Cognitive Social Exchange

This book chapter presents the role of telecommunications network in voice and data transmission. Switching, signaling and transmission are the technologies used to carry out this process. In landline call establishment, calls are routed from subscriber handset to a remote switching unit (RSU), a main switching unit (MSU), and to the internet protocol trunk automated exchange (IPTAX). Then, it is directed to the National Internet Backbone (NIB). On the receiver side, the IPTAX receives this signal from the NIB and directs to it to the MSU and RSU, respectively. The receiver side RSU delivers the information to the destination subscriber. In order to transmit the information from one place to other, it undergoes various process like modulation, demodulation, line coding, equalization, error control, bit synchronization and multiplexing, digitizing an analog message signal, and compression. This chapter also discusses the various services provided by BSNL and agencies governing the internet. Finally, it focuses on the National Internet Backbone facility of BSNL, India.

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.

Semantic-Based Indexing Approaches for Medical Document Clustering Using Cognitive Search

Biomedical literature is the primary repository of biomedical knowledge in which PubMed is the most absolute database for collecting, organizing and analyzing textual knowledge. The high dimensionality of the natural language text makes the text data quite noisy and sparse in the vector space. Hence, the data preprocessing and feature selection are important processes for the text processing issues. Ontologies select the meaningful terms semantically associated with the concepts from a document to reduce the dimensionality of the original text. In this chapter, semantic-based indexing approaches are proposed with cognitive search which makes use of domain ontology to extract relevant information from big and diverse data sets for users.

Data Mining for Social Network Analysis Using a CLIQUE Algorithm

Many different areas of computer science have been developed for many years in the world. Data mining is one of the fields which many algorithms, methods, and models have been built and applied to many commercial applications and research successfully. Many social networks have been invested and developed in the strongest way for the recent years in the world because they have had many big benefits as follows: they have been used by lots of users in the world and they have been applied to many business fields successfully. Thus, a lot of different techniques for the social networks have been generated. Unsurprisingly, the social network analysis is crucial at the present time in the world. To support this process, in this book chapter we have presented many simple concepts about data mining and social networking. In addition, we have also displayed a novel model of the data mining for the social network analysis using a CLIQUE algorithm successfully.

Cognitive Social Mining Analysis Using Data Mining Techniques

Social media are based on computer-mediated technologies that smooth the progress of the creation and distribution of information, thoughts, idea, career benefits and other forms of expression via implicit communities and networks. The social network analysis (SNA) has emerged with the increasing popularity of social networking services like Facebook, Twitter, etc. Therefore, information about group cohesion, contribution in activities, and associations among subjects can be obtained from the analysis of the blogs. The analysis of the blogs required well-known knowledge discovery tools to help the administrator to discover participant collaborative activities or patterns with inferences to improve the learning and sharing process. Therefore, the goal of this chapter is to provide the data mining tools for information retrieval, statistical modelling and machine learning to employ data pre-processing, data analysis, and data interpretation processes to support the use of social network analysis (SNA) to improve the collaborative activities for better performance.

Efficient Space Communication and Management (SCOaM) Using Cognitive Radio Networks Based on Deep Learning Techniques

The future of space communications has evolved towards being cognitive in order to improve energy and spectrum efficiency. Nowadays, near-Earth space satellites for weather, civil defense, and public commercial sectors are rapidly increasing, thereby resulting in congestion. Cognitive digital radio is a form of dynamic wireless communication in which a transceiver can intelligently detect the parts of communication channels that are currently not in use and instantly move into vacant channels while avoiding occupied ones. A challenge for communication between satellites and ground terminals involves calibrating both the time and frequency channels during rapid relative movement. A more dynamic and highly-precise algorithm for enhancing communication between satellites and base station terminals such as deep learning in cognitive radios is proposed that enables significant degree of automation in the space communication networks where spectrum interference is a key issue.

The Potential of Cognitive Social Mining Applications in Data Analytics for Protecting Social Media Users for National and International Security

Using social media has become an integral part of life for people of different demographics for information exchange, searching, maintaining contact networks, marketing, locating job opportunities etc. Social networking is used for education, research, business, advertisements and entertainment. Social media platforms are prone to cybercrime, which is a threat not only for the individual user but for national and international security according to the National Security Council Report. With the advent of big data storage and analytics abilities, decision making is a potential problem and requires smarter machines to organize data faster, make better sense of it, discover insights, learn, adapt and improve over time without direct programming. Cognitive computing makes it easy to unveil the patterns hidden in unstructured data and make more informed decision on consequential matters. In this chapter is a discussion of the challenges and opportunities in social mining services and the applications of cognitive technology towards national security.

Machine Learning Techniques for Healthcare Applications

Autism spectrum disorder (ASD) is one of the common disorders in brain. Early detection of ASD improves the overall mental health, which is very important for the future of the child. ASD affects social coordination, emotions, and motor activity of an individual. This is due to the difficulties in getting self-evaluation results and expressive experiences. In the first case study in this chapter, an efficient method to automatically detect the expressive states of individuals with the help of physiological signals is explored. In the second case study of the chapter, the authors explore breast cancer prediction using SMO and IBK. Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight. In this proposed system, the tumor is the feature that is used to identify the breast cancer presence in women. Tumors are basically of two types (i.e., benign or malignant). In order to provide appropriate treatment to the patients, symptoms must be studied properly, and an automatic prediction system is required that will classify the tumor into benign or malignant using SMO and IBK.

Cognitive Mining for Exploratory Data Analytics Using Clustering Based on Particle Swarm Optimization

This chapter examines the exploratory data analytics that require statistical techniques on data sets which are in the form of object-attribute-time format and referred to as three-dimensional data sets. It is very difficult to cluster and hence a subspace clustering method is used. Existing algorithms like CATSeeker are not actionable and its 3D structure complicates the clustering process, hence they are inadequate to solve this clustering problem. To cluster these three-dimensional data sets, a new centroid-based concept is introduced in the proposed system called clustering using particle swarm optimization (CPSO). This CPSO framework can be applied to financial and stock domain datasets through the unique combination of (1) singular value decomposition (SVD), (2) particle swarm optimization (PSO), and (3) 3D frequent item set mining which results in efficient performance. CPSO framework prunes the entire search space to identify the significant subspaces and clusters the datasets based on optimal centroid value.

A Detailed Study on Security Concerns of VANET and Cognitive Radio VANETs

Wireless ad hoc networks are dynamic networks in which nodes can move freely in the network. A new type of Vehicular Ad Hoc Network (VANET) that allows smart transport system to provide road security and reduces traffic jams through automobile-to-automobile and automobile-to-roadside communication. In this, vehicles rely on the integrity of received data for deciding when to present alerts to drivers. Because of wireless network the VANET messages are vulnerable to many attacks and the security concerns are also major issues. So, with respect to these methods, this article will discuss the Denial of Service (DoS) attack, masquerading, and their vulnerabilities. Also, it classifies the securities and their prevention mechanisms in overcoming these security issues in VANET and Cognitive Radio VANET perspectives.