Some latest hot topics in Computer Science:
- Data Warehousing
- Internet of Things(IoT)
- Big Data
- Cloud Computing
- Semantic Web
- Machine Learning
- Artificial Intelligence
- Data Mining
- Image Processing
- Quantum Computing
Data Warehousing is the process of analyzing data for business purposes. Data warehouse store integrated data from multiple sources at a single place which can later be retrieved for making reports. The data warehouse in simple terms is a type of database different and kept isolated from organization’s run-time database. The data in the warehouse is historical data which is helpful in understanding business goals and make decisions for future prospects. It is a relatively new concept and have high growth in future. Data Warehouse provides Online Analytical Processing(OLAP) tools for the systematic and effective study of data in a multidimensional view. Data Warehouse finds its application in the following areas:
- Financial Sector
- Banking Sector
- Retail Services
- Consumer goods
Internet of Things(IoT)
Internet of Things(IoT) is a concept of interconnection of various devices, a vehicle to the internet. IOT make use of actuators and sensors for transferring data to and from the devices. This technology is developed for better efficiency and accuracy apart from minimizing human interaction with the devices. The example for this is the traffic lights which changes its colors depending upon the traffic. Following are the application areas of Internet of Things(IoT):
- Home Automation
Big Data is a term to denote the large volume of data which is complex to handle. The data may be structured or unstructured. Structured data is an organized data while unstructured data is an unorganized data. The definition of big data is termed in terms of three Vs. These vs are:
- Volume: Volume defines large volume of data from different sources
- Velocity: It refers to the speed with which the data is generated
- Variety: It refers to the varied amount of data both structured and unstructured.
Cloud Computing is a comparatively new technology. It is an internet-based service that creates a shared pool of resources for consumers. There are three service models of cloud computing namely:
- Software as a Service(SaaS)
- Platform as a Service(PaaS)
- Infrastructure as a Service(IaaS)
Characteristics of cloud computing are:
- On-demand self-service
- Broad network access
- Shared pool of resources
- Measured service
Semantic Web is also referred to as Web 3.0 and is the next big thing in the field of communication. It is standardized by World Wide Web Consortium(W3C) to promote common data formats and exchange protocols over the web. It is machine-readable information based and is built on XML technology. It is an extension to Web 2.0. In the semantic web, the information is well defined to enable better cooperation between the computers and the people. In the semantic web, the data is interlinked for better understanding. It is different from traditional data sharing technologies.
MANET stands for mobile ad hoc network. It is an infrastructure-less network with mobile devices connected wirelessly and is self-configuring. It can change locations independently and can link to other devices through a wireless connection. Following are the various types of MANETS:
- Vehicular ad hoc network(VANET)
- Smartphone ad-hoc network(SPANET)
- Internet-based mobile ad hoc network(iMANET)
various simulation tools to study the functionality and working of MANET like OPNET, NETSIM, NS3 etc.
In MANET there is no need of central hub to receive and send messages. Instead, the nodes directly send packets to each other.
MANET finds its applications in the following areas:
- Environment sensors
- Vehicular ad hoc communication
- Road Safety
It is also a relatively new concept in the field of computer science and is a technique of guiding computers to act in a certain way without programming. It makes use of certain complex algorithms to receive an input and predict an output for the same. There are three types of learning;
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Machine Learning is closely related to statistics.
Data Mining is the process of identifying and establishing a relationship between large datasets for finding a solution to a problem through analysis of data. There are various tools and techniques in Data Mining which gives enterprises and organizations the ability to predict futuristic trends. Data Mining finds its application in various areas of research, statistics, genetics, and marketing. Following are the main techniques used in the process of Data Mining:
- Decision Trees
- Genetic Algorithm
- Induction method
- Artificial Neural Network
Advantages of Data Mining
- Data Mining helps marketing and retail enterprises to study customer behavior.
- Organizations into banking and finance business can get information about customer’s historical data and financial activities.
- Data Mining help manufacturing units to detect faults in operational parameters.
- Data Mining also helps various governmental agencies to track record of financial activities to curb on criminal activities.
Disadvantages of Data Mining
- Privacy Issues
- Security Issues
- Information extracted from data mining can be misused
Artificial Intelligence is the intelligence shown by machines and it deals with the study and creation of intelligent systems that can think and act like human beings. In Artificial Intelligence, intelligent agents are studied that can perceive its environment and take actions according to its surrounding environment.
Goals of Artificial Intelligence
Following are the main goals of Artificial Intelligence:
- Creation of expert systems
- Implementation of human intelligence in machines
- Problem-solving through reasoning
Application of Artificial Intelligence
Following are the main applications of Artificial Intelligence:
- Expert Systems
- Natural Language Processing
- Artificial Neural Networks
- Fuzzy Logic Systems
Strong AI – It is a type of artificial intelligence system with human thinking capabilities and can find a solution to an unfamiliar task.
Weak AI – It is a type of artificial intelligence system specifically designed for a particular task. Apple’s Siri is an example of Weak AI.
Turing Test is used to check whether a system is intelligent or not. Machine Learning is a part of Artificial Intelligence. Following are the types of agents in Artificial Intelligence systems:
- Model-Based Reflex Agents
- Goal-Based Agents
- Utility-Based Agents
- Simple Reflex Agents
Natural Language Processing – It is a method to communicate with the intelligent systems using human language. It is required to make intelligent systems work according to your instructions. There are two processes under Natural Language Processing – Natural Language Understanding, Natural Language Generation.
Natural Language Understanding involves creating useful representations from the natural language. Natural Language Generation involves steps like Lexical Analysis, Syntactic Analysis, Semantic Analysis, Integration and Pragmatic Analysis to generate meaningful information.
Image Processing is another field in Computer Science and a popular topic for a thesis in Computer Science. There are two types of image processing – Analog and Digital Image Processing. Digital Image Processing is the process of performing operations on digital images using computer-based algorithms to alter its features for enhancement or for other effects. Through Image Processing, essential information can be extracted from digital images. It is an important area of research in computer science. The techniques involved in image processing include transformation, classification, pattern recognition, filtering, image restoration and various other processes and techniques.
Main purpose of Image Processing
Following are the main purposes of image processing:
- Image Restoration
- Image Retrieval
- Pattern Measurement
- Image Recognition
Applications of Image Processing
Following are the main applications of Image Processing:
- UV Imaging, Gamma Ray Imaging and CT scan in medical field
- Transmission and encoding
- Robot Vision
- Color Processing
- Pattern Recognition
- Video Processing
Bioinformatics is a field that uses various computational methods and software tools to analyze the biological data. In simple words, bioinformatics is the field that uses computer programming for biological studies. This field is a combination of computer science, biology, statistics, and mathematics. It uses image and signal processing techniques to extract useful information from a large amount of data. Following are the main applications of bioinformatics:
- It helps in observing mutations in the field of genetics
- It plays an important role in text mining and organization of biological data
- It helps to study the various aspects of genes like protein expression and regulation
- Genetic data can be compared using bioinformatics which will help in understanding molecular biology
- Simulation and modeling of DNA, RNA, and proteins can be done using bioinformatics tools
Quantum Computing is a computing technique in which computers known as quantum computers use the laws of quantum mechanics for processing information. Quantum Computers are different from digital electronic computers in the sense that these computers use quantum bits known as qubits for processing. A lot of experiments are being conducted to build a powerful quantum computer. Once developed, these computers will be able to solve complex computational problems which cannot be solved by classical computers.
Quantum Computers work on quantum algorithms like Simon’s algorithm to solve problems. Quantum Computing finds its application in the following areas:
- Artificial Intelligence