This document discusses machine learning and its applications to email. It begins with definitions of machine learning and its goal of allowing computers to learn from data without being explicitly programmed. The document then discusses the history of machine learning, including early enthusiasm, dark ages, renaissance, and current maturity. It describes three main types of machine learning: supervised, semi-supervised, and unsupervised learning. The document focuses on applications of machine learning to email, including automatic answering, automatic organization into folders, email summarization, spam filtering, and dynamic feature selection for classification. It concludes by noting other potential applications like medical imaging and robotics.
This seminar gives an introduction to machine learning use-cases in real life. it talks about use of classification, regression and clustering with the help of use-cases that we see daily. it describes how neural networks and reinforcement learning can be leveraged in creating games
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Sentiment analysis of comments in social media IJECEIAES
Social media platforms are witnessing a significant growth in both size and purpose. One specific aspect of social media platforms is sentiment analysis, by which insights into the emotions and feelings of a person can be inferred from their posted text. Research related to sentiment analysis is acquiring substantial interest as it is a promising filed that can improve user experience and provide countless personalized services. Twitter is one of the most popular social media platforms, it has users from different regions with a variety of cultures and languages. It can thus provide valuable information for a diverse and large amount of data to be used to improve decision making. In this paper, the sentiment orientation of the textual features and emoji-based components is studied targeting “Tweets” and comments posted in Arabic on Twitter, during the 2018 world cup event. This study also measures the significance of analyzing texts including or excluding emojis. The data is obtained from thousands of extracted tweets, to find the results of sentiment analysis for texts and emojis separately. Results show that emojis support the sentiment orientation of the texts and those texts or emojis cannot separately provide reliable information as they complement each other to give the intended meaning.
This seminar gives an introduction to machine learning use-cases in real life. it talks about use of classification, regression and clustering with the help of use-cases that we see daily. it describes how neural networks and reinforcement learning can be leveraged in creating games
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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Sentiment analysis of comments in social media IJECEIAES
Social media platforms are witnessing a significant growth in both size and purpose. One specific aspect of social media platforms is sentiment analysis, by which insights into the emotions and feelings of a person can be inferred from their posted text. Research related to sentiment analysis is acquiring substantial interest as it is a promising filed that can improve user experience and provide countless personalized services. Twitter is one of the most popular social media platforms, it has users from different regions with a variety of cultures and languages. It can thus provide valuable information for a diverse and large amount of data to be used to improve decision making. In this paper, the sentiment orientation of the textual features and emoji-based components is studied targeting “Tweets” and comments posted in Arabic on Twitter, during the 2018 world cup event. This study also measures the significance of analyzing texts including or excluding emojis. The data is obtained from thousands of extracted tweets, to find the results of sentiment analysis for texts and emojis separately. Results show that emojis support the sentiment orientation of the texts and those texts or emojis cannot separately provide reliable information as they complement each other to give the intended meaning.
Desarrollo Local Integrado y Sostenible-- Created using PowToon -- Free sign up at http://www.powtoon.com/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Introduction on how social media has an impact on todays business and how to utilise the current scenario. This PPT talks about basics and target mainly for novice people.
Handelsbeleid in steden en gemeenten (UNIZO Burgemeestersonbijt 16/01/14)Bert Serneels
Presentatie voor Karel Van Eetvelt en Peter Aerts (UNIZO) rond omgaan met leegstand van winkelpanden in steden en gemeenten via een doordacht handelsbeleid.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
Unlocking the Potential of Artificial Intelligence_ Machine Learning in Pract...eswaralaldevadoss
Machine learning is a subset of artificial intelligence that involves training computers to learn from data and make predictions or decisions based on that data. It involves building algorithms and models that can learn patterns and relationships from data and use that knowledge to make predictions or take actions.
Here are some key concepts that can help beginners understand machine learning:
Data: Machine learning algorithms require data to learn from. This data can come from a variety of sources such as databases, spreadsheets, or sensors. The quality and quantity of data can greatly impact the accuracy and effectiveness of machine learning models.
Training: In machine learning, training involves feeding data into a model and adjusting its parameters until it can accurately predict outcomes. This process involves testing and tweaking the model to improve its accuracy.
Algorithms: There are many different algorithms used in machine learning, each with its own strengths and weaknesses. Common machine learning algorithms include decision trees, random forests, and neural networks.
Supervised vs. Unsupervised Learning: Supervised learning involves training a model on labeled data, where the desired outcome is already known. Unsupervised learning, on the other hand, involves training a model on unlabeled data and allowing it to identify patterns and relationships on its own.
Evaluation: After training a model, it's important to evaluate its accuracy and performance on new data. This involves testing the model on a separate set of data that it hasn't seen before.
Overfitting vs. Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, leading to poor performance on new data. Underfitting occurs when a model is too simple and fails to capture important patterns in the data.
Applications: Machine learning is used in a wide range of applications, from predicting stock prices to identifying fraudulent transactions. It's important to understand the specific needs and constraints of each application when building machine learning models.
Overall, machine learning is a powerful tool that can help businesses and organizations make more informed decisions based on data. By understanding the basic concepts and techniques of machine learning, beginners can begin to explore the potential applications and benefits of this exciting field.
Unit I and II Machine Learning MCA CREC.pptxtrishipaul
Machine Learning topics presentation covering the topics:
Unit I – Introduction: Towards Intelligent Machines, Well posed Problems, Example of Applications in diverse fields, Data Representation, Domain Knowledge for Productive use of Machine Learning, Diversity of Data: Structured / Unstructured, Forms of Learning, Machine Learning and Data Mining, Basic Linear Algebra in Machine Learning Techniques.
Unit II – Supervised Learning – Rationale and Basics: Learning from Observations: Why Learning Works, Bias and Variance: Computations Learning Theory, Occam’s Razor Principle and Overfitting Avoidance, Heuristic Search in Inductive Learning, Estimating Generalization Errors, Metrics for Assessing Regression, Metrics for Assessing Classification.
Machine learning is a technology design to build intelligent systems. These systems also have the ability to learn from past experience or analyze historical data. It provides results according to its experience.
Alpavdin defines Machine Learning as-
“Optimizing a performance criterion using example data and past experience”.
Data is the key concept of machine learning. We can also apply its algorithms on data to identify hidden patterns and gain insights. These patterns and gained knowledge help systems to learn and improve their performance.
Machine learning technology involves both statistics and computer science. Statistics allows one to draw inferences from the given data. To implement efficient algorithms we can also use computer science. It represents the required model, and evaluate the performance of the model.
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Workshop given to the staff for PhD and Masters Topic Selection in the area of Big Data, Data Science and Machine Learning. It has many interactive online demos to understanding on NLP social media analysis like sentiment analysis , topic modeling , language detection and intent detection. Some of the basic concept about classification and regression and clustering with interactive worksheets. Finally , hands-on machine learning models and comparisons in WEKA tool kit with case study of cars and diabetic patient data.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
ترتيل - جمعية تحفيظ القرآن الكريم بخليصFahd Allebdi
استحدثت جمعية تحفيظ القرآن الكريم بخليص مشروع وموقع ترتيل لادارة كافة انشطة الجمعية من نتائج وتقارير واختبارات بحيث يسهل على منسوبيها
من اداريين ومشرفين ومعلمين وطلاب الدخول على النظام وتسجيل النتائج او الاطلاع عليها بكل يسر وسهولة في أي وقت واي مكان
مما ساهم في تطوير المشروع واظهار روح المنافسة بين جميع افراد العمل والمسابقة للحصول على المراكز الاولى والمتقدمة.
http://trteel.com/
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
2. Artificial intelligence is a part of computer science that tries to make
computers more intelligent. One of the basic requirements for any
intelligent behavior is learning. Therefore, machine learning is one of
major branches of artificial intelligence and, indeed, it is one of the most
rapidly developing subfields of AI research.
So in this report we will discover The definition of it, history , types and
some real application in real world used.
3. Machine Learning is the domain of Artificial Intelligence which is
concerned with building adaptive computer systems that are able to
improve their competence and/or efficiency through learning from input
data or from their own problem solving experience.
Machine Learning is arguably the greatest export from computing to
other scientific fields.
4. Early enthusiasm (1955 - 1965):
• Learning without knowledge;
• Neural modeling (self-organizing systems and decision space techniques);
• Evolutionary learning;
• Rote learning (Samuel Checker’s player).
5. Dark ages (1962 - 1976):
• To acquire knowledge one needs knowledge;
• Realization of the difficulty of the learning process and of the limitations of the
explored methods (e.g. the perceptron cannot learn the XOR function);
• Symbolic concept learning (Winston’s influential thesis, 1972).
7. Maturity (1988 - present):
• Experimental comparisons;
• Revival of non-symbolic methods;
• Computational learning theory;
• Multistrategy learning;
• Integration of machine learning and knowledge acquisition;
8. Types Of Machine Learning :-
1. Supervised learning is fairly common in classification problems
because the goal is often to get the computer to learn a
classification system that we have created. Digit recognition,
once again, is a common example of classification learning
It is the most common technique for training neural networks and
decision trees. Both of these techniques are highly dependent on the
information given by the pre-determined classifications
9. Types Of Machine Learning :-
a learning paradigm concerned withislearningsupervised-Semi.2
the study of how computers and natural systems such as humans
learn in the presence of both labeled and unlabeled data
supervised learning is to understand how-of semigoalThe
combining labeled and unlabeled data may change the learning
behavior, and design algorithms that take advantage of such a
combination
10. Types Of Machine Learning :-
3. Unsupervised learning seems much harder :
the goal is to have the computer learn how to do Something
that we don't tell it how to do! There are actually two
approaches to unsupervised learning.
11. However, in the few last years due to various technological advances and
research efforts (e.g. evolution of the Web). Some of these modern
applications are learning from biological sequences, learning from
email data, and learning in complex environments such as Web.
12. People are sending and receiving many messages per day,
communicating with partners and friends, or exchanging files and
information. Unfortunately, the phenomenon of email overload has
grown over the past years becoming a personal headache for users
and a financial issue for companies.
› Automatic Answering :
› Automatic Mail Organization into Folders :
› Email and Thread Summarization :
› Spam Filtering :
› Dynamic Feature Space and Incremental Feature Selection for Email Classification :
13. Automatic Answering :
Large companies usually maintain email centers (call centers) with employees
committed to answer incoming messages. Those messages usually come from
company clients and partners and many times address the same problems
and queries .
Automatic email answering is an effort to build email centers or
personalized software that will be able to analyze an incoming message
and then propose or even send an applicable answer.
14. Automatic Mail Organization into Folders
The growth of email usage has forced users to find ways to organize archive
and manage their emails more efficiently. Many of them are organizing
incoming messages into separate folders. Folders can be topic-oriented like
“work”, ”personal” and ”funny”.
many users create manually some so-called rules to classify their email
Machine learning provide this task is the automatic classification of incoming
email by observing past and current classifications made by the user Thus,
the user does not need to create the rules by himself.
A lot of research has been recorded in the field and lots of those ideas
have been implemented into useful email tools .
15. Email and Thread Summarization :
email users that receive hundreds of messages per day. Some of them
are newsletters , colleagues, appointment arrangements etc.
It would be extremely useful for them if they could avoid reading all of
those messages and instead read only the most important and necessary
parts
Data mining techniques are explored in order to build trainable tools for
summarization .
16. Spam Filtering :
A spam filter is a program that is used to detect unsolicited and
unwanted email and prevent those messages from getting to a user's
inbox.
Many different machine learning classifiers have been tested in the
bibliography including Naïve Bayes , Support Vector Machines , Stacking
Classifiers and some of them have proved to be particularly accurate.
17. 1.5 Dynamic Feature Space and Incremental Feature Selection for Email Classification
In this section we present a computationally undemanding method that tackles with
this problem . Our approach uses two components in conjunction:
a) an incremental feature ranking method.
b) an incremental learning algorithm that can consider a subset of the features during
prediction.
18. Such methods evaluate each word based on cumulative statistics
concerning the number of times that it appears in each different
class of documents
19. Figure 1 . presents
algorithm Update for
the incremental update
of our approach .
20. There are also other interesting topics that were not discussed
in this report. Some of them are applications on MRI data,
astronomical data, robotics, video games, music data etc.