Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Assessment Methods of Cognitive Ability of Human Brains for Inborn Intelligence Potential Using Pattern Recognitions

This research aims to examine the scientific study related to fingerprint patterns and brains lobes. Generally, this method is used to find and develop the inborn potential and personality especially of children. Every person is having inborn potential and personality, which will help us to analyze strength and weakness. The present work is based only on the analysis and used as a reference for scientific research in the field of Galtian and statistical study conducted based on the fingerprint processing. Human brain is divided into two parts, left hemispheres and right hemispheres. Fingers of right hand represent the functions of left brain and fingers of left hand represent the functions of right brain. Human brain is divided into 10 lobes and each lobe is related with each finger. Each lobe represents different intelligences. A detailed analysis of the fingerprint would help the researchers to find the inborn talents. It will provide them the most appropriate learning habits from young age and improve learning ability effectively. The vital factor of an individual’s intelligence is determined by neural network connection of brain cells. Cognitive science is the scientific study that will help you to know about yourself

Player Modeling

Player modeling is the study of computational models of players in games. This includes the detection, modeling, prediction and expression of human player characteristics which are manifested through cognitive, affective and behavioral patterns. This chapter introduces a holistic view of player modeling and provides a high level taxonomy and discussion of the key components of a player\u27s model. The discussion focuses on a taxonomy of approaches for constructing a player model, the available types of data for the model\u27s input and a proposed classification for the model\u27s output. The chapter provides also a brief overview of some promising applications and a discussion of the key challenges player modeling is currently facing which are linked to the input, the output and the computational model

Integrated smoothed location model and data reduction approaches for multi variables classification

Smoothed Location Model is a classification rule that deals with mixture of continuous variables and binary variables simultaneously. This rule discriminates groups in a parametric form using conditional distribution of the continuous variables given each pattern of the binary variables. To conduct a practical classification analysis, the objects must first be sorted into the cells of a multinomial table generated from the binary variables. Then, the parameters in each cell will be estimated using the sorted objects. However, in many situations, the estimated parameters are poor if the number of binary is large relative to the size of sample. Large binary variables will create too many multinomial cells which are empty, leading to high sparsity problem and finally give exceedingly poor performance for the constructed rule. In the worst case scenario, the rule cannot be constructed. To overcome such shortcomings, this study proposes new strategies to extract adequate variables that contribute to optimum performance of the rule. Combinations of two extraction techniques are introduced, namely 2PCA and PCA+MCA with new cutpoints of eigenvalue and total variance explained, to determine adequate extracted variables which lead to minimum misclassification rate. The outcomes from these extraction techniques are used to construct the smoothed location models, which then produce two new approaches of classification called 2PCALM and 2DLM. Numerical evidence from simulation studies demonstrates that the computed misclassification rate indicates no significant difference between the extraction techniques in normal and non-normal data. Nevertheless, both proposed approaches are slightly affected for non-normal data and severely affected for highly overlapping groups. Investigations on some real data sets show that the two approaches are competitive with, and better than other existing classification methods. The overall findings reveal that both proposed approaches can be considered as improvement to the location model, and alternatives to other classification methods particularly in handling mixed variables with large binary size

Biometric fingerprint identification

Projekt sa zaoberá spracovaním a porovnaním otlačkov prstov. Preberá obecné princípy biometrie a rôzne metódy analýzy otlačkov prstov. Navrhuje vlastné riešenie problému formou adaptívnych maskových operátorov na detekciu markantov a štatistické spracovanie výsledkov.The aim of this project is an automatic analysis of fingerprint images. Basic principles of biometrics and commonly used methods of fingerprint analysis are studied. An algorithm is proposed, using adaptive mask operators for minutiae detection, followed by a statistical evaluation of the achieved results.

Using Gaze for Behavioural Biometrics

A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the unique- ness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The rel- evant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field

Securing Cloud Storage by Transparent Biometric Cryptography

With the capability of storing huge volumes of data over the Internet, cloud storage has become a popular and desirable service for individuals and enterprises. The security issues, nevertheless, have been the intense debate within the cloud community. Significant attacks can be taken place, the most common being guessing the (poor) passwords. Given weaknesses with verification credentials, malicious attacks have happened across a variety of well-known storage services (i.e. Dropbox and Google Drive) – resulting in loss the privacy and confidentiality of files. Whilst today's use of third-party cryptographic applications can independently encrypt data, it arguably places a significant burden upon the user in terms of manually ciphering/deciphering each file and administering numerous keys in addition to the login password. The field of biometric cryptography applies biometric modalities within cryptography to produce robust bio-crypto keys without having to remember them. There are, nonetheless, still specific flaws associated with the security of the established bio-crypto key and its usability. Users currently should present their biometric modalities intrusively each time a file needs to be encrypted/decrypted – thus leading to cumbersomeness and inconvenience while throughout usage. Transparent biometrics seeks to eliminate the explicit interaction for verification and thereby remove the user inconvenience. However, the application of transparent biometric within bio-cryptography can increase the variability of the biometric sample leading to further challenges on reproducing the bio-crypto key. An innovative bio-cryptographic approach is developed to non-intrusively encrypt/decrypt data by a bio-crypto key established from transparent biometrics on the fly without storing it somewhere using a backpropagation neural network. This approach seeks to handle the shortcomings of the password login, and concurrently removes the usability issues of the third-party cryptographic applications – thus enabling a more secure and usable user-oriented level of encryption to reinforce the security controls within cloud-based storage. The challenge represents the ability of the innovative bio-cryptographic approach to generate a reproducible bio-crypto key by selective transparent biometric modalities including fingerprint, face and keystrokes which are inherently noisier than their traditional counterparts. Accordingly, sets of experiments using functional and practical datasets reflecting a transparent and unconstrained sample collection are conducted to determine the reliability of creating a non-intrusive and repeatable bio-crypto key of a 256-bit length. With numerous samples being acquired in a non-intrusive fashion, the system would be spontaneously able to capture 6 samples within minute window of time. There is a possibility then to trade-off the false rejection against the false acceptance to tackle the high error, as long as the correct key can be generated via at least one successful sample. As such, the experiments demonstrate that a correct key can be generated to the genuine user once a minute and the average FAR was 0.9%, 0.06%, and 0.06% for fingerprint, face, and keystrokes respectively. For further reinforcing the effectiveness of the key generation approach, other sets of experiments are also implemented to determine what impact the multibiometric approach would have upon the performance at the feature phase versus the matching phase. Holistically, the multibiometric key generation approach demonstrates the superiority in generating the bio-crypto key of a 256-bit in comparison with the single biometric approach. In particular, the feature-level fusion outperforms the matching-level fusion at producing the valid correct key with limited illegitimacy attempts in compromising it – 0.02% FAR rate overall. Accordingly, the thesis proposes an innovative bio-cryptosystem architecture by which cloud-independent encryption is provided to protect the users' personal data in a more reliable and usable fashion using non-intrusive multimodal biometrics.Higher Committee of Education Development in Iraq (HCED