A Prediction Mechanism of Mail Retrieval Based on the User Behavior Analysis for Electronic Mail Database System

Lai, Wei-Ru; Liao, Che-Hui; Lu, Chang-Ching; Liao, Ming-Kuan

doi:10.1109/isda.2008.348

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…E-mail is a message that may contain text, files, images, or other attachments and is sent over a network to a specific individual or group of individuals (Lai et al 2008). Despite the availability of numerous chat apps such as WhatsApp, Facebook Messenger, and Snapchat,Email has remained an important part of daily digital life (Parasuraman 1982).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracy

Neeharika,

Kalaiarasi

2023

E3S Web Conf.

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The main aim is to compare Novel Recurrent Neural Network over Artificial Neural Network in predicting Email spammers with improved accuracy. Material and Methods : This research study contains two groups namely Novel Recurrent Neural Network and Artificial Neural Network. Each group consists of a sample size of 10 and the study parameters are calculated using clincalc with preset parameters as alpha 0.8, beta 0.2 and CI as 90%. Results and Discussion : The Novel Recurrent Neural Network has the highest accuracy 97.96% when compared to Artificial Neural Network it has 93.79% accuracy in Electronic Mail spam prediction with significance value p=0.000(p<0.05) that is significantly better. The G-power value is 80%. When used as a spam predictor for electronic mail, the Novel Recurrent Neural Network performance analysis outperforms the best results than the Artificial Neural Network performance.

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Section: Introductionmentioning

confidence: 99%

Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracy

Neeharika,

Kalaiarasi

2023

E3S Web Conf.

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“…Some researches focus on the prediction mechanism with the history user behavior data [4], such as the attacks and destructions. The mail prediction [5] describes how to reduce the response time of mail retrieves by saving the high possibility mail in cache with only the frequency of user action analyzed. In a word, for efficient pre-detection of the software faults in mobile devices, how to choose PSS based on user behavior is still a problem to be solved.…”

Section: Introductionmentioning

confidence: 99%

A User Behavior Clustering Based Fault Pre-detection Mechanism for QoE Assurance on Mobile Devices

Zhu¹,

Guo

Qiu³

et al. 2013

J. Inf. Comput. Sci.

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Pre-detection mechanisms are often used to ensure the users' QoE on mobile devices before potential service/application malfunctions really occur. In order to improve the efficiency of pre-detection, an optimized detection mechanism is proposed. In the mechanism, a priority set of services is selected by considering user's dependency degree on services, service's priority and network load. Firstly, a model based on fuzzy theories is proposed to weigh the dependency degree of user to each service. Then, a clustering model by analyzing user behavior is introduced to divide mobile users to several clusters. After that, an optimized algorithm for selecting priority set of services to be pre-detected is proposed by considering the user-service dependency degree generated before, combined with failure rate of services and network load status. Based on that, the service/application with higher usage rate and higher failure rate could have the priority of being diagnosed. Meanwhile, the proposed fault pre-detection mechanism is implemented on a prototype system, and the algorithm is validated by computing time and services availability comparisons under various network environments.

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A Prediction Mechanism of Mail Retrieval Based on the User Behavior Analysis for Electronic Mail Database System

Cited by 2 publications

References 6 publications

Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracy

Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracy

A User Behavior Clustering Based Fault Pre-detection Mechanism for QoE Assurance on Mobile Devices

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