The Experts below are selected from a list of 417924 Experts worldwide ranked by ideXlab platform
Marek Penhaker - One of the best experts on this subject based on the ideXlab platform.
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a combined negative selection algorithm particle swarm optimization for an email spam detection system
Engineering Applications of Artificial Intelligence, 2015Co-Authors: Ismaila Idris, Ali Selamat, Ngoc Thanh Nguyen, Sigeru Omatu, Ondrej Krejcar, Kamil Kuca, Marek PenhakerAbstract:Abstract Email is a convenient means of communication throughout the entire world today. The increased popularity of email spam in both text and images requires a Real-Time Protection mechanism for the media flow. The previous approach has been limited by the adaptive nature of unsolicited email spam. This research introduces an email detection system that is designed based on an improvement in the negative selection algorithm. Furthermore, particle swarm optimization (PSO) was implemented to improve the random detector generation in the negative selection algorithm (NSA). The algorithm generates detectors in the random detector generation phase of the negative selection algorithm. The combined NSA–PSO uses a local outlier factor (LOF) as the fitness function for the detector generation. The detector generation process is terminated when the expected spam coverage is reached. A distance measure and a threshold value are employed to enhance the distinctiveness between the non-spam and spam detectors after the detector generation. The implementation and evaluation of the models are analyzed. The results show that the accuracy of the proposed NSA–PSO model is better than the accuracy of the standard NSA model. The proposed model with the best accuracy is further used to differentiate between spam and non-spam in a network that is developed based on a client–server network for spam detection.
Ali Selamat - One of the best experts on this subject based on the ideXlab platform.
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a combined negative selection algorithm particle swarm optimization for an email spam detection system
Engineering Applications of Artificial Intelligence, 2015Co-Authors: Ismaila Idris, Ali Selamat, Ngoc Thanh Nguyen, Sigeru Omatu, Ondrej Krejcar, Kamil Kuca, Marek PenhakerAbstract:Abstract Email is a convenient means of communication throughout the entire world today. The increased popularity of email spam in both text and images requires a Real-Time Protection mechanism for the media flow. The previous approach has been limited by the adaptive nature of unsolicited email spam. This research introduces an email detection system that is designed based on an improvement in the negative selection algorithm. Furthermore, particle swarm optimization (PSO) was implemented to improve the random detector generation in the negative selection algorithm (NSA). The algorithm generates detectors in the random detector generation phase of the negative selection algorithm. The combined NSA–PSO uses a local outlier factor (LOF) as the fitness function for the detector generation. The detector generation process is terminated when the expected spam coverage is reached. A distance measure and a threshold value are employed to enhance the distinctiveness between the non-spam and spam detectors after the detector generation. The implementation and evaluation of the models are analyzed. The results show that the accuracy of the proposed NSA–PSO model is better than the accuracy of the standard NSA model. The proposed model with the best accuracy is further used to differentiate between spam and non-spam in a network that is developed based on a client–server network for spam detection.
Ismaila Idris - One of the best experts on this subject based on the ideXlab platform.
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a combined negative selection algorithm particle swarm optimization for an email spam detection system
Engineering Applications of Artificial Intelligence, 2015Co-Authors: Ismaila Idris, Ali Selamat, Ngoc Thanh Nguyen, Sigeru Omatu, Ondrej Krejcar, Kamil Kuca, Marek PenhakerAbstract:Abstract Email is a convenient means of communication throughout the entire world today. The increased popularity of email spam in both text and images requires a Real-Time Protection mechanism for the media flow. The previous approach has been limited by the adaptive nature of unsolicited email spam. This research introduces an email detection system that is designed based on an improvement in the negative selection algorithm. Furthermore, particle swarm optimization (PSO) was implemented to improve the random detector generation in the negative selection algorithm (NSA). The algorithm generates detectors in the random detector generation phase of the negative selection algorithm. The combined NSA–PSO uses a local outlier factor (LOF) as the fitness function for the detector generation. The detector generation process is terminated when the expected spam coverage is reached. A distance measure and a threshold value are employed to enhance the distinctiveness between the non-spam and spam detectors after the detector generation. The implementation and evaluation of the models are analyzed. The results show that the accuracy of the proposed NSA–PSO model is better than the accuracy of the standard NSA model. The proposed model with the best accuracy is further used to differentiate between spam and non-spam in a network that is developed based on a client–server network for spam detection.
Ngoc Thanh Nguyen - One of the best experts on this subject based on the ideXlab platform.
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a combined negative selection algorithm particle swarm optimization for an email spam detection system
Engineering Applications of Artificial Intelligence, 2015Co-Authors: Ismaila Idris, Ali Selamat, Ngoc Thanh Nguyen, Sigeru Omatu, Ondrej Krejcar, Kamil Kuca, Marek PenhakerAbstract:Abstract Email is a convenient means of communication throughout the entire world today. The increased popularity of email spam in both text and images requires a Real-Time Protection mechanism for the media flow. The previous approach has been limited by the adaptive nature of unsolicited email spam. This research introduces an email detection system that is designed based on an improvement in the negative selection algorithm. Furthermore, particle swarm optimization (PSO) was implemented to improve the random detector generation in the negative selection algorithm (NSA). The algorithm generates detectors in the random detector generation phase of the negative selection algorithm. The combined NSA–PSO uses a local outlier factor (LOF) as the fitness function for the detector generation. The detector generation process is terminated when the expected spam coverage is reached. A distance measure and a threshold value are employed to enhance the distinctiveness between the non-spam and spam detectors after the detector generation. The implementation and evaluation of the models are analyzed. The results show that the accuracy of the proposed NSA–PSO model is better than the accuracy of the standard NSA model. The proposed model with the best accuracy is further used to differentiate between spam and non-spam in a network that is developed based on a client–server network for spam detection.
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A combined negative selection algorithm-particle swarm optimization for an email spam detection system
'Elsevier BV', 2015Co-Authors: Idris Ismaila, Ngoc Thanh Nguyen, Selamat Ali, Omatu Sigeru, Krejcar Ondrej, Kuca Kamil, Penhaker MarekAbstract:Email is a convenient means of communication throughout the entire world today. The increased popularity of email spam in both text and images requires a Real-Time Protection mechanism for the media flow. The previous approach has been limited by the adaptive nature of unsolicited email spam. This research introduces an email detection system that is designed based on an improvement in the negative selection algorithm. Furthermore, particle swarm optimization (PSO) was implemented to improve the random detector generation in the negative selection algorithm (NSA). The algorithm generates detectors in the random detector generation phase of the negative selection algorithm. The combined NSA-PSO uses a local outlier factor (LOF) as the fitness function for the detector generation. The detector generation process is terminated when the expected spam coverage is reached. A distance measure and a threshold value are employed to enhance the distinctiveness between the non-spam and spam detectors after the detector generation. The implementation and evaluation of the models are analyzed. The results show that the accuracy of the proposed NSA-PSO model is better than the accuracy of the standard NSA model. The proposed model with the best accuracy is further used to differentiate between spam and non-spam in a network that is developed based on a client-server network for spam detection
P Mccullen - One of the best experts on this subject based on the ideXlab platform.
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the software and hardware architecture of the real time Protection of in vessel components in jet ilw
Nuclear Fusion, 2019Co-Authors: V Huber, P J Lomas, A Huber, D J Kinna, I Balboa, G F Matthews, G Sergienko, S Brezinsek, J Mailloux, P MccullenAbstract:For the first time, the JET operation in deuterium-tritium (D-T) plasma, which is scheduled to take place on JET in 2020, will be performed in the ITER mix of plasma-facing component materials. In ...
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the jet real time plasma wall load monitoring system
Fusion Engineering and Design, 2014Co-Authors: D Valcarcel, D Alves, P Card, B B Carvalho, S Devaux, R Felton, A Goodyear, P J Lomas, F Maviglia, P MccullenAbstract:In the past. the Joint European Torus (JET) has operated with a first-wall composed of Carbon Fibre Composite (CFC) tiles. The thermal properties of the wall were monitored in Real-Time during plasma operations by the WALLS system. This software routinely performed model-based thermal calculations of the divertor and Inner Wall Guard Limiter (IWGL) tiles calculating bulk temperatures and strike-point positions as well as raising alarms when these were beyond operational limits. Operation with the new ITER-like wall presents a whole new set of challenges regarding machine Protection. One example relates to the new beryllium limiter tiles with a melting point of 1278 degrees C, which can be achieved during a plasma discharge well before the bulk temperature rises to this value. This requires new and accurate power deposition and thermal diffusion models. New systems were deployed for safe operation with the new wall: the Real-Time Protection Sequencer (RTPS) and the Vessel Thermal Map (VTM). The former allows for a coordinated stop of the pulse and the latter uses the surface temperature map, measured by infrared (IR) cameras, to raise alarms in case of hot-spots. Integration of WALLS with these systems is required as RTPS responds to raised alarms and VTM, the primary Protection system for the ITER-like wall, can use WALLS as a vessel temperature provider. This paper presents the engineering design, implementation and results of WALLS towards D-T operation, where it will act as a primary Protection system when the IR cameras are blinded by the fusion reaction neutrons. The first operational results, with emphasis on its performance, are also presented.
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centralised coordinated control to protect the jet iter like wall
2011Co-Authors: G Arnoux, R Felton, A Goodyear, P J Lomas, P Mccullen, T Budd, J Harling, D Kinna, P D Thomas, I YoungAbstract:The JET ITER-like wall project (ILW) replaces the first wall carbon fibre composite tiles with beryllium and tungsten tiles which should have improved fuel retention characteristics but are less thermally robust. An enhanced Protection system using new control and diagnostic systems has been designed which can modify the pre-planned experimental control to protect the new wall. Key design challenges were to extend the Level-1 supervisory control system to allow configurable responses to thermal problems to be defined without introducing excessive complexity, and to integratethe new functionalitywith existing control and Protection systems efficiently and reliably. Alarms are generated by the vessel thermal map (VTM) system if infra-red camera measurements of tile temperatures are too high and by the plasma wall load system (WALLS) if component power limits are exceeded. The design introduces two new concepts: local Protection, which inhibits individual heating components but allows the discharge to proceed, and stop responses, which allow highly configurable early termination of the pulse in the safest way for the plasma conditions and type of alarm. These are implemented via the new Real-Time Protection system (RTPS), a centralised controller which responds to the VTM and WALLS alarms by providing override commands to the plasma shape, current, density and heating controllers. This paper describes the design and implementation of the RTPS system which is built with the Multithreaded Application Real-Time executor (MARTe) and will present results from initial operations.
