Emotion Detection using Artificial Intelligence presentation by Aryan Trisal.
In this ppt you will learn about emotion detection using AI and how will it change the world.
IF U WANT A PPT MADE AT VERY LOW PRICES CONTACT ME ON LINKEDIN -www.linkedin.com/in/aryan-trisal-420253190
iData Sciences is an IT company that provides software solutions to help customers improve process efficiency and productivity in data capture. It offers innovative solutions for data capture, analytics, and optimization from documents like invoices, bank statements, and medical bills. Headquartered in New Jersey with an offshore center in Chennai, India, iData's tools extract and analyze structured and unstructured data to provide valuable insights and intelligence to organizations.
How to use Adobe Analytics features for data analysis & personalization.pdfTech Modena
How to use Adobe Analytics features for data analysis & personalization
With Adobe Analytics features, businesses can conduct thorough data analysis, uncovering valuable insights that enable effective personalization strategies for delivering tailored and engaging experiences to their audience.
Organizations gather a lot of data to understand customers, improve products, and enhance experiences. But analyzing this data for useful insights can be a challenge. That’s where Adobe Analytics comes in.
Adobe Analytics, part of Adobe Experience Cloud, helps businesses make sense of data from various digital channels. It provides real-time insights, enabling effective marketing campaigns and better customer experiences. Integrated with other Adobe platforms, it enhances audience targeting and marketing activities.
Amidst the noise of data, our experts discuss how Adobe Analytics tackles challenges, making data-driven decisions easier and personalizing experiences for every user.
What is Adobe Analytics?
Adobe Analytics, once known as Omniture SiteCatalyst, is a top-notch tool for analyzing data across various channels. It’s a key component of the Adobe Marketing Cloud, offering plugins for assessing campaign performance not just on websites but also on mobile apps and video platforms.
Adobe Analytics empowers marketers by offering valuable insights to make informed decisions. By leveraging this data, marketers aim to increase revenues, boost conversions, and reduce advertising costs. Success with Adobe Analytics, like any data analytics platform, relies on accurately interpreting marketing metrics derived from available data.
Types of Data Captured by Adobe Analytics
Adobe Analytics gathers data from websites, mobile apps, and other sources to create reports. They call this data metrics or key performance indicators (KPIs), showing quantitative info about your website’s performance. The main tracked metric categories are:
Traffic metrics: Details about your visitors, revealing public attention through marketing, PR, and services.
Conversion metrics: Info on user actions to measure website engagement.
Calculated metrics: Custom metrics you create by combining existing ones.
Video metrics: Stats like total views, time spent, and completion rates.
Social metrics: Data to track your brand’s presence on social media.
To protect privacy, Adobe only collects non-personally identifiable information (PII), like a visitor’s zip code, avoiding sensitive data to ensure individual identity remains safe.
Benefits of Adobe Analytics
Quick Web Insights: Adobe Analytics helps you get speedy results by collecting web data in real-time. It’s like having a super-fast tool to understand what’s happening on your website.
Smart Marketing Know-How: We’re not just about analytics; we cover all the new digital stuff. With Adobe Analytics, you can see everything about your customers in one go, getting smart insights in no time.
Better Choices with Attribution: Figure out what’s
The document discusses image processing and describes its goals, applications, and system requirements. It defines image processing as altering existing images in a desired manner to extract important features and provide machine understanding. It provides examples of image processing applications like remote sensing, medical imaging, and character recognition. The proposed system allows users to modify images through tools for compression, rotation, resizing pixels and edge detection, and can process various file formats. Hardware requirements include at least 80GB storage, 512MB RAM, and a Pentium processor, while software requirements include Windows OS, Java/Swing technologies, Apache Tomcat server, and an Oracle or Access backend database.
Color based image processing , tracking and automation using matlabKamal Pradhan
Image processing is a form of signal processing in which the input is an image, such as a photograph or video frame. The output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. This project aims at processing the real time images captured by a Webcam for motion detection and Color Recognition and system automation using MATLAB programming.
In color based image processing we work with colors instead of object. Color provides powerful information for object recognition. A simple and effective recognition scheme is to represent and match images on the basis of color histograms.
Tracking refers to detection of the path of the color once the color based processing is done the color becomes the object to be tracked this can be very helpful in security purposes.
Automation refers to an automated system is any system that does not require human intervention. In this project I’ve automated the mouse that work with our gesture and do the desired tasks.
This document provides an overview of artificial intelligence (AI) and key AI concepts like machine learning, computer vision, natural language processing, anomaly detection, and knowledge mining. It discusses how machine learning works and is the foundation of most AI solutions. It also covers challenges and risks of AI like bias, errors, privacy/security issues, and the importance of developing AI responsibly. Microsoft Azure provides various cognitive services and tools to help build AI solutions while addressing issues of fairness, reliability, privacy, transparency, and more.
Emotion Detection using Artificial Intelligence presentation by Aryan Trisal.
In this ppt you will learn about emotion detection using AI and how will it change the world.
IF U WANT A PPT MADE AT VERY LOW PRICES CONTACT ME ON LINKEDIN -www.linkedin.com/in/aryan-trisal-420253190
iData Sciences is an IT company that provides software solutions to help customers improve process efficiency and productivity in data capture. It offers innovative solutions for data capture, analytics, and optimization from documents like invoices, bank statements, and medical bills. Headquartered in New Jersey with an offshore center in Chennai, India, iData's tools extract and analyze structured and unstructured data to provide valuable insights and intelligence to organizations.
How to use Adobe Analytics features for data analysis & personalization.pdfTech Modena
How to use Adobe Analytics features for data analysis & personalization
With Adobe Analytics features, businesses can conduct thorough data analysis, uncovering valuable insights that enable effective personalization strategies for delivering tailored and engaging experiences to their audience.
Organizations gather a lot of data to understand customers, improve products, and enhance experiences. But analyzing this data for useful insights can be a challenge. That’s where Adobe Analytics comes in.
Adobe Analytics, part of Adobe Experience Cloud, helps businesses make sense of data from various digital channels. It provides real-time insights, enabling effective marketing campaigns and better customer experiences. Integrated with other Adobe platforms, it enhances audience targeting and marketing activities.
Amidst the noise of data, our experts discuss how Adobe Analytics tackles challenges, making data-driven decisions easier and personalizing experiences for every user.
What is Adobe Analytics?
Adobe Analytics, once known as Omniture SiteCatalyst, is a top-notch tool for analyzing data across various channels. It’s a key component of the Adobe Marketing Cloud, offering plugins for assessing campaign performance not just on websites but also on mobile apps and video platforms.
Adobe Analytics empowers marketers by offering valuable insights to make informed decisions. By leveraging this data, marketers aim to increase revenues, boost conversions, and reduce advertising costs. Success with Adobe Analytics, like any data analytics platform, relies on accurately interpreting marketing metrics derived from available data.
Types of Data Captured by Adobe Analytics
Adobe Analytics gathers data from websites, mobile apps, and other sources to create reports. They call this data metrics or key performance indicators (KPIs), showing quantitative info about your website’s performance. The main tracked metric categories are:
Traffic metrics: Details about your visitors, revealing public attention through marketing, PR, and services.
Conversion metrics: Info on user actions to measure website engagement.
Calculated metrics: Custom metrics you create by combining existing ones.
Video metrics: Stats like total views, time spent, and completion rates.
Social metrics: Data to track your brand’s presence on social media.
To protect privacy, Adobe only collects non-personally identifiable information (PII), like a visitor’s zip code, avoiding sensitive data to ensure individual identity remains safe.
Benefits of Adobe Analytics
Quick Web Insights: Adobe Analytics helps you get speedy results by collecting web data in real-time. It’s like having a super-fast tool to understand what’s happening on your website.
Smart Marketing Know-How: We’re not just about analytics; we cover all the new digital stuff. With Adobe Analytics, you can see everything about your customers in one go, getting smart insights in no time.
Better Choices with Attribution: Figure out what’s
The document discusses image processing and describes its goals, applications, and system requirements. It defines image processing as altering existing images in a desired manner to extract important features and provide machine understanding. It provides examples of image processing applications like remote sensing, medical imaging, and character recognition. The proposed system allows users to modify images through tools for compression, rotation, resizing pixels and edge detection, and can process various file formats. Hardware requirements include at least 80GB storage, 512MB RAM, and a Pentium processor, while software requirements include Windows OS, Java/Swing technologies, Apache Tomcat server, and an Oracle or Access backend database.
Color based image processing , tracking and automation using matlabKamal Pradhan
Image processing is a form of signal processing in which the input is an image, such as a photograph or video frame. The output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. This project aims at processing the real time images captured by a Webcam for motion detection and Color Recognition and system automation using MATLAB programming.
In color based image processing we work with colors instead of object. Color provides powerful information for object recognition. A simple and effective recognition scheme is to represent and match images on the basis of color histograms.
Tracking refers to detection of the path of the color once the color based processing is done the color becomes the object to be tracked this can be very helpful in security purposes.
Automation refers to an automated system is any system that does not require human intervention. In this project I’ve automated the mouse that work with our gesture and do the desired tasks.
This document provides an overview of artificial intelligence (AI) and key AI concepts like machine learning, computer vision, natural language processing, anomaly detection, and knowledge mining. It discusses how machine learning works and is the foundation of most AI solutions. It also covers challenges and risks of AI like bias, errors, privacy/security issues, and the importance of developing AI responsibly. Microsoft Azure provides various cognitive services and tools to help build AI solutions while addressing issues of fairness, reliability, privacy, transparency, and more.
Lecture-1-Introduction to Deep learning.pptxJayChauhan100
Introduction To Deep Learning.
This presentation covers everything about deep learning. You will be familier with all the main concepts used in deep learning.
Includes topics like difference between deep learning and machine learning, Feature engineering in detail, Deep learning frameworks , applications of deep learning etc.
This presentation will surely help you to know about the deep learning.
For queries contact on the given email id.
Email - chauhanjay657@gmail.com
The document discusses the key steps in an AI project cycle:
1) Problem scoping involves understanding the problem, stakeholders, location, and reasons for solving it.
2) Data acquisition collects accurate and reliable structured or unstructured data from various sources.
3) Data exploration arranges and visualizes the data to understand trends and patterns using tools like charts and graphs.
4) Modelling creates algorithms and models by training them on large datasets to perform tasks intelligently.
5) Evaluation tests the project by comparing outputs to actual answers to identify areas for improvement.
This document discusses image processing and its applications. It begins with an abstract discussing image processing and its main types. It then discusses fundamental image processing steps like image acquisition, enhancement, restoration, and more. It also discusses topics like color image processing, wavelets, compression, morphological processing, segmentation, representation and description, and recognition. It provides examples of image processing applications like medical imaging, traffic sensing, image reconstruction, and face detection. It concludes by discussing benefits of image processing and providing examples of image processing techniques like filtering, affine transformations, erosion, dilation and more.
Expense Manager: An Expense Tracking Application using Image ProcessingIRJET Journal
This document describes an expense tracking mobile application called "Expense Manager" that uses image processing and machine learning techniques. The application allows users to take photos of receipts which are then processed to extract expense information like item names, costs, and dates. Machine learning is used to categorize expenses and detect patterns in spending. The extracted data is presented to users through graphs and reports to help them analyze their spending habits and budgets. The goals of the application are to help users better understand and manage their expenses through a simple interface that avoids tedious manual data entry. Future work may include integrating the app with financial accounts to automatically track spending from purchases.
The Rise of AI and Machine Learning in Power BI (1).pdfSparity1
Learn about the rise of AI and ML in Power BI and how AI-powered business Intelligence tools can improve data analysis, the new additions of Copilot and Microsoft Fabric that leverages AI to revolutionize data analysis and reporting in Power BI.
Topperworld offers a project-based internship program to help students gain skills for technical careers. The internship involves completing 3 tasks, maintaining project codes and documentation on GitHub and LinkedIn, and updating profiles to reflect the intern position. The first task involves developing a computer vision model for detecting road lane lines from images or videos. The second task is building a movie recommendation system using collaborative or content-based filtering algorithms. The third task is creating a system for detecting fake news articles using natural language processing and machine learning classification models.
This document presents a project on a face recognition system. It provides an abstract describing the use of biometric security systems like face detection and recognition to provide verification and identification capabilities. It then outlines the various sections that will be included in the report, such as introduction, methodology, tools/technologies, applications and future scope. The methodology section describes using an Agile development approach and details the requirements analysis, data modeling, and process modeling steps. Computer vision, image processing and machine learning tools and technologies are also listed.
This document provides an overview of machine learning and AI services available on Microsoft Azure. It discusses Azure Cognitive Services for computer vision, speech, language, and decision capabilities. It also covers Azure Machine Learning for building ML models using popular frameworks. Additional Azure services and tools mentioned include Databricks, ML VMs, Visual Studio Code, and hardware accelerators. Specific cognitive services like Face API, Custom Vision, Text Analytics, and Anomaly Detector/Metrics Advisor are described in more detail. Case studies and demos are referenced to illustrate real-world applications.
Artificial intelligence is a field of computer science that creates intelligent systems that can act like humans. It involves machine learning algorithms that allow systems to learn from data and make predictions without being explicitly programmed. Business intelligence is a set of processes and technologies that analyzes historical data to provide insights and information to support business decision making. It involves extracting, transforming, and loading data into data warehouses where it can be visualized through reports, dashboards, and data analysis. Machine learning is a key subset of artificial intelligence that uses algorithms to learn from data and make predictions without being explicitly programmed. It is used in applications like recommender systems, speech recognition, and self-driving cars.
It can bring the power of image recognition to CRM and third-party applications so that end users across sales, service, and marketing can discover new insights about their customers and predict outcomes that lead to smarter decisions.
A Machine learning based framework for Verification and Validation of Massive...IRJET Journal
This document presents a machine learning based framework for verification and validation of massive scale image data. It discusses the challenges of managing and analyzing large image datasets. The proposed framework uses techniques like data augmentation, feature extraction and selection, decision trees, cross-validation and test cases to systematically manage massive image data and validate machine learning algorithms and systems. It uses Cell Morphology Analysis (CMA) as a case study to demonstrate how the framework can verify and validate large datasets, software systems and algorithms. The effectiveness of the framework is shown through its application to CMA, which involves classifying cell images using machine learning.
It is the presentation of my project .In this ppt we tell you about our project . In inventory management system we handled the management of my shop . It is best in your helping material . So download our ppt and take rest .
Data pre-processing involves preparing raw data for machine learning models through several key steps:
1) Getting the raw dataset from various sources, 2) Importing necessary libraries, 3) Importing and storing large datasets in the cloud, 4) Cleaning data by handling missing values through techniques like deletion or approximation, 5) Encoding categorical variables as numbers, 6) Splitting the dataset into training and test sets, and 7) Feature scaling to normalize variable values for model training. These steps ensure the data is in a suitable format for building accurate machine learning models.
1. The document discusses optical character recognition (OCR), including its applications, how it works, and the platform used.
2. OCR involves using software to convert scanned images of text into machine-encoded text by recognizing glyphs and classifying characters through feature extraction and neural networks.
3. The authors explore using OCR for tasks like digitization and security monitoring to reduce human error, and discuss future enhancements like recognizing multiple characters and improving accuracy.
Providing intelligent data extraction solutions for an analytics relation spe...Netscribes1
The analytical insights provider wanted to optimize and automate its entire data extraction & analyzing process. To be the go-to expert as a leading data solution provider, the consulting firm reached out to Netscribes to develop an AR-based platform to assimilate accurate and informative data from various online media to obtain analytical solutions.
Explore more: https://www.netscribes.com/solutions/ai-business-solutions/
What is Computer Vision and How Does it Work.pdfSoftmaxAi
According to an artificial intelligence development company, there are many types of computer vision out of which the most common types of computer vision are object detection, image classification, pose estimation, semantic segmentation, image restoration, etc.
Top 5 Travel Analytics Solutions Companies.pptxKavika Roy
Data analytics has a crucial role in the ever-changing travel industry. It helps businesses stay at the top of the game while increasing ROI. Here, we’ll discuss the top travel analytics solutions provider to partner with in the US market.
Transforming Hotel Data Analytics with a Resilient Datawarehouse.pptxKavika Roy
The travel and hospitality industry is evolving through the adoption of data analytics and BI solutions. This is done by modernizing the hotel data analytics infrastructure. Here, we’ll discuss the ways to build a resilient data warehouse and the role of analytics in the industry.
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This presentation covers everything about deep learning. You will be familier with all the main concepts used in deep learning.
Includes topics like difference between deep learning and machine learning, Feature engineering in detail, Deep learning frameworks , applications of deep learning etc.
This presentation will surely help you to know about the deep learning.
For queries contact on the given email id.
Email - chauhanjay657@gmail.com
The document discusses the key steps in an AI project cycle:
1) Problem scoping involves understanding the problem, stakeholders, location, and reasons for solving it.
2) Data acquisition collects accurate and reliable structured or unstructured data from various sources.
3) Data exploration arranges and visualizes the data to understand trends and patterns using tools like charts and graphs.
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Expense Manager: An Expense Tracking Application using Image ProcessingIRJET Journal
This document describes an expense tracking mobile application called "Expense Manager" that uses image processing and machine learning techniques. The application allows users to take photos of receipts which are then processed to extract expense information like item names, costs, and dates. Machine learning is used to categorize expenses and detect patterns in spending. The extracted data is presented to users through graphs and reports to help them analyze their spending habits and budgets. The goals of the application are to help users better understand and manage their expenses through a simple interface that avoids tedious manual data entry. Future work may include integrating the app with financial accounts to automatically track spending from purchases.
The Rise of AI and Machine Learning in Power BI (1).pdfSparity1
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This document provides an overview of machine learning and AI services available on Microsoft Azure. It discusses Azure Cognitive Services for computer vision, speech, language, and decision capabilities. It also covers Azure Machine Learning for building ML models using popular frameworks. Additional Azure services and tools mentioned include Databricks, ML VMs, Visual Studio Code, and hardware accelerators. Specific cognitive services like Face API, Custom Vision, Text Analytics, and Anomaly Detector/Metrics Advisor are described in more detail. Case studies and demos are referenced to illustrate real-world applications.
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Read the Full Article: https://www.prepai.io/blog/most-common-elearning-challenges/
About PrepAI
PrepAI is a smart, easy-to-use, intuitive question-generation platform powered by the latest AI/ML technology. It helps you create text and media-rich-question papers in multiple forms, making tests less of a hassle for any class and course.
PrepAI: https://www.prepai.io/
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Read the Full Article: https://www.prepai.io/blog/ways-to-develop-metacognitive-skills/
About PrepAI
PrepAI is a smart, easy-to-use, intuitive question-generation platform powered by the latest AI/ML technology. It helps you create text and media-rich-question papers in multiple forms, making tests less of a hassle for any class and course.
PrepAI: https://www.prepai.io/
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Read the Full Article: https://www.prepai.io/blog/upskill-and-reskill-your-employees/
About PrepAI
PrepAI is a smart, easy-to-use, intuitive question-generation platform powered by the latest AI/ML technology. It helps you create text and media-rich-question papers in multiple forms, making tests less of a hassle for any class and course.
PrepAI: https://www.prepai.io/
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Read the Full Article: https://www.prepai.io/blog/most-common-elearning-challenges/
About PrepAI
PrepAI is a smart, easy-to-use, intuitive question-generation platform powered by the latest AI/ML technology. It helps you create text and media-rich-question papers in multiple forms, making tests less of a hassle for any class and course.
PrepAI: https://www.prepai.io/
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This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
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- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
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How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Fueling AI with Great Data with Airbyte WebinarZilliz
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Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
2. How to Export
Power BI
Dashboards using
Power Automate
Workspace – where the report will be hosted in
service
Report – Report Name
Export Format – PDF
Step 6: Fill in the details in the actions
Step 7: Create a new step and search for Gmail or
Outlook and select send an email as an action
Place the visual according to your choice and run
the flow to trigger the action and send a mail to
defined users.
3. Acquiring Image Datasets
The first step in functioning a computer
vision algorithm is determining the
acquisition strategy of the image
datasets.The options are endless; hence, the
software architecture must be designed to
accommodate the best possibilities.
Labeling Datasets
When it comes to a machine learning
algorithm, there are numerous data points to
be fed to the system. The labels instruct the
machine about the job.
4. Processing the Data
Now that you have labeled data, it is time to
undergo a meticulous quality check by
testing it against a training dataset. Here,
the images undergo a series of automated
processes that enhance the images. This
involves adding or removing pixels, sorting
misclassified data, or removing noise.
Data Augmentation
The images are further modified with various
operations such as cropping, compressing,
flipping horizontally or vertically, and blurring,
among others. The following exercise trains
the system for better image recognition
capabilities.
5. Understanding the Visuals
Your model is now prepared to work
autonomously with various visuals in form of
images or videos. The system continues to
improve when used regularly.
6. Common
Computer Vision
Solutions
Face Recognition: The A.I. models calculate
several face representations to confirm identity
accurately.
Emotion Recognition
Video Analytics: Classifies each object to enable
smart video analysis solutions that help users
with granular search, smart alerting, and
comprehensive reporting.
Optical Character Recognition: Optical character
recognition translates data into easily editable
text and is the soundest way of capturing data
from printed or written text.
7. Common
Computer Vision
Solutions
Image Processing: Once you have labelled your
image dataset accurately, it can be used for
numerous purposes like removing noise or
identifying an individual.
Object Detection: An average object detection
capability includes information retrieval from a
moving or still image.