Arabic nested noun compound extraction based on linguistic features and statistical measures

The extraction of Arabic nested noun compound is significant for several research areas such as sentiment analysis, text summarization, word categorization, grammar checker, and machine translation. Much research has studied the extraction of Arabic noun compound using linguistic approaches, statistical methods, or a hybrid of both. A wide range of the existing approaches concentrate on the extraction of the bi-gram or tri-gram noun compound. Nonetheless, extracting a 4-gram or 5-gram nested noun compound is a challenging task due to the morphological, orthographic, syntactic and semantic variations. Many features have an important effect on the efficiency of extracting a noun compound such as unit-hood, contextual information, and term-hood. Hence, there is a need to improve the effectiveness of the Arabic nested noun compound extraction. Thus, this paper proposes a hybrid linguistic approach and a statistical method with a view to enhance the extraction of the Arabic nested noun compound. A number of pre-processing phases are presented, including transformation, tokenization, and normalisation. The linguistic approaches that have been used in this study consist of a part-of-speech tagging and the named entities pattern, whereas the proposed statistical methods that have been used in this study consist of the NC-value, NTC-value, NLC-value, and the combination of these association measures. The proposed methods have demonstrated that the combined association measures have outperformed the NLC-value, NTC-value, and NC-value in terms of nested noun compound extraction by achieving 90%, 88%, 87%, and 81% for bigram, trigram, 4-gram, and 5-gram, respectively

Label assignment and failure recovery approaches for IP multicast communication in MPLS networks

Multiprotocol Label Switching (MPLS) is an Internet Engineering Task Force (IETF) framework that provides for the efficient designation, routing, forwarding, and switching of traffic flows through the network. MPLS is widely used for multicast traffic engineering. However, integrating MPLS with IP multicast communication is difficult. This thesis proposes solutions for two problems: label assignment and failure recovery. When a set of multicast flows (sessions) is deployed in an MPLS network, each multicast entry for the sessions consumes an MPLS label. However, MPLS labels are significant resources in MPLS networks, as the 20-bit field of the MPLS header limits the number of available labels. It is advantageous if different multicast sessions can share the same multicast tree in the network and re-use the MPLS label. In the first part of the thesis, we propose two algorithms to do this. The first one is called State Encoding (SE), in which a code is calculated for every tree built in an MPLS network. The second algorithm is called Tree Numbering (TN), where a number represents each tree. If the IP packets of different multicast sessions are delivered over the same tree, all those packets are then classified to the same Forwarding Equivalence Class (FEC) and only one label is used instead of using a number of labels equal to the number of those sessions. The second part of this thesis contributes to this area. To trade off between the large amount of bandwidth required for reserving backup paths in local recovery and the large recovery time taken in global recovery, a new tree division approach is proposed. In this approach, a multicast tree is divided into several domains, where each domain represents a local stand-alone sub-tree of the original one. Two MPLS-based methods are then proposed to set up the backup paths inside the domains. A comparison is made among the local recovery approach, the global recovery approach, a method that sets up the backup paths between the branching points, and the proposed approach. The comparison is based on three metrics: the total backup capacity, the maximum and the average notification times, and the average number of the notification messages that are produced as a result of a failure. In terms of the reserved capacity, the results have shown that our architecture consumes backup capacity close to that consumed by the global recover

When the environment and mutations affect organ systems

Atypical hemolytic uremic syndrome (aHUS) is a rare thrombotic microangiopathy (TMA) with a genetic predisposition. Like other TMAs, it presents clinically with thrombocytopenia and microangiopathic hemolytic anemia, which is accompanied by disruption of at least one organ system. We present a case of a 42-year-old female who presented with abdominal pain, nausea and vomiting. She had hemolytic anemia, thrombocytopenia and acute kidney injury suggestive of TMA.Includes bibliographical reference

Control of Small Spacecraft by Optimal Output Regulation: A Reinforcement Learning Approach

The growing number of noncooperative flying objects has prompted interest in sample-return and space debris removal missions. Current solutions are both costly and largely dependent on specific object identification and capture methods. In this paper, a low-cost modular approach for control of a swarm flight of small satellites in rendezvous and capture missions is proposed by solving the optimal output regulation problem. By integrating the theories of tracking control, adaptive optimal control, and output regulation, the optimal control policy is designed as a feedback-feedforward controller to guarantee the asymptotic tracking of a class of reference input generated by the leader. The estimated state vector of the space object of interest and communication within satellites is assumed to be available. The controller rejects the nonvanishing disturbances injected into the follower satellite while maintaining the closed-loop stability of the overall leader-follower system. The simulation results under the Basilisk-ROS2 framework environment for high-fidelity space applications with accurate spacecraft dynamics, are compared with those from a classical linear quadratic regulator controller, and the results reveal the efficiency and practicality of the proposed method

The Effectiveness of Dramatic Role-Playing on the Linguistic Achievement and the Development of Verbal Expressive Performance among the Basic 4th Grade Students in Jordan

This study aimed at examining the effectiveness of the dramatic role- playing upon the linguistic achievement and the development of verbal expressive performance among the basic 4th grade students in Jordan. To achieve the aims of the study, a linguistic patterns achievement test and the verbal expressive performance checklist were used. The sample of the study consisted of (52) 4th grade students from a school that was purposefully selected from public school of Al- Zarqa educational district. The sample was randomly distributed into two groups. The experimental group consisted of (25) students who were taught by             ) dramatic role- playing methods, and the control group consisted of 27( students who were taught by the conventional methods. The results indicated that there were statistically significant differences between the two groups in the linguistic pattern achievement test and the verbal expressive performance checklist in the favor of the experimental group. Key words: Dramatic, Role-playing, Linguistic Achievement, Verbal Expressive Performance

Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm

Image segmentation of brain magnetic resonance imaging (MRI) plays a crucial role among radiologists in terms of diagnosing brain disease. Parts of the brain such as white matter, gray matter and cerebrospinal fluids (CFS), have to be clearly determined by the radiologist during the process of brain abnormalities detection. Manual segmentation is grueling and may be prone to error, which can in turn affect the result of the diagnosis. Nature-inspired metaheuristic algorithms such as Harmony Search (HS), which was successfully applied in multilevel thresholding for brain tumor segmentation instead of the Patch-Levy Bees algorithm (PLBA). Even though the PLBA is one powerful multilevel thresholding, it has not been applied to brain tumor segmentation. This paper focuses on a comparative study of the PLBA and HS for brain tumor segmentation. The test dataset consisting of nine images was collected from the Tuanku Muhriz UKM Hospital (HCTM). As for the result, it shows that the PLBA has significantly outperformed HS. The performance of both algorithms is evaluated in terms of solution quality and stability

Machine Learning Methods for Breast Cancer Diagnostic

This chapter discusses radio-pathological correlation with recent imaging advances such as machine learning (ML) with the use of technical methods such as mammography and histopathology. Although criteria for diagnostic categories for radiology and pathology are well established, manual detection and grading, respectively, are tedious and subjective processes and thus suffer from inter-observer and intra-observer variations. Two most popular techniques that use ML, computer aided detection (CADe) and computer aided diagnosis (CADx), are presented. CADe is a rejection model based on SVM algorithm which is used to reduce the False Positive (FP) of the output of the Chan-Vese segmentation algorithm that was initialized by the marker controller watershed (MCWS) algorithm. CADx method applies the ensemble framework, consisting of four-base SVM (RBF) classifiers, where each base classifier is a specialist and is trained to use the selected features of a particular tissue component. In general, both proposed methods offer alternative decision-making ability and are able to assist the medical expert in giving second opinion on more precise nodule detection. Hence, it reduces FP rate that causes over segmentation and improves the performance for detection and diagnosis of the breast cancer and is able to create a platform that integrates diagnostic reporting system