Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0

Πεππές, Νικόλαος; Daskalakis, Emmanouil; Alexakis, Theodoros; Adamopoulou, Evgenia; Demestichas, Konstantinos

doi:10.3390/s21227475

Cited by 48 publications

(27 citation statements)

References 44 publications

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“…The workflow diagram of the stacking ensemble model is depicted in Figure 6 . Voting: The voting classifier in machine learning uses an ensemble of multiple models to forecast an output (class) based on the chance that the result will fall into one of the specified classes [ 38 ]. Using the most popular class as a starting point, this classifier predicts the output class based on an average of all the classifiers that have been submitted to it.…”

Section: Methodsmentioning

confidence: 99%

Employment of Ensemble Machine Learning Methods for Human Activity Recognition

Hasan

Karim

Mahadi

et al. 2022

Journal of Healthcare Engineering

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The endeavor to detect human activities and behaviors is targeted as a real-time detection mechanism that tends to predict the form of human motions and actions. Though sensors like accelerometer and gyroscopes are noticeable in human motion detection, categorizing unique and individual human gestures require software-based assistance. With the widespread implementation of machine learning algorithms, human actions can be distinguished into multiple classes. Several state-of-the-art machine learning algorithms can be applied to this specified field which will give suitable outcomes, yet due to the bulk of the dataset, complexity can be made apparent, which will reduce the efficiency of the model. In our proposed research, ensemble learning methods have been established by assembling several trained and tuned machine learning models. The adopted dataset for the model has been preprocessed through PCA (principal component analysis), SMOTE oversampling (synthetic minority oversampling technique), and K-means clustering, which reduced the dataset to essentials, keeping the weight of the features intact and reducing complexity. Maximum accuracy of 99.36% was achieved from both stacking and voting ensemble methods.

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Section: Methodsmentioning

confidence: 99%

Employment of Ensemble Machine Learning Methods for Human Activity Recognition

Hasan

Karim

Mahadi

et al. 2022

Journal of Healthcare Engineering

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“…In hard voting, the final prediction is the class that acquires the most voting from the classifiers. In contrast, in soft voting, the predicted class is the class that achieves the highest average of probability assigned to it by the classifiers [ 43 ]. In this study, the hard voting approach is used, as shown in Equation ( 18 ), where

is the final prediction, C i are the predictions of i th observations, and x is the data sample [ 44 ].…”

Section: Methodsmentioning

confidence: 99%

A Novel Ensemble-Based Technique for the Preemptive Diagnosis of Rheumatoid Arthritis Disease in the Eastern Province of Saudi Arabia Using Clinical Data

Olatunji

Alansari

Alkhorasani

et al. 2022

Computational and Mathematical Methods in Medicine

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Rheumatoid arthritis (RA) is a chronic inflammatory disease caused by numerous genetic and environmental factors leading to musculoskeletal system pain. RA may damage other tissues and organs, causing complications that severely reduce patients’ quality of life. According to the World Health Organization (WHO), over 1.71 billion individuals worldwide had musculoskeletal problems in 2021. Rheumatologists face challenges in the early detection of RA since its symptoms are similar to other illnesses, and there is no definitive test to diagnose the disease. Accordingly, it is preferable to profit from the power of computational intelligence techniques that can identify hidden patterns to diagnose RA early. Although multiple studies were conducted to diagnose RA early, they showed unsatisfactory performance, with the highest accuracy of 87.5% using imaging data. Yet, imaging data requires diagnostic tools that are challenging to collect and examine and are more costly. Recent studies indicated that neither a blood test nor a physical finding could early confirm the diagnosis. Therefore, this study proposes a novel ensemble technique for the preemptive prediction of RA and investigates the possibility of diagnosing the disease using clinical data before the symptoms appear. Two datasets were obtained from King Fahad University Hospital (KFUH), Dammam, Saudi Arabia, including 446 patients, with 251 positive cases of RA and 195 negative cases of RA. Two experiments were conducted where the former was developed without upsampling the dataset, and the latter was carried out using an upsampled dataset. Multiple machine learning (ML) algorithms were utilized to assemble the novel voting ensemble, including support vector machine (SVM), logistic regression (LR), and adaptive boosting (Adaboost). The results indicated that clinical laboratory tests fed to the proposed voting ensemble technique could accurately diagnose RA preemptively with an accuracy, recall, and precision of 94.03%, 96.00%, and 93.51%, respectively, with 30 clinical features when utilizing the original data and sequential forward feature selection (SFFS) technique. It is concluded that deploying the proposed model in local hospitals can contribute to introducing a method that aids medical specialists in preemptively diagnosing RA and stopping or delaying the course using clinical laboratory tests.

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“…SVM uses a hyperplane to divide the positive and negative samples. The selection of the hyperplane is shown in Figure 3 , which can be described as [ 30 ]

where

denotes the normal vector, and b indicates the distance between the plane and coordinate origin.…”

Section: Ai-based Discriminant Algorithmsmentioning

confidence: 99%

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks

Zhang

Zhou

et al. 2023

Sensors

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The Internet-of-Things (IoT) massive access is a significant scenario for sixth-generation (6G) communications. However, low-power IoT devices easily suffer from remote interference caused by the atmospheric duct under the 6G time-division duplex (TDD) mode. It causes distant downlink wireless signals to propagate beyond the designed protection distance and interfere with local uplink signals, leading to a large outage probability. In this paper, a remote interference discrimination testbed is originally proposed to detect interference, which supports the comparison of different types of algorithms on the testbed. Specifically, 5,520,000 TDD network-side data collected by real sensors are used to validate the interference discrimination capabilities of nine promising AI algorithms. Moreover, a consistent comparison of the testbed shows that the ensemble algorithm achieves an average accuracy of 12% higher than the single model algorithm.

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Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0

Cited by 48 publications

References 44 publications

Employment of Ensemble Machine Learning Methods for Human Activity Recognition

Employment of Ensemble Machine Learning Methods for Human Activity Recognition

A Novel Ensemble-Based Technique for the Preemptive Diagnosis of Rheumatoid Arthritis Disease in the Eastern Province of Saudi Arabia Using Clinical Data

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks

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