Web clustering Engines are emerging trend in the field of data retrieval. They organize search results by topic, thus providing a complementary view to the flat ranked list returned by the standard search engines.
Web clustering Engines are emerging trend in the field of data retrieval. They organize search results by topic, thus providing a complementary view to the flat ranked list returned by the standard search engines.
Adversarial and reinforcement learning-based approaches to information retrievalBhaskar Mitra
Traditionally, machine learning based approaches to information retrieval have taken the form of supervised learning-to-rank models. Recently, other machine learning approaches—such as adversarial learning and reinforcement learning—have started to find interesting applications in retrieval systems. At Bing, we have been exploring some of these methods in the context of web search. In this talk, I will share couple of our recent work in this area that we presented at SIGIR 2018.
Text Categorization Using Improved K Nearest Neighbor AlgorithmIJTET Journal
Abstract— Text categorization is the process of identifying and assigning predefined class to which a document belongs. A wide variety of algorithms are currently available to perform the text categorization. Among them, K-Nearest Neighbor text classifier is the most commonly used one. It is used to test the degree of similarity between documents and k training data, thereby determining the category of test documents. In this paper, an improved K-Nearest Neighbor algorithm for text categorization is proposed. In this method, the text is categorized into different classes based on K-Nearest Neighbor algorithm and constrained one-pass clustering, which provides an effective strategy for categorizing the text. This improves the efficiency of K-Nearest Neighbor algorithm by generating the classification model. The text classification using K-Nearest Neighbor algorithm has a wide variety of text mining applications.
Hey friends, here is my "query tree" assignment. :-) I have searched a lot to get this master piece :p and I can guarantee you that this one gonna help you In Sha ALLAH more than any else document on the subject. Have a good day :-)
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
Text document clustering and similarity detection is the major part of document management, where every document should be identified by its key terms and domain knowledge. Based on the similarity, the documents are grouped into clusters. For document similarity calculation there are several approaches were proposed in the existing system. But the existing system is either term based or pattern based. And those systems suffered from several problems. To make a revolution in this challenging environment, the proposed system presents an innovative model for document similarity by applying back propagation time stamp algorithm. It discovers patterns in text documents as higher level features and creates a network for fast grouping. It also detects the most appropriate patterns based on its weight and BPTT performs the document similarity measures. Using this approach, the document can be categorized easily. In order to perform the above, a new approach is used. This helps to reduce the training process problems. The above framework is named as BPTT. The BPTT has implemented and evaluated using dot net platform with different set of datasets.
Application of Clustering in Data Science using Real-life Examples Edureka!
Clustering data into subsets is an important task for many data science applications. It is considered as one of the most important unsupervised learning technique. Keeping this in mind, we have come with a free webinar ‘Application of Cluster in Data Science using Real-life examples.’
Adversarial and reinforcement learning-based approaches to information retrievalBhaskar Mitra
Traditionally, machine learning based approaches to information retrieval have taken the form of supervised learning-to-rank models. Recently, other machine learning approaches—such as adversarial learning and reinforcement learning—have started to find interesting applications in retrieval systems. At Bing, we have been exploring some of these methods in the context of web search. In this talk, I will share couple of our recent work in this area that we presented at SIGIR 2018.
Text Categorization Using Improved K Nearest Neighbor AlgorithmIJTET Journal
Abstract— Text categorization is the process of identifying and assigning predefined class to which a document belongs. A wide variety of algorithms are currently available to perform the text categorization. Among them, K-Nearest Neighbor text classifier is the most commonly used one. It is used to test the degree of similarity between documents and k training data, thereby determining the category of test documents. In this paper, an improved K-Nearest Neighbor algorithm for text categorization is proposed. In this method, the text is categorized into different classes based on K-Nearest Neighbor algorithm and constrained one-pass clustering, which provides an effective strategy for categorizing the text. This improves the efficiency of K-Nearest Neighbor algorithm by generating the classification model. The text classification using K-Nearest Neighbor algorithm has a wide variety of text mining applications.
Hey friends, here is my "query tree" assignment. :-) I have searched a lot to get this master piece :p and I can guarantee you that this one gonna help you In Sha ALLAH more than any else document on the subject. Have a good day :-)
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
Text document clustering and similarity detection is the major part of document management, where every document should be identified by its key terms and domain knowledge. Based on the similarity, the documents are grouped into clusters. For document similarity calculation there are several approaches were proposed in the existing system. But the existing system is either term based or pattern based. And those systems suffered from several problems. To make a revolution in this challenging environment, the proposed system presents an innovative model for document similarity by applying back propagation time stamp algorithm. It discovers patterns in text documents as higher level features and creates a network for fast grouping. It also detects the most appropriate patterns based on its weight and BPTT performs the document similarity measures. Using this approach, the document can be categorized easily. In order to perform the above, a new approach is used. This helps to reduce the training process problems. The above framework is named as BPTT. The BPTT has implemented and evaluated using dot net platform with different set of datasets.
Application of Clustering in Data Science using Real-life Examples Edureka!
Clustering data into subsets is an important task for many data science applications. It is considered as one of the most important unsupervised learning technique. Keeping this in mind, we have come with a free webinar ‘Application of Cluster in Data Science using Real-life examples.’
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A SURVEY ON SIMILARITY MEASURES IN TEXT MINING mlaij
The Volume of text resources have been increasing in digital libraries and internet. Organizing these text documents has become a practical need. For organizing great number of objects into small or minimum number of coherent groups automatically, Clustering technique is used. These documents are widely used for information retrieval and Natural Language processing tasks. Different Clustering algorithms require a metric for quantifying how dissimilar two given documents are. This difference is often measured by similarity measure such as Euclidean distance, Cosine similarity etc. The similarity measure process in text
mining can be used to identify the suitable clustering algorithm for a specific problem. This survey discusses the existing works on text similarity by partitioning them into three significant approaches; String-based, Knowledge based and Corpus-based similarities.
A Novel Multi- Viewpoint based Similarity Measure for Document ClusteringIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATIONIJDKP
This article will introduce some approaches for improving text categorization models by integrating
previously imported ontologies. From the Reuters Corpus Volume I (RCV1) dataset, some categories very
similar in content and related to telecommunications, Internet and computer areas were selected for models
experiments. Several domain ontologies, covering these areas were built and integrated to categorization
models for their improvements.
Clustering Using Shared Reference Points Algorithm Based On a Sound Data ModelWaqas Tariq
A novel clustering algorithm CSHARP is presented for the purpose of finding clusters of arbitrary shapes and arbitrary densities in high dimensional feature spaces. It can be considered as a variation of the Shared Nearest Neighbor algorithm (SNN), in which each sample data point votes for the points in its k-nearest neighborhood. Sets of points sharing a common mutual nearest neighbor are considered as dense regions/ blocks. These blocks are the seeds from which clusters may grow up. Therefore, CSHARP is not a point-to-point clustering algorithm. Rather, it is a block-to-block clustering technique. Much of its advantages come from these facts: Noise points and outliers correspond to blocks of small sizes, and homogeneous blocks highly overlap. This technique is not prone to merge clusters of different densities or different homogeneity. The algorithm has been applied to a variety of low and high dimensional data sets with superior results over existing techniques such as DBScan, K-means, Chameleon, Mitosis and Spectral Clustering. The quality of its results as well as its time complexity, rank it at the front of these techniques.
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Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
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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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
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See how to accelerate model training and optimize model performance with active learning
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Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
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Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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:
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- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.