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Subsethood, which is to measure the degree of set inclusion relation, is predominant in fuzzy set theory. This paper introduces some basic concepts of spatial granules, coarse-fine relation, and operations like meet, join, quotient meet and quotient join. All the atomic granules can be hierarchized by set-inclusion relation and all the granules can be hierarchized by coarse-fine relation. Viewing an information system from the micro and the macro perspectives, we can get a micro knowledge space and a micro knowledge space, from which a rough set model and a spatial rough granule model are respectively obtained. The classical rough set model is the special case of the rough set model induced from the micro knowledge space, while the spatial rough granule model will be play a pivotal role in the problem-solving of structures. We discuss twelve axioms of monotone increasing subsethood and twelve corresponding axioms of monotone decreasing supsethood, and generalize subsethood and supsethood to conditional granularity and conditional fineness respectively. We develop five conditional granularity measures and five conditional fineness measures and prove that each conditional granularity or fineness measure satisfies its corresponding twelve axioms although its subsethood or supsethood measure only hold one of the two boundary conditions. We further define five conditional granularity entropies and five conditional fineness entropies respectively, and each entropy only satisfies part of the boundary conditions but all the ten monotone conditions.

This research investigates the appropriateness of Information-Theoretic-Based (ITB) metrics compliant with finite sense stationarity (FSS) and derived from the Variance Fractal Dimension Trajectory (VFDT), to augment network security against traffic anomalies. From the distinct and vast cyberattacks (infection, exploitation, probing, deception, cracking, concurrency, and unknown) types, this research focuses in those stemming from concurrency and specifically in Distributed Denial-of-Service (DDoS) cyberattacks. In this research, the design and application of robust methodologies and metrics to achieve powerful descriptors is pursued. The strength of ITB metrics, applied in alternate research areas like steganography, is a robust justification for this study. The usage of ITB metrics, rooted in multi- and polyscale analysis, for detecting network disruptions is novel in the network security area. This thesis introduces a novel multiscale analysis methodology, multiscalors, which permits the usage of arbitrary operators and transforms to be functional in the multiscale domain for inspecting complex signals. Multiscalors provide an analysis depth and insights into the signals that exceeds by far what other types of monoscale based analysis offer. Multiscale-based metrics have been scarcely utilized in the cybersecurity ecosystem. This thesis also showcases specific applications of metrics and methodologies powered by multiscale analysis for DDoS detection. The methodology presented formulates robust features, based on multi- and polyscale analysis, and successfully classifies DDoS disruptions. Such methodology integrates knowledge from: (i) Data acquisition, by verifying DDoS instances and deriving complementary data from them; (ii) design and implementation of ITB metrics, based on multiscalors operators for analysis; (iii) feature extraction, by applying such metrics to the PREDICT datasets, (iv) preparation of feature vectors that are highly representative of the I

This research investigates the appropriateness of Information-Theoretic-Based (ITB) metrics compliant with finite sense stationarity (FSS) and derived from the Variance Fractal Dimension Trajectory (VFDT), to augment network security against traffic anomalies. From the distinct and vast cyberattacks (infection, exploitation, probing, deception, cracking, concurrency, and unknown) types, this research focuses in those stemming from concurrency and specifically in Distributed Denial-of-Service (DDoS) cyberattacks. In this research, the design and application of robust methodologies and metrics to achieve powerful descriptors is pursued. The strength of ITB metrics, applied in alternate research areas like steganography, is a robust justification for this study. The usage of ITB metrics, rooted in multi- and polyscale analysis, for detecting network disruptions is novel in the network security area. This thesis introduces a novel multiscale analysis methodology, multiscalors, which permits the usage of arbitrary operators and transforms to be functional in the multiscale domain for inspecting complex signals. Multiscalors provide an analysis depth and insights into the signals that exceeds by far what other types of monoscale based analysis offer. Multiscale-based metrics have been scarcely utilized in the cybersecurity ecosystem. This thesis also showcases specific applications of metrics and methodologies powered by multiscale analysis for DDoS detection. The methodology presented formulates robust features, based on multi- and polyscale analysis, and successfully classifies DDoS disruptions. Such methodology integrates knowledge from: (i) Data acquisition, by verifying DDoS instances and deriving complementary data from them; (ii) design and implementation of ITB metrics, based on multiscalors operators for analysis; (iii) feature extraction, by applying such metrics to the PREDICT datasets, (iv) preparation of feature vectors that are highly representative of the I

Text classification is a process of classifying documents into predefined categories through different classifiers learned from labelled or unlabelled training samples. Many researchers who work on binary text classification attempt to find a more effective way to separate relevant texts from a large data set. However, current text classifiers cannot unambiguously describe the decision boundary between positive and negative objects because of uncertainties caused by text feature selection and the knowledge learning process. This paper proposes a three-way decision model for dealing with the uncertain boundary to improve the binary text classification performance based on the rough set techniques and centroid solution.

Nigeria is a developing nation in West Africa and the manufacturing management in this nation in the past twenty years has not been well documented. This paper sets out the research design for investigating Nigeria manufacturing management. The project is a combination of 'explanatory and exploratory researches' because it strives to explore the issues related to the Nigerian manufacturing organizations with the help of the existing research studies as well as statistical survey work.

A common problem found in statistics, signal processing, data analysis and image processing research is the estimation of mutual information, which tends to be difficult. The aim of this survey is threefold: an introduction for those new to the field, an overview for those working in the field and a reference for those searching for literature on different estimation methods. In this paper comparison studies on mutual information estimation is considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on the performance of different estimation methods and some future challenges.

In this paper, we have proposed a prototype biometrics authentication system based upon fingerprint recognition. The major functional blocks of the proposed authentication system are presented in details. A simple but effective algorithm is proposed to detect and remove false minutia, which is able to considerably improve the system performance. The performance of the developed system is demonstrated to have a high level of accuracy through experimentation.

Cyclic Delay Diversity (CDD) is a simple and efficient space-time diversity technique. It can be used in OFDM and DFT-Spread-OFDM. The traditional CDD has low complexity and system overhead, however, also has some limitations on performance and is sensitive to propagation environment. In this paper, two improved CDD scheme including open-loop and close-loop strategies for uplink and downlink of EUTRA LTE are introduced. Compared with the conventional method, the new schemes can obtain higher performance and less sensitivity to environment.

In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system.

In this paper, we propose a novel model for web-based database systems based on the multicast and anycast' protocols. In the model, we design a middleware, castway, which locates between the database server and the Web server. Every castway in a distributed system operates as a multicast node and an anycast node independently, respectively. The proposed mechanism can balance the workload among the distributed database servers, and offers the "best" server to serve for a query. Three algorithms are employed for the model: the requirement-based probing algorithm for anycast routing, the atomic multicast update algorithm for database synchronization, and the job deviation algorithm for system workload balance. The simulations and experiments show that the proposed model works very well.

Concept learning of text documents can be viewed as the problem of acquiring the definition of a general category of documents. To definite the category of a text document, the Conjunctive of keywords is usually be used. These keywords should be fewer and comprehensible. A naïve method is enumerating all combinations of keywords to extract suitable ones. However, because of the enormous number of keyword combinations, it is impossible to extract the most relevant keywords to describe the categories of documents by enumerating all possible combinations of keywords. Many heuristic methods are proposed, such as GA-base, immune based algorithm. In this work, we introduce pruning power technique and propose a robust enumeration-based concept learning algorithm. Experimental results show that the rules produce by our approach has more comprehensible and simplicity than by other methods.

In this paper an investigation on the utilisation of the degree of dependency and quality of classification measures in the extended variable precision rough sets model is undertaken. The use of (l, u)-graphs enable these measures to aid in the classification of objects to a number of categories for a choice of l and u values and selection of a (l, u)-reduct.