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Muhammad, M.A. Device-type Profiling using Packet Inter-Arrival Time for Network Access Control 2021 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded  phdthesis URL 
BibTeX:
@phdthesis{Muhammad2016,
  author = {Muhammad, Musa Abubakar},
  title = {Device-type Profiling using Packet Inter-Arrival Time for Network Access Control},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded},
  year = {2021},
  note = {2016 - 2021},
  url = {https://dora.dmu.ac.uk/handle/2086/21278}
}
Wang, R. Motivation Modelling and Computation for Personalised Learning of People with Dyslexia 2020 School: Faculty of Technology, De Montfort University - [second supervisor] Funding Source: DMU University Fees Only Scholarship  phdthesis URL 
BibTeX:
@phdthesis{Wang2015,
  author = {Wang, Ruijie},
  title = {Motivation Modelling and Computation for Personalised Learning of People with Dyslexia},
  school = {Faculty of Technology, De Montfort University - [second supervisor] Funding Source: DMU University Fees Only Scholarship},
  year = {2020},
  note = {2015-2020},
  url = {https://dora.dmu.ac.uk/handle/2086/20247}
}
Alotaibi, A.G. A Framework for University Admission in Saudi Arabia (UASA): Current and Potential Position 2017 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded with employer support  phdthesis URL 
BibTeX:
@phdthesis{Alotaibi2010,
  author = {Alotaibi, Awad Gazzai},
  title = {A Framework for University Admission in Saudi Arabia (UASA): Current and Potential Position},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded with employer support},
  year = {2017},
  url = {https://dora.dmu.ac.uk/handle/2086/20256}
}
Lim, Y.M. Detecting and Modelling Stress Levels in E-Learning Environment Users 2017 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship  phdthesis URL 
BibTeX:
@phdthesis{Lim2010,
  author = {Lim, Yee Mei},
  title = {Detecting and Modelling Stress Levels in E-Learning Environment Users},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship},
  year = {2017},
  url = {http://hdl.handle.net/2086/14210}
}
Al-Azawi, R.K.A. Agent Oriented Software Engineering (AOSE) Approach to Game Development Methodology 2015 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded with employer support  phdthesis URL 
BibTeX:
@phdthesis{Al-Azawi2011,
  author = {Al-Azawi, Rula Khalid Abbas},
  title = {Agent Oriented Software Engineering (AOSE) Approach to Game Development Methodology},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded with employer support},
  year = {2015},
  url = {http://hdl.handle.net/2086/11120}
}
Abdelhamid, N. Deriving Classifiers with Single and Multi-Label Rules using New Associative Classification Methods 2014 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded  phdthesis URL 
Abstract: Associative Classification (AC) in data mining is a rule based approach that uses association rule techniques to construct accurate classification systems (classifiers).The majority of existing AC algorithms extract one class per rule and ignore other class labels even when they have large data representation.Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the exponential growth of rules in AC has been investigated in this thesis aiming to minimise the number of candidate rules, and therefore reducing the classifier size so end-user can easily exploit and maintain it. Moreover, an investigation to both rule ranking and test data classification steps have been conducted in order to improve the performance of AC algorithms in regards to predictive accuracy. Overall, this thesis investigates different problems related to AC not limited to the ones listed above, and the results are new AC algorithms that devise single and multi-label rules from different applications data sets, together with comprehensive experimental results. To be exact, the first algorithm proposed named Multi-class Associative Classifier (MAC): This algorithm derives classifiers where each rule is connected with a single class from a training data set. MAC enhanced the rule discovery, rule ranking, rule filtering and classification of test data in AC. The second algorithm proposed is called Multi-label Classifier based Associative Classification (MCAC) that adds on MAC a novel rule discovery method which discovers multi-label rules from single label data without learning from parts of the training data set. These rules denote vital information ignored by most current AC algorithms which benefit both the end-user and the classifier's predictive accuracy. Lastly, the vital problem related to web threats called "website phishing detection" was deeply investigated where a technical solution based on AC has been introduced in Chapter 6. Particularly, we were able to detect new type of knowledge and enhance the detection rate with respect to error rate using our proposed algorithms and against a large collected phishing data set. Thorough experimental tests utilising large numbers of University of California Irvine (UCI) data sets and a variety of real application data collections related to website classification and trainer timetabling problems reveal that MAC and MCAC generates better quality classifiers if compared with other AC and rule based algorithms with respect to various evaluation measures, i.e. error rate, Label-Weight, Any-Label, number of rules, etc. This is mainly due to the different improvements related to rule discovery, rule filtering, rule sorting, classification step, and more importantly the new type of knowledge associated with the proposed algorithms. Most chapters in this thesis have been disseminated or under review in journals and refereed conference proceedings.
BibTeX:
@phdthesis{Abdelhamid2014,
  author = {Abdelhamid, Neda},
  title = {Deriving Classifiers with Single and Multi-Label Rules using New Associative Classification Methods},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded},
  year = {2014},
  url = {https://www.dora.dmu.ac.uk/handle/2086/10120}
}
Nikolaev, P. Policy-based Planning for Student Mobility Support in e-Learning Systems 2013 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Bi-literal agreement scheme  phdthesis URL 
BibTeX:
@phdthesis{Nikolaev2008-2013,
  author = {Nikolaev, Pavel},
  title = {Policy-based Planning for Student Mobility Support in e-Learning Systems},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Bi-literal agreement scheme},
  year = {2013},
  url = {http://hdl.handle.net/2086/10132}
}
Alruily, M. Using Text Mining to Identify Crime Patterns from Arabic Corpus 2012 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship  phdthesis URL 
Abstract: Most text mining techniques have been proposed only for English text, and even here, most research has been conducted on specific texts related to special contexts within the English language, such as politics, medicine and crime. In contrast, although Arabic is a widely spoken language, few mining tools have been developed to process Arabic text, and some Arabic domains have not been studied at all. In fact, Arabic is a language with a very complex morphology because it is highly inflectional l, and therefore, dealing with texts written in Arabic is highly complicated.

This research studies the crime domain in the Arabic language, exploiting unstructured text using text mining techniques. Developing a system for extracting important information from crime reports would be useful for police investigators, for accelerating the investigative process (instead of reading entire reports) as well as for conducting further or wider analyses. We propose the Crime Profiling System (CPS) to extract crime-related information (crime type, crime location and nationality of persons involved in the event), automatically construct dictionaries for the existing information, cluster crime documents based on certain attributes and utilize visualisation techniques to assist in crime data analysis.

The proposed information extraction approach is novel, and it relies on computational linguistic techniques to identify the abovementioned information, i.e. without using predefined dictionaries (e.g. lists of location names) and annotated corpus. The language used in crime reporting is studied to identify patterns of interest using a corpus-based approach. Frequency analysis, collocation analysis and concordance analysis are used to perform the syntactic analysis in order to discover the local grammar.

Moreover, the Self Organising Map (SOM) approach is adopted in order to perform the clustering and visualisation tasks for crime documents based on crime type, location or nationality. This clustering technique is improved because only refined data containing meaningful keywords extracted through the information extraction process are inputted into it, i.e. the data is cleaned by removing noise. As a result, a huge reduction in the quantity of data fed into the SOM is obtained, consequently, saving memory, data loading time and the execution time needed to perform the clustering. Therefore, the computation of the SOM is accelerated. Finally, the quantization error is reduced, which leads to high quality clustering. The outcome of the clustering stage is also visualised and the system is able to provide statistical information in the form of graphs and tables about crimes committed within certain periods of time and within a particular area.
BibTeX:
@phdthesis{ALRUILY2012,
  author = {Alruily, Meshrif},
  title = {Using Text Mining to Identify Crime Patterns from Arabic Corpus},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship},
  year = {2012},
  url = {https://www.dora.dmu.ac.uk/handle/2086/7584}
}
Daoud, M.S.H. An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network 2012 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded  phdthesis URL 
Abstract: One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient mobility prediction technique is particularly needed for these networks. The mobility prediction technique incurs overheads on the transmission process. These overheads affect properties of the cellular communications network such as delay, denial of services, manual filtering and bandwidth. The main goal of this research is to enhance a mobility prediction scheme in cellular communications networks through three phases. Firstly, current mobility prediction techniques will be investigated. Secondly, innovation and examination of new mobility prediction techniques will be based on three hypothesises that are suitable for cellular communications network and mobile user (MU) resources with low computation cost and high prediction success rate without using MU resources in the prediction process. Thirdly, a new mobility prediction scheme will be generated that is based on different levels of mobility prediction. In this thesis, a new mobility prediction scheme for LBSs is proposed. It could be considered as a combination of the cell and routing area (RA) prediction levels. For cell level prediction, most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the New Markov-Based Mobility Prediction (NMMP) and Prediction Location Model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression and insufficient accuracy. In this thesis, Location Prediction based on a Sector Snapshot (LPSS) is introduced, which is based on a Novel Cell Splitting Algorithm (NCPA). This algorithm is implemented in a micro cell in parallel with the new prediction technique. The LPSS technique, compared with two classic prediction techniques and the experimental results, shows the effectiveness and robustness of the new splitting algorithm and prediction technique. In the cell side, the proposed approach reduces the complexity cost and prevents the cell level prediction technique from performing in time slots that are too close. For these reasons, the RA avoids cell-side problems. This research discusses a New Routing Area Displacement Prediction for Location-Based Services (NRADP) which is based on developed Ant Colony Optimization (ACO). The NRADP, compared with Mobility Prediction based on an Ant System (MPAS) and the experimental results, shows the effectiveness, higher prediction rate, reduced search stagnation ratio, and reduced computation cost of the new prediction technique.
BibTeX:
@phdthesis{DAOUD2012,
  author = {Daoud, Mohammad Sharif Hamdan},
  title = {An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded},
  year = {2012},
  url = {https://www.dora.dmu.ac.uk/handle/2086/8697}
}
Blewitt, W.F. Exploration of Emotion Modelling through Fuzzy Logic 2011 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: EPSRC doctoral training program  phdthesis URL 
Abstract: This work outlines a programme of research tasked with the exploration of representing psychologically grounded theories of emotion through fuzzy logic systems. It presents an introduction to the specific goals of the project, followed by an overview of the wider, multi-disciplinary field of emotion representation. Two emotion theories are explored in detail. One, rooted in behaviourism, proposed by J. R. Millenson in 1967; the other, the Geneva Emotion Wheel proposed by K. R. Scherer in 2005. Each of these theories is independently abstracted mathematically, and represented in terms of both type-1 and type-2 fuzzy logic systems. Six potential implementations of these systems are presented. Of these, five are tested within this report. The results of these tests are analysed and discussed in the context of both computational behaviour and psychological analogue. There follows a critical review where the effectiveness of the different implementations and models is considered, informed by both testing results and the psychology upon which they are based. A prototype of one implementation applied to govern the behaviour of an agent in a predator-prey scenario is included. Discussion of this prototype includes examples of how the implementation was practically applied to the environment, and an assessment of the behaviours of the agent in testing. The work concludes with an overview of the thesis, including discussion of the results of the project and future avenues of research related to the completed work. The contributions of the thesis are explicitly outlined: the research of pre-existing, psychologically grounded models of emotional state suitable for computational representation; construction of mathematical representations of two models of emotion, using both type-1 and type-2 fuzzy logic; and, the presentation of five computational implementations of those representations, of which four are explicitly tested, compared and critically reviewed.
BibTeX:
@phdthesis{BLEWITT2011,
  author = {Blewitt, William Frederick},
  title = {Exploration of Emotion Modelling through Fuzzy Logic},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: EPSRC doctoral training program},
  year = {2011},
  url = {https://www.dora.dmu.ac.uk/handle/2086/6443}
}
Al-Obaidy, M.A.H. ENAMS: Energy Optimization Algorithm for Mobile Wireless Sensor Networks using Evolutionary Computation and Swarm Intelligence 2010 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded with employer support  phdthesis URL 
Abstract: Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of the wireless communication and the performance of networking protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis contributes toward the design of a new hybrid optimization algorithm; ENAMS (Energy optimizatioN Algorithm for Mobile Sensor networks) which is based on the Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks. The presented algorithm is suitable for large scale mobile sensor networks and provides a robust and energy- efficient communication mechanism by dividing the sensor-nodes into clusters, where the number of clusters is not predefined and the sensors within each cluster are not necessary to be distributed in the same density. The presented algorithm enables the sensor nodes to move as swarms within the search space while keeping optimum distances between the sensors. To verify the objectives of the proposed algorithm, the LEGO-NXT MIND-STORMS robots are used to act as particles in a moving swarm keeping the optimum distances while tracking each other within the permitted distance range in the search space.
BibTeX:
@phdthesis{AL-OBAIDY2010,
  author = {Al-Obaidy, Mohanned A Hameed},
  title = {ENAMS: Energy Optimization Algorithm for Mobile Wireless Sensor Networks using Evolutionary Computation and Swarm Intelligence},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded with employer support},
  year = {2010},
  url = {https://www.dora.dmu.ac.uk/handle/2086/5187}
}
Alzahrani, S.S. Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE) 2010 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship  phdthesis URL 
Abstract: e-Learning is becoming an influential role as an economic method and a flexible mode of study in the institutions of higher education today which has a presence in an increasing number of college and university courses. e-Learning as system of systems is a dynamic and scalable environment. Within this environment, e-learning is still searching for a permanent, comfortable and serviceable position that is to be controlled, managed, flexible, accessible and continually up-to-date with the wider university structure. As most academic and business institutions and training centres around the world have adopted the e-learning concept and technology in order to create, deliver and manage their learning materials through the web, it has become the focus of investigation. However, management, monitoring and collaboration between these institutions and centres are limited. Existing technologies such as grid, web services and agents are promising better results. In this research a new architecture has been developed and adopted to make the e-learning environment more dynamic and scalable by dividing it into regional data grids which are managed and monitored by agents. Multi-agent technology has been applied to integrate each regional data grid with others in order to produce an architecture which is more scalable, reliable, and efficient. The result we refer to as Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE). Our RDADeLE architecture is an agent-based grid environment which is composed of components such as learners, staff, nodes, regional grids, grid services and Learning Objects (LOs). These components are built and organised as a multi-agent system (MAS) using the Java Agent Development (JADE) platform. The main role of the agents in our architecture is to control and monitor grid components in order to build an adaptable, extensible, and flexible grid-based e-learning system. Two techniques have been developed and adopted in the architecture to build LOs' information and grid services. The first technique is the XML-based Registries Technique (XRT). In this technique LOs' information is built using XML registries to be discovered by the learners. The registries are written in Dublin Core Metadata Initiative (DCMI) format. The second technique is the Registered-based Services Technique (RST). In this technique the services are grid services which are built using agents. The services are registered with the Directory Facilitator (DF) of a JADE platform in order to be discovered by all other components. All components of the RDADeLE system, including grid service, are built as a multi-agent system (MAS). Each regional grid in the first technique has only its own registry, whereas in the second technique the grid services of all regional grids have to be registered with the DF. We have evaluated the RDADeLE system guided by both techniques by building a simulation of the prototype. The prototype has a main interface which consists of the name of the system (RDADeLE) and a specification table which includes Number of Regional Grids, Number of Nodes, Maximum Number of Learners connected to each node, and Number of Grid Services to be filled by the administrator of the RDADeLE system in order to create the prototype. Using the RST technique shows that the RDADeLE system can be built with more regional grids with less memory consumption. Moreover, using the RST technique shows that more grid services can be registered in the RDADeLE system with a lower average search time and the search performance is increased compared with the XRT technique. Finally, using one or both techniques, the XRT or the RST, in the prototype does not affect the reliability of the RDADeLE system.
BibTeX:
@phdthesis{ALZAHRANI2010,
  author = {Alzahrani, Saleh Saeed},
  title = {Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE)},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship},
  year = {2010},
  url = {https://www.dora.dmu.ac.uk/handle/2086/3814}
}
Al Ratrout, S. A Hybrid Multi-Agent Architecture and Heuristics Generation for Solving Meeting Scheduling Problem 2009 School: Faculty of Technology, De Montfort University - [second supervisor] Funding Source: Self-funded  phdthesis URL 
BibTeX:
@phdthesis{ALRATROUT2009,
  author = {Al Ratrout, Serein},
  title = {A Hybrid Multi-Agent Architecture and Heuristics Generation for Solving Meeting Scheduling Problem},
  school = {Faculty of Technology, De Montfort University - [second supervisor] Funding Source: Self-funded},
  year = {2009},
  url = {https://www.dora.dmu.ac.uk/handle/2086/2409}
}
Alkhawlani, M.M. Access Network Selection in Heterogeneous Networks 2008 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded with employer support  phdthesis URL 
Abstract: The future Heterogeneous Wireless Network (HWN) is composed of multiple Radio Access Technologies (RATs), therefore new Radio Resource Management (RRM) schemes and mechanisms are necessary to benefit from the individual characteristics of each RAT and to exploit the gain resulting from jointly considering the whole set of the available radio resources in each RAT. These new RRM schemes have to support mobile users who can access more than one RAT alternatively or simultaneously using a multi-mode terminal. An important RRM consideration for overall HWN stability, resource utilization, user satisfaction, and Quality of Service (QoS) provisioning is the selection of the most optimal and promising Access Network (AN) for a new service request. The RRM mechanism that is responsible for selecting the most optimal and promising AN for a new service request in the HWN is called the initial Access Network Selection (ANS). This thesis explores the issue of ANS in the HWN. Several ANS solutions that attempt to increase the user satisfaction, the operator benefits, and the QoS are designed, implemented, and evaluated. The thesis first presents a comprehensive foundation for the initial ANS in the HVN. Then, the thesis analyses and develops a generic framework for solving the ANS problem and any other similar optimized selection problem. The advantages and strengths of the developed framework are discussed. Combined Fuzzy Logic (FL), Multiple Criteria Decision Making (MCDM) and Genetic Algorithms (GA) are used to give the developed framework the required scalability, flexibility, and simplicity. The developed framework is used to present and design several novel ANS algorithms that consider the user, the operator, and the QoS view points. Different numbers of RATs, MCDM tools, and FL inference system types are used in each algorithm. A suitable simulation models over the HWN with a new set of performance evolution metrics for the ANS solution are designed and implemented. The simulation results show that the new algorithms have better and more robust performance over the random, the service type, and the terminal speed based selection algorithms that are used as reference algorithms. Our novel algorithms outperform the reference algorithms in- terms of the percentage of the satisfied users who are assigned to the network of their preferences and the percentage of the users who are assigned to networks with stronger signal strength. The new algorithms maximize the operator benefits by saving the high cost network resources and utilizing the usage of the low cost network resources. Usually better results are achieved by assigning the weights using the GA optional component in the implemented algorithms.
BibTeX:
@phdthesis{ALKHAWLANI2008,
  author = {Alkhawlani, Mohammed Mohssen},
  title = {Access Network Selection in Heterogeneous Networks},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded with employer support},
  year = {2008},
  url = {https://www.dora.dmu.ac.uk/handle/2086/5217}
}
Al-Marghilani, A. Application of Self-Organizing Maps to Multilingual Text Mining (Arabic-English) 2008 School: Faculty of Technology, De Montfort University - [second supervisor] Funding Source: Overseas Scholarship  phdthesis URL 
BibTeX:
@phdthesis{AL-MARGHILANI2008,
  author = {Al-Marghilani, Abdulsamad},
  title = {Application of Self-Organizing Maps to Multilingual Text Mining (Arabic-English)},
  school = {Faculty of Technology, De Montfort University - [second supervisor] Funding Source: Overseas Scholarship},
  year = {2008},
  url = {https://www.dora.dmu.ac.uk/handle/2086/4261}
}
Alqrainy, S. A Morphological - Syntactical Analysis Approach for Arabic Textual Tagging 2008 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship  phdthesis URL 
Abstract: Part-of-Speech (POS) tagging is the process of labeling or classifying each word in written text with its grammatical category or part-of-speech, i.e. noun, verb, preposition, adjective, etc. It is the most common disambiguation process in the field of Natural Language Processing (NLP). POS tagging systems are often preprocessors in many NLP applications. The Arabic language has a valuable and an important feature, called diacritics, which are marks placed over and below the letters of the word. An Arabic text is partiallyvocalisedl when the diacritical mark is assigned to one or maximum two letters in the word. Diacritics in Arabic texts are extremely important especially at the end of the word. They help determining not only the correct POS tag for each word in the sentence, but also in providing full information regarding the inflectional features, such as tense, number, gender, etc. for the sentence words. They add semantic information to words which helps with resolving ambiguity in the meaning of words. Furthermore, diacritics ascribe grammatical functions to the words, differentiating the word from other words, and determining the syntactic position of the word in the sentence. 1. Vocalisation (also referred as diacritisation or vowelisation). This thesis presents a rule-based Part-of-Speech tagging system called AMT - short for Arabic Morphosyntactic Tagger. The main function of the AMT system is to assign the correct tag to each word in an untagged raw partially-vocalised Arabic corpus, and to produce a POS tagged corpus without using a manually tagged or untagged lexicon (dictionary) for training. Two different techniques were used in this work, the pattem-based technique and the lexical and contextual technique. The rules in the pattem-based technique technique are based on the pattern of the testing word. A novel algorithm, Pattern-Matching Algorithm (PMA), has been designed and introduced in this work. The aim of this algorithm is to match the testing word with its correct pattern in pattern lexicon. The lexical and contextual technique on the other hand is used to assist the pattembased technique technique to assign the correct tag to those words not have a pattern to follow. The rules in the lexical and contextual technique are based on the character(s), the last diacritical mark, the word itself, and the tags of the surrounding words. The importance of utilizing the diacritic feature of the Arabic language to reduce the lexical ambiguity in POS tagging has been addressed. In addition, a new Arabic tag set and a new partially-vocalised Arabic corpus to test AMT have been compiled and presented in this work. The AMT system has achieved an average accuracy of 91 %.
BibTeX:
@phdthesis{ALQRAINY2008,
  author = {Alqrainy, Shihadeh},
  title = {A Morphological - Syntactical Analysis Approach for Arabic Textual Tagging},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship},
  year = {2008},
  url = {https://www.dora.dmu.ac.uk/handle/2086/4819}
}
Alserhan, H. Extraction of Arabic Word Roots: An Approach Based on Computational Model and Multi-Backpropagation Neural Network 2008 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship  phdthesis URL 
Abstract: Stemming is a process of extracting the root of a given word, by stripping off the affixes attached to this word. Many attempts have been made to address the stemming of Arabic words problem. The majority of the existing Arabic stemming algorithms require a complete set of morphological rules and large vocabulary lookup tables. Furthermore, many of them give more than one potential stem or root for a given Arabic word. According to Ahmad [11], the Arabic stemming process based on the language morphological rules is still a very difficult task due to the nature of the language itself. The limitations of the current Arabic stemming methods have motivated this research in which we investigate a novel approach to extract the word roots of Arabic language named here as MUAIDI-STEMMER 2. This approach attempts to exploit numerical relations between Arabic letters, avoiding having a list of the root and pattern of each word in the language, and giving one root solution. This approach is composed of two phases. Phase I depends on a basic calculations extracted from linguistic analysis of Arabic patterns and affixes. Phase II is based on artificial neural network trained by backpropagation learning rule. In this proposed phase, we formulate the root extraction problem as a classification problem and the neural network as a classifier tool. This study demonstrates that a neural network can be effectively used to ex- tract the word roots of Arabic language The stemmer developed is tested using 46,895 Arabic word types3. Error counting accuracy evaluation was employed to evaluate the performance of the stemmer. It was successful in producing the stems of 44,107 Arabic words from the given test datasets with accuracy of 94.81%. 2.Muaidi is the author father's name. 3.Types mean distinct or unique words.
BibTeX:
@phdthesis{ALSERHAN2008,
  author = {Alserhan, Hasan},
  title = {Extraction of Arabic Word Roots: An Approach Based on Computational Model and Multi-Backpropagation Neural Network},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship},
  year = {2008},
  url = {https://www.dora.dmu.ac.uk/handle/2086/4921}
}
Zahary, A.T. Improving Routing Performance of Multipath Ad Hoc On-demand Distance Vector in Mobile Ad Hoc Networks 2008 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded with employer support  phdthesis URL 
BibTeX:
@phdthesis{ZAHARY2008,
  author = {Zahary, Ammar Thabit},
  title = {Improving Routing Performance of Multipath Ad Hoc On-demand Distance Vector in Mobile Ad Hoc Networks},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded with employer support},
  year = {2008},
  url = {https://www.dora.dmu.ac.uk/handle/2086/6443}
}
Rasol, A.M.M. An Agent-Based Framework for Forest Planning 2007 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship  phdthesis  
BibTeX:
@phdthesis{RASOL2007,
  author = {Rasol, Abdul Manaf Mohd},
  title = {An Agent-Based Framework for Forest Planning},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Overseas Scholarship},
  year = {2007}
}
Al Hudhud, G. Multi-Agent Communication Protocols with Emergent Behaviour 2006 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded  phdthesis URL 
BibTeX:
@phdthesis{ALHUDHUD2006,
  author = {Al Hudhud, Ghada},
  title = {Multi-Agent Communication Protocols with Emergent Behaviour},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Self-funded},
  year = {2006},
  url = {https://dora.dmu.ac.uk/handle/2086/19295}
}
Gavrilov, A. Welding Process Engineering with Artificial Neural Networks 2006 School: Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Bi-literal agreement scheme  phdthesis  
BibTeX:
@phdthesis{GAVRILOV2006,
  author = {Gavrilov, Alexander},
  title = {Welding Process Engineering with Artificial Neural Networks},
  school = {Faculty of Technology, De Montfort University - [first supervisor (director of study)] Funding Source: Bi-literal agreement scheme},
  year = {2006}
}
Tyler, L. A Hybrid Methodology for the Experimental Study of Collective Behaviour in Robot Groups 2006 School: Faculty of Technology, De Montfort University - [second supervisor] Funding Source: EPSRC doctoral training program  phdthesis  
BibTeX:
@phdthesis{TYLER2006,
  author = {Tyler, Laurence},
  title = {A Hybrid Methodology for the Experimental Study of Collective Behaviour in Robot Groups},
  school = {Faculty of Technology, De Montfort University - [second supervisor] Funding Source: EPSRC doctoral training program},
  year = {2006}
}
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