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In medicine, artificial intelligence (AI) is being used more and more regularly, particularly in diagnosis and treatment planning. AI and machine learning have become effective diagnostic tools in recent years. By offering more accurate diagnoses, this technology can potentially change healthcare. Artificial intelligence facilitates healthcare management, automation, administration, and workflows in medical diagnostics. AI in medical diagnostics has demonstrated tremendous potential over the past several years in altering healthcare standards while easing medical services’ intense pressures.

Source: Knowledge At Wharton

What is AI Medical Diagnosis?

AI medical diagnosis refers to the application of artificial intelligence (AI) and machine learning techniques in the field of medicine to aid in diagnosing diseases and medical conditions. It involves using algorithms to analyze patient data, such as medical images, lab results, electronic health records, and other clinical data, to assist healthcare professionals in making accurate and timely diagnoses.

AI Algorithms in Medical Diagnosis

Here are some areas where AI in helping in medical diagnosis:

AI Algorithms Can Analyze Medical Data And Aid In The Diagnosis

Electronic health records (EHRs), imaging technology, genetic data, and portable sensor data are just a few of the types of medical data being collected at a new level today. These numerous data may be processed and analyzed by AI algorithms, which can yield insightful information to help with medical diagnosis. AI algorithms can produce estimates and concepts by reviewing a patient’s medical history, symptoms, testing results, and other relevant data.

Source: ScientificAmercian

The Use Of Machine Learning And Deep Learning Techniques

AI algorithms for medical diagnosis rely extensively on machine learning (ML) approaches. Large datasets with labeled samples can be used to train ML systems to discover relationships and trends. Deep Learning (DL) algorithms have transformed medical imaging analysis by improving tumor identification, categorization, and classification efficiency.

DL algorithms can also combine other data types, including textual data, genetic information, and medical imaging, to provide a more thorough analysis. The accuracy of the diagnosis is improved, and this comprehensive approach makes a more profound comprehension of complicated conditions possible.

The Ability Of AI To Detect Patterns And Make Predictions

AI algorithms can analyze a vast amount of data to find hidden associations, biomarkers, and disease-associated risks that practitioners might not be able to see. The AI algorithms may offer a comprehensive view of an individual’s health by considering multiple factors simultaneously. Hence, resulting in more precise diagnoses and individualized treatment strategies.

AI Applications in Medical Imaging

Source: TowardsAI

The Use Of AI In Analyzing Medical Images Such As X-Rays, MRIs, And CT Scans

AI algorithms have shown remarkable skills in processing medical images. It allows healthcare workers to acquire accurate and in-depth findings based on these diagnostic scans. AI can process X-ray images, MRIs, and CT scans in a fraction of the time, which helps human specialists, who can more quickly spot patterns, analyze vast volumes of data, and retrieve relevant data.

The Role Of AI In Identifying Abnormalities, Tumors, And Other Medical Conditions

AI has demonstrated outstanding proficiency in identifying and locating tumors, anomalies, and other medical problems using medical images. The AI algorithms can effectively analyze extensive collections of medical images to identify and classify tumors in the case of cancer. AI medical diagnosis systems can offer oncologists insightful information about the tumor’s stage, rate of growth, and potential for metastasis by comparing these outcomes with prior data, enabling personalized treatment plans.

The Potential For AI To Improve Diagnostic Accuracy And Efficiency

The application of AI to medical imaging has enormous potential to improve the efficiency and accuracy of diagnosis. AI medical diagnosis systems can help radiologists by offering different viewpoints, decreasing the possibility of misinterpretation, and boosting overall diagnostic accuracy. It can also speed up image analysis, enabling faster responses and more effective medical care.

AI for Early Disease Detection and Prevention

The Use Of AI In Early Disease Detection And Risk Assessment

AI plays a crucial role in early disease recognition by analyzing vast patient records and finding minute patterns and abnormalities that may indicate a disease’s presence. AI tools can use machine learning algorithms to learn from various datasets. It includes medical records, imaging studies, and smart device data to identify hazardous factors and early warning indications.

The Application Of AI In Analyzing Patient Data, Genetic Information, And Biomarkers

AI can evaluate genetic data and find genetic variants linked to a higher risk of developing a given ailment. AI medical diagnosis systems could generate personalized risk assessment scores by integrating genetic data with factors affecting lifestyles, environmental effects, and medical history. This allows patients to make informed choices about their well-being and take preventive measures. Additionally, AI can assess biomarkers, including blood tests and imaging results, to find disease-related early warning symptoms that could still not be clinically evident.

Also Read: AI Tool Developed to Detect Parkinson’s Disease Years Before Symptoms

Source: Arrow

The Potential For AI To Assist In Personalized Medicine And Preventive Care AI-assisted Diagnosis Support Systems

The Development Of AI-Assisted Diagnosis Support Systems For Healthcare Professionals

Source: Capestart

AI Can Assist In Differential Diagnosis And Treatment Decision-Making

Differential diagnosis can be difficult and time-consuming because it involves finding the most probable cause for a patient’s symptoms. AI medical diagnosis assistance systems use machine learning algorithms to assess patient records and provide suggested diagnoses according to identified trends and scientific facts. AI can reduce the number of possible diagnoses and direct medical personnel toward the most likely reasons by providing ordered lists of prospective diagnoses, together with supportive data and probability estimates. This may result in faster and more precise diagnoses.

The Importance Of Combining AI With Human Expertise For Optimal Results

AI must be integrated with human expertise and clinical knowledge to get the best results when making decisions about diagnosis and therapy. Professionals in the healthcare industry provide important contextual insight, creative thinking abilities, and the capacity to consider complicated patient factors that AI systems may not entirely understand. With the assistance of human experience, we can test and improve AI-generated solutions. Thus, ensuring that choices are made after carefully considering each patient’s specific circumstances.

Ethical and Legal Considerations The Ethical Implications Of Using AI Medical Diagnosis

It is important to thoroughly explore the ethical implications of using AI for medical diagnosis. Healthcare practitioners and AI system developers must ensure that AI systems operate transparently, clearly explaining the process behind diagnoses and the rationale for their suggestions. It is important to provide patients with accurate information about the use of AI in their healthcare so that they can make well-informed decisions.

Issues Related To Patient Privacy, Data Security, And Algorithm Bias

Medical professionals and developers must prioritize implementing strong data protection measures, including anonymization, encryption, and private storage to safeguard patient data. Additionally, patients should be informed about data usage and allowed to participate through open consent practices. Addressing the potential biases in historical data that AI programs learn from is crucial, as they can impact their performance.

Healthcare results for different demographics can vary due to biases in medical diagnosis algorithms. Efforts must be made to curate and diversify training datasets to eliminate discrimination thoroughly, and AI medical diagnosis systems should be evaluated for equity and impartiality regularly.

The Need For Regulations And Guidelines In The Use Of AI In Healthcare

It is crucial to set rules and guidelines to control the usage of AI medical diagnosis, given its potential impact on healthcare. These should cover matters like transparency, accountability, confidentiality, data security, algorithm verification, and ongoing performance assessment of AI systems.

Developing rules that encourage responsible and ethical AI implementation and ensure patient safety and high-quality care is a joint effort between regulatory bodies and professional organizations.

Challenges and Limitations

The Challenges And Limitations Of AI Medical Diagnosis

Despite the promising potential, limited practical AI-enabled solutions are used in clinical practice. In addition to privacy issues, AI technology has additional analytical and technical flaws. AI medical diagnosis must overcome several challenges and limitations to optimize its efficacy and dependability. These challenges encompass concerns about data quality, the comprehensibility of AI systems, and their interaction with the healthcare systems currently in use.

Source: Stanford

Issues Such As Data Quality, Interpretability, And Integration With Existing Healthcare Systems

The reliability and accuracy of AI medical diagnosis depend significantly on the data quality. Healthcare data can be inconsistent, insufficient, and varied, impacting how well AI systems work. More specifically, biases in the data can result in discrepancies in diagnoses among marginalized groups.

Several AI algorithms, including deep learning models, function like black boxes, making comprehending their predictions’ logic impossible. To accept and validate the suggestions, healthcare professionals need arguments and justifications for AI-generated diagnostics.

Embedding AI medical diagnosis into conventional healthcare structures might be challenging due to interoperability issues and the varied nature of healthcare procedures. Different data formats, privacy laws, and systems might make integration difficult.

The Ongoing Research And Development Efforts To Overcome These Challenges

Researchers and developers are concentrating on creating compatible approaches, standard data formats, and reliable data transfer channels to seamlessly integrate AI into existing healthcare systems. To solve these integration problems, collaboration between IT experts, medical professionals, and AI developers is essential. We must address the issues and constraints to create more precise, effective, and reliable AI medical diagnosis systems. Therefore, ongoing study, innovation, and collaboration will be essential.

Future Directions and Impact

The Potential Future Impact Of AI Medical Diagnosis

AI medical diagnosis can completely change healthcare in several ways. AI systems might help medical personnel make more precise diagnoses, identify hidden patterns in imaging studies, and predict how patients may react to particular therapies. This could improve treatment plans, reduce clinical errors, and improve the accuracy of diagnosis.

Emerging Technologies And Advancements In The Field The Importance Of Continued Research And Collaboration In AI And Healthcare

Research should concentrate on overcoming AI’s current drawbacks, such as problems with data quality, understanding, and integration. This entails creating reliable techniques for gathering, curating, and validating data and improving the readability and transparency of AI models. Sharing knowledge, resources, and information requires close cooperation between researchers, healthcare practitioners, data scientists, and technological specialists. Such collaboration can make creating standardized protocols, regulations, and moral frameworks easier to ensure AI’s ethical and moral application in healthcare.

Conclusion

AI has revolutionized many different fields of study and is innovating every day. AI medical diagnosis is an innovative approach to simplify and enhance conventional healthcare practices for excellent medical care for the human race. Medical science utilizes AI in various ways. We will experience more revolutionized AI techniques and therapies in the coming years.

If you belong to the medical field, and want to know more about data science and AI, now is the best time to take the first step. Gone are the days when it was mandatory to know coding to indulge in these fields. We offer an exclusive No Code AI program that helps professionals across industries to learn and implement AI and Data Science in their day-to-day work life. Explore the course to know more.

Frequently Asked Questions

Q1. What is the role of AI in medical laboratories?

A. AI helps improve diagnostic accuracy and decreases the result time for lab tests. It reduces human error in laboratories and provides cost-effective healthcare solutions. It also enables better tracking of patients’ records.

Q2. When was AI first used in healthcare?

A. In the 1980s, researchers first utilized Bayesian networks, artificial neural networks, and hybrid intelligence in clinical settings, employing AI.

Q3. What algorithms are used in medical devices?

A. Medical devices use AI and machine learning algorithms.

Q4. What diseases can AI diagnose?

A. AI has shown promising capabilities in diagnosing a range of diseases. It has been applied to identify conditions such as cancer, heart disease, diabetes, Alzheimer’s, and various infections. However, the effectiveness and accuracy of AI-based diagnostic systems vary across different diseases, and human expertise remains essential for final diagnosis and treatment decisions.

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How Does Artificial Intelligence Work?

Artificial Intelligence is the field of study that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. These tasks include problem-solving, speech recognition, decision-making, and language translation. AI systems aim to simulate human intelligence and adapt to different situations by learning from experience.

See More : Is Hotpot AI Legit? A Comprehensive Review

AI systems require a significant amount of labeled training data to learn from. This data is ingested into the AI system and analyzed for correlations and patterns. By examining the data, the AI system can identify relationships between different variables and extract meaningful insights.

Based on the patterns identified in the data, AI systems can make predictions about future states. For example, a chatbot trained on a vast amount of text data can learn to generate lifelike exchanges with people. Similarly, an image recognition tool can identify and describe objects in images by reviewing millions of examples.

Advancements in machine learning have played a pivotal role in the development of AI. Machine learning algorithms enable computers to process large amounts of data, recognize patterns, and make informed decisions. By leveraging machine learning techniques, AI systems can be trained to accomplish specific tasks.

One of the latest developments in AI is the use of neural networks. These are machine learning models inspired by the structure of the human brain. Neural networks are designed to learn increasingly complex patterns from information. They consist of interconnected layers of artificial neurons that process and analyze data, enabling the AI system to gain a deeper understanding of the underlying patterns.

AI systems have the capability to make real-time decisions, replicating human discernment. Through extensive training and processing of data, AI systems can think, act, and respond just like a real human. This has opened up possibilities for applications such as autonomous vehicles, fraud detection systems, and personalized recommendations.

AI is a broad field with multiple approaches to building intelligent systems. These approaches include symbolic AI, machine learning, evolutionary algorithms, and hybrid models. Symbolic AI focuses on the use of logical rules and representations, while machine learning relies on training algorithms with data. Evolutionary algorithms mimic the process of natural evolution to optimize AI systems. Hybrid models combine different approaches to leverage their respective strengths.

Reactive Machines are the simplest form of AI systems that can only react to specific inputs without any memory or ability to learn from past experiences. These machines analyze the current situation and produce an output based on predefined rules. They do not have the capability to form memories or use past experiences to inform future decisions. Examples of reactive machines include chess-playing computers and voice assistants like Siri or Alexa.

Limited Memory AI systems have the ability to store and recall past experiences to inform future decisions. These systems use historical data to learn and improve their performance over time. One prominent example of limited memory AI is seen in self-driving cars. They store data about road conditions, traffic patterns, and past experiences to recognize and respond to traffic signals, pedestrians, and other vehicles on the road.

Theory of Mind AI is an area of research that aims to develop machines capable of understanding human emotions, beliefs, and intentions. The goal is to create AI systems that can interact with humans in a more natural and intuitive manner. This type of AI would have the ability to perceive and interpret human emotions, allowing for more effective communication and collaboration between humans and machines.

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AI can also be categorized based on its capabilities. The three main categories are:

This type of AI is designed to perform a specific task or a set of tasks within a narrow domain. Examples include voice recognition systems, recommendation algorithms, and image recognition software. Weak AI systems excel in their specific area but lack general intelligence.

General AI refers to machines that possess the ability to understand, learn, and apply knowledge across a wide range of tasks similar to human intelligence. General AI systems would have the capacity to transfer knowledge from one domain to another, demonstrating adaptability and flexibility.

AI systems can also be categorized based on their functionality, which includes:

As mentioned earlier, reactive machines are AI systems that react to specific inputs without memory or learning capabilities.

Limited memory AI systems can store and utilize past experiences to make decisions.

Theory of Mind AI systems aim to understand human emotions, beliefs, and intentions to enhance human-machine interactions.

Artificial Intelligence has revolutionized numerous industries by enabling machines to perform tasks that were once exclusive to humans. By ingesting and analyzing vast amounts of data, AI systems can learn patterns, make predictions, and execute real-time decision making. Advancements in machine learning and neural networks have played a crucial role in enhancing the capabilities of AI systems.

Q1: How does AI learn from data?

AI learns from data by ingesting large amounts of labeled training data and analyzing it for patterns and correlations. This allows the AI system to recognize relationships and make predictions based on the observed patterns.

Q2: Can AI systems adapt to new situations?

Yes, AI systems can adapt to new situations. Through machine learning algorithms, AI systems can learn from new data and adjust their predictions and responses accordingly.

Q3: What are some practical applications of AI?

AI finds applications in various fields such as healthcare, finance, transportation, and customer service. It is used for medical diagnosis, fraud detection, autonomous vehicles, and virtual assistants, to name a few.

Q4: Is AI capable of creativity?

AI systems can exhibit creative behavior by generating novel solutions or artworks. However, the level of creativity is still limited compared to human creativity.

Q5: How can businesses benefit from implementing AI?

Businesses can benefit from implementing AI by automating repetitive tasks, improving decision-making processes, enhancing customer experiences, and increasing operational efficiency.

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How Does Kubernetes Container Work?

Introduction to Kubernetes Container

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What is a Kubernetes container?

The container in Kubernetes is the software package that has all the information which are needful to run the software like code which is important at runtime, system libraries, and it also has the default values for required settings, in which it needs to have fewer system assets and the applications which are running in the container that can be implemented simply for various operating systems and their hardware platform, every container which is running that can be repeatable it means it follows the standardization from having admiration that means we can get the paired bearing at where we can run it, the container image is a ready-to-run software package having all the information which is needful to run the software including code, libraries and it also needs to have default values for the setting.

How does the Kubernetes container work?

The container is a detachment in the application layer of the Kubernetes, which contains the code and its dependencies together; for sharing the operating system, different containers can run on an identical machine, and every machine can work out of the way in space, the cluster of the Kubernetes can assist the main cloud providers environment so that we can turn the cluster and implement the application by using some commands when Kubernetes can control the bunch of information, the container has a platform is a client-server software can make easier the implementation of the container with its working elements.

A container can start its compilation from the bottom image, and a trial application has been packed into a container image, and that has been implemented via the platform the container; in the computing environment, there are many levels which are starting from the bottom level means bottom level will be the level 1 in which it may be the infrastructure of the container and level 2 having host operating system, level 3 having a platform for container and at the top there are two levels in which one having libraries and next level having application code for services and that are connected with the images and network which is available, this is the working of the container.

Kubernetes container images

We can able to assign the names to the container images for the specification; after that, we can add the tag to the image also consisting of uppercase and lowercase letters, we can also able to update the images.

Kubernetes container environment

The Kubernetes container environment can have some major assets related to the container, the environment of Kubernetes has a filesystem that contains the images and one or more volumes, it also has all the information which are related to the container which is running itself, it also contains the information regarding other devices in the cluster, it has two assets such as container information and cluster information let us discuss them,

1. Container information 2. Cluster information

The cluster information has all the information which are related to the container, and it can be available at the time of generating the container, and that list is restricted to the services under the same namespace when the new pod has been generated and Kubernetes can able to support the plane services.

Kubernetes container runtime

The container runtime is also known as a container engine in which it is an element of software that can allow running the container on a host operating system, for example, docker, runC, containered, and windows containers, etc., it can also accept the requests from user like command-line options, and pull images and it can runs the container in user’s point of view, the container runtime allows to Kubernetes that to utilize the different variety of container without to run the container repeatedly, the Kubernetes can utilize any container runtime which can be deployed CRI for controlling the pods, container and container images.

Conclusion

In this article, we conclude that the Kubernetes container is a software package that has a container image in which it is a software packet containing binary data and its dependencies, container runtime that allows us to run the container on the host operating system; this article will help to understand the concept of Kubernetes container.

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How Does Privilege Escalation Work?

Privilege Escalation − What Is It?

WordPress offers a function that lets an administrator grant access to other users to edit the website. However, you might not want to offer each user total freedom to make any changes they like. User roles play a role in this.

Subscribers, contributors, authors, editors, admins, and super admins are just a few of the six user roles that are available. In this case, subscribers have the fewest rights while super admins have complete control over everything on the website.

For the sake of network security, it’s desirable to adopt these specific user roles rather than giving every user administrator rights.

If a hacker, for instance, gained access to an author’s account without authorization, they would only be able to edit, publish, and remove their own writings. They are unable to alter anything else.

However, a hacker can use a privilege escalation vulnerability to bypass these restrictions if it exists. They then begin evading user account control. They can now access features that are supposed to be available only to local administrators after just having author rights previously.

If this occurs, you never know what information the hackers might take or what nefarious deeds they might commit in your name!

Types of Privilege Escalation

Vertical and horizontal privilege escalation are the two categories into which privilege escalation may be divided. In vertical privilege escalation, the attacker attempts to take control of a higher-level account. However, with Horizontal Privilege Escalation (HPE), the hacker first takes control of an account before attempting to acquire access to system-level privileges. Both kinds of activities are accomplished by exploiting current operating system flaws.

Why Is It Important to Prevent Privilege Escalation Attacks?

Privilege escalation serves as a tool for attackers. It enables them to enter a system, maintain and expand their access, and engage in increasingly harmful activities. As an illustration, privilege escalation can turn a minor malware infection into a serious data breach.

Attackers can introduce new attack methods on a target system by escalating their privileges. For illustration, it might entail

Gaining access to connected systems elsewhere

Modifying the privileges or security settings

Gaining access to software or data on a system with more rights than the compromised account originally allowed

Obtaining root access to a target system or an entire network is possible in some circumstances

Investigating thoroughly is crucial when security teams suspect privilege escalation. Malware on sensitive systems, shady logins, and strange network communications are all indications of privilege escalation.

Depending on the organization’s compliance requirements, each privilege escalation incident must be handled as a severe security incident and may need to be reported to the authorities.

How Does Privilege Escalation Work?

One form of hack that takes place in a number of other hacking operations is privilege escalation. We’ll use an example to demonstrate how a privilege escalation attack operates.

Step 1 − Hack into any WordPress website user account

Consider that you manage a website with 10 members. Others are privileged users with admin capabilities who have enhanced access and can manage the entire website, while some are contributors and authors who can publish content.

Let’s say that a contributor account is utilizing the flimsy password “password123.” Hackers can easily deduce this. The hacker tries a variety of passwords to log into the account using a different kind of hacking technique known as a brute force assault (They can quickly test hundreds of passwords.) The hacker is successful since the password is so simple, and he or she now has contributor access.

A contributor, however, has limited rights. They are only able to create and edit their own posts; they are unable to publish them. This limits what the hacker may do with the account. Since having admin access would grant them total power over the website, they would want to elevate their credentials.

Step 2 − Upgrade Privileges by Disregarding Restrictions

There are plugins and themes installed on every WordPress website. Themes and plugins make the website stand out by enhancing the look and feel as well as the functionality.

A hacker may be able to increase the account’s privileges thanks to one of these vulnerabilities.

Step 3 − Execute the Attack

Privilege escalation is used to get ready for larger or more targeted attacks. The hacker might start pursuing their true objective once they have access to an admin account or the data they need. Some of the typical criminal behaviors these miscreants engage in include −

Stealing confidential and sensitive data from your company.

Stealing data and information from customers or clients, which they can then sell for money or use to carry out other hacks.

Stealing more login information from legitimate accounts on your website.

Deleting content and data from your website.

Sending unsolicited emails and messages to your consumers.

Using malware to sell illegal goods or drugs on your website.

Using malware to trick people into downloading it from your website.

Sending users to other websites, such as pornographic or spam sites, via your website.

Launching larger (DDoS) attacks from your website to knock down well-known websites.

How Does Ansible Wait_For Work With Sample Code

Introduction to Ansible wait_for

In Ansible, we face many situations where a task is dependent on a service, port or system’s state. But that state is expected to occur after some time due to a connection reset, server reboot, execution time taken by a code or time taken to start a service. So we need to wait for it to happen. Now Ansible is so rich in its module treasure that it has a module named wait_for which enables our playbook to wait for a state or task completion on remote target machines. Using this module we will wait for a condition to occur before continuing with next tasks our playbook.

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What is Ansible wait_for?

Ansible wait_for module can actually be used with or without a condition on remote target hosts and it checks for that condition’s state after a specified time in seconds.

If you just need to wait and not checking any condition, then use only timeout parameter with time in seconds to wait. This completely work on your assumption of required waiting time.

This module can be used for conditions like for starting a guest to with virt module, File to exits, match a regex in file.

For Microsoft Windows remote target machines, use module named win_wait_for.

This module is having backward compatibility support going ahead in next version, by Ansible core team.

How does Ansible wait_for work?

To work with Ansible wait_for module, we must know about the acceptable parameters and their acceptable options. Some of the parameters have default values set, so this must also be noted.

Given below is a list of parameters:

1. timeout: Default is 300 seconds. Maximum number of seconds to wait for. This shouldn’t be used with other conditions, because then it will fail.

2. state: Below are the acceptable states:

started: whether port is open.

stopped: whether port is closed.

drained: Whether connection is active.

present: Whether a string is present in a file or string.

absent: whether file is removed or absent.

3. sleep: Default is 1 second. This is to sleep between checks.

4. search_regex: To check a string in file or socket connection.

5. port: Port number to check.

6. path: Path of a file which must be existing before continuing.

7. msg: This is the message which will be printing instead of normal error message in case of failures.

8. host: The IP or hostname to wait for.

9. exclude_hosts: The list of hosts, not to check when looking for active connections (TCP).

10. delay: Default is 0. This represents the number of seconds to wait for next polling.

11. connect_timeout: Default is 5 seconds. This is to specify the time to wait for a connection to establish before closing and try again.

12. active_connection_states: This is the list of connections (TCP) which can be counted as active session. Default values are “TIME_WAIT”, “FIN_WAIT2, “FIN_WAIT1”, “ESTABLISHED”, “SYN_SENT”, “SYN_RECV”.

The unique return values by this module, this will enable us to know the actual execution happened on remote target systems.

1. elapsed: The seconds elapsed while waiting.

2. match_groupdict: This is the dictionary containing all the subgroups name matched by key given.

3. match_groups: Tuple containing all the subgroup of the match.

Examples of Ansible wait_for

Given below are the examples mentioned:

Here we have one Ansible controller node named as ansible-controller. As target nodes, we have two remote machines. First machine is a Red Hat Enterprise Linux machine named as host-one and second machine is an Ubuntu machine named as host-two. We will run our playbooks on Ansible controller machine and make changes on remote target machines.

Example #1

In this example, we have a playbook like below, using which we will try to reboot the remote machines and the use wait_for module to wait for 30 seconds and then we check the uptime on same machines.

Code:

var: var_uptime.stdout_lines

Now running this playbook like below, we have below output where we can see that servers were rebooted and then we waited for 30 seconds and then uptime was displayed.

Output:

Example #2

In this example, we have a playbook like below where we are checking for a chúng tôi file, if this file exists then make modification in this file. If this file doesn’t exists then wait for it. For this we have a playbook like below.

Code:

insertafter: ‘^#Listen ‘ line: Listen 8443

ansible-playbook ansible_wait_for_file.yaml -v

Output:

Conclusion

As we saw in this article, this module is really helpful in practical or real time scenarios, where you really have to wait for some time so that a condition can complete on remote target systems. Operations like patching, provisioning leverages it very well. But as you know we shall be very aware about its available options before using it. So learn it first and then use it.

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This is a guide to Ansible wait_for. Here we discuss what is ansible wait_for? how does ansible wait_for work with respective examples. You may also have a look at the following articles to learn more –

How Does Property Work In Jsp With Example?

Introduction to JSP property

Web development, programming languages, Software testing & others

Syntax

The syntax used by the JSP property is :

<jsp:setProperty name="Name_to_identify_bean" <jsp:getProperty name="Name_to_identify_bean" <jsp:useBean

The “setProperty” or “getProperty” after colon JSP is the identifier for the compiler. By this compiler identifies that the property functionality of JSP has been used. “Name” tag is used to set the name to identify the JAVA bean, which will carry the property name along with values. “Property” is used to assign property names.

The useBean tag mentioned in the end is used to locate the remotely located bean, which can then be used for processing of JAVA beans. Note here that “name” on property and “id” in usebean JSP tags should be the same.

How does property work in JSP?

Name: This is a mandatory attribute. This attribute is used to identify the EJB. Refer to syntax carefully as this name should match to JSPBean’s name.

Property: This is a mandatory attribute. This defines which properties should be set. For example, if “*” is given as a value for this parameter, then all the properties are set as per the setter method in EJB.

Param: This attribute is optional. This attribute is equal to the request parameter name. This parameter is getting the incoming value that would be assigned to the property. “Param” and “Value” can not be used together as both of these attributes are used to assign value to the property in the tag itself, although the method of assigning values is different.

Value: This attribute is optional. This attribute is used to set a value to the property in the tag itself rather than fetching it from some other place. If this attribute is not present or the value is zero, then the “setProperty” property is being ignored by the compiler.

EJB: A JAVA bean should be defined with getting and set methods that would take the property values fetched from property tags and then assign them to the variables. These variables are sent again wherever required. We can understand more about its working with the help of an example provided in the below section.

Examples of JSP property

This is an example where we will create an index page with a form that collects email id, name, and password and then finally displays it in the output by carrying the user input name, password and email via properties. There are some files to be created to accomplish the task.

File: Index.html

File: Test1.java package test1JSP; public class Test1 { private String n,p,e; public String getName() { return n; } public void setName(String name) { this.n = name; } public String getPassword() { return p; } public void setPassword(String password) { this.p = password; } public String getEmail() { return e; } public void setEmail(String email) { this.e = email; } } File: Test1.Class / Compiled from chúng tôi (version 12 : 56.0, super bit) public class test1JSP.Test1 { private java.lang.String n; private java.lang.String p; private java.lang.String e; public Test1(); 0  aload_0 [this] 1  invokespecial java.lang.Object() [12] 4  return Line numbers: [pc: 0, line: 3] Local variable table: [pc: 0, pc: 5] local: this index: 0 type: test1JSP.Test1 File: useBean2.jsp

Output:

Explanation:

In this example, three files are created named “Index.html”, “useBean2.jsp” and “Test1.java”. “Index.html” is simply the web page to input the values. Here the form is used to collect the details required. The “action” attribute in the form tag links this static HTML page to the dynamic JSP page named “useBean2.jsp”. This JSP page sets the property with the help of JSP property tags. One thing to notice here is that there is a “useBean” tag also defined on the JSP page, which establishes a link between the JSP page and the JAVA class. The name of the properties, the id of useBean tag is the same, which is “u”. “setProperty” sets all the properties with the value fetched from the HTML page and “getProperty” gets the value which is set via the “setProperty” tag. In the background, the JSP bean connects with the JAVA class page “Test1.java” and uses get and set methods to set the values. Get the method to fetch the value and displays it on the output screen.

Conclusion

JSP property is used commonly to streamline the data flow between multiple pages in the backend when we run any web application. In addition, it helps in enhancing user interactive sessions by the use of dynamic web content loading. One of the most useful tools for front end developers.

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This is a guide to JSP property. Here we discuss How does property work in JSP and Examples along with the codes and outputs. You may also have a look at the following articles to learn more –

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