This paper proposes a new fraud detection method using multiple classifiers to handle skewed data distributions. The method partitions the minority class using oversampling and trains Naive Bayes, C4.5, and backpropagation classifiers on each partition. The classifiers are then combined using stacking and bagging. Experimental results found that stacking and bagging the classifiers achieved higher cost savings than single classifiers, with stacking-bagging performing the best. Oversampling performed better than undersampling or SMOTE for this skewed fraud detection data.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
MATH 533 Education Specialist / snaptutorial.comMcdonaldRyan97
For more classes visit
www.snaptutorial.com
MATH 533 Week 1 Homework
MATH 533 Week 1 Quiz
MATH 533 Week 2 DQ 1 Case Let's Make a Deal
MATH 533 Week 2 Homework (2 Sets)
MATH 533 Week 2 Quiz
MATH 533 Week 3 DQ 1 Ethics in Statistics Readings and Discussion
Spectral Element Methods in Large Eddy Simulationgaurav dhir
Lower Order Finite Volume based RANS simulations have become an industry standard in Computational Fluid Dynamics. However, there's a major interest among academics to advance the state of the art by employing spectral element methods in Large Eddy Simulation. In this presentation, we study major issues which inhibit spectral element methods from becoming a mainstream CFD method
Prediction model of algal blooms using logistic regression and confusion matrix IJECEIAES
Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.
Multiclass classification of imbalanced dataSaurabhWani6
Pydata Talk on Classification of imbalanced data.
It is an overview of concepts for better classification in imbalanced datasets.
Resampling techniques are introduced along with bagging and boosting methods.
Use of Definitive Screening Designs to Optimize an Analytical MethodPhilip Ramsey
discusses using a definitive screening design to characterize and optimize a glycoprofiling method and compares the definitive screening results to a much larger central composite design results
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
selection of relevant feature from a given set of feature is one of the important issues in the field of
data mining as well as classification. In general the dataset may contain a number of features however it is not
necessary that the whole set features are important for particular analysis of decision making because the
features may share the common information‟s and can also be completely irrelevant to the undergoing
processing. This generally happen because of improper selection of features during the dataset formation or
because of improper information availability about the observed system. However in both cases the data will
contain the features that will just increase the processing burden which may ultimately cause the improper
outcome when used for analysis. Because of these reasons some kind of methods are required to detect and
remove these features hence in this paper we are presenting an efficient approach for not just removing the
unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information
theory to detect the information gain from each feature and minimum span tree to group the similar features
with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the
algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the
results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the
performances of the classifier.
MATH 533 RANK Achievement Education--math533rank.comkopiko162
FOR MORE CLASSES VISIT
www.math533rank.com
MATH 533 Week 1 Homework
MATH 533 Week 1 Quiz
MATH 533 Week 2 DQ 1 Case Let's Make a Deal
MATH 533 Week 2 Homework (2 Sets)
MATH 533 Week 2 Quiz
BridgeStreet Worldwide offer fully furnsihed 1, 2, & 3 bedroom apartments locally, nationally, and internationally. You get resort style living at prices equal to or less than most hotel suite and extended stay hotels.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
MATH 533 Education Specialist / snaptutorial.comMcdonaldRyan97
For more classes visit
www.snaptutorial.com
MATH 533 Week 1 Homework
MATH 533 Week 1 Quiz
MATH 533 Week 2 DQ 1 Case Let's Make a Deal
MATH 533 Week 2 Homework (2 Sets)
MATH 533 Week 2 Quiz
MATH 533 Week 3 DQ 1 Ethics in Statistics Readings and Discussion
Spectral Element Methods in Large Eddy Simulationgaurav dhir
Lower Order Finite Volume based RANS simulations have become an industry standard in Computational Fluid Dynamics. However, there's a major interest among academics to advance the state of the art by employing spectral element methods in Large Eddy Simulation. In this presentation, we study major issues which inhibit spectral element methods from becoming a mainstream CFD method
Prediction model of algal blooms using logistic regression and confusion matrix IJECEIAES
Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.
Multiclass classification of imbalanced dataSaurabhWani6
Pydata Talk on Classification of imbalanced data.
It is an overview of concepts for better classification in imbalanced datasets.
Resampling techniques are introduced along with bagging and boosting methods.
Use of Definitive Screening Designs to Optimize an Analytical MethodPhilip Ramsey
discusses using a definitive screening design to characterize and optimize a glycoprofiling method and compares the definitive screening results to a much larger central composite design results
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
selection of relevant feature from a given set of feature is one of the important issues in the field of
data mining as well as classification. In general the dataset may contain a number of features however it is not
necessary that the whole set features are important for particular analysis of decision making because the
features may share the common information‟s and can also be completely irrelevant to the undergoing
processing. This generally happen because of improper selection of features during the dataset formation or
because of improper information availability about the observed system. However in both cases the data will
contain the features that will just increase the processing burden which may ultimately cause the improper
outcome when used for analysis. Because of these reasons some kind of methods are required to detect and
remove these features hence in this paper we are presenting an efficient approach for not just removing the
unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information
theory to detect the information gain from each feature and minimum span tree to group the similar features
with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the
algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the
results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the
performances of the classifier.
MATH 533 RANK Achievement Education--math533rank.comkopiko162
FOR MORE CLASSES VISIT
www.math533rank.com
MATH 533 Week 1 Homework
MATH 533 Week 1 Quiz
MATH 533 Week 2 DQ 1 Case Let's Make a Deal
MATH 533 Week 2 Homework (2 Sets)
MATH 533 Week 2 Quiz
BridgeStreet Worldwide offer fully furnsihed 1, 2, & 3 bedroom apartments locally, nationally, and internationally. You get resort style living at prices equal to or less than most hotel suite and extended stay hotels.
Detecting fraud in cellular telephone networksJamal Meselmani
A Thesis Presented in Partial Fulfillment of the Requirement for the Degree in "MBA" by Hiyam Ali El Tawashi
Telecommunication fraud is a problem that has grown dramatically over the past ten years.
Fraud become a serious global issue for mobile network service providers, it has
undoubtedly become a significant source of revenue losses and bad debts to
telecommunication industry, and with the expected continuing growth in revenue it can be
expected that fraud will increase proportionally.
The research project therefore, focused on how Jawwal Company managing and detecting
the fraud, in order to modify the current tools for more effective fraud prevention and
detection, for this reason the researcher undertook a set of actions that are reported as
follow:
First step it was necessary to understand the problem of telecom fraud, then to know what
makes people perpetrate the fraud, and which are the most prevalent fraud types that are
occurring, clarifying which is the likely products and services to be attacked, what source
of information to facilitate the fraud, how fraudsters perpetrate the fraud finally explaining
the fraud detection and prevention procedures.
Then apply the study on Jawwal Company as study case, by distributing 200
questionnaires to targeted sections, and analyzing the result which shows that the current
fraud management at Jawwal Company is not efficient and needs to be modified.
Fraud Detector - The easy-to-customize, high ROI, IT solution for detecting ...112Motion
Fraud Detector is a standard, easy-to-customize, Harmony solution that uses business rules and expressions to detect suspicious transactions.
It is Do It Yourself IT - aimed at domain experts - who can easily add new rules and it is considered to be one of the most compelling, innovative, IT solutions available today.
The slideshare describes the benefits & shows how it's implemented in Harmony (in a matter of hours !!!
Overview of misappropriation fraud, CPA responsibilities in relation to fraud, fraud investigative methods, common red flags, simple preventive controls for small business
The Donald W. Reynolds National Center for Business Journalism presented the free, three-hour workshop, "Detecting Corporate Fraud: Tips from a Crook and a Sleuth," before the Investigative Reporters and Editors Conference on June 25, 2014.
Roddy Boyd, investigative reporter and founder of the Southern Investigative Reporting Foundation, led this investigative training with Sam E. Antar, former CFO of Crazy Eddie, Inc. and convicted felon.
To access all training materials provided during the series of "Detecting Corporate Fraud" workshops, please visit http://bit.ly/cofraud14.
For information about business journalism training and resources, please visit http://businessjournalism.org.
NYC R Conference slides on lessons learned at Socure deploying real-time prediction web services based on fraud models developed in R. Leveraging AWS IAAS and Docker containerization to deploy auto-scaling redundant deployment units with clear separation of modelling and dev-op concerns.
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!
This session will go into best practices and detail on how to architect a near real-time application on Hadoop using an end-to-end fraud detection case study as an example. It will discuss various options available for ingest, schema design, processing frameworks, storage handlers and others, available for architecting this fraud detection application and walk through each of the architectural decisions among those choices.
Telecom Fraud Detection - Naive Bayes ClassificationMaruthi Nataraj K
To create a fraud management classification model that is powerful enough to handle the subscription fraud that the company has encountered and flexible enough to potentially apply to things that had not been witnessed yet
Analysis of Common Supervised Learning Algorithms Through Applicationaciijournal
Supervised learning is a branch of machine learning wherein the machine is equipped with labelled data which it uses to create sophisticated models that can predict the labels of related unlabelled data. the literature on the field offers a wide spectrum of algorithms and applications. however, there is limited research available to compare the algorithms making it difficult for beginners to choose the most efficient algorithm and tune it for their application.
This research aims to analyse the performance of common supervised learning algorithms when applied to sample datasets along with the effect of hyper-parameter tuning. for the research, each algorithm is applied to the datasets and the validation curves (for the hyper-parameters) and learning curves are analysed to understand the sensitivity and performance of the algorithms. the research can guide new researchers aiming to apply supervised learning algorithm to better understand, compare and select the appropriate algorithm for their application. Additionally, they can also tune the hyper-parameters for improved efficiency and create ensemble of algorithms for enhancing accuracy.
ANALYSIS OF COMMON SUPERVISED LEARNING ALGORITHMS THROUGH APPLICATIONaciijournal
Supervised learning is a branch of machine learning wherein the machine is equipped with labelled data
which it uses to create sophisticated models that can predict the labels of related unlabelled data.the
literature on the field offers a wide spectrum of algorithms and applications.however, there is limited
research available to compare the algorithms making it difficult for beginners to choose the most efficient
algorithm and tune it for their application.
This research aims to analyse the performance of common supervised learning algorithms when applied to
sample datasets along with the effect of hyper-parameter tuning.for the research, each algorithm is applied
to the datasets and the validation curves (for the hyper-parameters) and learning curves are analysed to
understand the sensitivity and performance of the algorithms.the research can guide new researchers
aiming to apply supervised learning algorithm to better understand, compare and select the appropriate
algorithm for their application. Additionally, they can also tune the hyper-parameters for improved
efficiency and create ensemble of algorithms for enhancing accuracy.
Predicting automobile insurance fraud using classical and machine learning mo...IJECEIAES
Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Analysis of Common Supervised Learning Algorithms Through Applicationaciijournal
Supervised learning is a branch of machine learning wherein the machine is equipped with labelled data
which it uses to create sophisticated models that can predict the labels of related unlabelled data. the
literature on the field offers a wide spectrum of algorithms and applications. However, there is limited
research available to compare the algorithms making it difficult for beginners to choose the most efficient
algorithm and tune it for their application.
This research aims to analyse the performance of common supervised learning algorithms when applied to
sample datasets along with the effect of hyper-parameter tuning. for the research, each algorithm is
applied to the datasets and the validation curves (for the hyper-parameters) and learning curves are
analysed to understand the sensitivity and performance of the algorithms. The research can guide new
researchers aiming to apply supervised learning algorithm to better understand, compare and select the
appropriate algorithm for their application. Additionally, they can also tune the hyper-parameters for
improved efficiency and create ensemble of algorithms for enhancing accuracy.
Accounting for variance in machine learning benchmarksDevansh16
Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
A MODEL-BASED APPROACH MACHINE LEARNING TO SCALABLE PORTFOLIO SELECTIONIJCI JOURNAL
This study proposes a scalable asset selection and allocation approach using machine learning that
integrates clustering methods into portfolio optimization models. The methodology applies the Uniform
Manifold Approximation and Projection method and ensemble clustering techniques to preselect assets
from the Ibovespa and S&P 500 indices. The research compares three allocation models and finds that the
Hierarchical Risk Parity model outperformed the others, with a Sharpe ratio of 1.11. Despite the
pandemic's impact on the portfolios, with drawdowns close to 30%, they recovered in 111 to 149 trading
days. The portfolios outperformed the indices in cumulative returns, with similar annual volatilities of
20%. Preprocessing with UMAP allowed for finding clusters with higher discriminatory power, evaluated
through internal cluster validation metrics, helping to reduce the problem's size during optimal portfolio
allocation. Overall, this study highlights the potential of machine learning in portfolio optimization,
providing a useful framework for investment practitioners.
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETSEditor IJCATR
This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
Nearly 80% of banks surveyed by Enigma are evaluating or already using machine learning in their sanctions screening programs. This deck enables you to move beyond the buzzwords and provides key information about machine learning for your sanctions screening strategy.
Here's what the deck contains:
- Why sanctions screening can benefit from machine learning
- Which machine learning models are a good fit for sanctions screening
- What best practices you need to successfully integrate machine learning
Empirical analysis of ensemble methods for the classification of robocalls in...IJECEIAES
With the advent of technology, there has been an excessive use of cellular phones. Cellular phones have made life convenient in our society. However, individuals and groups have subverted the telecommunication devices to deceive unwary victims. Robocalls are quite prevalent these days and they can either be legal or used by scammers to trick one out of their money. The proposed methodology in the paper is to experiment two ensemble models on the dataset acquired from the Federal Trade Commission(DNC Dataset). It is imperative to analyze the call records and based on the patterns the calls can classify as a robocall or not a robocall. Two algorithms Random Forest and XgBoost are combined in two ways and compared in the paper in terms of accuracy, sensitivity and the time taken.
Classification of Breast Cancer Diseases using Data Mining Techniquesinventionjournals
Medical data mining has great deal for exploring new knowledge from large amount of data. Classification is one of the important data mining techniques for classification of data. In this research work, we have used various data mining based classification techniques for classification of cancer diseases patient or not. We applied the Breast Cancer-Wisconsin (Original) data set into different data mining techniques and compared the accuracy of models with two different data partitions. BayesNet achieved highest accuracy as 97.13% in case of 10-fold data partitions. We have also applied the info gain feature selection technique on BayesNet and Support Vector Machine (SVM) and achieved best accuracy 97.28% accuracy with BayesNet in case of 6 feature subset.
Re-mining item associations: Methodology and a case study in apparel retailingGurdal Ertek
Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price,
time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques.
http://research.sabanciuniv.edu.
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.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- 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.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
11/04 Regular Meeting: Monority Report in Fraud Detection Classification of Skewed Data
1. Minority Report in Fraud Detection: Classification of Skewed Data Clifton Phua, Damminda Alahakoon, and Vincent Lee SIGKDD 2004 Reporter: Ping-Hua Yang
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