A distributed density estimation algorithm and its application to naive Bayes classification

Khajenezhad, Ahmad; Bashiri, Mohammad; Beigy, Hamid

doi:10.1016/j.asoc.2020.106837

Cited by 18 publications

(8 citation statements)

References 8 publications

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“…Then, the example of case 1 tends to Social Phobia Disorder. The Naïve Bayes model has efficiency in estimating a wide range of probability density functions [24].…”

Section: Steps Of Naïve Bayes Methodsmentioning

confidence: 99%

Mental Health Helper: Intelligent Mobile Apps in the Pandemic Era

Prasasti

Rahmi

Nurahmani

et al. 2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…Then, the example of case 1 tends to Social Phobia Disorder. The Naïve Bayes model has efficiency in estimating a wide range of probability density functions [24].…”

Section: Steps Of Naïve Bayes Methodsmentioning

confidence: 99%

Mental Health Helper: Intelligent Mobile Apps in the Pandemic Era

Prasasti

Rahmi

Nurahmani

et al. 2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…The approach can detect anomalies with 0.99 detection rate for this dataset. In [29], the authors propose nested Log-Poly, a communicationally efficient model for distributed density estimation in naive Bayes classification. The method is evaluated on the N-BaIoT dataset.…”

Section: Work On N-baiot Datasetmentioning

confidence: 99%

A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection

2023

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Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, which often provide one separate model per device or per attack. These solutions are not suited to the scale and dynamism of modern IoT networks. This paper proposes a novel IoT-driven cross-device method, which allows learning a single IDS model instead of many separate models atop the traffic of different IoT devices. A semi-supervised approach is adopted due to its wider applicability for unanticipated attacks. The solution is based on an all-in-one deep autoencoder, which consists of training a single deep neural network with the normal traffic from different IoT devices. Extensive experimentation performed with a widely used benchmarking dataset indicates that the all-in-one approach achieves within 0.9994–0.9997 recall, 0.9999–1.0 precision, 0.0–0.0071 false positive rate and 0.9996–0.9998 F1 score, depending on the device. The results obtained demonstrate the validity of the proposal, which represents a lightweight and device-independent solution with considerable advantages in terms of transferability and adaptability.

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“…Algorithm. e ML model, i.e., Naive Bayes [8], is a classification model based on the Bayesian model and the independence assumption of feature conditions. is Bayesian classification model has a simple concept, but it is difficult to calculate the posterior probability, so the following independence assumptions are introduced.…”

Section: Classification Of English Grammatical Errors Based On the Na...mentioning

confidence: 99%

Identification of Grammatical Errors of English Language Based on Intelligent Translational Model

Hongli

2022

Mobile Information Systems

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To address the problem of low kappa, precision and recall values, and high misjudgment rate in traditional methods, this study proposes an English grammatical error identification method based on a machine translation model. For this purpose, a bidirectional long short-term memory (Bi-LSTM) model is established to diagnose English grammatical errors. A machine learning (ML) model, i.e., Naive Bayes is used for the result classification of the English grammatical error diagnosis, and the N-gram model is utilized to effectively point out the location of the error. According to the preprocessing results, a grammatical error generation model is designed, a parallel corpus is built from which a training dataset for the model training is generated, and different types of grammatical errors are also checked. The overall architecture of the machine translation model is given, and the model parameters are trained on a large-scale modification of the wrong learner corpus, which greatly improves the accuracy of grammatical error identification. The experimental outcomes reveal that the model used in this study significantly improves the kappa value, the precision and recall values, and the misjudgment rate remains below 1.0, which clearly demonstrates that the detection effect is superior.

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A distributed density estimation algorithm and its application to naive Bayes classification

Cited by 18 publications

References 8 publications

Mental Health Helper: Intelligent Mobile Apps in the Pandemic Era

Mental Health Helper: Intelligent Mobile Apps in the Pandemic Era

A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection

Identification of Grammatical Errors of English Language Based on Intelligent Translational Model

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