Mclarens @ Data Science Sg
•
1 like•651 views
1) The document discusses how data and analytics can be used to change games in various industries like transportation, health and wellness, motorsports, energy, consumer, and rail. 2) It provides examples of how McLaren uses a data cycle of simulation, pre-race analysis, live monitoring and strategy, and post-race analysis with data management and visualization to improve performance. 3) The document outlines focus areas like human performance/condition monitoring, disease progression monitoring, and remaining useful life prediction using sensors, data capture, analytics, and other techniques to extract insights from data in areas like gait, biotelemetry, and health conditions.
Report
Share
Report
Share
Recommended
AXA x DSSG Meetup Sharing (Feb 2016)
Philippe MARIE-JEANNE, Head of Global Data Innovation Lab , “What's an insurer like AXA doing in the Big Data world?"
Fraud Detection in Insurance with Machine Learning for WARTA - Artur Suchwalko
Fraud Detection in Insurance with Machine Learning for WARTA - Artur SuchwalkoInstitute of Contemporary Sciences
Fraud detection is a popular application of Machine Learning. But is not that obvious and not that common as it seems. I'll tell how QuantUp implemented it for WARTA insurance company (a subsidiary of Talanx International AG).
The models developed gave between 10% and 30% of reduction of losses. The project was not a simple one because of the complex process of handling claims and using really rich dataset. The tools applied were R (modeling) and DataWalk (data peparation). You will learn what is important in development of such solutions in general, what was difficult in this particular project, and how to overcome possible difficulties in similar projects.IoT Big Data Analytics Insights from Patents
Patents are a good information resource for obtaining IoT (Internet of Things) technology development status. IOT big data analytics is becoming important to process unimaginably large amounts of information and data that are obtained by the sensor embedded interconnected IoT devices. The typical IoT big data analytics is Hadoop, an open-source software framework that supports data-intensive distributed applications, and the running of applications on large clusters of commodity hardware. Hadoop, that is based on the architectural framework MapReduce, collects both structured data and unstructured data, processes the collected data set in a distributed network cluster in parallel, and extracts valuable information from the processed data set within a short time. Followings illustrate some examples of patents that provide current status of the IoT big data analytics technology development.
Fraud Analytics with Machine Learning and Big Data Engineering for Telecom
Presentation of a successful project executed on telecom fraud analytics @ 3rd International conference for businees analytics and intelligence, Indian Institute of Management Bangalore
Internet of things & predictive analytics
How the data captured using Internet of things is pulled using Hive kind of tools for importing and creating database for analytics of data
A Journey Through The Far Side Of Data Science
This document summarizes a presentation on data science and artificial intelligence. It discusses how AI is transforming businesses in many ways, including automating repetitive tasks, improving customer experiences, and driving revenue growth. It also mentions that while data is important, AI is needed to transform organizations through intelligent process optimization and innovation. The document provides examples of how various companies are applying AI in sales, customer service, and other areas. It emphasizes that AI strategies should focus on innovation, identifying high-impact use cases, and developing people's data science skills.
[Ai in finance] AI in regulatory compliance, risk management, and auditing
AI to Improve Regulatory Compliance, Governance & Auditing. How AI identifies and prevents risks, above and beyond traditional methods. Techniques and analytics that protect customers and firms from cyber-attacks and fraud. Using AI to quickly and efficiently provide evidence for auditing requests.
Fraud analytics
Fraud Analytics is the process of integration of analytical tools and techniques with human interaction.
Recommended
AXA x DSSG Meetup Sharing (Feb 2016)
Philippe MARIE-JEANNE, Head of Global Data Innovation Lab , “What's an insurer like AXA doing in the Big Data world?"
Fraud Detection in Insurance with Machine Learning for WARTA - Artur Suchwalko
Fraud Detection in Insurance with Machine Learning for WARTA - Artur SuchwalkoInstitute of Contemporary Sciences
Fraud detection is a popular application of Machine Learning. But is not that obvious and not that common as it seems. I'll tell how QuantUp implemented it for WARTA insurance company (a subsidiary of Talanx International AG).
The models developed gave between 10% and 30% of reduction of losses. The project was not a simple one because of the complex process of handling claims and using really rich dataset. The tools applied were R (modeling) and DataWalk (data peparation). You will learn what is important in development of such solutions in general, what was difficult in this particular project, and how to overcome possible difficulties in similar projects.IoT Big Data Analytics Insights from Patents
Patents are a good information resource for obtaining IoT (Internet of Things) technology development status. IOT big data analytics is becoming important to process unimaginably large amounts of information and data that are obtained by the sensor embedded interconnected IoT devices. The typical IoT big data analytics is Hadoop, an open-source software framework that supports data-intensive distributed applications, and the running of applications on large clusters of commodity hardware. Hadoop, that is based on the architectural framework MapReduce, collects both structured data and unstructured data, processes the collected data set in a distributed network cluster in parallel, and extracts valuable information from the processed data set within a short time. Followings illustrate some examples of patents that provide current status of the IoT big data analytics technology development.
Fraud Analytics with Machine Learning and Big Data Engineering for Telecom
Presentation of a successful project executed on telecom fraud analytics @ 3rd International conference for businees analytics and intelligence, Indian Institute of Management Bangalore
Internet of things & predictive analytics
How the data captured using Internet of things is pulled using Hive kind of tools for importing and creating database for analytics of data
A Journey Through The Far Side Of Data Science
This document summarizes a presentation on data science and artificial intelligence. It discusses how AI is transforming businesses in many ways, including automating repetitive tasks, improving customer experiences, and driving revenue growth. It also mentions that while data is important, AI is needed to transform organizations through intelligent process optimization and innovation. The document provides examples of how various companies are applying AI in sales, customer service, and other areas. It emphasizes that AI strategies should focus on innovation, identifying high-impact use cases, and developing people's data science skills.
[Ai in finance] AI in regulatory compliance, risk management, and auditing
AI to Improve Regulatory Compliance, Governance & Auditing. How AI identifies and prevents risks, above and beyond traditional methods. Techniques and analytics that protect customers and firms from cyber-attacks and fraud. Using AI to quickly and efficiently provide evidence for auditing requests.
Fraud analytics
Fraud Analytics is the process of integration of analytical tools and techniques with human interaction.
Predictive analytics solution for claims fraud detection
Predictive analytics solution for claims fraud detection - Gain better control over fraudulent claims
Xavier Conort, DataScience SG Meetup - Challenges in insurance pricing
Modeling challenges for insurance pricing include:
1) Claims costs often follow skewed distributions with tails and are affected by discontinuities from policy limits, changing risk pools, and other factors.
2) Regulation and operational constraints require models to be simple and transparent while meeting requirements like premiums increasing with coverage.
3) Predicting future claims is difficult as models may not extrapolate to new situations and can be naive in how they learn from past data.
Machine learning in credit risk modeling : a James white paper
In Machine Learning in Credit Risk Modeling, we provide an explanation of the main Machine Learning models used in James so that Efficiency does not come at the expense of Explainability.
(Contact Yvan De Munck for more info or to receive other and future updates on the subject @yvandemunck or yvan@james.finance)
Credit Card Fraud Detection Client Presentation
Presenting a Proposal to get the order from the client for implementing a Fraud Detection System in a Bank
Credit Card Fraud Detection
Suraj Patro and M. Binayak Kumar Reddy presented their B.Tech major project on credit card fraud detection. They aimed to build an ensemble classifier using machine learning algorithms like decision trees, logistic regression, neural networks and gradient boosting to detect fraudulent transactions. They discussed challenges in fraud detection, implemented the project in Python using various libraries, and evaluated the performance using metrics like precision, recall and F1 score. The outcome would be an ensemble classifier model for credit card fraud detection.
IOT & Machine Learning
Internet of Things (IOT) and Machine learning are new technology trends that are booming individually: we will look at how to combine these concepts and technologies by layering machine learning on top of IOT data and driving significant insights for clients via specific use cases like predictive maintenance. Let’s look at some state of the art use cases and subsequent benefits delivered in this space to dig deeper into the “art of the possible”.
Masters thesis - Fraud & Big Data
This document discusses managing fraud risk in the banking industry using big data from a consulting perspective. It begins by acknowledging those who contributed to the research.
Traditional fraud risk management methods are still widely used but struggle with big data's high volume, velocity, and variety of data. Big data technologies can leverage large amounts of diverse data in real-time to more efficiently and accurately detect fraud. However, fully utilizing big data solutions is complex and costly for banks and requires centralizing operations and hiring skilled professionals.
Consulting firms have an opportunity to help banks address big data challenges in fraud risk management. They must understand how to implement big data solutions while considering a bank's needs to centralize operations and manage change. A
Fraud detection ML
This document discusses anomaly and fraud detection using machine learning. It outlines different applications of anomaly detection such as cybersecurity and fraud detection. It compares supervised versus unsupervised learning approaches for financial sector applications. Specific algorithms discussed for unsupervised anomaly detection include isolation forest, DBSCAN, HDBSCAN, local outlier factor, and Gaussian mixture models.
AI: A risk and way to manage risk
AI can be used to create sophisticated tools to monitor and analyze behavior and activities in real time. Since these systems can adapt to changing risk environments, they continually enhance the organization’s monitoring capabilities in areas such as regulatory compliance and corporate governance.
AI systems
can adapt to changing risk environments
continually enhance the organization’s monitoring capabilities
Better manage regulatory compliance and corporate governance.
Machine Learning for Fraud Detection
Machine Learning (ML) for Fraud Detection.
- fraud is a big problem (big data, big cost)
- ML on bigger data produces better results
- Industry standard today (for detecting fraud)
- How to improve fraud detection!
Top industry use cases for streaming analytics
Organizations need to get high value from streaming data to gain new clients and capitalize on market opportunities. Discover how IBM Streams is best suited for use cases that has the need for high speed and low latency.
Creditcard
This document presents a genetic algorithm approach for detecting credit card fraud in real time. It aims to reduce the relation between loss and available credit limit. The proposed genetic algorithm system overcomes issues with existing database-dependent systems by optimizing interval-valued parameters to detect fraud based on customer behavior, minimizing false alerts and producing optimized results. Hardware requirements include a Pentium IV 2.4 GHz system with 40GB hard disk and 256MB RAM, while the software used are Windows XP, Java, and NetBeans IDE.
Fraudulent credit card cash-out detection On Graphs
See all on-demand Graph + AI Sessions: https://www.tigergraph.com/graph-ai-world-sessions/
Get TigerGraph: https://www.tigergraph.com/get-tigergraph/
Ethics of Analytics and Machine Learning
An introductory take on the ethical issues surrounding the use of algorithms and machine learning in finance, education, law enforcement and defense. This work was stimulated by, but is not a product or authorized content from the IEEE P7003 WG.
Disclaimer: This work is mine alone and does not reflect view of IEEE, IEEE 7003 WG, my employer.
Credit card fraud detection using python machine learning
This document provides an overview of machine learning tools, technologies, and the data preparation process. It discusses collecting and selecting relevant data, data visualization, labeling data for supervised learning, and transforming raw data into a tidy format. The document also covers various data preprocessing techniques, including data cleaning, formatting, handling missing values and outliers, smoothing, aggregation, generalization, and data reduction methods. The goal of these preprocessing steps is to prepare raw data into a structured format suitable for machine learning modeling.
Forecasting and Predictive Digital Marketing. Daniele Donzella, Gianluigi Spa...
Forecasting and Predictive Digital Marketing. Daniele Donzella, Gianluigi Spa...Data Driven Innovation
1) The document discusses forecasting and predictive digital marketing. It defines forecasting as using past observations to predict future events in digital business.
2) It discusses different forecasting timeframes (short, medium, long term), common issues with forecasting like lack of patterns or anomalies, and how forecasting differs from planning which describes what should happen rather than what will happen.
3) It provides an example application of forecasting digital marketing data using exploratory data analysis, data collection and processing, modeling and validation, and the Prophet forecasting model to generate forecasts.Quant university MRM and machine learning
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
Big data, Machine learning and the Auditor
This document discusses how auditors can use big data, machine learning, and analytics. It defines big data and machine learning, describing techniques like supervised vs. unsupervised learning. It provides examples of how auditors could use these approaches for risk management, fraud detection, process mining, compliance, and more. Specific use cases are outlined, like vendor collusion identification and predictive analytics for bad debts. Statistical methods like logistic regression that could support predictive analytics are also mentioned. The document suggests auditors are well-positioned to help companies implement big data and machine learning for assurance, automation, and controls.
Generali - Fraud Analytics
This document discusses Generali Group, an international insurance company, and its efforts to detect insurance fraud. It provides an overview of Generali Group, describing it as one of the leading insurers in the world with operations in over 60 countries. It then discusses the problem of insurance fraud and some common types. It outlines Generali's solution to fraud detection, which involves using a graph database and applying techniques like rule-based analysis, social network analysis, and machine learning to processed insurance claims data.
Insurance Applications of Machine Learning - The 9th International Istanbul I...
Insurance Applications of Machine Learning - The 9th International Istanbul I...SigortaTatbikatcilariDernegi
Insurance Applications of Machine Learning - The 9th International Istanbul Insurance Confrence - Emrah Gökmen (2017)Big data: status atual e tendências
This document discusses big data perspectives, current and future. It notes that smartphones, tablets, and other connected devices now equal the world's population. It also discusses how connected vehicles utilize various systems and analytics. Finally, it discusses forecasts that 50 billion devices will be connected to the web by 2020 and how organizations are currently exploring and implementing big data technologies and analyzing traditional customer and transaction data.
Chris Gage - IBM
This document discusses how various organizations are using data and analytics to gain competitive advantages. It provides examples of how sports teams like England Rugby and former Leicester City manager Nigel Pearson use data from wearables, video, and other sensors to optimize player performance. Formula One and construction companies are also using sensor data from equipment, mobile inspections, and other sources to improve safety, reliability, productivity and reduce costs. The All England Lawn Tennis Club uses social media data and analytics to engage fans digitally. The document speculates that in the future, data analysis and machines will play a larger role in decision making and help technical teams.
More Related Content
What's hot
Predictive analytics solution for claims fraud detection
Predictive analytics solution for claims fraud detection - Gain better control over fraudulent claims
Xavier Conort, DataScience SG Meetup - Challenges in insurance pricing
Modeling challenges for insurance pricing include:
1) Claims costs often follow skewed distributions with tails and are affected by discontinuities from policy limits, changing risk pools, and other factors.
2) Regulation and operational constraints require models to be simple and transparent while meeting requirements like premiums increasing with coverage.
3) Predicting future claims is difficult as models may not extrapolate to new situations and can be naive in how they learn from past data.
Machine learning in credit risk modeling : a James white paper
In Machine Learning in Credit Risk Modeling, we provide an explanation of the main Machine Learning models used in James so that Efficiency does not come at the expense of Explainability.
(Contact Yvan De Munck for more info or to receive other and future updates on the subject @yvandemunck or yvan@james.finance)
Credit Card Fraud Detection Client Presentation
Presenting a Proposal to get the order from the client for implementing a Fraud Detection System in a Bank
Credit Card Fraud Detection
Suraj Patro and M. Binayak Kumar Reddy presented their B.Tech major project on credit card fraud detection. They aimed to build an ensemble classifier using machine learning algorithms like decision trees, logistic regression, neural networks and gradient boosting to detect fraudulent transactions. They discussed challenges in fraud detection, implemented the project in Python using various libraries, and evaluated the performance using metrics like precision, recall and F1 score. The outcome would be an ensemble classifier model for credit card fraud detection.
IOT & Machine Learning
Internet of Things (IOT) and Machine learning are new technology trends that are booming individually: we will look at how to combine these concepts and technologies by layering machine learning on top of IOT data and driving significant insights for clients via specific use cases like predictive maintenance. Let’s look at some state of the art use cases and subsequent benefits delivered in this space to dig deeper into the “art of the possible”.
Masters thesis - Fraud & Big Data
This document discusses managing fraud risk in the banking industry using big data from a consulting perspective. It begins by acknowledging those who contributed to the research.
Traditional fraud risk management methods are still widely used but struggle with big data's high volume, velocity, and variety of data. Big data technologies can leverage large amounts of diverse data in real-time to more efficiently and accurately detect fraud. However, fully utilizing big data solutions is complex and costly for banks and requires centralizing operations and hiring skilled professionals.
Consulting firms have an opportunity to help banks address big data challenges in fraud risk management. They must understand how to implement big data solutions while considering a bank's needs to centralize operations and manage change. A
Fraud detection ML
This document discusses anomaly and fraud detection using machine learning. It outlines different applications of anomaly detection such as cybersecurity and fraud detection. It compares supervised versus unsupervised learning approaches for financial sector applications. Specific algorithms discussed for unsupervised anomaly detection include isolation forest, DBSCAN, HDBSCAN, local outlier factor, and Gaussian mixture models.
AI: A risk and way to manage risk
AI can be used to create sophisticated tools to monitor and analyze behavior and activities in real time. Since these systems can adapt to changing risk environments, they continually enhance the organization’s monitoring capabilities in areas such as regulatory compliance and corporate governance.
AI systems
can adapt to changing risk environments
continually enhance the organization’s monitoring capabilities
Better manage regulatory compliance and corporate governance.
Machine Learning for Fraud Detection
Machine Learning (ML) for Fraud Detection.
- fraud is a big problem (big data, big cost)
- ML on bigger data produces better results
- Industry standard today (for detecting fraud)
- How to improve fraud detection!
Top industry use cases for streaming analytics
Organizations need to get high value from streaming data to gain new clients and capitalize on market opportunities. Discover how IBM Streams is best suited for use cases that has the need for high speed and low latency.
Creditcard
This document presents a genetic algorithm approach for detecting credit card fraud in real time. It aims to reduce the relation between loss and available credit limit. The proposed genetic algorithm system overcomes issues with existing database-dependent systems by optimizing interval-valued parameters to detect fraud based on customer behavior, minimizing false alerts and producing optimized results. Hardware requirements include a Pentium IV 2.4 GHz system with 40GB hard disk and 256MB RAM, while the software used are Windows XP, Java, and NetBeans IDE.
Fraudulent credit card cash-out detection On Graphs
See all on-demand Graph + AI Sessions: https://www.tigergraph.com/graph-ai-world-sessions/
Get TigerGraph: https://www.tigergraph.com/get-tigergraph/
Ethics of Analytics and Machine Learning
An introductory take on the ethical issues surrounding the use of algorithms and machine learning in finance, education, law enforcement and defense. This work was stimulated by, but is not a product or authorized content from the IEEE P7003 WG.
Disclaimer: This work is mine alone and does not reflect view of IEEE, IEEE 7003 WG, my employer.
Credit card fraud detection using python machine learning
This document provides an overview of machine learning tools, technologies, and the data preparation process. It discusses collecting and selecting relevant data, data visualization, labeling data for supervised learning, and transforming raw data into a tidy format. The document also covers various data preprocessing techniques, including data cleaning, formatting, handling missing values and outliers, smoothing, aggregation, generalization, and data reduction methods. The goal of these preprocessing steps is to prepare raw data into a structured format suitable for machine learning modeling.
Forecasting and Predictive Digital Marketing. Daniele Donzella, Gianluigi Spa...
Forecasting and Predictive Digital Marketing. Daniele Donzella, Gianluigi Spa...Data Driven Innovation
1) The document discusses forecasting and predictive digital marketing. It defines forecasting as using past observations to predict future events in digital business.
2) It discusses different forecasting timeframes (short, medium, long term), common issues with forecasting like lack of patterns or anomalies, and how forecasting differs from planning which describes what should happen rather than what will happen.
3) It provides an example application of forecasting digital marketing data using exploratory data analysis, data collection and processing, modeling and validation, and the Prophet forecasting model to generate forecasts.Quant university MRM and machine learning
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
Big data, Machine learning and the Auditor
This document discusses how auditors can use big data, machine learning, and analytics. It defines big data and machine learning, describing techniques like supervised vs. unsupervised learning. It provides examples of how auditors could use these approaches for risk management, fraud detection, process mining, compliance, and more. Specific use cases are outlined, like vendor collusion identification and predictive analytics for bad debts. Statistical methods like logistic regression that could support predictive analytics are also mentioned. The document suggests auditors are well-positioned to help companies implement big data and machine learning for assurance, automation, and controls.
Generali - Fraud Analytics
This document discusses Generali Group, an international insurance company, and its efforts to detect insurance fraud. It provides an overview of Generali Group, describing it as one of the leading insurers in the world with operations in over 60 countries. It then discusses the problem of insurance fraud and some common types. It outlines Generali's solution to fraud detection, which involves using a graph database and applying techniques like rule-based analysis, social network analysis, and machine learning to processed insurance claims data.
Insurance Applications of Machine Learning - The 9th International Istanbul I...
Insurance Applications of Machine Learning - The 9th International Istanbul I...SigortaTatbikatcilariDernegi
Insurance Applications of Machine Learning - The 9th International Istanbul Insurance Confrence - Emrah Gökmen (2017)What's hot (20)
Predictive analytics solution for claims fraud detection
Predictive analytics solution for claims fraud detection
Xavier Conort, DataScience SG Meetup - Challenges in insurance pricing
Xavier Conort, DataScience SG Meetup - Challenges in insurance pricing
Machine learning in credit risk modeling : a James white paper
Machine learning in credit risk modeling : a James white paper
Fraudulent credit card cash-out detection On Graphs
Fraudulent credit card cash-out detection On Graphs
Credit card fraud detection using python machine learning
Credit card fraud detection using python machine learning
Forecasting and Predictive Digital Marketing. Daniele Donzella, Gianluigi Spa...
Forecasting and Predictive Digital Marketing. Daniele Donzella, Gianluigi Spa...
Insurance Applications of Machine Learning - The 9th International Istanbul I...
Insurance Applications of Machine Learning - The 9th International Istanbul I...
Similar to Mclarens @ Data Science Sg
Big data: status atual e tendências
This document discusses big data perspectives, current and future. It notes that smartphones, tablets, and other connected devices now equal the world's population. It also discusses how connected vehicles utilize various systems and analytics. Finally, it discusses forecasts that 50 billion devices will be connected to the web by 2020 and how organizations are currently exploring and implementing big data technologies and analyzing traditional customer and transaction data.
Chris Gage - IBM
This document discusses how various organizations are using data and analytics to gain competitive advantages. It provides examples of how sports teams like England Rugby and former Leicester City manager Nigel Pearson use data from wearables, video, and other sensors to optimize player performance. Formula One and construction companies are also using sensor data from equipment, mobile inspections, and other sources to improve safety, reliability, productivity and reduce costs. The All England Lawn Tennis Club uses social media data and analytics to engage fans digitally. The document speculates that in the future, data analysis and machines will play a larger role in decision making and help technical teams.
HPE - "‘Software Defined’ Infrastructure, zeg je? Noem het ‘Generatie Z’ van ...
This document discusses hybrid IT infrastructure and how IT is evolving to support new workloads and intelligent data processing. It provides examples of how optimal hybrid IT infrastructure can support Internet of Things use cases. Specifically, it discusses using examples in areas like asset monitoring, location services, security and surveillance, and remote presence to illustrate how hybrid IT can intelligently process data.
Deep Learning Image Processing Applications in the Enterprise
The presentation has many use cases covering the following Image classification: "The process of identifying and detecting an object or a feature in a digital image or video," the report states. In retail, deep learning models "quickly scan and analyze in-store imagery to intuitively determine inventory movement."
Voice recognition: "The ability to receive and interpret dictation or to understand and carry out spoken commands. Models are able to convert captured voice commands to text and then use natural language processing to understand what is being said and in what context." In transportation, deep learning "uses voice commands to enable drivers to make phone calls and adjust internal controls - all without taking their hands off the steering wheel."
Anomaly detection: "Deep learning technique strives to recognize abnormal patterns which don't match the behaviors expected for a particular system, out of millions of different transactions. These applications can lead to the discovery of an attack on financial networks, fraud detection in insurance filings or credit card purchases, even isolating sensor data in industrial facilities signifying a safety issue."
Recommendation engines: "Analyze user actions in order to provide recommendations based on user behavior."
Sentiment analysis: "Leverages deep learning-heavy techniques such as natural language processing, text analysis, and computational linguistics to gain clear insight into customer opinion, understanding of consumer sentiment, and measuring the impact of marketing strategies."
Video analysis: "Process and evaluate vast streams of video footage for a range of tasks including threat detection, which can be used in airport security, banks, and sporting events."
Automation analytics
A well-publicised PwC report projects that by 2030, “smart automation” technologies could contribute up to 14% of global GDP (around 10% to UK GDP). It also believesthat the long-term net effect of automation on the economy will be positive. Thus, despite the above uncomplimentary words from a scientist whose work still drives our modern digital economy,the verdict is in – technologies like Robotic Process Automation (RPA) are here to stay.
Economics & Statistics Insights in Data Science by DataPerts Technologies
DATA is an inevitable part of our life today. These tiny pieces of information from which we derive valuable insights
have their genesis in the domain of ECONOMICS and STATISTICS.
[WSO2Con EU 2018] Streaming SQL in the Real World
Every continuous collection of events represent an action, that can be categorized and recognized in this universe. Steaming SQL is one of the techniques utilized to process massive amounts of streaming events to find real-time responsiveness on changing real-world conditions. In this session, we discuss how Steaming SQL has been heavily utilized to battle real-world challenges.
Jorge Sebastiao "Using AI for Smart traffic Management"
AI and machine learning are enabling smart traffic management by collecting and analyzing large amounts of data from sensors like cameras and cellular towers. Machine learning models can detect traffic violations, optimize traffic light timing, and reduce congestion. One city saw a 5x increase in traffic management efficiency and 10% reduction in congestion by integrating AI from multiple vendors onto a single platform to analyze video, sensor, and traffic data and control lights and traffic flow. The future of smart transportation will include autonomous vehicles connected through technologies like 5G, using AI to enable features such as vehicle-to-everything communication.
Credit card fraud detection through machine learning
This document discusses using machine learning algorithms for credit card fraud detection. It proposes using principal component analysis for feature selection followed by logistic regression and decision tree models. It finds that logistic regression has higher accuracy at 79.91% compared to 71.41% for decision tree. The proposed approach aims to better handle imbalanced data and reduce fraudulent transactions. Future work could implement the approach in Python and produce experimental results.
Caroline Hargrove - Lessons from 8 World Championships - McLaren Applied Tech...
Caroline Hargrove - Lessons from 8 World Championships - McLaren Applied Tech...Business of Software Conference
McLaren Applied Technologies uses data and analytics from Formula 1 racing to develop IoT solutions. They use real-time data collection, predictive analytics, and visualization to optimize race strategy. McLaren is now applying these skills to develop smart systems for production line optimization, medical devices, surgical wearables, and personalized healthcare.Data Economy 2016
Economic data or economic statistics are data (quantitative measures) describing an actual economy, past or present. These are typically found in time-series form, that is, covering more than one time period (say the monthly unemployment rate for the last five years) or in cross-sectional data in one time period (say for consumption and income levels for sample households). Data may also be collected from surveys of for example individuals and firms[1] or aggregated to sectors and industries of a single economy or for the international economy. A collection of such data in table form comprises a data set.
Methodological economic and statistical elements of the subject include measurement, collection, analysis, and publication of data.[2] 'Economic statistics' may also refer to a subtopic of official statistics produced by official organizations (e.g. statistical institutes, intergovernmental organizations such as United Nations, European Union or OECD, central banks, ministries, etc.). Economic data provide an empirical basis for economic research, whether descriptive or econometric. Data archives are also a key input for assessing the replicability of empirical findings[3] and for use in decision making as to economic policy.
ThingTech - TracIT for Public Works
Learn about ThingTech's real-time connected solution for Public Works departments and Smarter Cities Initiatives.
JRI 2021 AIOps for Preventive& Automated Incident Management
This document discusses using AI and machine learning (AIOps) for prescriptive and automated incident management in operations. It outlines how observability, predictive analytics, and prescriptive maintenance can help automate operations and maintenance tasks. The document then provides examples of AIOps use cases like anomaly detection, predictive network issues, and automated ticket resolution. Finally, it discusses future research opportunities in programming languages, algorithms, and distributed systems for AI in operations.
No Time Like the Present – The Case for Streaming Analytics
The Briefing Room with John Myers and SQLstream
Live Webcast Jan. 14, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?AT=pb&SP=EC&rID=8028437&rKey=f7805e721c625b13
Many of today's opportunities and challenges come and go faster than a data warehouse can perform. That's one reason why streaming analytics are starting to take off. For all kinds of Big Data, streaming analytics can provide business value almost instantaneously. Whether for protecting critical infrastructure, knowing which offer to make to a high-value customer, or detecting fraud before serious damage is done, streaming analytics can provide tangible solutions in near-real-time.
Register for this episode of The Briefing Room to hear industry Analyst John Myers as he explains the changing dynamics of modern analytics. He'll be briefed by Damian Black of SQLstream who will discuss his company's streaming Big Data Platform which uses a standards-based approach to provide new levels of visibility into a wide range of log file, sensor, network and machine-generated data. He'll also detail their latest offerings – StreamApp templates, which are pre-configured streaming application components for specific industries.
Visit InsideAnalysis.com for more information
Data Science by Chappuis Halder & Co.
This document discusses opportunities and challenges around data science in the financial services industry. It begins with defining data science and providing an overview of trends in data science. It then discusses four key challenges facing the financial industry: compliance, profitability and solvency growth, competitive advantage, and gaining insights from customer behavior, risk management, product optimization, and reporting. The document outlines various levels of analytics maturity and provides examples of how descriptive, predictive, and integrated analytics can be used in areas like customer analytics, risk management, and portfolio optimization. It concludes by discussing future trends in data science and artificial intelligence.
EPC Solutions LLP- Presentation [ENERGY MANAGEMENT]
EPC Solutions LLP, which is into Large Scale Infra Projects (Metro, Airports, Stadiums & Other Mega Projects), Energy Solutions (EPC Solutions for Transmission & Distributed System upto 765kV, Contour & Route Survey, Soil Investigating etc.), Solar (EPC), Structure Supply - For Infra, Energy T&D & Solar Segment, MEP Services, SEZ & Other Consultancy Services, BIM Services (Upto LOD 500) & Geographical Information System, IT Services (Web Development, Software Solutions & Manpower Solutions).
Customer_Experience_for_Telco
The document discusses a data-driven customer experience management framework for the mobile telecommunication industry. It summarizes research from 8,000 customer experience executives and 53 authorities that found problems in customer perception despite investments, due to disjoint practices, silo mentalities, and obsolete measurement methods. The framework is based on descriptive, predictive, and prescriptive analytics to form an integrated, actionable solution by aligning company delivery to customer value chains through effective internal communication. Oxford Data Science Centre offers services including customer segmentation, journey mapping, personalization, quality assurance, and predictive analysis to help develop an analytical framework and apply analyses.
Heart Health & Drowsiness Analysis of Driver for RoadSafety
This document describes a project to develop a system that monitors a driver's heart health and drowsiness for road safety. The system would analyze the driver's face using a camera to detect drowsiness and measure heart rate using a PPG sensor. This could help reduce accidents caused by driver health issues like sleep deprivation or heart problems. The document reviews similar existing systems and proposed methods for the project, including using eye aspect ratio analysis of camera images to detect drowsiness and an IoT system to monitor heart rate. The goal is to alert drivers if issues are detected and help prevent accidents related to driver health and fatigue.
Drive Profitability with Big Data Analytics: from Asset to Operations Optimiz...
Industrial companies are using big data analytics in ways
that impact the bottom line - from asset optimization
resulting from proactive management through operations
optimization derived from analytic insights. See how
Predictivity solutions are leading the way to enable
powerful financial impacts along the full spectrum of
analytic adoption across multiple business lines.
Similar to Mclarens @ Data Science Sg (20)
HPE - "‘Software Defined’ Infrastructure, zeg je? Noem het ‘Generatie Z’ van ...
HPE - "‘Software Defined’ Infrastructure, zeg je? Noem het ‘Generatie Z’ van ...
Deep Learning Image Processing Applications in the Enterprise
Deep Learning Image Processing Applications in the Enterprise
Economics & Statistics Insights in Data Science by DataPerts Technologies
Economics & Statistics Insights in Data Science by DataPerts Technologies
Jorge Sebastiao "Using AI for Smart traffic Management"
Jorge Sebastiao "Using AI for Smart traffic Management"
Credit card fraud detection through machine learning
Credit card fraud detection through machine learning
Caroline Hargrove - Lessons from 8 World Championships - McLaren Applied Tech...
Caroline Hargrove - Lessons from 8 World Championships - McLaren Applied Tech...
JRI 2021 AIOps for Preventive& Automated Incident Management
JRI 2021 AIOps for Preventive& Automated Incident Management
No Time Like the Present – The Case for Streaming Analytics
No Time Like the Present – The Case for Streaming Analytics
EPC Solutions LLP- Presentation [ENERGY MANAGEMENT]
EPC Solutions LLP- Presentation [ENERGY MANAGEMENT]
Heart Health & Drowsiness Analysis of Driver for RoadSafety
Heart Health & Drowsiness Analysis of Driver for RoadSafety
Drive Profitability with Big Data Analytics: from Asset to Operations Optimiz...
Drive Profitability with Big Data Analytics: from Asset to Operations Optimiz...
Recently uploaded
原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
学校原件一模一样【微信：741003700 】《(Deakin毕业证书)迪肯大学毕业证学位证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
原版制作(swinburne毕业证书)斯威本科技大学毕业证毕业完成信一模一样
学校原件一模一样【微信：741003700 】《(swinburne毕业证书)斯威本科技大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdf
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
The affect of service quality and online reviews on customer loyalty in the E...
Journal of Management and Creative Business.published book./
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
UIUC毕业证offer【微信95270640】☀《伊利诺伊大学|厄巴纳-香槟分校毕业证购买》GoogleQ微信95270640《UIUC毕业证模板办理》加拿大文凭、本科、硕士、研究生学历都可以做,二、业务范围：
★、全套服务：毕业证、成绩单、化学专业毕业证书伪造《伊利诺伊大学|厄巴纳-香槟分校大学毕业证》Q微信95270640《UIUC学位证书购买》
(诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书；
2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。
3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。
办理伊利诺伊大学|厄巴纳-香槟分校伊利诺伊大学|厄巴纳-香槟分校毕业证offer流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
-办理真实使馆公证（即留学回国人员证明）
-办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度）
-全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！
（详情请加一下 文凭顾问+微信:95270640）欢迎咨询！的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下须绕过高楼旁边的垃圾堆下八个台阶才到父亲的家很狭小除了一张单人床和一张小方桌几乎没有多余的空间山娃一下子就联想起学校的男小便处山娃很想笑却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气间
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
原版定制【微信:41543339】【(BCU毕业证书)伯明翰城市大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
一比一原版(Dalhousie毕业证书)达尔豪斯大学毕业证如何办理
原版定制【微信:41543339】【(Dalhousie毕业证书)达尔豪斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Malana- Gimlet Market Analysis (Portfolio 2)
A market analysys on Spotify's parent Podcast company Gimlet.
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
Recently uploaded (20)
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdf
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdf
The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
Influence of Marketing Strategy and Market Competition on Business Plan
Influence of Marketing Strategy and Market Competition on Business Plan
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
Mclarens @ Data Science Sg
- 1. CHANGE THE GAME with DATA & ANALYTICS Dr CAO Hong Head of Analytics, APAC
- 6. Sensors Analytics Electric Propulsion Data Driven Design Real Time Control Decision Support CORE TECHNOLOGY COMPETENCY Informed Intervention Planned Intervention Auto- Regulated Response Sensors, Data Capture and Transmission Embedded Control Visualisation Decision Support Next Time Right Time Real Time Sensors Data Logging and Telemetry Volume, Speed, Real-Time Electric Propulsion Data Driven Design Decision Support Embedded Analysis Simulation Analytics Historic and Real-Time Data 6
- 7. OUR FOCUS: INDUSTRIES 7 TRANSPORT HEALTH & WELLNESS MOTORSPORT ENERGY CONSUMER RAIL
- 8. Our Analytics Role and Approach 8 FORMULATION GOVERNANCE PREPROCESSING DISTILLING REPORTINGMODELLING Problem Statement and Data MEASURE MONITOR INTEPRET MANAGE To work on: - Data crunching and insight discovery - Formulation and modelling - Algorithm design and validation
- 9. Human Performance / Condition Monitoring 9
- 10. 10
- 11. CONTINUOUS MONITORING OF HEALTH AND PREDICTING OF DETERIORATION 11 Automating the Heart Condition Monitoring
- 12. LIFEINSIGHT: UNDERLYING MODELS 12 Sleep Quality Gait/ Activities Perceived Stress Cognitive Performance Human Condition / Performance Monitoring Leveraging Economic Sensors
- 13. Health Case I GAIT OUTLIER ANALYSIS 13 HOW MUCH INSIGHT CAN WE GET FROM FROM OUR GAIT?
- 14. Health Case 2: 14 HOW CAN BIOTELEMETRY ASSIST IN PATIENT TREATMENT? > AMYOTROPHIC LATERAL SCLEROSIS (ALS) ALS Disease Progression Monitoring Study Background: • Hard to diagnose • Short remaining lifetime with some exceptional cases • Severely depleted life quality ALS Functional Scale (ALSFR): • Speech, Salivation, Swallowing, Handwriting, Cutting food, Dressing and Hygienne, Turning in bed, Walking, Climbing Stairs, Dyspnea, Orthopnea, Respiratory Insufficiency
- 15. Health DISEASE PROGRESSION MONITORING 15 HOW CAN BIOTELEMETRY ASSIST IN PATIENT TREATMENT? > AMYOTROPHIC LATERAL SCLEROSIS (ALS) ALS Disease Progression Heart Rate Activity Speech