Computer Science Engineering
One of the most sought courses amongst engineering students, Computer Science Engineering (CSE) is an academic programme which integrates the field of Computer Engineering and Computer Science. The programme, which emphasises the basics of computer programming and networking, comprises a plethora of topics.
Digital image processing involves the handling of images using digital devices. Its use has exponentially increased in recent decades. The applications include medicine, sports, mineral processing and remote sensing. One of the foundations of the new information society, multimedia networks depend heavily on digital imaging. The field of digital image processing is extensive, covering both digital signal processing techniques and image-specific techniques.
An picture can be shown as f (x, y) of two continuous variables x and y. To be digitally stored, it must be sampled and converted into a number matrix. Since a machine displays the numbers with finite accuracy, these numbers must be digitally represented. The creation of digital images involves the manipulation of these precision numbers. The optical image processing can be divided into many classes: image enhancement, image restore, image analysis and image compression. In improved images, an image, mostly using heuristic techniques, is manipulated in order to retrieve valuable information from a human audience. Image reconstruction methods attempt to process degraded images using a predictive or mathematical explanation of the decay in order to reverse it. Image analysis techniques enable the processing of an image to automatically derive information from it. Imaging segmentation, edge extraction, texture analysis and motion analysis are examples of the image analysis. The vast volume of detail necessary to depict images is a significant feature of the pictures. Say 512 × 512, also a grey image of moderate resolution requires 512 × 512 × 8 ≈ 2 × 106 bit for its representation. For this reason, if digital images are to be stored and transmitted practically, you need some kind of photo compression, using image replication to reduce the amount of bits used to display them.
Image processing is a technique for conducting such image operations such that an improved picture can be obtained or valuable information can be extracted. It is a kind of signal processing in which the image input is an image, and the image output may be an image or features. The rendering of images is now one of the fast-growing technologies. It is also a central area of study in the fields of engineering and computer science.
Basically, image processing consists of the following three steps:
- Import the image using image acquisition tools;
- processing and manipulation;
- Output in which image or report dependent on image processing may be changed.
There are two processes, analogue and optical image processing, used for image processing. For hard copies such as prints and photos, analogue image processing can be used. Image analysts use different interpretive fundamentals when employing these visual strategies. Digital image processing helps manipulate digital images with computers. The three general phases which all data types must undergo during the digital processing, improvement and display are the extraction of information. we will discuss a number of basic concepts such as image, digital image and digital image processing. Various digital image sources will be addressed and examples will be given for each source. This lecture would explore the spectrum from image recognition to machine vision. Finally, we will discuss the acquisition of images and various image sensor forms. PhD projects in Digital Image Processing provide the PhD/MS pupils with a brainy atmosphere to increase thinking. A significant total of PhD projects in digital image processing have been completed by our wise team. They have the ability to use appropriate techniques and vocabulary.
Area of Application
- Remotely sensed Robotic vision medical image processing
- Forensics digital
- Blueprint Analysis
- Cyber safety
Talk to us about your project
With Vidhya IT technical support the tagging, sorting and data recovery process is a lucrative monetary and operating process. To learn how our offerings will make the profits easier
We offer one of the best Python Tutorial in India- Installation, control statements, Strings, Lists, Tuples, Dictionary, Modules, Exceptions, Date and Time, File I/O, Programs, and more are all covered in our Python tutorial. Python interview questions are also included to help you properly grasp Python programming.
Python is a programming language. Python basics and intermediate topics are included in this Python tutorial. Our Python tutorial is suitable for both beginners and experts. Python is an object-oriented programming language that is simple, general-purpose, and high-level. Python is also an interpreted programming language. Guido Van Rossum is credited for inventing Python programming.
What is Python?
Python is a general, complex, high-level and interpreted language of programming. It follows object-oriented application development programming methodology. It is quick to read and offers many high-level data structures. Python is still strong and scalable, making it desirable for the development of application. With its syntax and dynamic typing, Python makes it an excellent language for fast programme creation and scripting.Python embraces various patterns of programming, including object-oriented, imperative, sequential or procedural types. Python is not meant to function in a specific field, like web programming. This is why this language is called multipurpose programming since it can be used for the Internet, business, 3D CAD, etc.We don’t have to use data types to declare vector, because it’s dynamically typed so we can write a=10 in an integer variable. Python develops and debugges quickly since the implementation of Python does not take a compilation stage and the edit-test-debug period is quite quick.
In 1991, Guido van Rossum developed Python at CWI in the Netherlands. The definition of Python was drawn from the ABC programming language, or we might assume that ABC was a Python language precursor.Behind the word Python is indeed a reality. Guido van Rossum was a follower of the famous “Monty Python’s Flying Circus” comedy show of the period. So he chose Python for his freshly invented language of programming.Python has the world’s vast audience and publishes its update in the short term.
Why the Name Python?
Why to learn Python?
Python offers the programmer with several helpful features. These characteristics make the language most common and commonly used. Below are some of the most important features of Python.
- Easy to use and learn
- Language Expressive
- Language interpreted
- Language oriented towards objects
- Language Open Root
- Standard Library Learn
- Support for GUI Programming
- Memory allocation dynamic
- Wide range of frameworks and libraries
Where is Python used?
Python is a common, universal programming language and is used in nearly every technological area. The following are the different fields of Python application. Data Science, Date Mining, Desktop Applications, Console-based Applications, Mobile Applications, Software Development, Artificial Intelligence, Web Applications, Enterprise Applications
What is MAT LAB?
MATLAB is a high-performance mathematical computation, simulation, and programming environment software suite. It offers an immersive platform with hundreds of built-in advanced computing, graphics, and animation features. Matrix Laboratory is the abbreviation for MATLAB Software. MATLAB was created to apply the LINPACK (Linear system package) and EISPACK (Eigen system package) projects’ basic approach to matrix applications. MATLAB is a modern programming environment with advanced data structures, built-in editing and debugging facilities, and object-oriented programming support. In addition to the world, MATLAB is also a programming language. Although its name includes the term Matrix, MATLAB is all programming based on mathematical matrices and arrays. MATLAB’s all variable types contain data only in the context of an array, let it be an integer type, character type, or string type variable. MATLAB allows multiple task styles such as matrix manipulation, execution of an algorithm, data, and plotting functions and can interface with programmes in other programming languages.
The MATLAB was developed in the mid 1970s by the chairman of the Department of Computer Science at the University of New Mexico, Cleve Moler. Cleve expected his students to be able to use LINPACK & EISPACK (FORTRAN app libraries) without studying FORTRAN. In 1984, Cleve Moler tried to rewrite MATLAB in C with Jack Little & Steve Bangert and formed MathWorks. At the time, these libraries were known as JACKPAC, later revised as LAPACK for matrix manipulation in 2000. The integrated functions of MATLAB include excellent methods for linear algebra simulations, data analysis, signal processing, optimisation, numerical solution of ordinary differential equations, squares and a wide variety of other forms of mathematical calculations.
Any of these tasks use cutting-edge algorithms. There are various 2-D and 3-D graphics and animation capabilities.In order to run the programmes from MATLAB, MATLAB supports an external interface. The consumer is not restricted to built-in functions; he can use MATLAB to write his functions.
History of MATLAB
Multiple optional “toolboxes” are also available from MATLAB developers. These toolboxes provide a set of functions for primary purposes such as symbolic calculations, image analysis, statistics, the architecture of the control system and neural networks.The matrix is the necessary construction components of MATLAB. The basic data form is the list. Vectors, scalars, actual matrices, and complex matrices are all processed automatically as key data form special cases. MATLAB likes matrices and functions of matrices. The integrated functions for vector functions are streamlined. Vectorized commands or codes thus run even more quickly in MATLAB.
Main Features and Capabilities of MATLAB
- Environmental Development – This is the compilation of applications and installations to help you use MATLAB files and operations. Many of these tools are the user experience. It consists of a MATLAB desktop and command window, a command history, an editor and debugger, as well as browsers for support, working room, reports and search route.
- MATLAB Library of Mathematical Function- This is a comprehensive list of simple functions, such as number, sinus, cosine and complex mathematics, to more advanced features such as inverse matrix, self-values matrix, Bessel functions and Fourier’s quick transformation.
- Language MATLAB-This is a high-level array language with flow sentence, function, data structure, input/output and object-oriented programming. It allows “small programming” to quickly and dirty throw-out programmes and “broad programming” to construct massive and complicated application functions.
- Graphs Charts-MATLAB offers detailed vector and matrix displays as diagrams, and the possibility to annotate and print these graphs. It includes high-level frameworks for the simulation, animation, and display of two-dimensional and three-dimensional data. It also includes low-level frameworks, which allow us to completely customise graphics display and to create full graphical user interfaces on our MATLAB applications.
- MATLAB External Interfaces/API- It is a library where we can write C and FORTRAN programmes interacting with MATLAB. It offers the capability to call MATLAB (dynamic link) routines, call MATLAB as a computer engine, and read and write MAT-files.
MATLAB can be used as a simulation device for different electrical networks but MATLAB has developed a highly competitive tool for artificial intelligence, robotics, imaging, wireless communications, machine learning, data analytics and more. While its use is vast, mainly used by circuit branches and mechanical engineering to solve a number of simple problems. It is a method for computing, scripting and visualising the results graphically.
As the name implies, MATLAB’s basic data element is the matrix or array. MATLAB toolboxes are properly constructed and allow you to make your creativity a reality. The programming of the MATLAB is quite similar to C and allows you to begin working with a little brush of your basic programming skills.
Advantage of MATLAB
Statistics and machine learning (ML)- This MATLAB toolbox can be very useful for programmers. It can effectively apply statistical techniques such as descriptive or inferential. The same is true in machine learning. Different models should be used to solve contemporary problems. The used algorithms can also be used for programmes of large data.
Fitting the curve- The toolbox for curve-fitting allows to interpret the pattern of data. After a certain trend is achieved, it can be a curve or a surface, the possible trends can be expected. Additional plotting, integral calculation, derivatives, interpolation etc may be performed.
System of control-Nature systems can be accessed. Control and observability of such factors as the closed-loop, the open-loop, the bode map, the Nyquist plot, etc. can be obtained. Different control methods like PD, PI and PID can be visualised. Time domain or frequency domain analysis may be performed.
Signal processing-In different engineering streams, signals and structures as well as digital signal processing are taught. Yet MATLAB offers the ability to visualise this properly. Different transformations like Laplace, Z etc. can be performed on any given signal. Validation of theorems could be possible. Analysis may be performed in the time or frequency domain. Multiple built-in functions are available.
The mapping processes-Mapping has many uses in different fields. For example, the MapReduce tool is really important in big data and has many implementations in the real world. Data mapping can be used for theft investigation or financial fraud detection, regression simulations, contingency analysis, social media prediction methods, data tracking, and so on.
Deep learning-It’s a form of machine learning that can be used for things like speech recognition, detecting financial fraud, and analysing medical images. Time-series analysis, artificial neural networks (ANN), fuzzy logic, or a mixture of these methods may all be used.
Network and Security
Computer network protection consists of steps taken by companies or by such organisations, to detect unlicensed entry by external threats to deter them from doing so.Based on computer network complexity, various approaches to computer network security management have different criteria. For eg, a home office requires simple network protection, while large enterprises need high maintenance to deter malware threats from the network. Network Administrator manages network access to data and applications. A network administrator assigns the designated person the user ID and password.
Aspect of network security
Privacy: privacy implies secrecy with both the sender and the receiver. The message sent should only be delivered to the intended recipient, while the communication should be invisible to all users. Just the sender and recipient can understand the message as eavesdroppers can decrypt the message. Therefore, the code must be encrypted such that the message cannot be intercepted. This secrecy feature is widely used for safe correspondence.
Message reliability: Data integrity ensures that the information must reach the recipient precisely as it has been delivered. Data content does not alter during transmission, maliciously or accidentally, in a transit. When monetary transactions on the internet are increasing, data privacy is more and more important. For safe correspondence, data confidentiality must be maintained.
Authentication of the end-point: Authentication ensures that the recipient is certain of the identity of the sender, i.e. that no impostor has received a letter.
Non-repudiation: Non-repudiation ensures that the recipient must be able to show that the letter sent is from a certain source. The sender does not refuse to deliver a reply. The recipient has the responsibility of verifying the name. For eg, if a customer receives an application to move the money on a separate account, the bank may provide evidence that the customer has requested the transaction.
What is a Computer Network?
Computer networking is the process of interfacing two or more computer computers for the purposes of data sharing. Computer networks are designed of hardware and software combinations.
Features Of Computer network
Computer networks are used to perform certain functions by knowledge exchange.
- Any of the topics used by networks include:
- email, film, text messaging and other ways communication
- Tools include printers, scanners and photocopiers sharing
- Share files
- Remote system applications and operating programmes
- Enables network administrators to view and retain knowledge effectively
Computer Network Components–
Computer network modules are the main components used for software installation. NIC, transform, wire, hub, router and modem are several essential network components. Any network modules can also be omitted depending on the type of network we need to add. The wireless network, for example, would not need a cable.
NIC(National interface card)
NIC is a system for communicating with another device. The network interface card holds the hardware addresses and this address is used by the protocol for the data-link layer to define the network device to move the data to a correct place.
Two types of NICs are available: NIC wireless and NIC wired.
Wireless NIC: Wireless NIC is used for all new laptops. With the Wireless NIC, the antenna that uses radio wave technologies is connected.
Wired NIC: the wired NIC cables are used for transferring data over a medium.
Hub is a central device which divides the network link into several devices. If the machine asks for information from a computer, the request is sent to the hub. This request is sent by Hub to all the interconnected computers.
Router is a LAN connecting system to the internet. The router is used primarily to link the different networks or to connect the internet to many machines.
Switch is a networking system that groups together all network equipment to transfer data to another device. It does not relay the message across the network, i.e. it transmits the message to the system on which it belongs. There’s a safer turn than Hub. Then we should conclude that the switch simply delivers a message from the source to the destination.
Modem links the device via the current phone line to the internet. A modem is not compatible with the chipset of the unit. A modem is a different component of the motherboard’s PC slot.
Computer Network Uses
Resource sharing: Resource sharing involves sharing resource such as programmes, printers and data between network users without requiring the resource and user’s physical position.
Model Server-Client: In the server client model, computer networking is included. A server is a central device that is used by the system administrator to store and manage the records. Clients are computers used for securely accessing the details contained on the computer.
Medium of contact: The computer network acts as a means of communication between users. For example, an enterprise includes more than one machine with a regular communications email system used by workers.
E-commerce: The network of computers is essential in companies as well. We will do this on the internet. Amazon.com, for example, does its business through the internet, i.e. does its business via the internet.
Machine Learning Tutorial offers simple and advanced machine learning principles. Our machine learning tutorial is for students and practitioners. Machine learning is a developing technique that allows machines to learn from previous data automatically. Machine learning uses many algorithms to construct mathematical models and simulate using historical knowledge or evidence. It is currently used for different purposes, for example image recognition, voice recognition, email scanning, auto-tagging, Facebook recommendations and much more.This tutorial provides you with a reference to machine learning, coupled with a broad variety of machine learning techniques such as guided, uncontrolled and strengthened learning. You’ll find out about regression and sorting models, classification processes, secret Markov models and sequential models. Machine Learning is said to be a subset of artificial intelligence primarily dedicated to the creation of algorithms which enable a machine to learn from its own data and experience. Arthur Samuel invented the word machine learning in 1959. We will summarise it in the following way:
What is machine learning?
In the modern world, we are surrounded by people who will learn more of their learning activities and have computers or software that interact on our orders. But can a computer, like a person, still benefit from interactions or past data? So this is Machine Learning’s function.
Machine Learning features:
- Machine learning utilises data in a specified dataset to identify different trends.
- It will benefit from previous data and automatically change.
- It is a technology powered by evidence.
- Machine learning is just like data mining as it often covers the enormous volume of data.
Machine Learning Requirement
The need for automatic learning is growing every day. The explanation why machine learning is essential is that it can accomplish tasks that are too difficult to be carried out directly by a human. As human beings, we have some shortcomings because we can’t manually access the vast volume of info, so we need some computer systems and here comes machine education for us.
By supplying them with the huge amount of data, we can train machine learning algorithms, allow them to explore the data, create models and automatically predict the appropriate results. The efficiency of the machine learning algorithm is calculated by the quantity of data and the cost function. We will save time and resources with the aid of machine learning.The value of machine learning can clearly be grasped via its applications. Machine learning is also used in self-driving vehicles, cyber fraud prevention, facial recognition and Facebook friendship advice. Several top organisations including Netflix and Amazon have developed machine learning algorithms that use a wide variety of data to analyse customer interest and then suggest items.
Machine Learning Classification
Machine learning may be divided into three categories at a general level:
- Supervised learning -Supervised learning is a kind of machine-learning process where we supply the machine-learning framework with sample labelled data to train it and thus forecast its performance.The algorithm generates a model using labelled data to understand the databases and learn about each data; after preparation and processing is completed, we validate the model with sample data to see if the same outcome is predicted.The aim of supervised learning is to chart performance data. Education is controlled and is the same as learning something under the supervision of the instructor from a pupil. Spam filtering is the example of guided learning.
- Unsupervised learning- Unsupervised learning is a form of learning where a computer will acquire without guidance.The training is provided to the computer with data which has not been named, identified or categorised, and without any oversight the algorithm needs to work on that data. The purpose of unattended study is to restructure the input data into new characteristics or a set of related items.
- Reinforcement learning- Reinforcement learning is a feedback-based learning system in which a learning agent receives credit for every correct behaviour and a punishment for every incorrect action. The agent absorbs these suggestions immediately and increases its results. In enhancement learning, the agent communicates and experiences the world. An agent’s objective is to achieve the most incentive points and thereby increase its results.The robotic dog, which knows the manipulation of his arms automatically, is an illustration of Reinforcement learning.
You can learn in depth about machine learning in our tutorial programme-
- What is the core of the apprenticeship?
- What are the various forms of machine learning?
- What are the various algorithms accessible for designing models of machine learning?
- What are the resources used to build these models?
- What are the language options for programming?
- What frameworks help Machine Learning software creation and deployment?
- What IDEs are possible (Integrated Development Environment)?
- How to improve your skills rapidly in this critical field?
Data Mining Tutorial
Data mining tutorial offers simple and advanced data mining principles. Our Data Mining tutorial contains all data mining topics such as apps, data mining against machine learning, data mining methods, social media data mining, data mining tactics, data mining clustering, data mining challenges, etc. Our data mining tutorial is for students and professionals. Data mining is one of the most useful tools for helping developers, analysts and individuals gain insightful knowledge from large data sets. Data mining is sometimes called Database Information Discovery (KDD). The method of information exploration involves data purification, data integration, data collection, data transformation, data analysis, pattern assessment and knowledge presentation.
Our Data Mining tutorial contains all data mining topics such as apps, data mining against machine learning, data mining methods, social media data mining, data mining tactics, data mining clustering, data mining challenges, etc.
What is the data mining?
Data Mining is the practice of collecting knowledge in order to detect patterns, developments and usable data that enables the company to decide data based on large groups of data. In other terms, data mining is the practise of investigating secret trends of knowledge from different viewpoints on the categorisation of relevant data gathered and organised in specific areas such as data warehouse, effective research, algorithms for the mining of data, decision making and more data needs for ultimate cost-cutting and revenue generation.
Data mining is the automated quest for broad information stores to find trends and patterns which go beyond simple analytical procedures. For data segments, data mining uses sophisticated statistical algorithms and evaluates the likelihood of future events. Data mining is also known as data discovery (KDD).
Data mining is a method that companies use to retrieve data from enormous datasets in order to resolve business challenges. It mostly transforms raw data into usable material. Data mining is analogous to the data science performed by an individual on a certain dataset with a goal in a specific scenario. Included in this phase are different forms of services like text mining, online mining, audio and video mining, image mining and social network mining. It is achieved by basic or extremely specialised applications. All analysis can be accomplished quicker with low operating costs by outsourcing data mining. Specialized companies may often utilise emerging tools to gather data that cannot be manually located. Tons of resources are available on different websites, but very little expertise is available. The greatest difficulty is to interpret the data to obtain important knowledge for solving a problem or for the growth of a business. Mine data have several powerful tools and techniques accessible and can be best understood.
The following categories of data may be used for data mining:
Relational Database: A relational database consists of a list of several data sets arranged formally by tables, documents and columns, from which data can access in different forms without identifying the database tables. Tables relay and exchange knowledge, making it easier to find, report and organise details.
Data warehouses:A data warehouse is the infrastructure that gathers data from different outlets in the company to offer practical insights into market. The enormous volume of information emerges from other fields like marketing and finance. The derived data is used for analytical purposes and helps to make decisions for a business. The data centre is structured to analyse data and not to process transactions.
Data Repositories: The data repository usually corresponds to a data collection destination. But many IT experts use the word more specifically to apply to a particular type of configuration in an IT system. For eg, a database category in which an institution has kept different types of records.
Object-Relational Database: An object-relational architecture is considered a hybrid of an object-oriented database model and a related database model. It supports classes, items, legacy, etc. One of the main purposes of the Object-Relational Database model is to close the distance between the relation database and object-oriented models commonly used in many programming languages, e.g. C++, Java, C#, etc.
Transactional Database :A transaction database relates to a database management system (DBMS) that can reverse a database transaction if not properly carried out. While this has been a special feature for quite a long time, most of the connection database structures already allow transactional database operations.
Data Mining Advantages
- Information Mining allows companies to collect information dependent on intelligence.
- Data mining allows companies to make lucrative operational and output changes.
- Data mining is cost-effective compared to other computational data applications.
- Data mining supports an organization’s decision-making mechanism.
- It helps to automatically uncover secret dynamics and to forecast trends and behaviour.
- The latest structure and the current platforms can be induced.
- It is a fast method that facilitates the analysis of huge volumes of data by new users in a short time.
AI is done by researching how the human brain is thinking and how people are learning, decide and working while attempting to solve a problem and then utilising the results of this research as a framework for designing intelligent applications and systems. Since computers or devices were invented, their capacity to execute different functions has increased exponentially. Humans also developed the influence of computer systems with regard to their various working areas, their rising pace and their time reduction.
An artificial intelligence division of computer science seeks to create computers or robots as smart as human beings.
What does artificial intelligence include?
Artificial intelligence is not only a component of informational technology but also so extensive and requires several other aspects that can lead to it. In order to construct the AI, we should first know how intelligence is made, because intelligence is an intangible component of the brain that combines reasoning, learning, problem-solving thinking, language awareness, etc.
We should know what AI’s significance is and why we should study it before we learn artificial intelligence. Here are some of the key causes for AI:
- By using AI, you can create apps or devices that can quite quickly and accurately solve real problems such as health concerns, promotions, traffic issues, etc.
- You may build your personal virtual assistant using AI, such as Cortana, Google Assistant, Siri, etc.
- Using AI, you can create robots that can function in an area in which human life can be at danger.
- Other emerging technology, new devices and new opportunities are opened up by AI.
The following discipline is needed to achieve the above factors for a computer or software Artificial Intelligence:
- Computer Science
- Neurons Study
Artificial Intelligence Goals
The core objectives of Artificial Intelligence are as follows:
- Replicate human understanding
- Solve skill-intensive activities
- A smart link between thought and behaviour
- Build a computer that can do human intelligence activities such as:
- Proof of a theorem
- Chess Play
- Plan any surgical procedure
- Driving a traffic vehicle
- Create a machine that can show smart behaviour, learn new concepts by itself, illustrate, clarify and inform the consumer.
The Cloud Computing Tutorial offers basic and advanced cloud computing topics. Our cloud computing tutorial is intended for experts and newcomers.
Cloud Computing is a technology focused on virtualization that enables us to build, configure and customise apps through an internet connection. A programming framework, hard drive, web application and storage are part of cloud technologies.
What is Cloud Computing
The word cloud refers to an internet or a network. It is a technology that uses remote internet servers instead of local drives to store, control and view data electronically. Data can be anything like files, photographs, records, audio, video, etc.
The following operations we can do with the help of cloud computing are all here:
- New software and services development
- Data storage, backup and recovery
- Blogs and websites hosting
- Computer delivery on demand
- Data analysis
- Video and audio streaming
Why Cloud Computing?
Small and large IT firms adopt the conventional IT infrastructure practises. This means we need a server room for any IT company that is the simple necessity of the IT industries.
This server room should have a web server, mail server, networking, firewalls, modem, router, transform, QPS (Query per Second means how many requests or loads are done on the server).We need to invest a lot of money to build such IT infrastructure. Cloud computing is available to solve all these issues and reduce IT infrastructure costs. Whenever you ride by train or bus, you take a ticket to your destination and stay on your seat until you reach your destination. Likewise, other passengers take a ticket and ride with you on the same bus and it would hardly disturb you. When you stop, you get off the bus and thank the driver. Cloud storage is as such a bus, transporting data and information to various customers and enabling the service to be used at reduced cost.