Machine Learning and Deep learning

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How Useful is Self-Supervised Learning?

July 22, 2021

How Useful is Self-Supervised Learning? Self-supervised learning is a way of training computers to do tasks without humans providing labelled data. It is a main subset of unsupervised learning where outputs or are derived by machines that label, categorize, and analyze information on their own then draws conclusions based on correlations and connections. Self-supervised learning can also be an autonomous form of supervised learning because it does not require human input in the form of data labelling. In contrast to unsupervised learning, self-supervised learning does not focus on clustering and grouping that is commonly associated with unsupervised learning. The concept of self-supervised learning aims to address challenges in supervised learning when it comes to collecting, handling, cleaning, labeling, and analyzing data. Developers who want to create an image classification algorithm , therefore, create supervised learning-capable systems to co

What is the Role of Predictive Analytics in Present world?

July 01, 2021

What is the Role of Predictive Analytics in Present world? Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities. Predictive analytics is often associated with big data and data science. Companies today are swimming in data that resides across transactional databases, equipment log files, images, video, sensors or other data sources. To gain insights from this data, data scientists use deep learning and machine learning algorithms to find patterns and make predictions about future events. These include linear and nonlinear regression, neural networks, support vector machines and decision trees. Learning obtained through predictive analytics can then be used further within prescriptive analytics to drive actions

What is the difference between Computer Vision and Image Processing?

June 04, 2021

Computer vision comes from demonstrating picture preparing utilizing the methods of AI. PC vision applies AI to perceive designs for understanding of pictures. Similar as the interaction of visual thinking of human vision; we can recognize objects, characterize them, sort them as indicated by their size, etc. PC vision, similar to picture preparing, accepts pictures as information and gives yield as data on size, shading power and so forth. Image Processing is mostly related to the usage and application of mathematical functions and transformations over images regardless of any intelligent inference being done over the image itself. It simply means that an algorithm does some transformations on the image such as smoothing, sharpening, contrasting, stretching on the image. Some techniques which are used in digital image processing include: Hidden Markov models Image editing and restoration Linear filtering and Bilateral filtering Neural networks

What is Computer Aided Diagnosis?

May 28, 2021

What is Computer Aided Diagnosis? Computer aided detection additionally called computer aided diagnosis, are frameworks that help specialists in the understanding of clinical pictures. Imaging strategies in X-beam, MRI, and ultrasound diagnostics yield a lot of data that the radiologist or other clinical expert needs to break down and assess thoroughly in a brief time frame. Computer aided design frameworks measure advanced pictures for normal appearances and to feature obvious segments, like potential infections, to offer contribution to help a choice taken by the expert. Computer aided design likewise has expected future applications in computerized pathology with the appearance of entire slide imaging and AI calculations. So far its application has been restricted to measuring immunostaining but on the other hand is being researched for the standard H&E stain. CAD is used in the diagnosis of breast cancer , lung cancer, colon cancer, prostate cancer, bone metastases, coro

What is Biometric Technology?

May 21, 2021

What is Biometric Technology? It includes utilizing biometric security programming to consequently perceive individuals dependent on their conduct characteristics. The biometric innovation presently utilized frequently in actual access control is unique finger impression acknowledgment due to its lower cost. Among 2D unique finger impression sensors, multispectral are regularly a superior decision over optical sensors. They're somewhat more costly however offer higher exactness and more dependable execution. Different identifiers utilized incorporate finger veins, palm veins, faces and irises. For high-security conditions, iris acknowledgment gives the best exactness, trailed by palm vein acknowledgment. Some biometric security frameworks confirm characters utilizing at least one recognition advances, while others don't check the personality at all to minimize expenses. Benefits of biometric security system: High security and assurance User experience is convenient and fas

What is Automatic Machine Translation?

May 14, 2021

What is Automatic Machine Translation? Machine translation , sometimes referred to by the abbreviation MT, is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another. Current machine translation software often allows for customization by domain or profession, improving output by limiting the scope of allowable substitutions. This technique is particularly effective in domains where formal or formulaic language is used. It follows that machine translation of government and legal documents more readily produces usable output than conversation or less standardised text. The technique referred to as rule-based machine translation may be used. Rule-based Transfer-based machine translation Interlingual Dictionary-based Hybrid MT Neural MT