World Wide Web plays an important role in providing various knowledge sources to the world, which helps many applications to provide quality service to the consumers. As the years go on the web is overloaded with lot of information and it becomes very hard to extract the relevant information from the web. This gives way to the evolution of the Big Data and the volume of the data keeps increasing rapidly day by day. Data mining techniques are used to find the hidden information from the big data. In this paper we focus on the review of Big Data, its data classification methods and the way it can be mined using various mining methods.
An Investigation on Scalable and Efficient Privacy Preserving Challenges for ...IJERDJOURNAL
ABSTRACT:- Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big data refers to huge amount of digital information collected from multiple and different sources. With the development of application of Internet/Mobile Internet, social networks, Internet of Things, big data has become the hot topic of research across the world, at the same time; big data faces security risks and privacy protection during collecting, storing, analyzing and utilizing. Since a key point of big data is to access data from multiple and different domains security and privacy will play an important role in big data research and technology. Traditional security mechanisms, which are used to secure small scale static data, are inadequate. So the question is which security and privacy technology is adequate for efficient access to big data. This paper introduces the functions of big data, and the security threat faced by big data, then proposes the technology to solve the security threat, finally, discusses the applications of big data in information security. Main expectation from the focused challenges is that it will bring a novel focus on the big data infrastructure.
Big data analytics and its impact on internet usersStruggler Ever
Big Data Analytic tools are promising techniques for a future prediction in many aspects of our life. The need for such predictive techniques has been exponentially increasing. even though, there are many challenges and risks are still of concern of researchers and decision makers, the outcome from the use of these techniques will considerable revolutionize our world to a new era of technology.
An Investigation on Scalable and Efficient Privacy Preserving Challenges for ...IJERDJOURNAL
ABSTRACT:- Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big data refers to huge amount of digital information collected from multiple and different sources. With the development of application of Internet/Mobile Internet, social networks, Internet of Things, big data has become the hot topic of research across the world, at the same time; big data faces security risks and privacy protection during collecting, storing, analyzing and utilizing. Since a key point of big data is to access data from multiple and different domains security and privacy will play an important role in big data research and technology. Traditional security mechanisms, which are used to secure small scale static data, are inadequate. So the question is which security and privacy technology is adequate for efficient access to big data. This paper introduces the functions of big data, and the security threat faced by big data, then proposes the technology to solve the security threat, finally, discusses the applications of big data in information security. Main expectation from the focused challenges is that it will bring a novel focus on the big data infrastructure.
Big data analytics and its impact on internet usersStruggler Ever
Big Data Analytic tools are promising techniques for a future prediction in many aspects of our life. The need for such predictive techniques has been exponentially increasing. even though, there are many challenges and risks are still of concern of researchers and decision makers, the outcome from the use of these techniques will considerable revolutionize our world to a new era of technology.
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 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.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
- Know how big data can be turned into smart data
- Be able to apply the key terms regarding big data
Duration of the module: approximately 1 – 2 hours
Story of Bigdata and its Applications in Financial Institutionsijtsrd
The importance of BigData is indeed nothing new, but being able to manage data efficiently is just now becoming more attainable. Although data management has evolved considerably since the 1800's, advancements made in recent years that have made the process even more efficient. Technique of Data mining, is much used in the banking industry, which helps banks compete in the market and provide the right product to the right customer. While collecting and combining different sources of data into a single significant volumetric Golden Source of TRUTH can be achieved by applying the right combination of tools. In this paper Author introduced BIGDATA technologies in brief along with its applications. Phani Bhooshan | Dr. C. Umashankar "Story of Bigdata and its Applications in Financial Institutions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29145.pdf Paper URL: https://www.ijtsrd.com/computer-science/database/29145/story-of-bigdata-and-its-applications-in-financial-institutions/phani-bhooshan
Implementation of application for huge data file transferijwmn
Nowadays big data transfers make people’s life difficult. During the big data transfer, people waste so
much time. Big data pool grows everyday by sharing data. People prefer to keep their backups at the cloud
systems rather than their computers. Furthermore considering the safety of cloud systems, people prefer to
keep their data at the cloud systems instead of their computers. When backups getting too much size, their
data transfer becomes nearly impossible. It is obligated to transfer data with various algorithms for moving
data from one place to another. These algorithms constituted for transferring data faster and safer. In this
Project, an application has been developed to transfer of the huge files. Test results show its efficiency and
success.
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Big Data is the new technology or science to make the well informed decision in
business or any other science discipline with huge volume of data from new sources of
heterogeneous data. . Such new sources include blogs, online media, social network, sensor network,
image data and other forms of data which vary in volume, structure, format and other factors. Big
Data applications are increasingly adopted in all science and engineering domains, including space
science, biomedical sciences and astronomic and deep space studies. The major challenges of big
data mining are in data accessing and processing, data privacy and mining algorithms. This paper
includes the information about what is big data, data mining with big data, the challenges in big data
mining and what are the currently available solutions to meet those challenges.
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
A REVIEW ON CLASSIFICATION OF DATA IMBALANCE USING BIGDATAIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories
or collection of data to provide more accurate predictions and analysis. Classification using supervised
learning method aims to identify the category of the class to which a new data will fall under. With the
advancement of technology and increase in the generation of real-time data from various sources like
Internet, IoT and Social media it needs more processing and challenging. One such challenge in
processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes
causing the machine learning classifiers to be more biased towards majority classes and also most
classification algorithm predicts all the test data with majority classes. In this paper, the author analysis
the data imbalance models using big data and classification algorithm
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
Big Data Analytics Presentation at International Workshop Colloquium Exploring Research Opportunity. School of Business and Management (SBM) - ITB. Bandung, 8 August 2019.
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 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.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
- Know how big data can be turned into smart data
- Be able to apply the key terms regarding big data
Duration of the module: approximately 1 – 2 hours
Story of Bigdata and its Applications in Financial Institutionsijtsrd
The importance of BigData is indeed nothing new, but being able to manage data efficiently is just now becoming more attainable. Although data management has evolved considerably since the 1800's, advancements made in recent years that have made the process even more efficient. Technique of Data mining, is much used in the banking industry, which helps banks compete in the market and provide the right product to the right customer. While collecting and combining different sources of data into a single significant volumetric Golden Source of TRUTH can be achieved by applying the right combination of tools. In this paper Author introduced BIGDATA technologies in brief along with its applications. Phani Bhooshan | Dr. C. Umashankar "Story of Bigdata and its Applications in Financial Institutions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29145.pdf Paper URL: https://www.ijtsrd.com/computer-science/database/29145/story-of-bigdata-and-its-applications-in-financial-institutions/phani-bhooshan
Implementation of application for huge data file transferijwmn
Nowadays big data transfers make people’s life difficult. During the big data transfer, people waste so
much time. Big data pool grows everyday by sharing data. People prefer to keep their backups at the cloud
systems rather than their computers. Furthermore considering the safety of cloud systems, people prefer to
keep their data at the cloud systems instead of their computers. When backups getting too much size, their
data transfer becomes nearly impossible. It is obligated to transfer data with various algorithms for moving
data from one place to another. These algorithms constituted for transferring data faster and safer. In this
Project, an application has been developed to transfer of the huge files. Test results show its efficiency and
success.
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Big Data is the new technology or science to make the well informed decision in
business or any other science discipline with huge volume of data from new sources of
heterogeneous data. . Such new sources include blogs, online media, social network, sensor network,
image data and other forms of data which vary in volume, structure, format and other factors. Big
Data applications are increasingly adopted in all science and engineering domains, including space
science, biomedical sciences and astronomic and deep space studies. The major challenges of big
data mining are in data accessing and processing, data privacy and mining algorithms. This paper
includes the information about what is big data, data mining with big data, the challenges in big data
mining and what are the currently available solutions to meet those challenges.
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
A REVIEW ON CLASSIFICATION OF DATA IMBALANCE USING BIGDATAIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories
or collection of data to provide more accurate predictions and analysis. Classification using supervised
learning method aims to identify the category of the class to which a new data will fall under. With the
advancement of technology and increase in the generation of real-time data from various sources like
Internet, IoT and Social media it needs more processing and challenging. One such challenge in
processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes
causing the machine learning classifiers to be more biased towards majority classes and also most
classification algorithm predicts all the test data with majority classes. In this paper, the author analysis
the data imbalance models using big data and classification algorithm
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
Big Data Analytics Presentation at International Workshop Colloquium Exploring Research Opportunity. School of Business and Management (SBM) - ITB. Bandung, 8 August 2019.
Encroachment in Data Processing using Big Data TechnologyMangaiK4
Abstract—The nature of big data is now growing and information is present all around us in different kind of forms. The big data information plays crucial role and it provides business value for the firms and its benefits sectors by accumulating knowledge. This growth of big data around all the concerns is high and challenge in data processing technique because it contains variety of data in enormous volume. The tools which are built on the data mining algorithm provides efficient data processing mechanisms, but not fulfill the pattern of heterogeneous, so the emerging tools such like Hadoop MapReduce, Pig, SPARK, Cloudera, Impala and Enterprise RTQ, IBM Netezza and Apache Giraphe as computing tools and HBase, Hive, Neo4j and Apache Cassendra as storage tools useful in classifying, clustering and discovering the knowledge. This study will focused on the comparative study of different data processing tools, in big data analytics and their benefits will be tabulated.
Isolating values from big data with the help of four v’seSAT Journals
Abstract
Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. It includes business transactions, e-mail messages, photos, surveillance videos and activity logs. It also includes unstructured text posted on the Web, such as blogs and social media. Big Data has shown lot of potential in real world industry and research community. We support the power and Potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 4 Vs. 4th V as ‘Value’ is desired output for industry challenges and issues. We provide a brief survey study of 4 Vs. of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.
Keywords: Big Data, Surveillance videos, blogs, social media, four Vs.
Project 3 – Hollywood and IT· Find 10 incidents of Hollywood p.docxstilliegeorgiana
Project 3 – Hollywood and IT
· Find 10 incidents of Hollywood portraying IT security incorrectly
· You can use movies or TV episodes
· Write 2-5 paragraphs for each incident. Use supporting citations for each part.
· What has Hollywood portrayed wrong? Describe the scene and what is being shown. Make sure to state whether it is partially wrong or totally fictitious.
· How would you protect/secure against what they show (answers might include install firewall, load Antivirus etc.)
· Use APA formatting for your sources on everything.
· Make sure to put your name on assignment.
Big Data and Social Media
Colgate Palmolive
Agenda Of socail media use
Buisness intellegence and Social media concenpts
Intellegent organization
Data Anaylysis and Data trustworthiness
Conclusion
Buisness intellegence and Social media concenpts
No-Hassle Documentation
Gain Trusted Followers
Spy on Competition
Learn Customer Demographics
Research and Analyze Events
Advertise More Accurately
Intellegent organization
They consistently use (big) data proactively
They know exactly where they want to go: all-round vision
They continuously discuss business matters: alignment
They talk to each other regarding positive and negative performance
They know their customers through and through
They think and work in an agile way
Data Anaylysis and Data trustworthiness
Data completeness and accuracy
Data credibility
Data consistency
Data processing and algorithms
Data Validity
Conclusion
How Colgate benefit from Big Data and Social Media
Social media increases sales and customers
Big data shows popular trends and popular companies
All around they are both beneficial
Big Data can find trends that can benefit you greatly
Criteria
Title Page:
Name, Contact info, title of Presentation
Slide 1
Adenda : Topic you going to cover in order
Slide 2
Discuss how big data, social media concepts and knowledge to successfully create business intellegence (Support your bullets points with data, analysis, charts)
Slide 3
Describe how big data can be used to build an intelligent organization
Slide 4
Discuss the importance of data source trustworthiness and data analysis
Slide 5
Conclusion
Slide 6
Big Data And Business Intelligence
Business Value With Big Data
For business to survive in a competitive environment, organizational change requires improved governance, sponsorship, processes, and controls, in addition to new skill sets and technology all work in harmony to deliver the benefits of big data. See Fig. 13.2
Data science has taken the business world by storm. Every field of study and area of business has been affected as companies realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de fac to programming language for data science. Its flexibility, power, sophistication, and expressiveness have ma ...
Introduction to Big Data: Definition, Characteristic Features, Big Data Applications, Big Data vs Traditional Data, Risks of Big Data, Structure of Big Data, Challenges of Conventional Systems, Web Data, Evolution of Analytic Scalability, Evolution of Analytic Processes, Tools and methods, Analysis vs Reporting, Modern Data Analytic Tools
Here's how big data and the Internet of Things work together: a vast network of sensors (IoT) collect a boatload of information (big data) that is then used to improve services and products in various industries, which in turn generate revenue.
Real World Application of Big Data In Data Mining Toolsijsrd.com
The main aim of this paper is to make a study on the notion Big data and its application in data mining tools like R, Weka, Rapidminer, Knime,Mahout and etc. We are awash in a flood of data today. In a broad range of application areas, data is being collected at unmatched scale. Decisions that previously were based on surmise, or on painstakingly constructed models of reality, can now be made based on the data itself. Such Big Data analysis now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences. The paper mainly focuses different types of data mining tools and its usage in big data in knowledge discovery.
Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.
The software development process is complete for computer project analysis, and it is important to the evaluation of the random project. These practice guidelines are for those who manage big-data and big-data analytics projects or are responsible for the use of data analytics solutions. They are also intended for business leaders and program leaders that are responsible for developing agency capability in the area of big data and big data analytics .
For those agencies currently not using big data or big data analytics, this document may assist strategic planners, business teams and data analysts to consider the value of big data to the current and future programs.
This document is also of relevance to those in industry, research and academia who can work as partners with government on big data analytics projects.
Technical APS personnel who manage big data and/or do big data analytics are invited to join the Data Analytics Centre of Excellence Community of Practice to share information of technical aspects of big data and big data analytics, including achieving best practice with modeling and related requirements. To join the community, send an email to the Data Analytics Centre of Excellence
Similar to Big Data: Review, Classification and Analysis Survey (20)
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas