A Robust UWSN Handover Prediction System Using Ensemble Learning

Eldesouky, Esraa; Bekhit, Mahmoud; Fathalla, Ahmed; Salah, Ahmad; Ali, Ahmed

doi:10.3390/s21175777

Cited by 18 publications

(8 citation statements)

References 32 publications

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“…As previously stated, the Korea Hydrographic and Oceanographic Agency dataset includes gathered real-time observed marine data [ 54 ]. This data is updated every 30 minutes from the located underwater network at latitude 34.223611 and longitude 128.4205552.…”

Section: Resultsmentioning

confidence: 99%

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

Ali

Fathalla

Salah

et al. 2021

Computational Intelligence and Neuroscience

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Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors’ knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model.

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Section: Resultsmentioning

confidence: 99%

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

Ali

Fathalla

Salah

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

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show abstract

“…However, NB requires few estimated parameters, it is not sensitive to missing data, and the assumption is relatively simple, so the accuracy of the algorithm is affected. According to different assumptions, NB includes Gaussian NB (GNB), Multinomial NB (MNB), Complement NB (CNB), Bernoulli NB (BNB), Categorical NB, and so on [ 35 ].…”

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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“…Machine learning and deep neural networks have recently exhibited top-notch performance in a wide range of applications [23]- [26], For instance, various natural language processing (NLP) and computer vision tasks, such as language modeling [27], speech recognition [23], [24], computer vision [28], sentence classification [29], and machine translation [30], require the processing of text, images, and speech.…”

Section: Background and Literature Review A Backgroundmentioning

confidence: 99%

Predicting Insolvency of Insurance Companies in Egyptian Market Using Bagging and Boosting Ensemble Techniques

et al. 2022

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Insolvency is a crucial problem for several insurance companies that suffer from it. This problem has direct or indirect effects on both the people working in the financial business and normal citizens. Thus, in insurance companies, the ability to predict insolvency is in great demand. There are several efforts proposed to predict insurance company insolvency using computer science methods (e.g., support vector machine and fuzzy systems). Each country has its own data patterns due to interior matters. Thus, insurance companies from different countries may have different data patterns. Consequently, the utilized predictive model should adapt to the dataset at hand. To our best knowledge, despite there are several efforts to build an insolvency predictive model, none of these efforts explored the Egyptian market. In addition, even the existing efforts did not utilize the ensemble learning methods in the insolvency prediction problem. In this context, we have two main contributions to this work. First, we proposed the first public access dataset of Egyptian insurance companies. The collected dataset was gathered from 11 Egyptian insurance companies during the years 1999 to 2019. The dataset consists of a set of 22 ratios (21 input features and one output feature), e.g., retention and investment yield alongside the solvency ration (i.e., the target feature). In the second contribution, we proposed exploring the performance of the ensemble learning methods to address the insolvency prediction problem. Thus, we proposed building several insolvency predictive models using ensemble learning and classic machine learning models. Next, the proposed models are evaluated on different accuracy metrics, e.g., Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The experimental results revealed that the ensemble learning-based models outperformed the classic machine learning-based models. Moreover, the correlation analysis between the utilized 22 financial ratios revealed that the most significant ratios, for the task of predicting the solvency ratio, are the technical provisions to shareholders' funds, insurance companies' debit balances to shareholders, and earnings after taxes to shareholders' funds.

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A Robust UWSN Handover Prediction System Using Ensemble Learning

Cited by 18 publications

References 32 publications

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

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

Predicting Insolvency of Insurance Companies in Egyptian Market Using Bagging and Boosting Ensemble Techniques

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