Uses of Machine Learning in Healthcare

Now you can see, so many cases are coming due to coronavirus that spread from Wuhan, China. With the help of technologies, doctors are able to cure the patients. This thing only possible with the help of Machine learning.

Machine learning helps to identify the potentially harmful content and then it sends for evaluation to humans reviewers for assessment. Machine learning uses in healthcare to improve the health of the patients and reduce the cost by using superior diagnostic tools and effective treatment plans for patients.

Apart from this, there are many benefits of Machine Learning in Healthcare:

Identify the diseases and Diagnosis

Nowadays we can say, machine learning is more accurate and faster at diagnosis as compared to doctors. Machine learning in healthcare is the detection and diagnosis of diseases and conditions that are difficult to treat. This can include anything from cancers that are tough to detect at the initial stage. Researchers have developed machine learning algorithms to identify the cancerous tumors on mammograms.

Aid in Coronavirus Response

Machine learning and data analytics will help accelerate solutions and reduces the impact of the virus. It helps in expediting the drug development process, predict infection levels and help in screen patients more quickly. By the use of machine learning to identify those that produce the antibodies that help that person neutralize the virus. 

Patient risk Identification

Nowadays, the healthcare sector also started using tools built from machine learning models which helps in predicting various diseases like cancer, heart attack, tumors, strokes & more serious complications. These tools use data from the patient’s medical records,

daily evaluations, and measurements of vital signs in real-time, such as heart rate, sugar level, and blood pressure, to alert doctors about patient risks so they can immediately take preventive actions.  

Visual data Detection for tumors

Earlier days technologies were not so advanced as compared to now. So, Many diseases were not being able to detect at an early stage & due to lack of treatment at the right time people died. One of the most dangerous diseases is a tumor. Researchers have developed deep learning algorithms trained on previously captured radiographic images to recognize the early development of tumors in various areas of the body such as lungs, breasts, brain, etc. Algorithms can be trained to recognize complex patterns in radiographic imaging data.

Neurosurgeons are more confident than ever about their patient’s brain tumor diagnosis, thanks to the integration of a new system that will allow them to quickly see the diagnostic tissue & tumor margins in near real-time. Without the need for a pathology lab, neuropathologists can review the images, reducing the long waiting time needed for conventional processing, staining, and analysis.

Accelerating Medical Research Insight  

The use of NLP tools and neural networks to parse literature will provide usefully insights for medical researchers in the years ahead. NLP is also being used to mine unstructured data for insights in EHRs, such as data from electrocardiogram tests or copies of manually written notes that have been submitted to a patient’s record, but not included in form fields. CTakes is one example of the open-source NLP projects by Mayo clinic, Boston children’s hospital, and other organizations to develop a tool that analyzes unstructured data in EHRs for insights extraction.

Smart Health Records

Maintaining the up to date health records is a lengthy process and technology has played its part in easing the process of data entry. The fact is that even now, most of the process requires lot of time to complete. The primary function of machine learning in healthcare is to ease the process that saves time, energy and money. Documents classification methods are increasingly gaining momentum using vector machines and OCR recognization based on machine learning such as google’s cloud vision API and MATLAB’s machine learning-based recognization technology for handwriting.   

Using Convolutional Neural Networks for Diagnosis of Skin Cancer 

CNN’s are important tools for detecting and classifying images. Many researchers have used them to create machine learning models for skin cancer detection with 87-95% accuracy using TensorFlow, sci-kit-learn, Keras and other open-source tools. In comparison, dermatologists detect melanomas with an accuracy rate of 65 percent to 85 percent. Models are trained using thousands of images of malignant and benign skin lesions.

Outbreak Prediction

Also now, AI-based technology and machine learning are being used to predicting and forecast epidemics around the world. Scientists have access to a large amount of data collected from satellites, real-time social media notifications, website information, etc.with the help of Artificial neural networks it collects information and predicts everything from malaria outbreaks to serve chronic infectious diseases. Predicting these outbreaks are particularly helpful in third-world countries because they are lack in crucial medical infrastructure and educational systems.

Conclusion

Machine learning Algorithms provide disciplines with reproducible or standardized processes with immediate benefits. Those with large image datasets are also good candidates, such as radiology, cardiology, and pathology. Machine learning can be trained to look at pictures, detect abnormalities and point to areas that require attention, thus improving the accuracy of all these processes. Machine learning at the bedside can support the family practitioner or internist in the long term. Machine learning offers an unbiased opinion to improve performance, reliability, and accuracy.

Also Read: How Machine Learning Can Help your Business

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The Evolution in online Education with the use of Artificial Intelligence

As we know we are in the 21st century and the world is changing rapidly around us. As days passed it getting more difficult to keep up with the innovation decade change thing as we now learn. So the introduction of Artificial Intelligence is a step to make our work more efficient and effective. 

Artificial Intelligence is the implementation of some aspects of human ability like we do such as capable of thinking, learning, objects or speech recognition. Now It took over in every sector and spreading every possible industry right now and It's also major impact on the education sector. 

AI brings a great impact on education especially in online education and it also changing rapidly in education sectors. Education sectors are changing in the innovative world of technology.

In this digital world, Most students, learners, instructors as well as teachers are able to interact with each other through online forums. Many schools, colleges, universities, and other educational institutes are removing the offline grading system and assessments of students. Even midterm and a final examination at the graduate and postgraduate levels are being held online. students and teachers using AI tools and techniques for completing their respective tasks.

AI has completely revolutionized and here some beneficial effects of AI on online education:

Personalized Learning

AI began for the purpose of making e-learning more personalized. Students who pursued the traditional education system did not have much scope for one-on-one interaction with their teachers or instructor. However, the online education system has ensured a personal touch to teaching and learning and also a good scope for the interface between teachers and students. Now universities can use AI to create specific algorithms that suit particular students considering their needs. 

Spectacular and Dynamic transformation

AI algorithms will use to analyze the mass capacity of the students and modify the teaching strategies to improve the teaching and learning experience in this way AI will help reshape in online education.

Customized content

The publishing firm of textbooks and study material will take advantage of AI algorithms for creating course materials and workbooks.

For a better learning experience

AI will be easier for students to make the most of cloud-based and IoT services for getting assistance from educators and instructors situated across the globe. At the same time, learners will able to learn better in the classroom and become more focused.

Feedback and monitoring 

Students are hesitant to approach their teachers to get feedback and the same teachers also face such kind of problems that whether the course quality is being accommodated well or not so using AI will help teachers and instructor to note if students are facing challenges and suggest them to get advice from professional writers to get ahead in their performance.

Information portal

Most of the universities around the globe are working with software developing companies to create their own virtual assistants. This will make it much easier for any students to access information about anything that should be in the education system. The ability of AI is to redirect the wandering mind of students by just keeping track of activities on a smart device.

Classes Beyond the Classroom

For decades, our education system has been limited within the four walls of the classroom. While smart learning is being introduced and popular companies are providing to tablets to our students. AI learning will be more immersive with all advantages. Anytime or anywhere students can able to learn new things and interacts with a new learning experience. 

Why AI is important in online education?

Nowadays, AI plays a very important role in online education. AI helps to interact with students and teachers even those who situated globally. AI Changing the education system in this way:

• AI analyzes the search result of the user and offers easy access to education.
• Help in monitoring performance
• Help to learn more in globalized classroom
• Help student to select a study program and institution according to their preferences and needs

How AI algorithms can help promote education

AI algorithms will play an important role in traditional education:

• On the basis of learner’s preferences and needs, AI will help offer bespoke coaching beyond the classroom
• AI will help the teachers to identify the weakness of the students and enabling the instructor to develop the best academic practices.
• It will help to analyze the mass capacity of the students and modify the teaching strategies to improve the learning experience.
• It will help the examiners to automated grading of assessment and answer sheets.

How AI is going to influence eLearning in coming days

In the future, educators, instructors, and teachers will need to master big data analytics and AI algorithms for imparting education via the internet and have to function very much like software and programmers.

AI can be used more effectively for taking benefits of all aspects of online education that make it so prospective for all concerned. AI is already transforming the key sector of the economy in a precedented manner and there is a good likelihood it will greatly impact education. 

AI-driven education the software will be able to concentrate on specific areas where students need to enhance, thereby making teaching and learning experience more learner-centric. AI with education will be more effective in both online and offline.

  

Conclusion

In the coming decades, most of the universities, schools, colleges, and institutions will adopt AI and will change the learning approach of education. Definitely, the AI approach will change the education sector in any way. It will help the students as well as teachers to change their learning approach and gather information globally. Suppose If you are looking for the best online machine learning training in Bangalore then with the help of AI you can able to learn the approach of machine learning and able to interact with teachers or instructors.

Also Read: Top Artificial Intelligence trends in 2020 

Artificial Intelligence training institute

Nearlearn is the best online Artificial Intelligence training Institute in Bangalore where students get real-time experience in Artificial Intelligence and they provide internship facilities where they get practical knowledge and also provide placements support guarantee to students in various companies.

Nearlearn provides regular online batch for students who situated globally. They provide the best trainers having 10+ years of industry experience who help students to gain detailed information in the field of Artificial Intelligence. 

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How to Develop AI-Based Software Testing?

Almost every day new technologies are introduced in the world but not all of them have the power to transform industries and organizations all across the globe. Artificial Intelligence is widely utilized to understand and enhance the business. AI entering in every sector of life and has a huge impact on e-commerce, healthcare, cybersecurity, application development or software testing. Artificial Intelligence is the key to making the companies competitive and ready for the future.

According to Research, AI will take over all sectors of technology. Thus it will make its high position among the top investment priority CIOs this year, That means new software testing horizon and its scope will be wider than ever before. Today application interacts with people, other apps, devices, and networks as well. They do all these tasks at the same time.

Why AI needs in software Testing?

Through the help of AI developers and testers identify the issue, no matter how careful manual testers are, machines are more accurate. Since these tools are used to perform software testing and tester believes that manual testing gives better results. AI used to help in preventing task repetition and helps in speeding up the software testing process. Machines are used to test software applications and these processes take less time as compared to testing. That is why human testers do not have to do repetitive testing activities and even if there is a repetition, software testing tools can do all the work efficiently. 

Today most of the businesses trying to harness the potential of artificial intelligence in different ways. Artificial Intelligence could ease some major testings problems which include:

• Identifying the precise use cases
• Lack of awareness on what to do
• Based on input data, verifying the behavior of the applications.
• Checkings the app’s quality on various parameters such as stability, performance, and security that too in the least amount of time.

Developing AI-based testing 

Automating API tests generation and maintenance

Artificial Intelligence can be used to develop a set of complete automated test scenarios using manual UI tests. Developers can logic to understand the basic patterns and relationships in the different API calls that are made while executing the user interface. Once the behavior is understood, the next step would be to create a series of API calls in the user interface phase that represent the underlying interface calls. Artificial Intelligence plays an important role in researching and understanding the entire process around API resources. Upon learning and storing the test in a proper data structure will help. The aim of AI is to create more advanced tests and not simply automate existing ones. Para soft’s smart API test generator is one such tool that goes beyond records and playback testing, using AI technology to identify the pattern within the traffic, Building a comprehensive data model of observed parameters along with generating automated API tests that can learn and upgrade themselves.

Self-Heal Execution of selenium tests

The AI-based tool is used can be made for learning internal data during routine selenium tests such as detailed web UI content information, an attribute of DOM elements, locators, etc. The AI platform becomes smarter and saves a lot of time in observation if you understand and know through the historical study of these tests. Para soft’s tool selenic is a classic execution of this definition.

Automating unit test generation and parameterization

The gaps in testing can create a lot of problems for JAVA developers, especially when starting from a sparse JUnit Harness. On the other hand, AI-based software testing tools, along with automated test case creation, can include static analysis, unit testing, coverage, and testability. JAVA application can be tested with added automation in one click. 

Defect Analytics

Artificial Intelligence can be used is in defect analytics. Cigniti’s Intelligence test case management strategy includes an algorithm for Defect analytics. This makes sure that every error causing a defect is caught at its source. AI-based algorithms can quickly learn from past trends and go through the standard process evaluating defects in the least amount of time. 

Conclusion

Artificial Intelligence and Machine Learning can have more than just a few software testing applications. AI helps developers in identifying the errors that can be used to improve the code. Artificial Intelligence occupies every domain of our lives. AI changes software testing for good, by improving quality software development efficiency and processes. Nowadays few companies implement AI technologies for product development, There is no doubt that AI is here to stay and develop early.

Also Read: Artificial Intelligence trends in 2020

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Artificial Intelligence trends in 2020

Nowadays, Artificial intelligence is one of the fastest moving and less predictable industries. As we know Artificial intelligence is Expanding and growing Every day. It literally took over every sector and it spreading over every possible industry right now. In the coming days, it will be the biggest boost industry in the global economy and the demand for the job in artificial intelligence will rise.

Artificial intelligence is becoming widely utilized to understand, enhance business and customer satisfaction. Today, It is defined as the implementation of some aspect of human abilities such as capable of thinking, learning, object or speech recognization, like we do.

Here, I am sharing some predictions on key Artificial Intelligence trends that will happen in 2020. 

Improve the Accuracy and Efficiency of Neural Network 

Architectures of the neural networks will continue to grow in size and depth. It will produce more accurate results and become better at mimicking the performance of humans on the tasks that involve data analysis. At the same time, It will also develop methods for improving the efficiency of neural networks. 

The methods of neural generations such as deep fake will also continue to improve and create more realistic manipulations of text, photos, videos, audios and other multimedia that are unidentifiable to humans. As AI enters more and more fields, there will be new issues and concerns will arise. The reliability and bias behind these AI approaches will be more scrutinized and become more widely deployed in society. 

AI in Healthcare

In 2020, Artificial intelligence plays a greater role in Healthcare. This technology will make healthcare more accurate and less costly. The ability of AI is to acquire and analyze data in real-time from various hospital information flows- electronic health records, staffing level information, Equipment utilization, emergency department admission, automated reporting, and interpreting data in meaningful ways.

AI in Manufacturing

Manufacturing industries will embrace AI to modernize the product line. Quality control is one of the biggest challenges in the manufacturing industry. AI will perform manufacturing, quality control, shorten design time, reduce waste of material, increase reuse of production and many more. With AI adoption, they can make quick, data-driven decisions, optimize manufacturing processes, reduce operating cost and improve the way they serve their customer. 

AI in Media

Artificial Intelligence aims to change the media and entertainment industry- impacting everything from content creation to the consumer experience. It will affect all type of media value chain will help content creators to be more innovative and help a content consumer to find the content that suits their requirement and needs.AI methods are also used in the development of stories, production & Scriptwriting.

AI in Cybersecurity

Hacking phishing and cyber attack are becoming more resistant and continuously increasing at a faster rate. AI is used cyber threat detection to identify the threat and deter online criminal activities. It enhances the cybersecurity solution that companies used to fight cybercrime and help to keep organizations and customers safe.

AI will become less data-hungry

Based on Deep learning, AI algorithms could only work with real-time data, which was complicated by the need for feeding it with the right kind of data and volume of data. It can be challenging and time-consuming. Data synthesis methodologies are helping AI tech leverage old videos and other media to create new scenarios and customize experiences.

AI and chatbots

In the coming years, real-time and individual interaction will drive marketing. AI professionals play an important role in making that happen. conversational AI of like Alexa of Amazon, Google assistant, and others are occupying the space. Similarly, banks use AI chatbots for customer services instead of a human interface. Now chatbots available in both text mode and voice mode.

AI and Governance

The governance and ethical issue related to AI technology will grow in the coming year. Successful adoption and scalability of AI will depend on those complexities being resolved. It involves in creating policies around biased data that contribute to technical discrimination.

Also Read: Artificial intelligence and why it matters?

Edge computing

Without Internet of Things (IoT), many destined artificial intelligence innovations would not be possible. Both combinations can be loosely called Edge Computing. The Data thus collected would be leveraged by AI specialists to customize decision-making models and programs. For Dominos 30 minutes delivery promise becomes possible with IoT & AI- a combination of geolocation and data to forecast time frames and automate notifications. 

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