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BicATPlus: An automatic comparative tool for Bi/Clustering of gene expression data obtained using microarrays

In the last few years the gene expression microarray technology has become a central tool in the field of functional genomics in which the expression levels of thousands of genes in a biological sample are determined in a single experiment. Several clustering and biclustering methods have been introduced to analyze the gene expression data by identifying the similar patterns and grouping genes into subsets that share biological significance. However, it is not clear how the different methods compare with each other with respect to the biological relevance of the biclusters and clusters as well

Artificial Intelligence
Healthcare

Maximum likelihood estimator for signal intensity in STEAM-based MR imaging techniques

Stimulated echo acquisition mode (STEAM) is a generic imaging technique that lies at the core of many magnetic resonance imaging (MRI) techniques such MRI tagging, displacement encoded MRI, black-blood cardiac imaging. Nevertheless, tissue deformation causes frequency shift of the MR signal and leads to severe signal attenuation. In this work, a maximum likelihood estimator for the signal amplitude is proposed and used to correct the image artifacts. Numerical simulation and real MR data are used to test and validate the proposed method. © 2011 IEEE.

Artificial Intelligence
Healthcare

Fuzzy gaussian classifier for combining multiple learners

In the field of pattern recognition multiple classifier systems based on the combination of outputs from different classifiers have been proposed as a method of high performance classification systems. The objective of this work is to develop a fuzzy Gaussian classifier for combining multiple learners, we use a fuzzy Gaussian model to combine the outputs obtained from K-nearest neighbor classifier (KNN), Fuzzy K-nearest neighbor classifier and Multi-layer Perceptron (MLP) and then compare the results with Fuzzy Integral, Decision Templates, Weighted Majority, Majority Naïve Bayes, Maximum

Artificial Intelligence
Circuit Theory and Applications

An innovative approach for the wormhole attack detection and prevention in wireless ad hoc networks

Due to their diverse applications, ad hoc networks are appealing for use in many domains. However, their features of open medium, absence of infrastructure, dynamic changing network topology, cooperative algorithms, lack of centralized monitoring and management point, resource constraints and lack of a clear line of defense, they are vulnerable to many attacks. Therefore, there is a major concern about their security. Amongst attacks we are particularly interested in a severe attack called the wormhole attack. In this paper, we propose a scheme for the wormhole attack detection and prevention

Artificial Intelligence

Immunizing the SAODV protocol against routing information disclosure

Secure routing protocols presents one of the most important challenges of Mobile Ad hoc Networks (MANETs). This is due to their special characteristics such as shared wireless medium, stringent resource constraints and highly dynamic network topology. This paper presents a solution to the problem of routing information disclosure and traffic analysis in a new way that doesn't require exchanging a group secret key between one-hop neighbors. In addition, the proposed solution maintains the routing data integrity and node authentication features. Furthermore, the solution provides a new method

Artificial Intelligence

Organizational risk assessment based on attacks repetition

Risk assessment is a very critical and important process to protect the organization assets and reputation against security threats and risks. It provides a clear picture of the current threats that the organization is facing and helps the top management to take the right decision to eliminate or mitigate those risks. Usually if the vulnerability is exploited, the same attack may be happen twice or more in a different time periods because the vulnerability has been exploited and not mitigated. In this paper, we are illustrating our observation of the relation between the risk value and the

Artificial Intelligence

A novel proximity based trust model for opportunistic networks

Trust should be earned. This is a famous quote that we use everyday implicitly or explicitly. Trust often is an inherent characteristic of our daily life, but in the digital community and between devices how can we represent trust? Since our mobile and digital devices became our confidants, we cannot share the information embedded in these devices with other devices without establishing trust. Hence, in this research a proximity based trust model based on Homophily principle is proposed. Earlier social studies have shown that people tend to have similarities with others in close proximity. In

Artificial Intelligence

SAODV and modified SAODV performance comparison

Routing plays a vital role in ad hoc networks and Ad hoc On-demand Distance Vector (AODV) protocol is considered one of the most famous routing protocols in ad hoc networks. Unfortunately it doesn't specify security measures. This has motivated the researchers to design secured version of AODV. However Security always collides with performance. The higher the security level is, the lower the performance level. This paper presents a performance comparison between Secure AODV (SAODV) and Modified SAODV (MSAODV). © 2013 IEEE.

Artificial Intelligence

Lightweight authentication protocol deployment over FlexRay

In-vehicle network security is becoming a major concern for the automotive industry. Although there is significant research done in this area, there is still a significant gap between research and what is actually applied in practice. Controller area network (CAN) gains the most concern of community but little attention is given to FlexRay. Many signs indicate the approaching end of CAN usage and starting with other promising technologies. FlexRay is considered one of the main players in the near future. We believe that migration era is near enough to change our mindset in order to supply

Artificial Intelligence

A Real-Time Social Network- Based Traffic Monitoring Vehicle Tracking System

Social networking has become an essential part of our daily lives. tTe integration of social networking communication model and Internet of Things (IoT) provides the users with a greater advantage than the benefit of using each one alone. This paper presents a real-time traffic monitoring and vehicle tracking for the public or private transportation sectors. The proposed system uses a social network service to provide traffic monitoring for individual users. A fully functional prototype model is developed and presented to demonstrate the system operation and to evaluate its performance. © 2018

Artificial Intelligence