In the purview of most educational sectors today, numerous reviews regarding data mining have been the primary focus, with goals of discovering vast knowledge patterns for students' data. This paper focuses on bui...
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Data mining has become an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering interesting and previously unknown knowledge from...
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Data mining has become an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering interesting and previously unknown knowledge from very large real world database. These databases contain potential gold mine of valuable information, but it is beyond human ability to analyze massive amount of data and elicit meaningful patterns by using conventional techniques. In this study, DNA sequence was analyzed to locate promoter which is a regulatory region of DNA located upstream of a gene, providing a control point for regulated gene transcription. In this study, some supervised learning algorithms such as artificial neural network (ANN), RULES-3 and newly developed keREM rule induction algorithm were used to analyse to DNA sequence. In the experiments different option of keREM, RULES-3 and ANN were used, and according to the empirical comparisons, the algorithms appeared to be comparable to well-known algorithms in terms of the accuracy of the extracted rule in classifying unseen data.
The paper proposes the architecture of distributed multilevel detection system of malicious software in local area networks. Its feature is the synthesis of its requirements of distribution, decentralization, multilev...
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The paper proposes the architecture of distributed multilevel detection system of malicious software in local area networks. Its feature is the synthesis of its requirements of distribution, decentralization, multilevel. This allows you to use it autonomously. In addition, the feature of autonomous program modules of the system is the same organization, which allows the exchange of knowledge in the middle of the system, which, unlike the known systems, allows you to use the knowledge gained by separate parts of the system in other parts. The developed system allows to fill it with subsystems of detection of various types of malicious software in local area networks.
The complexity of pairwise RNA structure alignment depends on the structural restrictions assumed for both the input structures and the computed consensus structure. For arbitrarily crossing input and consensus struct...
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Multithreaded Object-Oriented programming in concurrency environment using object-oriented technology is a complex activity. Programmers need to be aware of issues unrelated to their domain of problem, and are often u...
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Multithreaded Object-Oriented programming in concurrency environment using object-oriented technology is a complex activity. Programmers need to be aware of issues unrelated to their domain of problem, and are often unprepared for the challenges of the concurrent object-oriented programming brings. The interaction of their components becomes more complex, and makes it difficult to validate the design and correctness of the implemented program. Supporting separation of concerns in the design and implementation of the multithreaded object-oriented programming can provide a number of benefits such as comprehension, reusability, extensibility and adaptability in both design and implementation. We have tackled this problem by adopting the technique of separation of concerns in multithreaded object-oriented programming. In this paper we demonstrate an aspect-oriented approach that can be used for multithreaded object-oriented programming. We also show how better the separation of concerns in components. Readers/Writers problem is demonstrated using an aspect-oriented approach. Our methodology, which is based on aspect-oriented techniques as well as language and architecture independence, is an aspect-oriented framework.
Collaborative work, with the need to keep HTML/XML code up-to-date, is now becoming vital particularly in the Web Development field. In order to fully support collaborative work and resolve related problems the need h...
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The notion of "time" plays an important role when coordinating large, heterogeneous, distributed software systems. We present a generic coordination architecture that supports relative and absolute, discrete...
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The multilevel inverters (MLIs) are classified into three topologies such as Diode Clamped, Flying Capacitor and Cascade Multilevel Inverter (CMLI). CMLI topologies include two kind of structure that is named symmetri...
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Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptom...
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Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptoms, varies from patient to another. Since the seizures are not predictable, and most of epileptic patients suffer from physical risky symptoms during the seizure, such patients are not able to perform daily work activities. The objective of this project is to design and implement a monitoring system for epileptic patients; the system should continuously check some vital signs, analyze the measurements, and decide whether the patient is nearly to have a seizure or not. Whenever a seizure is predicted, the system initiates an alarm. In addition, a notification should be sent to the health care responsible, as well as one preferred contact. By implementing the monitoring system, people who suffer from epilepsy will have more chance to work and live a normal life. Thus, this paper presents the concept of the overall system and shows results of the implemented systems: EEG, ECG and Fall Detection system. Results have shown that the fall detection accuracy reached 99.89% whereas the accuracy of the prediction using the ANN was about 97.34%.
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