What is Domain Expertise ?
One of the critical element for data mining is something called "domain expertise. Generally defined, domain expertise implies knowledge and understanding of the essential aspects of specific field of inquiry. It is essential to effective risk and threat assessment.
Domain expertise or domain knowledge is nothing but expertise in a particular field, and when I say field it can anything like field of Healthcare, Education, etc. It in knowledge and understanding of particular field.
Who is Domain Expert ?
A domain expert is a person with the special knowledge or skills in particular area or field. The term domain expert is frequently used in expert system software development, and there the team always refers to the domain other than the software domain.
Does Domain Expertise really matter ?
Domain expertise is the most popular way, I have seen to filter candidates for software testing jobs. Testers are expected to be masters of at least to talk about many different areas testing programming, development process and the business.
The term domain expert is frequently used in expert system software development, and there the team always refers to the domain other than the software domain.
A domain expert is a person with the special knowledge or skills in a particular field. An accountant is an expert in the domain of accountancy, for example
The development of accounting software requires knowledge in two different domain namely accounting and software. Some of the development workers may be experts in one domain and not the other.
Some realtime example are as follows:
1. Telecommunication :
What is Telecommunication ?
Telecommunication is the transmission of signals over long distance. A complete telecommunication arrangement is made up of two or more stations equipped with transmitter and receiver devices. A single co-arrangement of transmitters and receivers, called a transceiver, may also be used in many telecommunication stations.
Telecommunication includes signs, signals, messages, words, writing, images and sounds or information of any nature by wire , radio, optical or other electromagnetic system. Some real example are mobile .
Components:
Telecommunication system use electronic signals to communicate information. Digital technology has made telecommunication system includes signals, communication channels and communications networks.
i.Signals
The data traveling through a telecommunication system use analog and digital electronic signals. The analog signal is a continuous waveform used for voice communication that goes through communication medium. Digital signals, on the other hand, transmit data coded as one bits and zero bits or no-off electric pulses. Computers communication using digital signals. Whenever a computer needs to communicate over an analog line it needs a modern to translate the signals. A modern to translate the signals. A modern translates analog signals into digital and digital signals into analog.
ii.Communication Channels
The transmission of information over a telecommunications system also requires communication channels. communication channels use different mediums to transmit information from one device to another. The speed in which the information flows depends on the transmission media. High-speed transmission is more expensive because the infrastructure to support the high-speed transmission costs more than the infrastructure used to support low-speed transmission. Example of media used for transmission include wireless,fiber optics, coaxial cable and twisted wire
iii.Communications Networks
Telecommunication network provides a variety of functions and receive a classification based on their geographic capacity and the type of service they provide. the topology of a network and the network connection indicate how a specific network perform its tasks. The most recognized topologies are star, bus and ring network. The star network uses a central computer connected to different terminals or small computers. The bus network uses a single circuit to link computer. The ring network, on the other hand, is the most independent type of network and does not rely on a central host computer.
2. Cloud Computing :
What is Cloud Computing ?
Cloud computing is the delivery of different services through the Internet. These resources include tools and applications like data storage, servers, databases, networking, and software.
Cloud computing is a popular option for people and businesses for a number of reasons including cost savings, increased productivity, speed and efficiency, performance, and security
Cloud Computing takes the technology, services and applications to those on the Internet and turns then into a self-service utility. Cloud sever are located in data centers all over the world. Using cloud computing, users and companies don't have to manage physical servers themselves or run software application on their own machines.
The cloud enables users to access the same files and applications from almost any device, because the computing and storage takes place on servers in a data center, instead of locally on the user device. This is why a user can log into their Instagram account on a new phone after their old phone breaks and still find their old account in place, with all their photos, videos, and conversation history. It works same for Facebook also, and any social accounts. It works the same way with cloud email providers like Gmail or Microsoft Office 365, and with cloud storage providers like Dropbox, Google Drive or Google photos.
Cloud Computing Services:
There are three types are as follows-
i.Software as a Service (SaaS)
A vendor provides clients pay as you go access to storage network, operating system, servers and other computing resources in cloud. Organisation use their own platforms and application within a service provider infrastructure instead of purchasing hardware users pay for IaaS on demand. Infrastructure id scalable depending on processing and storage needs.
ii.Platform as a Service (PaaS)
It provides users with cloud environment in which they can develop, manage and deliver application to provide platform with tools to test and host application in some environment. It facilitates collaborative work even if team works remotely.
iii.Infrastructure as a Service (IaaS)
It is cloud computing that provides users with access to vendors cloud based software. Data is secure in cloud, equipment failure does not result in loss of data application are accessible from at most any internet connected device virtually from anywhere in cloud.
Why Cloud Computing important for business ?
In today’s world, cloud computing has changed every business. Nowadays businesses of every size are turning to cloud services. According to the latest research, private and public cloud adoption has increased over the past year. It is very crucial for big and small businesses today.
Now, days globally competitive world when larger enterprises have already moved to IoT, AI and BigData, it is critical for SMEs too, to explore cloud adoption seriously and use platforms that can help make their businesses smart.
This is because it has seen the fastest adoption into the mainstream than any other technology in the domain, and it increases efficiency, helps improve cash flow and offers many more benefits
1. Flexibility
2. Disaster recovery
3. Automatic software updates
4. Capital-expenditure Free
5. Increased collaboration
6. Work from anywhere
7. Document control
8. Security
9. Competitiveness
10. Environmentally friendly
3. Data Analytics :
Data analytics is the process of examining data sets in order to draw conclusions about the information they contain. And the analysis of data contain large set of big data, by the use of computer software. It is the science of using data to build model that lead to better decision that in turn add value to individual companies.
There are three types of Data Analytics such as;
i.Descriptive : It helps the business understand how thing are going.
ii.Predictive : It helps the business forecast future behavior and results.
iii.Prescriptive : It helps the business prescribe right course of action.
Data analytics technologies and techniques are widely used in commercial industries. It can help in business to increase income, improve operational efficiency(operational efficiency can be defines as the ratio between an output gained from the business operation), respond more quickly to emerging market trends
4. Computer Vision :
Computer vision is a field of artificial
intelligence that trains computers to interpret and understand the visual
world. Using digital images from cameras and videos and deep learning models,
machines can accurately identify and classify objects
For example, cars could be fitted with computer
vision which would be able to identify and distinguish objects on and around
the road such as traffic lights, pedestrians, traffic signs and so on, and act
accordingly. The intelligent device could provide inputs to the driver or even
make the car stop if there is a sudden obstacle on the road.
When a human who is driving a car sees someone suddenly move into the path of the car, the driver must react instantly. In a split second, human vision has completed a complex task, that of identifying the object, processing data and deciding what to do. Computer vision's aim is to enable computers to perform the same kind of tasks as humans with the same efficiency.
Early experiments in computer vision took place in the 1950s, using some of the first neural networks to detect the edges of an object and to sort simple objects into categories like circles and squares. In 1966, Seymour Papert and Marvin Minsky, two pioneers of artificial intelligence, launched the Summer Vision Project, a two-month, 10-man effort to create a computer system that could identify objects in images.
In the 1970s, the first commercial use of computer vision interpreted typed or handwritten text using optical character recognition. This advancement was used to interpret written text for the blind. In 1979, Japanese scientist Kunihiko Fukushima proposed the neocognitron, a computer vision system based on neuroscience research done on the human visual cortex. Although Fukushima's neocognitron failed to perform any complex visual tasks, it laid the groundwork for one of the most important developments in the history of computer vision.
As the internet matured in the 1990s, making large sets of images available online for analysis, facial recognition programs flourished. These growing data sets helped make it possible for machines to identify specific people in photos and videos.
To accomplish the task, a computer program had to be able to determine which pixels belonged to which object. This is a problem that the human vision system, powered by our vast knowledge of the world and billions of years of evolution, solves easily. But for computers, whose world consists only of numbers, it is a challenging task.
At the time of this project, the dominant branch of artificial intelligence was symbolic AI, also known as rule-based AI: Programmers manually specified the rules for detecting objects in images. But the problem was that objects in images could appear from different angles and in various lighting. The object might appear against a range of different backgrounds or be partially occluded by other objects. Each of these scenarios generates different pixel values, and it's practically impossible to create manual rules for every one of them.
How computer vision works
Today’s AI systems can go a step further and take actions based on an understanding of the image. There are many types of computer vision that are used in different ways:
Image segmentation partitions an image into multiple regions or pieces to be examined separately.
Object detection identifies a specific object in an image. Advanced object detection recognizes many objects in a single image: a football field, an offensive player, a defensive player, a ball and so on. These models use an X,Y coordinate to create a bounding box and identify everything inside the box.
Facial recognition is an advanced type of object detection that not only recognizes a human face in an image, but identifies a specific individual.
Edge detection is a technique used to identify the outside edge of an object or landscape to better identify what is in the image.
Pattern detection is a process of recognizing repeated shapes, colors and other visual indicators in images.
Applications of Computer Vision
Many of the applications you use every day employ computer-vision technology. Google uses it to help you search for objects and scenes - say, "dog" or "sunset" - in your Images library.
Other companies use computer vision to help enhance images. One example is Adobe Lightroom CC, which uses machine-learning algorithms to enhance the details of zoomed images. Traditional zooming uses interpolation techniques to color the zoomed-in areas, but Lightroom uses computer vision to detect objects in images and sharpen their features when zooming in.
One field that has seen remarkable progress thanks to advances in computer vision is facial recognition. Apple uses facial-recognition algorithms to unlock iPhones. Facebook uses facial recognition to detect users in pictures you post online. In China, many retailers now provide facial-recognition payment technology, relieving their customers of the need to reach into their pockets.
Content moderation is another important application for computer vision. Most social-media networks use deep-learning algorithms to analyze posts and flag those that contain banned content. Self-driving cars also rely heavily on computer vision to make sense of their surroundings. Deep-learning algorithms analyze video feeds from cameras installed on the vehicle and detect people, cars, roads, and other objects to help the car navigate its environment.
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