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Email Filtering Using Hybrid Feature Selection Model

Computer Modeling in Engineering & Sciences 2022年第8期132卷 435-450页

作者： Adel Hamdan Mohammad Sami Smadi tariq alwada’n Computer Science Department The World Islamic Sciences and Education UniversityAmmanJordan Information System and networks Department The World Islamic Sciences and Education UniversityAmmanJordan network and Cybersecurity Department Teesside UniversityMiddlesbroughUK

Undoubtedly,spam is a serious problem,and the number of spam emails is increased ***,the massive number of spam emails prompts the need for spam detection *** methods and algorithms are used for spam ***,some emergent spam detection techniques use machine learning methods and feature *** methods and algorithms have been introduced for spam detecting and *** research proposes two models for spam detection and feature *** first model is evaluated with the email spam classification dataset,which is based on reducing the number of keywords to its *** results of this model are promising and highly *** second proposed model is based on creating features for spam detection as a first ***,the number of features is reduced using three well-known metaheuristic algorithms at the second *** algorithms used in the second model are Artificial Bee Colony(ABC),Ant Colony Optimization(ACO),and Particle Swarm Optimization(PSO),and these three algorithms are adapted to fit the proposed ***,the authors give it the names AABC,AACO,and APSO,*** dataset used for the evaluation of this model is ***,well-known criteria are used for the evaluation purposes of this model,such as true positive,false positive,false negative,precision,recall,and *** outcomes of the second proposed model are highly significant compared to the first one.

关键词： Feature selection artificial bee colony ant colony optimization particle swarm optimization spam detection emails filtering

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Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

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Computers, Materials & Continua 2022年第10期73卷 133-150页

作者： Adel Hamdan Mohammad tariq alwada’n Omar Almomani Sami Smadi nidhal ElOmari Computer Science Department The World Islamic Sciences and Education UniversityAmmanJordan network and Cybersecurity Department Teesside UniversityMiddlesbroughUnited Kingdom Information and network Security The World Islamic Sciences and Education UniversityAmmanJordan Software Engineering The World Islamic Sciences and Education UniversityAmmanJordan

Intrusion detection is a serious and complex *** due to a large number of attacks around the world,the concept of intrusion detection has become very *** research proposes a multilayer bioinspired feature selection model for intrusion detection using an optimized genetic ***,the proposed multilayer model consists of two layers(layers 1 and 2).At layer 1,three algorithms are used for the feature *** algorithms used are Particle Swarm Optimization(PSO),Grey Wolf Optimization(GWO),and Firefly Optimization Algorithm(FFA).At the end of layer 1,a priority value will be assigned for each feature *** layer 2 of the proposed model,the Optimized Genetic Algorithm(GA)is used to select one feature set based on the priority *** are done on standard GA to perform optimization and to fit the proposed *** Optimized GA is used in the training phase to assign a priority value for each feature ***,the priority values are categorized into three categories:high,medium,and ***,the Optimized GA is used in the testing phase to select a feature set based on its *** feature set with a high priority will be given a high priority to be *** the end of phase 2,an update for feature set priority may occur based on the selected features priority and the calculated *** proposed model can learn and modify feature sets priority,which will be reflected in selecting *** evaluation purposes,two well-known datasets are used in these *** first dataset is UnSW-nB15,the other dataset is the *** evaluation criteria are used,such as precision,recall,and *** experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.

关键词： Intrusion detection Machine learning Optimized Genetic Algorithm(GA) Particle Swarm Optimization algorithms(PSO) Grey Wolf Optimization algorithms(GWO) FireFly Optimization Algorithms(FFA) Genetic Algorithm(GA)

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Early fault prediction and detection of hydrocephalus shunting system

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Journal of Biomedical Science and Engineering 2013年第3期6卷 280-290页

作者： Abdel Rahman Alkharabsheh Lina Momani Waleed Al-nuaimy Jafar Ababneh tariq alwada’n Abeer Hawatmeh Department of Electrical Engineering University of Liverpool Liverpool UK Faculty of Science and Information Technology The World Islamic Sciences and Education Technology Amman Jordan Mechatronics Engineering Department Faculty of Engineering and Technology Al-Balqa’ Applied University Amman Jordan

Trends of various intracranial pressure (ICP) parameters for high pressure hydrocephalus patients are utilized to detect various shunt faults in their early stages, as well as, to monitor the effect of such faults on shunt performance. A method was proposed to predict the time required for ICP to be abnormal and for the valve to reach full blockage condition. Furthermore, an auto valve schedule updating method is proposed and used to temporarily deal with detected faults until the patient is checked up by his/her physician. The proposed algorithms were evaluated using numerical simulation.

关键词： Hydrocephalus Shunts Shunt Malfunctions Faults Detection

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