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In this paper we propose an efficient and secure elliptic curve scalar multiplication algorithm over odd prime fields. For this purpose, we propose an explicit algorithm for short addition-subtraction chain method which utilizes a 2's Complement with window method. We term it as W2CASC. Our proposed scalar multiplication algorithm based on W2CASC algorithm has preceded by 12.7% to 28% 160 bit multiplier...
Modern business goals are often fulfilled with workflows that may cross many organisations and utilise services on a variety of devices and/or supported by different platforms. Current workflows are inherently context-aware. Each context is governed and constrained by its own policies and rules to prevent unauthorised participants from executing sensitive tasks and also to prevent tasks from accessing...
Sufficient test coverage for Software Agents that operate in an open and dynamic environment is unlikely to be achieved during the agents' development. Especially when agents exhibit self properties and are constantly adapting to changes in their environment it is important to limit their autonomy to ensure that their behaviour lies within safe boundaries. To increase the trust in the agents, once...
Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and infrastructures. In this paper, we effectively introduced intrusion detection system by using Principal Component Analysis (PCA) with Support Vector Machines (SVMs) as an approach to select the optimum feature subset. We verify the effectiveness and the feasibility...
A blind color image steganalyzer is proposed, in which the features are extracted from Contourlet domain. Statistical features of Contourlet coefficients and cooccurrence metrics of subband images are used as features. For evaluating the proposed steganalysis method, some popular steganography methods such as OutGuess, JPHS, Model-based and Jsteg are used with payloads of 10% to 25%. To reduce the...
The success of any statistical steganalysis algorithm depends on the choice of features extracted and the classifier employed. This paper proposes steganalysis using random forests (SURF) employing HCS (Huffman Code Statistics) features and FR Index (ratio of File size to Resolution). The proposed algorithm is validated over an image database of over 30,000 images spanning various sizes, resolutions,...
Signature is a popular method of seeking approval and authentication between various parties in many transaction applications. Signature pattern recognition is done by processing a set of data that consists of (x, y) coordinates, representing online signature. Particle Swarm Optimisation technique is used to find and analyse the baseline feature that exists within the signature. Signatures were taken...
Stemming is a fundamental step in processing textual data preceding the tasks of text mining, Information Retrieval (IR), and natural language processing (NLP). The common goal of stemming is to standardize words by reducing a word to its base (root or stem), thus can be also considered a feature reduction technique. This paper aims at presenting a new dictionary free, content-based Arabic stemmer...
Many existing grid authorization systems adopt an inefficient structure of storing security policies for the available resources. That leads to huge repetitions in checking security rules. One of the efficient mechanisms that handle these repetitions is the Hierarchical Clustering Mechanism (HCM) [1]. HCM reduces the redundancy in checking security rules compared to the Brute Force Approach as well...
Fuzzy C-means (FCM) and Rough K-means (RKM) algorithms are two popular soft clustering algorithms that allow for overlapping clusters. The overlapping clusters can be useful in applications where restrictions imposed by crisp clustering that force assignment of every object to a unique cluster may not be practical. Likewise RKM and FCM, interval set representation of clusters would also generate overlapping...
In this paper, we apply classification system denoted Belief Rough Set Classifier (BRSC) based on the hybridization of belief functions and rough sets to learn decision rules from uncertain data consisting of web usage. The uncertainty appears only in decision attributes and is handled by the Transferable Belief Model (TBM), one interpretation of the belief function theory. The web usage mining dataset...
Non-negative matrix factorization is an important method helpful in the analysis of high dimensional datasets. It has a number of applications including pattern recognition, data clustering, information retrieval or computer security. One its significant drawback lies in its computational complexity. In this paper, we introduce a new method allowing fast approximate transformation from input space...
Recent library digitization projects attempt to provide large collections of printed material from varying sources in a searchable format. The scanned documents are typically processed using Optical Character Recognition (OCR), which typically introduces errors in the text. This paper proposes a technique for correction of OCR degraded text that is independent of character-level OCR errors, and hence...
A system of Multiple Neural Networks has been proposed to solve the face recognition problem. Our idea is that a set of expert networks specialized to recognize specific parts of face are better than a single network. This is because a single network could no longer be able to correctly recognize the subject when some characteristics partially change. For this purpose we assume that each network has...
Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts, nevertheless, there are some disadvantages. The main problem is how to interpret the out-of-control signal of a multivariate chart. The MEWMA quality control chart is a very powerful scheme to detect small shifts in the mean vector. There are no previous...
This article presents two classifiers based on machine learning methods, aiming to detect physiologic anomalies considering Poincaré plots of heart rate variability. It was developed a preprocessing procedure to encoding the plots, based on the Cellular Features Extraction Method. Simulation of different classifiers, artificial neural networks and support vector machine, has been performed and the...
This paper discusses the application of two unsupervised methods in classifying type of soils. Soils that are suitable for agricultural activities can be classified into four classes which are hill soil, organic soil, alteration soil and alluvium soil. In addition, no specific support system is able to classify the type of soil and retrieve the information for location and suitable plants for local...
Research in learning and planning in real-time strategy (RTS) games is very interesting in several industries such as military industry, robotics, and most importantly game industry. A recent published work on online case-based planning in RTS Games does not include the capability of online learning from experience, so the knowledge certainty remains constant, which leads to inefficient decisions...
One of the most important features of fuzzy set theory is its potential for the modeling of natural language expressions. Most works done on this topic focus on some parts of natural language, mostly those that correspond to the so-called “evaluating linguistic expressions”. We build constraints for the mathematical substitutes of these expressions to mark characteristic limits on an ordered scale...
In group decision making problems is common the necessity of achieving a consensus before making a decision. Many consensus reaching processes have been introduced in the literature but not many intelligent systems have finally been implemented to deal with such processes. In this contribution an initial prototype of a consensus support system supported on a multi-agent paradigm is presented, showing...
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