company name may just transfer information, which
does not affect financial results.
Another important factor is a choice of preparation of
training set. Two methods were used. One of the methods
was a manual labeling sentiment value of messages. This
method allows to more accurately label training data but is
large training sets. The other
applying SentiWordNet, which is a lexical
resource for sentiment opinion mining.
It enabled to create bigger training datasets, which resulted
in building more accurate models. Last factor that is
ediction is number of appearing messages
per time interval. Although model trainedwithdatasetswith
company name were not accurateincomparisontotheother
datasets, there is bigger number of tweets per time interval.
It allowed performing prediction for shorter time intervals,
which were not possible for dataset with messages
containing company stock symbol. Described methods can
be also used with other stock predictions procedures in
order to maintain higher accuracy. It is also important to
stock prediction methods are not able to predict
sudden events called ‘black swans’ [7] .
The purpose of this study is to compare the performance of
the three prediction algorithms Multiple Linear Regression,
Support Vector Machine, Artificial Neural Network in the
stock market. The Multiple Linear Regression
3. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD26660 | Volume – 3 | Issue – 5 | July - August 2019 Page 1446
algorithm is less developed state which measures the
relationship between the stock price and volume. The
Support Vector Machine algorithm is a two-class classifier
for the learning model.
The Artificial Neural Network is the classification algorithm
for deep learning. The result exhibits that the deep learning
algorithm performs better than the MLR and SVM. In deep
learning algorithm the hidden layer neuron learns
in every prediction. Hence the output layerneuronproduces
the best outcome. Artificial Neural Network is the best
predicting algorithm.
References
[1] Z. Da, J. Engelberg, P. Gao: In Search of Attention,
The Journal of FinanceVolume66,Issue5,pages 1461–
1499, October 2011, doi: 10.1111/j.1540-
6261.2011.01679.x
[2] J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C.
Roxburgh, and A.H. Byers. Big data: The next frontier
for innovation, competition, and productivity,
McKinsey, May 2011..
[3] Edd Dumbill. What is big data? : an introduction to
the big data landscape. http://
radar.oreilly.com/2012/01/what-is big-data.html,
2012 R. Nicole, “Title of paper with only first word
capitalized,” J. Name Stand. Abbrev., in press.
[4] What is Twitter and How does it work?
http://www.lifewire.com/ what-is –twitter.
[5] Hagenau, Michael, Michael Liebmann, Markus
Hedwig, and Dirk Neumann. "Automated news reading:
Stock price prediction based on financial news using
context-specific features." In System Science (HICSS),
2012 45th Hawaii International Conference on, pp.
1040-1049. IEEE, 2012.
[6] Khan, Zabir Haider, Tasnim Sharmin Alin, and Md
Akter Hussain. "Price prediction of share market using
artificial neural network (ANN)." InternationalJournal
of Computer Applications 22, no. 2 (2011): 42-47
[7] N. N. Taleb, Common Errors in the Interpretation
of the Ideas of Then Black Swan andAssociatedPapers.