As we move forward into the digital age, One of the modern innovations we’ve seen is the creation of Machine Learning. This incredible form of artificial intelligence is already being used in various industries and professions. For Example, Image and Speech Recognition, Medical Diagnosis, Prediction, Classification, Learning Associations, Statistical Arbitrage, Extraction, Regression. Today we’re looking at all these Machine Learning Applications in today’s modern world.
As we move forward into the digital age, One of the modern innovations we’ve seen is the creation of Machine Learning. This incredible form of artificial intelligence is already being used in various industries and professions. For Example, Image and Speech Recognition, Medical Diagnosis, Prediction, Classification, Learning Associations, Statistical Arbitrage, Extraction, Regression. Today we’re looking at all these Machine Learning Applications in today’s modern world.
Using Data Mining Techniques in Customer SegmentationIJERA Editor
Data mining plays important role in marketing and is quite new. Although this field expands rapidly, data mining is still foreign issue for many marketers who trust only their experiences. Data mining techniques cannot substitute the significant role of domain experts and their business knowledge. In the other words, data mining algorithms are powerful but cannot effectively work without the active support of business experts. We can gain useful results by combining these techniques and business expertise. For instance ability of a data mining technique can be substantially increased by combining person experience in the field or information of business can be integrated into a data mining model to build a more successful result. Moreover, these results should always be evaluated by business experts. Thus, business knowledge can help and enrich the data mining results. On the other hand, data mining techniques can extract patterns that even the most experienced business people may have missed. In conclusion, the combination of business domain expertise with the power of data mining techniques can help organizations gain a competitive advantage in their efforts to optimize customer management. Clustering algorithms, a group of data mining technique, is one of most common used way to segment data set according to their similarities. This paper focuses on the topic of customer segmentation using data mining techniques. In the other words, we theoretically discuss about customer relationship management and then utilize couple of data mining algorithm specially clustering techniques for customer segmentation. We concentrated on behavioral segmentation.
Content analytics is the process of analyzing data to understand and improve the
content on a website or other digital platform. This can include anything from
understanding what content is most popular to identifying which topics are being
discussed the most on social media. The goal of content analytics is to use this
information to make better decisions about what content to produce and how to best
present it.
Managing knowledge, communication and informationRichard Docc
Managing Knowledge, Communication and Information Report discuss business management and method to perform better and grab more knowledge in specific target in organization.
1.1.The words ‘data’ and ‘information’ are often used as though they.pdfaquadreammail
1.1.The words ‘data’ and ‘information’ are often used as though they mean exactly the same
thing, but there is a difference. Data are the undigested facts and figures that are collected on
innumerable subjects. You may gather data yourself or use data that have been gathered by other
people.
1.2..Strengths, weaknesses and suggestions. for the main other information sources reviewed .
1.3.The data must be collected carefully because organisation decision-making processes are
based on the generated information from this data. While selecting data and information for
decision-making we must apply some criteria to this selection such as accuracy, validity, clarity
etc.
1.4.
However simple or complex your data set, think about what you might need to do to ensure that
your management of the data respects the terms of your consent, and in particular, the
confidentiality and anonymity that participants were promised.
Take advice from relevant staff in your institution. Your Data Protection manager can advise you
on protocols for handling personal data. Your computing or information services department
should be able to advise you on setting up secure databases for the different forms of data that
will be generated by your research.
As with everything in this guidebook, the earlier you can start to think about these issues, the
better. When you are preparing your research proposal, you need to plan for data management -
this is a requirement for ESRC applications, and increasingly for other funders. If your work will
generate complex or sensitive datasets, you may need to plan and cost some time for a database
manager or information specialist to develop and manage the systems that you need to keep your
data secure.
Do you have suitable arrangements in place for archiving data? Befor you access or collect your
data, you should check institution what requirements they have in place for data storage, and
what facilities are available (e.g. for data archiving).
2.2. Identify a problem or opportunity, Gather information, Analyze the situation,Develop
options, Evaluate alternatives, Select a preferred alternative,Act on the decision.
2.3.There are numerous ways of presenting data to a client but you have to ask yourself several
questions. Who is the client? To what will they respond best? What response do you require?
Will the information and the decisions you made be transferrable between presentational styles?
Will it be a formalpresentation or can you have some fun?
Once you have answered these questions you can then set about shaping your presentation.
Personally if you are able to do so (as in whether the environment within which you will be
presenting is accommodating and data allows you do) I would recommend the use of pictures,
charts and visual stimuli as much as possible; a picture paints a thousand words after all. Of
course if the decisions you have made cannot be transferred into numerical data any pie charts,
bar graphs or statistical analysi.
TRUST METRICS IN RECOMMENDER SYSTEMS: A SURVEYaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
Trust Metrics In Recommender System : A Surveyaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
data science course with placement in hyderabadmaneesha2312
360DigiTMG delivers data science course with placement in hyderabad, where you can gain practical experience in key methods and tools through real-world projects. Study under skilled trainers and transform into a skilled Data Scientist. Enroll today!
what is ..how to process types and methods involved in data analysisData analysis ireland
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
Using Data Mining Techniques in Customer SegmentationIJERA Editor
Data mining plays important role in marketing and is quite new. Although this field expands rapidly, data mining is still foreign issue for many marketers who trust only their experiences. Data mining techniques cannot substitute the significant role of domain experts and their business knowledge. In the other words, data mining algorithms are powerful but cannot effectively work without the active support of business experts. We can gain useful results by combining these techniques and business expertise. For instance ability of a data mining technique can be substantially increased by combining person experience in the field or information of business can be integrated into a data mining model to build a more successful result. Moreover, these results should always be evaluated by business experts. Thus, business knowledge can help and enrich the data mining results. On the other hand, data mining techniques can extract patterns that even the most experienced business people may have missed. In conclusion, the combination of business domain expertise with the power of data mining techniques can help organizations gain a competitive advantage in their efforts to optimize customer management. Clustering algorithms, a group of data mining technique, is one of most common used way to segment data set according to their similarities. This paper focuses on the topic of customer segmentation using data mining techniques. In the other words, we theoretically discuss about customer relationship management and then utilize couple of data mining algorithm specially clustering techniques for customer segmentation. We concentrated on behavioral segmentation.
Content analytics is the process of analyzing data to understand and improve the
content on a website or other digital platform. This can include anything from
understanding what content is most popular to identifying which topics are being
discussed the most on social media. The goal of content analytics is to use this
information to make better decisions about what content to produce and how to best
present it.
Managing knowledge, communication and informationRichard Docc
Managing Knowledge, Communication and Information Report discuss business management and method to perform better and grab more knowledge in specific target in organization.
1.1.The words ‘data’ and ‘information’ are often used as though they.pdfaquadreammail
1.1.The words ‘data’ and ‘information’ are often used as though they mean exactly the same
thing, but there is a difference. Data are the undigested facts and figures that are collected on
innumerable subjects. You may gather data yourself or use data that have been gathered by other
people.
1.2..Strengths, weaknesses and suggestions. for the main other information sources reviewed .
1.3.The data must be collected carefully because organisation decision-making processes are
based on the generated information from this data. While selecting data and information for
decision-making we must apply some criteria to this selection such as accuracy, validity, clarity
etc.
1.4.
However simple or complex your data set, think about what you might need to do to ensure that
your management of the data respects the terms of your consent, and in particular, the
confidentiality and anonymity that participants were promised.
Take advice from relevant staff in your institution. Your Data Protection manager can advise you
on protocols for handling personal data. Your computing or information services department
should be able to advise you on setting up secure databases for the different forms of data that
will be generated by your research.
As with everything in this guidebook, the earlier you can start to think about these issues, the
better. When you are preparing your research proposal, you need to plan for data management -
this is a requirement for ESRC applications, and increasingly for other funders. If your work will
generate complex or sensitive datasets, you may need to plan and cost some time for a database
manager or information specialist to develop and manage the systems that you need to keep your
data secure.
Do you have suitable arrangements in place for archiving data? Befor you access or collect your
data, you should check institution what requirements they have in place for data storage, and
what facilities are available (e.g. for data archiving).
2.2. Identify a problem or opportunity, Gather information, Analyze the situation,Develop
options, Evaluate alternatives, Select a preferred alternative,Act on the decision.
2.3.There are numerous ways of presenting data to a client but you have to ask yourself several
questions. Who is the client? To what will they respond best? What response do you require?
Will the information and the decisions you made be transferrable between presentational styles?
Will it be a formalpresentation or can you have some fun?
Once you have answered these questions you can then set about shaping your presentation.
Personally if you are able to do so (as in whether the environment within which you will be
presenting is accommodating and data allows you do) I would recommend the use of pictures,
charts and visual stimuli as much as possible; a picture paints a thousand words after all. Of
course if the decisions you have made cannot be transferred into numerical data any pie charts,
bar graphs or statistical analysi.
TRUST METRICS IN RECOMMENDER SYSTEMS: A SURVEYaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
Trust Metrics In Recommender System : A Surveyaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
data science course with placement in hyderabadmaneesha2312
360DigiTMG delivers data science course with placement in hyderabad, where you can gain practical experience in key methods and tools through real-world projects. Study under skilled trainers and transform into a skilled Data Scientist. Enroll today!
what is ..how to process types and methods involved in data analysisData analysis ireland
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
Decision making is necessary for every organization and for proper decision making, certain things are needed such as information and knowledge, without which decisions cannot be made.
This takes a look at the architectural constructs that are used for building business intelligence systems and how they are used in business processes to improve marketing, better serve customers, and maximize organizational 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:
- 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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
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
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.
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.
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.
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.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
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
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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/
12. The sales data progressively becomes an information when processed with other data such as the budget and the new product sales.
13.
14.
15. The poor quality of an information due to various factors would create confusion and misunderstanding, which is equivalent to a ‘Noise’ and ‘Distortion’ in the communication model.
24. The principle of the message routing achieves the spread of information to the appropriate quarters.
25. Knowledge is a power and an intelligent person in the organization can misuse this power to achieve personal goals undermining the functional and organizational goals.
26.
27.
28.
29. While choosing the appropriate method of communicating information, a care has to be taken to see that it is not biased.
30. For example, while using the techniques of classification or filtering the information, it should not happen that certain information is to be avoided.
31. This bias enters because people try to block a sensitive information which affect them.
32.
33. In this case one who draws inferences may have a bias in the process of collection, processing and presentation of data and information.
34. Organizations have departments like corporate planning, Market Research, R and D, HRD and so on, which collect the data and analyze for the company and communicate the inferences.
35.
36. If the information is presented with a criteria of exception, the choice of exception and deviation from the exception creates a bias by design itself.
37.
38.
39.
40. Redundancy is considered as an essential feature to ensure that the information is received and digested.
![Chapter-7: Information, Knowledge, Business Intelligence ,[object Object]](https://image.slidesharecdn.com/book-2chapter-7-110512084059-phpapp02/85/Book-2-chapter-7-1-320.jpg)
