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It is the scientific and practical approach to computation and its applications and the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures (or algorithms) that underlie the acquisition, representation, processing, storage, communication of, and access to, information. An alternative, more succinct definition of computer science is the study of automating algorithmic processes that scale.

In this concept, we are doing projects in the following areas: 

Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity. Big Data has the potential to help companies improve operations and make faster, more intelligent decisions. The data is collected from a number of sources including emails, mobile devices, applications, databases, servers and other means. This data, when captured, formatted, manipulated, stored and then analyzed, can help a company to gain useful insight to increase revenues, get or retain customers and improve operations.

MATT001-Text Sentiment Analysis Based on Multi-Granularity Joint Solution(IEEE-2018) 

MATT002-On Power Law Growth of Social Network(IEEE-2018)

MATT003-Fast and Parallel Trust Computing Scheme Based on Big Data Analysis For Collaboration Cloud Service(IEEE-2018)

MATT004-Effective Variational Data Assimilation in Air-Pollution Prediction(IEEE-2018)

 

The domain name net is a generic top-level domain (gTLD) used in the Domain Name System of the Internet. The name is derived from the word network, indicating it was originally intended for organizations involved in networking technologies, such as Internet service providers and other infrastructure companies. However, restrictions were never enforced and the domain is now a general purpose namespace. It is still popular with network operators and the advertising sector and it is often treated as an alternative to com.

MATT001-Blockchain For Large-Scale Internet of Things Data Storage and Protection(IEEE 2018)

MATT002-Blockchain-based Traceability in Agri-Food Supply Chain Management: A Practical Implementation(IEEE 2018)

MATT003-Proof-of-Property – A Lightweight and Scalable Blockchain protocal (IEEE 2018)

MATT004-Untangling Blockchain: A Data Processing View of Blockchain Systems(IEEE2017)

MATT005-Blockchain as a Service for IoT(IEEE 2017)

MATT006-Blockchain Based Security Framework for IoT Implementations(IEEE 2017)

MATT007-Application of Blockchain Technology in Smart City Infrastructure(IEEE 2017)

 
 
 
 

 

 
 
 

Network security consists of the policies and practices adopted to prevent and monitor unauthorized access, misuse, modification, or denial of a computer network and network-accessible resources. Network security involves the authorization of access to data in a network, which is controlled by the network administrator. Users choose or are assigned an ID and password or other authenticating information that allows them access to information and programs within their authority. Network security covers a variety of computer networks, both public and private, that are used in everyday jobs; conducting transactions and communications among businesses, government agencies and individuals. Networks can be private, such as within a company, and others which might be open to public access.

MATT003-Sequence Covering for Efficient Host-Based Intrusion Detection(IEEE 2018)

MATT004-The Impact of Head of Line Blocking in Highly Dynamic WLANs (IEEE 2018)

MATT006-Trust-based Collaborative Privacy Management in Online Social Networks (IEEE 2018)

MATT007-Associative Search through Formal Concept Analysis in Criminal Intelligence Analysis(IEEE 2018)

MATT008-A Hybrid Approach for Detecting Automated Spammers in Twitter(IEEE 2018)

MATT009-Semi Supervised Spam Detection in Twitter Stream(IEEE 2018)

MATT010-CoDetect Financial Fraud Detection With Anomaly Feature Detection(IEEE 2018)

 
 

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

 

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