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.
The document discusses tools and techniques for big data analytics, including A/B testing, crowdsourcing, machine learning, and data mining. It provides an overview of the big data analysis pipeline, including data acquisition, information extraction, integration and representation, query processing and analysis, and interpretation. The document also discusses fields where big data is relevant like industry, healthcare, and research. It analyzes tools like A/B testing, machine learning, and data mining techniques in more detail.
A survey of big data and machine learning IJECEIAES
This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.
Data Management for Internet of things : A Survey and DiscussionIRJET Journal
This document discusses data management for the Internet of Things (IoT). It begins with an abstract that outlines the need for improved data management techniques to handle the massive volumes of data produced by IoT devices. The document then provides background on IoT data characteristics that make traditional database solutions inadequate. It surveys current research in IoT data management and proposes a framework that considers the full data lifecycle from collection to deletion. Finally, it performs a gap analysis of existing solutions based on factors like data format, storage architecture, processing speed, and server response time.
IRJET- Comparative Analysis of Various Tools for Data Mining and Big Data...IRJET Journal
This document compares and analyzes various tools for data mining and big data mining. It discusses traditional open source data mining tools like Orange, R, Weka, Shogun, Rapid Miner and KNIME. Each tool has different capabilities for data preprocessing, machine learning algorithms, visualization, platforms and programming languages. The document aims to help researchers select the most appropriate data mining tool for their needs and research.
KM - Cognitive Computing overview by Ken Martin 13Apr2016HCL Technologies
This document provides an introduction to cognitive computing and how it relates to knowledge management strategies. It begins with an overview of Ken Martin's background and the agenda. It then defines key cognitive computing concepts and technologies like natural language processing, machine learning, and pattern recognition. The document contrasts traditional and cognitive systems, noting cognitive systems are interactive, self-learning, and expand conversations. It maps cognitive capabilities to the KM lifecycle, showing how capabilities like natural language processing, text mining, and social network analysis can enhance each stage.
Knowledge Management and Predictive Analytics in IT Project Risksijtsrd
"Knowledge management and predictive analytics are considered to be unusual partners in today’s technology. However, they can be very good tools that would solve current problems in valuing data. Predictive analytics has now become one of the forecasting tools that is of huge help in information management. Its application in IT project development risk management is very important, where a lot of raw data is involved with risk analysis and prediction. The use of IT project risk management as supported by knowledge management KM will help increase the success rate of IT projects. Knowledge management will bring about additional value to the data needed. This paper presents the usage of KM and predictive analytics to increase the success ratings of projects by predicting the risks that might happen during project development. It explores how KM and predictive analytics can identify risks in IT project development and give recommendations in evaluating the risks that could affect successful completion of IT projects. Mia Torres-Dela Cruz | Subashini A/P Ganapathy | Noor Zuhaili Binti Mohd Yasin ""Knowledge Management and Predictive Analytics in IT Project Risks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19142.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/19142/knowledge-management-and-predictive-analytics-in-it-project-risks/mia-torres-dela-cruz"
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
The document discusses tools and techniques for big data analytics, including A/B testing, crowdsourcing, machine learning, and data mining. It provides an overview of the big data analysis pipeline, including data acquisition, information extraction, integration and representation, query processing and analysis, and interpretation. The document also discusses fields where big data is relevant like industry, healthcare, and research. It analyzes tools like A/B testing, machine learning, and data mining techniques in more detail.
A survey of big data and machine learning IJECEIAES
This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.
Data Management for Internet of things : A Survey and DiscussionIRJET Journal
This document discusses data management for the Internet of Things (IoT). It begins with an abstract that outlines the need for improved data management techniques to handle the massive volumes of data produced by IoT devices. The document then provides background on IoT data characteristics that make traditional database solutions inadequate. It surveys current research in IoT data management and proposes a framework that considers the full data lifecycle from collection to deletion. Finally, it performs a gap analysis of existing solutions based on factors like data format, storage architecture, processing speed, and server response time.
IRJET- Comparative Analysis of Various Tools for Data Mining and Big Data...IRJET Journal
This document compares and analyzes various tools for data mining and big data mining. It discusses traditional open source data mining tools like Orange, R, Weka, Shogun, Rapid Miner and KNIME. Each tool has different capabilities for data preprocessing, machine learning algorithms, visualization, platforms and programming languages. The document aims to help researchers select the most appropriate data mining tool for their needs and research.
KM - Cognitive Computing overview by Ken Martin 13Apr2016HCL Technologies
This document provides an introduction to cognitive computing and how it relates to knowledge management strategies. It begins with an overview of Ken Martin's background and the agenda. It then defines key cognitive computing concepts and technologies like natural language processing, machine learning, and pattern recognition. The document contrasts traditional and cognitive systems, noting cognitive systems are interactive, self-learning, and expand conversations. It maps cognitive capabilities to the KM lifecycle, showing how capabilities like natural language processing, text mining, and social network analysis can enhance each stage.
Knowledge Management and Predictive Analytics in IT Project Risksijtsrd
"Knowledge management and predictive analytics are considered to be unusual partners in today’s technology. However, they can be very good tools that would solve current problems in valuing data. Predictive analytics has now become one of the forecasting tools that is of huge help in information management. Its application in IT project development risk management is very important, where a lot of raw data is involved with risk analysis and prediction. The use of IT project risk management as supported by knowledge management KM will help increase the success rate of IT projects. Knowledge management will bring about additional value to the data needed. This paper presents the usage of KM and predictive analytics to increase the success ratings of projects by predicting the risks that might happen during project development. It explores how KM and predictive analytics can identify risks in IT project development and give recommendations in evaluating the risks that could affect successful completion of IT projects. Mia Torres-Dela Cruz | Subashini A/P Ganapathy | Noor Zuhaili Binti Mohd Yasin ""Knowledge Management and Predictive Analytics in IT Project Risks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19142.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/19142/knowledge-management-and-predictive-analytics-in-it-project-risks/mia-torres-dela-cruz"
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
This document summarizes a research paper that proposes using an ensemble of k-nearest neighbor (k-NN) classifiers with genetic programming to improve network intrusion detection. The researchers trained classifiers on the KDD Cup 1999 dataset, which contains network traffic labeled as normal or an attack of various types. They preprocessed the data to remove redundancy and applied feature selection before training. The ensemble of k-NN classifiers classified data into five categories - one normal and four attack types - and achieved 99.97% accuracy on testing after genetic programming optimized the ensemble.
Rao Mikkilineni discusses the emergence of cognitive computing models and a new cognitive infrastructure. He argues that increasing data volumes and the need for real-time insights are driving the need for intelligent, sentient, and resilient systems. The new cognitive infrastructure will include a cognitive and infrastructure agnostic control overlay, composable services, and cognitive deep learning integration. It will enable a post-hypervisor cognitive computing era with intelligent, distributed systems.
An overview of information extraction techniques for legal document analysis ...IJECEIAES
In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing.
Data Mining And Visualization of Large DatabasesCSCJournals
Data Mining and Visualization are tools that are used in databases to further analyse and understand the stored data. Data mining and visualization are knowledge discovery tools used to find hidden patterns and to visualize the data distribution. In the paper, we shall illustrate how data mining and visualization are used in large databases to find patterns and traits hidden within. In large databases where data is both large and seemingly random, mining and visualization help to find the trends found in such large sets. We shall look at the developments of data mining and visualization and what kind of application fields usage of such tools. Finally, we shall touch upon the future developments and newer trends in data mining and visualization being experimented for future use.
What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
Full Paper: Analytics: Key to go from generating big data to deriving busines...Piyush Malik
This document discusses how analytics can help organizations derive business value from big data. It describes how statistical analysis, machine learning, optimization and text mining can extract meaningful insights from social media, online commerce, telecommunications, smart utility meters, and improve security. While tools exist to analyze big data, challenges remain around data security, privacy, and developing skilled talent. The paper aims to illustrate how existing algorithms can generate value from different industry use cases.
Finely Chair talk: Every company is an AI company - and why Universities sho...Amit Sheth
Video: https://youtu.be/ZS8rGSzb_9I
The context of this talk is this statement from the host institution's provost: "We are trying to mobilize our campus activities around AI.” I connect academic initiatives in Interdisciplinary AI with industry needs.
--- Original abstract -----
Every company now is an AI company: Now, Near Future, or Distant Future?
Amit Sheth, AI Institute, University of South Carolina
“Every company now is an AI company. The industrial companies are changing, the supply chain…every single sector, it’s not only tech.” said Steven Pagliuca, CEO of Bain Capital at the 2019 World Economic Forum. With this statement as the context, I will provide an overview of AI landscape -- what AI capabilities are for real, what is being oversold, what is nonexistent, what is unlikely in our lifetime. I will also provide an anecdote-supported review through a broad variety of current and eminent applications of AI that rely on some of the well-developed and emerging AI capabilities. The objective is to help those considering AI applications start thinking of new business opportunities, new products and services, and new revenue/business models in the context of rapid penetration of AI technologies everywhere. I will seek to answer: Is AI just hype or something already happening? If it has not happened in your industry, is it impending? Do bad impacts of AI outweigh the good?
Electronics health records and business analytics a cloud based approachIAEME Publication
This document discusses using business analytics and cloud computing to analyze electronic health records (EHRs). It proposes using pattern recognition algorithms within an intelligent agent on the cloud to better utilize resources and optimize the time needed to analyze EHR requests. The rest of the document outlines related work involving EHR and cloud environments, business scopes and trends related to EHR investments, and a proposed architectural model.
Role of artificial intelligence in cloud computing, IoT and SDN: Reliability ...IJECEIAES
Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps.
The effect of technology-organization-environment on adoption decision of bi...IJECEIAES
Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organizationenvironment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
Artificial intelligence: Simulation of IntelligenceAbhishek Upadhyay
1. The document discusses the history and development of artificial intelligence and machine learning, from early concepts in probability and statistics in the 18th century to modern algorithms and applications.
2. It outlines important early milestones like the McCulloch-Pitts neural network model from 1943 and the Turing Test in 1950. Major algorithms like perceptron and modern frameworks like TensorFlow are also mentioned.
3. The text advocates for applying machine learning to solve real-world business problems by understanding the problem domain, acquiring relevant data, selecting an appropriate algorithm, and iterating through the problem solving process.
Evidence Data Preprocessing for Forensic and Legal AnalyticsCSCJournals
The document discusses best practices for preprocessing evidentiary data from legal cases or forensic investigations for use in analytical experiments. It outlines key steps like identifying the analytical aim or problem based on the case scope or investigation protocol, understanding the case data through assessment and exploration of its format, features, quality, and potential issues. Challenges of working with common text-based case data like emails, social media posts are also discussed. The goal is to clean and transform raw data into a suitable format for machine learning or other advanced analytical techniques while maintaining integrity and relevance to the case.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...ijdpsjournal
The paper aims at proposing a solution for designing and developing a seamless automation and integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to seamlessly handle and scale very large amount of unstructured and structured data from diversified and heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing component; 3) the ability to automatically select the most appropriate libraries and tools to compute and accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different application problem domains, with high accuracy, robustness, and scalability. This paper highlights the research methodologies and research activities that we propose to be conducted by the Big Data researchers and practitioners in order to develop and support seamless automation and integration of machine learning capabilities for Big Data analytics.
Content an Insight to Security Paradigm for BigData on Cloud: Current Trend a...IJECEIAES
The sucesssive growth of collabrative applications producing Bigdata on timeline leads new opprutinity to setup commodities on cloud infrastructure. Mnay organizations will have demand of an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presented
This document discusses big data challenges for e-governance systems in distributed systems. It proposes a map reducing architecture model to provide the basics for building interoperable big data infrastructure for e-governance. The model includes stages for data management, access control, and security. It explains how this can be implemented using distributed structures and a provisioning model. Key challenges addressed include the exponential growth of data from various sources, need for data sharing across organizations, and ensuring security, access control, and data integrity in distributed systems.
New approaches of Data Mining for the Internet of things with systems: Litera...IRJET Journal
This document provides a literature review of new approaches to data mining for the Internet of Things (IoT). It discusses data mining from three perspectives: data view, method view, and application view. In the data view, it examines data mining functions like classification, clustering, association analysis, time series analysis, and anomaly detection. In the method view, it reviews machine learning, statistical, and deep learning techniques used for data mining. In the application view, it discusses data mining applications in industries like business, healthcare, and government. It also discusses challenges of big data mining for IoT and proposes a framework for large-scale IoT data mining using open source tools.
The document describes an AI-driven Occupational Skills Generator (AIOSG) that aims to automate the process of creating occupational skills reference documents. The AIOSG utilizes an intelligent web crawler, natural language processing, neural networks, and a blockchain to gather data on occupational skills from various sources, analyze the data, and generate standardized skills reference documents. It is intended to reduce the time and resources required to manually produce these documents while ensuring more comprehensive and up-to-date skills information. The AIOSG system architecture and its use of analytics, artificial intelligence, and blockchain technologies are explained in detail.
IRJET- Big Data Privacy and Security Challenges in IndustriesIRJET Journal
This document discusses the privacy and security challenges of big data in industries. It begins by defining big data as extremely large data sets used for computational analysis. The three main characteristics of big data are volume, velocity, and variety. The document then discusses related work on big data privacy and security, including categories of big data privacy like data privacy, data administration, integrity security, and framework security. It outlines challenges related to privacy like inaccurate analytics and security challenges like real-time monitoring and protected database storage. The document concludes that big data brings benefits but also privacy and security challenges that must be addressed through appropriate protocols and further research.
Big data analytics in Business Management and Businesss Intelligence: A Lietr...IRJET Journal
This document discusses big data analytics and its role in business management and business intelligence. It provides an overview of big data analytics, how it differs from traditional data analysis methods, and how organizations can use big data analytics to improve performance. Some key points include:
- Big data analytics uses large, complex datasets from various sources to uncover hidden patterns and trends for business insights.
- It differs from traditional analytics in its ability to handle larger, more unstructured data in real-time.
- Organizations can use big data analytics across various business functions like supply chain, marketing, and HR to improve decision-making and gain competitive advantages.
- When combined with business intelligence, big data analytics provides insights that can improve customer
This document summarizes a research paper that proposes using an ensemble of k-nearest neighbor (k-NN) classifiers with genetic programming to improve network intrusion detection. The researchers trained classifiers on the KDD Cup 1999 dataset, which contains network traffic labeled as normal or an attack of various types. They preprocessed the data to remove redundancy and applied feature selection before training. The ensemble of k-NN classifiers classified data into five categories - one normal and four attack types - and achieved 99.97% accuracy on testing after genetic programming optimized the ensemble.
Rao Mikkilineni discusses the emergence of cognitive computing models and a new cognitive infrastructure. He argues that increasing data volumes and the need for real-time insights are driving the need for intelligent, sentient, and resilient systems. The new cognitive infrastructure will include a cognitive and infrastructure agnostic control overlay, composable services, and cognitive deep learning integration. It will enable a post-hypervisor cognitive computing era with intelligent, distributed systems.
An overview of information extraction techniques for legal document analysis ...IJECEIAES
In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing.
Data Mining And Visualization of Large DatabasesCSCJournals
Data Mining and Visualization are tools that are used in databases to further analyse and understand the stored data. Data mining and visualization are knowledge discovery tools used to find hidden patterns and to visualize the data distribution. In the paper, we shall illustrate how data mining and visualization are used in large databases to find patterns and traits hidden within. In large databases where data is both large and seemingly random, mining and visualization help to find the trends found in such large sets. We shall look at the developments of data mining and visualization and what kind of application fields usage of such tools. Finally, we shall touch upon the future developments and newer trends in data mining and visualization being experimented for future use.
What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
Full Paper: Analytics: Key to go from generating big data to deriving busines...Piyush Malik
This document discusses how analytics can help organizations derive business value from big data. It describes how statistical analysis, machine learning, optimization and text mining can extract meaningful insights from social media, online commerce, telecommunications, smart utility meters, and improve security. While tools exist to analyze big data, challenges remain around data security, privacy, and developing skilled talent. The paper aims to illustrate how existing algorithms can generate value from different industry use cases.
Finely Chair talk: Every company is an AI company - and why Universities sho...Amit Sheth
Video: https://youtu.be/ZS8rGSzb_9I
The context of this talk is this statement from the host institution's provost: "We are trying to mobilize our campus activities around AI.” I connect academic initiatives in Interdisciplinary AI with industry needs.
--- Original abstract -----
Every company now is an AI company: Now, Near Future, or Distant Future?
Amit Sheth, AI Institute, University of South Carolina
“Every company now is an AI company. The industrial companies are changing, the supply chain…every single sector, it’s not only tech.” said Steven Pagliuca, CEO of Bain Capital at the 2019 World Economic Forum. With this statement as the context, I will provide an overview of AI landscape -- what AI capabilities are for real, what is being oversold, what is nonexistent, what is unlikely in our lifetime. I will also provide an anecdote-supported review through a broad variety of current and eminent applications of AI that rely on some of the well-developed and emerging AI capabilities. The objective is to help those considering AI applications start thinking of new business opportunities, new products and services, and new revenue/business models in the context of rapid penetration of AI technologies everywhere. I will seek to answer: Is AI just hype or something already happening? If it has not happened in your industry, is it impending? Do bad impacts of AI outweigh the good?
Electronics health records and business analytics a cloud based approachIAEME Publication
This document discusses using business analytics and cloud computing to analyze electronic health records (EHRs). It proposes using pattern recognition algorithms within an intelligent agent on the cloud to better utilize resources and optimize the time needed to analyze EHR requests. The rest of the document outlines related work involving EHR and cloud environments, business scopes and trends related to EHR investments, and a proposed architectural model.
Role of artificial intelligence in cloud computing, IoT and SDN: Reliability ...IJECEIAES
Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps.
The effect of technology-organization-environment on adoption decision of bi...IJECEIAES
Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organizationenvironment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
Artificial intelligence: Simulation of IntelligenceAbhishek Upadhyay
1. The document discusses the history and development of artificial intelligence and machine learning, from early concepts in probability and statistics in the 18th century to modern algorithms and applications.
2. It outlines important early milestones like the McCulloch-Pitts neural network model from 1943 and the Turing Test in 1950. Major algorithms like perceptron and modern frameworks like TensorFlow are also mentioned.
3. The text advocates for applying machine learning to solve real-world business problems by understanding the problem domain, acquiring relevant data, selecting an appropriate algorithm, and iterating through the problem solving process.
Evidence Data Preprocessing for Forensic and Legal AnalyticsCSCJournals
The document discusses best practices for preprocessing evidentiary data from legal cases or forensic investigations for use in analytical experiments. It outlines key steps like identifying the analytical aim or problem based on the case scope or investigation protocol, understanding the case data through assessment and exploration of its format, features, quality, and potential issues. Challenges of working with common text-based case data like emails, social media posts are also discussed. The goal is to clean and transform raw data into a suitable format for machine learning or other advanced analytical techniques while maintaining integrity and relevance to the case.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...ijdpsjournal
The paper aims at proposing a solution for designing and developing a seamless automation and integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to seamlessly handle and scale very large amount of unstructured and structured data from diversified and heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing component; 3) the ability to automatically select the most appropriate libraries and tools to compute and accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different application problem domains, with high accuracy, robustness, and scalability. This paper highlights the research methodologies and research activities that we propose to be conducted by the Big Data researchers and practitioners in order to develop and support seamless automation and integration of machine learning capabilities for Big Data analytics.
Content an Insight to Security Paradigm for BigData on Cloud: Current Trend a...IJECEIAES
The sucesssive growth of collabrative applications producing Bigdata on timeline leads new opprutinity to setup commodities on cloud infrastructure. Mnay organizations will have demand of an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presented
This document discusses big data challenges for e-governance systems in distributed systems. It proposes a map reducing architecture model to provide the basics for building interoperable big data infrastructure for e-governance. The model includes stages for data management, access control, and security. It explains how this can be implemented using distributed structures and a provisioning model. Key challenges addressed include the exponential growth of data from various sources, need for data sharing across organizations, and ensuring security, access control, and data integrity in distributed systems.
New approaches of Data Mining for the Internet of things with systems: Litera...IRJET Journal
This document provides a literature review of new approaches to data mining for the Internet of Things (IoT). It discusses data mining from three perspectives: data view, method view, and application view. In the data view, it examines data mining functions like classification, clustering, association analysis, time series analysis, and anomaly detection. In the method view, it reviews machine learning, statistical, and deep learning techniques used for data mining. In the application view, it discusses data mining applications in industries like business, healthcare, and government. It also discusses challenges of big data mining for IoT and proposes a framework for large-scale IoT data mining using open source tools.
The document describes an AI-driven Occupational Skills Generator (AIOSG) that aims to automate the process of creating occupational skills reference documents. The AIOSG utilizes an intelligent web crawler, natural language processing, neural networks, and a blockchain to gather data on occupational skills from various sources, analyze the data, and generate standardized skills reference documents. It is intended to reduce the time and resources required to manually produce these documents while ensuring more comprehensive and up-to-date skills information. The AIOSG system architecture and its use of analytics, artificial intelligence, and blockchain technologies are explained in detail.
IRJET- Big Data Privacy and Security Challenges in IndustriesIRJET Journal
This document discusses the privacy and security challenges of big data in industries. It begins by defining big data as extremely large data sets used for computational analysis. The three main characteristics of big data are volume, velocity, and variety. The document then discusses related work on big data privacy and security, including categories of big data privacy like data privacy, data administration, integrity security, and framework security. It outlines challenges related to privacy like inaccurate analytics and security challenges like real-time monitoring and protected database storage. The document concludes that big data brings benefits but also privacy and security challenges that must be addressed through appropriate protocols and further research.
Big data analytics in Business Management and Businesss Intelligence: A Lietr...IRJET Journal
This document discusses big data analytics and its role in business management and business intelligence. It provides an overview of big data analytics, how it differs from traditional data analysis methods, and how organizations can use big data analytics to improve performance. Some key points include:
- Big data analytics uses large, complex datasets from various sources to uncover hidden patterns and trends for business insights.
- It differs from traditional analytics in its ability to handle larger, more unstructured data in real-time.
- Organizations can use big data analytics across various business functions like supply chain, marketing, and HR to improve decision-making and gain competitive advantages.
- When combined with business intelligence, big data analytics provides insights that can improve customer
This document provides an overview of big data, including its definition, characteristics, examples, analysis methods, and challenges. It discusses how big data is characterized by its volume, variety, and velocity. Examples of big data are given from various industries like healthcare, retail, manufacturing, and web/social media. Analysis methods for big data like MapReduce, Hadoop, and HPCC are described and compared. The document also covers privacy and security issues that arise from big data analytics.
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 refers to large data sets that are too large or complex for traditional data processing systems. It is characterized by high volume, velocity, and variety. Common challenges with big data include analysis, storage, search, sharing, transfer, and privacy. Traditional systems are inadequate for big data, which requires massively parallel software running on many servers. Architectures for handling big data include distributed file systems, MapReduce frameworks like Hadoop, and data lake systems.
Big data refers to massive amounts of structured and unstructured data that is difficult to process using traditional databases. It is characterized by volume, variety, velocity, and veracity. Major sources of big data include social media posts, videos uploaded, app downloads, searches, and tweets. Trends in big data include increased use of sensors, tools for non-data scientists, in-memory databases, NoSQL databases, Hadoop, cloud storage, machine learning, and self-service analytics. Big data has applications in banking, media, healthcare, energy, manufacturing, education, and transportation for tasks like fraud detection, personalized experiences, reducing costs, predictive maintenance, measuring teacher effectiveness, and traffic control.
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
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.
IRJET- Scope of Big Data Analytics in Industrial DomainIRJET Journal
This document discusses the scope of big data analytics in industrial domains. It begins by defining big data and its key characteristics, known as the "7 V's" - volume, velocity, variety, variability, veracity, value, and volatility. It then discusses how big data is generated in various fields like social media, search engines, healthcare, online shopping, and stock exchanges. The document focuses on how big data analytics can be applied in industrial Internet of Things (IoT) to extract meaningful information from large and continuous data streams generated by IoT devices using machine learning techniques.
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.
IRJET- Big Data Management and Growth EnhancementIRJET Journal
1. The document discusses big data management and growth, including definitions of big data, properties of big data like volume, variety, and velocity, and applications of big data in various domains.
2. It describes how big data is used in education to improve student outcomes, in healthcare to enable prevention and more personalized care, and in industries like banking and fraud detection to enhance customer segmentation and risk assessment.
3. Big data analytics refers to analyzing large and complex datasets to extract useful insights and make better decisions. The document provides examples of machine learning and predictive analytics techniques used for big data analysis.
trends of information systems and artificial technologymilkesa13
This document provides an overview of emerging technologies transforming the information technology industry, as discussed in recent literature. It examines technologies like cloud computing, the internet of things, artificial intelligence, blockchain, big data analytics, and more. For each technology, the document summarizes key points from 5-8 research papers on their characteristics, advantages, and challenges. The goal is to help researchers and practitioners understand these important trends by synthesizing information from multiple sources, rather than reading numerous individual papers. Artificial intelligence is discussed in more depth as an example, outlining how it is used through machine learning and deep learning, and its impact on enhancing security and automating processes within information systems.
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.
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.
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANC...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.
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.
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.
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.
Similar to DEALING CRISIS MANAGEMENT USING AI (20)
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
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politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
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Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
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1. International Journal of Computer Science, Engineering and Applications (IJCSEA)
Vol.11, No.5, October 2021
DOI: 10.5121/ijcsea.2021.11502 15
DEALING CRISIS MANAGEMENT USING AI
Yew Kee Wong
School of Information Engineering, HuangHuai University, Henan, China
ABSTRACT
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.
KEYWORDS
Artificial Intelligence, Big Data, Business Operations, Crisis Management
1. INTRODUCTION
Artificial intelligence (AI) is a way of making a computer, a computer-controlled robot, or a
software think intelligently, in the similar manner the intelligent humans think. AI is
accomplished by studying how human brain thinks, and how people learn, decide, and work
while trying to solve a problem, and then using the outcomes of this study as a basis of
developing intelligent software and systems [1]. AI is a science and innovation based on
disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and
Engineering. A major thrust of AI is in the development of computer functions associated with
human intelligence, for example, reasoning, learning, and problem solving. Out of the following
areas, one or multiple areas can contribute to build an intelligent system [2]. This paper aims to
analyse some of the use of big data for the AI development and its applications in various
business operations and crisis management.
2. WHAT IS BIG DATA
The Big data refers to significant volumes of data that cannot be processed effectively with the
traditional applications that are currently used. The processing of big data begins with raw data
that isn’t aggregated and is most often impossible to store in the memory of a single computer. A
buzzword that is used to describe immense volumes of data, unstructured, structured and semi-
structured, big data can inundate a business on a day-to-day basis. Big data is used to analyse
insights, which can lead to better decisions and strategic business moves [3]. The definition of
big data: “Big data is high-volume, and high-velocity or high-variety information assets that
demand cost-effective, innovative forms of information processing that enable enhanced insight,
2. International Journal of Computer Science, Engineering and Applications (IJCSEA)
Vol.11, No.5, October 2021
16
decision making, and process automation.” The characteristics of Big Data are commonly
referred to as the four Vs:
Volume of Big Data
The volume of data refers to the size of the data sets that need to be analysed and processed,
which are now frequently larger than terabytes and petabytes. The sheer volume of the data
requires distinct and different processing technologies than traditional storage and processing
capabilities. In other words, this means that the data sets in Big Data are too large to process with
a regular laptop or desktop processor. An example of a high-volume data set would be all credit
card transactions on a day within Asia.
Velocity of Big Data
Velocity refers to the speed with which data is generated. High velocity data is generated with
such a pace that it requires distinct (distributed) processing techniques. An example of a data that
is generated with high velocity would be Instagram messages or Wechat posts.
Variety of Big Data
Variety makes Big Data really big. Big Data comes from a great variety of sources and generally
is one out of three types: structured, semi structured and unstructured data. The variety in data
types frequently requires distinct processing capabilities and specialist algorithms. An example
of high variety data sets would be the CCTV audio and video files that are generated at various
locations in a city.
Veracity of Big Data
Veracity refers to the quality of the data that is being analysed. High veracity data has many
records that are valuable to analyse and that contribute in a meaningful way to the overall results.
Low veracity data, on the other hand, contains a high percentage of meaningless data. The non-
valuable in these data sets is referred to as noise. An example of a high veracity data set would
be data from a medical experiment or trial.
Data that is high volume, high velocity and high variety must be processed with advanced tools
(analytics and algorithms) to reveal meaningful information. Because of these characteristics of
the data, the knowledge domain that deals with the storage, processing, and analysis of these data
sets has been labelled Big Data [4].
3. International Journal of Computer Science, Engineering and Applications (IJCSEA)
Vol.11, No.5, October 2021
17
Figure 1. Big Data Architecture. (arccil.com)
2.1. Types of Big Data
There are 3 types of big data; unstructured data, structured data and semi-structured data.
Unstructured data:
Any data with unknown form or the structure is classified as unstructured data.
Structured data:
Any data that can be stored, accessed and processed in the form of fixed format is termed as a
'structured' data.
Semi-structured data:
Semi-structured data can contain both the forms of data.
Dealing with unstructured and structured data, data science is a field that comprises everything
that is related to data cleansing, preparation, and analysis. Data science is the combination of
statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the
ability to look at things differently, and the activity of cleansing, preparing, and aligning data [5].
This umbrella term includes various techniques that are used when extracting insights and
information from data.
Big data benefits:
Big data makes it possible for you to gain more complete answers because you have more
information.
More complete answers mean more confidence in the data, which means a completely
different approach to tackling problems.
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2.2. What is Big Data Analytics
Data analytics involves applying an algorithmic or mechanical process to derive insights and
running through several data sets to look for meaningful correlations. It is used in several
industries, which enables organizations and data analytics companies to make more informed
decisions, as well as verify and disprove existing theories or models [6] [7]. The focus of data
analytics lies in inference, which is the process of deriving conclusions that are solely based on
what the researcher already knows.
Figure 2. Big Data Analytics Architecture
3. USING AI IN SENSITIVE BUSINESS OPERATIONS
The artificial intelligence rules define the way the online learning system assigned learning
materials and exercises for the learner to follow [8]. These are the basic rules which we have
carry out in our experiments, in which we find it effective in improving the learners
understanding.
3.1. Financial Industry
Artificial intelligence (AI), along with other financial technology (fintech) innovations, are
significantly changing the ways that financial business are being run, especially in the fields like
trading and insurance, leading the traditional financial industry into a new era [9].
Robots replacing humans
Back in 2000, Goldman Sach's New York headquarters employed 600 traders, buying and selling
stock on the orders of the investment bank's clients. Today there are just two equity traders left,
as automated trading programs have taken over the rest of the work. Meanwhile, BlackRock, the
world's biggest money manager, also cut more than 40 jobs earlier this year, replacing some of its
human portfolio managers with artificially intelligent, computerized stock-trading algorithms.
Those two big companies are not the only financial institutions replacing human jobs with robots.
By 2025, AI technologies will reduce employees in the capital markets by 230,000 people
worldwide, according to a report by the financial services consultancy Opimas [10].
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Big new frontiers are only just beginning to opening up in fintech from AI, block chain and
robotics to biometrics, augmented reality and cybersecurity. Among all the fintech innovations,
the prospect of the block chain has the highest expectation. The block chain will change the way
people store information, which is real, spreading fast and cross-border, and its 'de-centric'
feature will allow everyone to know what other people are doing. The application of block chain
in finance will once again bring about a revolutionary impact on the industry, just like AI does.
3.2. Health Industry
The Artificial intelligence (AI) is reshaping operations across industries. Arguably, healthcare is
where these changes are poised to make the biggest impact – optimizing uptime and availability
of the treatment solutions. Using AI-powered tools capable of processing large amounts of data
and making real-time recommendations, healthcare organizations are learning they can reduce
administrative waste in a number of areas, from medical equipment maintenance to hospital bed
assignments [11].
Artificial intelligence is reinventing and reinvigorating modern healthcare through technologies
that can predict, comprehend, learn and act. The ability of AI to transform clinical care has
received widespread attention, but the technology’s potential extends beyond patient care to
processes across the spectrum of healthcare operations. In healthcare and other industries that
depend on reliable equipment performance, few things are more disruptive than unexpected
outages. These unplanned stops create costly emergency situations, such as extended downtime,
rush delivery of parts and overtime to repair the equipment.
Facing pressure to improve profitability and efficiency, many healthcare organizations are
turning to emerging technologies like AI and big data analytics to improve upon existing
maintenance operations. Until recently, maintenance typically involved either reacting to an
unexpected problem or adhering to a preventive maintenance schedule, which can sometimes
result in unnecessary maintenance. line.
3.3. Manufacturing Industry
AI is core to manufacturing's real-time future. Real-time monitoring provides many benefits,
including troubleshooting production bottlenecks, tracking scrap rates, meeting customer delivery
dates, and more. It's an excellent source of contextually relevant data that can be used for training
machine learning models. Supervised and unsupervised machine learning algorithms can interpret
multiple production shifts' real-time data in seconds and discover previously unknown processes,
products, and workflow patterns [12].
The manufacturing industry has exploited the use of AI technology, and in particular knowledge-
based systems, throughout the manufacturing lifecycle. This has been motivated by the
competitive challenge of improving quality while at the same time decreasing costs and reducing
design and production time. Just-in-time manufacturing and simultaneous engineering have
further required companies to focus on exploiting technology to improve manufacture planning
and coordination, and on providing more intelligent processing in all aspects of manufacturing.
The objective is to improve quality, to reduce costs, and to speed up the design and
manufacturing process.
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4. USING AI IN CRISIS MANAGEMENT
4.1. Extreme Weather Forecast
According to the UN Office for the Coordination of Human Affairs, in 2016 over 100 million
people were affected by natural disasters including earthquakes, hurricanes and floods.
Technology has a vital role to play in providing the appropriate situational awareness that then
shapes practical, life-saving decisions for effective crisis management. These decisions may
involve the evacuation of the most dangerous areas after an earthquake, or explore tactical
options about how and where to position critical resources like medicine, food, clean water and
shelter. Through utilising the data tweeted and texted by citizens in a crisis zone, rescuers have
access to the knowledge needed to devise a strategy for immediate rescue attempts and for longer
term help [13].
Issues can arise, however, due to the volume of available data, and high-quality filtering systems
are needed to avoid using inaccurate data that could misdirect humanitarian aid, potentially
wasting time, resources, and human trust in the system. Humanitarian responders may,
understandably, question the specificity of information, therefore, building their trust and
encouraging uptake of AI technology is a socially meaningful endeavour; without this, a system
is unlikely to be adopted in the field. Machine learning, understood as the refinement of how AI
‘learns’ to use algorithms and other data, offers a solution to detecting key information taken
from social media messages. Hence, researchers are focusing efforts on improving how the
millions of messages are sifted by algorithms to overcome inaccuracy, ensuring that only the
most important data is identified and shared.
4.2. Man-Made Environmental Disaster
The case of BP oil spill in 2010 provides an important example for understanding how these
principles are valued by public opinion in a crisis situation, and how the communication actions
by a corporation in this type of circumstances might have long-term effect on the brand image of
the organization. On April 20, 2010, a BP’s Deepwater Horizon oil rig exploded, causing what
has been called the worst environmental disaster in U.S. history and taking the lives of 11 rig
workers. For 87 straight days, oil and methane gas spewed from an uncapped well-head, 1 mile
below the surface of the ocean. The federal government estimated 4.2 million barrels of oil
spilled into the Gulf of Mexico [14].
The accumulation of unsafe supervisory action had resulted in risk levels substantially increasing.
Not only were risks increasing, but they were also incrementally becoming more aggressive in
nature. For instance, one of the first acts of unsafe supervision is illustrated when BP neglected
its responsibility of ensuring safety protocols were carried out after the completion of the
Macondo Well. This was a major mistake on BP’s part, violating safety protocols which may
have identified the issues present with the cementing of the well. Should these issues have been
identified sooner, the likeliness of the crisis happening would potentially be slim. In addition to
this, there was also very little supervision during and after works were carried out. This can be
attributed to the aforementioned organisational restructuring which created much confusion
regarding who was accountable for the assurance of safety [15].
4.3. Natural Disaster
Researchers have found that AI can be used to predict natural disasters. With enormous amounts
of good quality datasets, AI can predict the occurrence of numerous natural disasters, which can
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be the difference between life and death for thousands of people [16]. Some of the natural
disasters that can be predicted by AI are:
Earthquakes:
AI systems can be trained with the help of seismic data to analyse the magnitude and patterns of
earthquakes and predict the location of earthquakes and aftershocks.
Floods:
Various researchers and technology experts are developing AI-based applications with the help of
rainfall records and flood simulations to predict and monitor flooding.
Volcanic eruptions:
AI-powered systems can accurately predict volcanic eruptions with the help of seismic data and
geological information.
Hurricanes:
AI can use satellite to predict and monitor the path and intensity of hurricanes and tornadoes.
5. CONCLUSIONS
The study is assessing new frameworks for effective prevention measures and how AI can fit in
and foster the early warning process. So further experiments and understanding the interrelation
between AI and big data, what frameworks and systems that worked, and how AI can impact on
different business operations whether by introducing new innovations that foster crisis
management learning process and early prevention measures. The study from various reviews
show promising results in using AI to learn specific industry big data and further evaluation and
research is in progress.
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AUTHOR
Prof. Yew Kee Wong (Eric) is a Professor of Artificial Intelligence (AI) &
Advanced Learning Technology at the HuangHuai University in Henan, China. He
obtained his BSc (Hons) undergraduate degree in Computing Systems and a Ph.D.
in AI from The Nottingham Trent University in Nottingham, U.K. He was the
Senior Programme Director at The University of Hong Kong (HKU) from 2001 to
2016. Prior to joining the education sector, he has worked in international
technology companies, Hewlett-Packard (HP) and Unisys as an AI consultant. His
research interests include AI, online learning, big data analytics, machine learning, Internet of Things
(IOT) and blockchain technology.