Pothole and Plain Road Classification Using Adaptive Mutation Dipper Throated Optimization and Transfer Learning for Self Driving Cars

Alhussan, Amel Ali; Khafaga, Doaa Sami; El-kenawy, El-Sayed M.; Ibrahim, Abdelhameed; Eid, Marwa M.; Abdelhamid, Abdelaziz A.

doi:10.1109/access.2022.3196660

Cited by 45 publications

(31 citation statements)

References 59 publications

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“…• Selection of the best solution: To ensure that the solutions are of excellent quality, the BER chooses the best to employ in the next cycle. While the elitism technique is more effective, it may lead to quick convergence [58,59]. The BER can deliver cutting-edge exploration capabilities by taking a mutational approach and scanning the explorers' surrounding area.…”

Section: ) Al-biruni Earth Radius (Ber) Optimizationmentioning

confidence: 99%

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Hadjouni

Abdelhamid

El-kenawy³

et al. 2023

IEEE Access

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Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middleincome countries. An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth. There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images. However, these are still lacking high precision in oral cancer detection. Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy. In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN). The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER). Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%. In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach. The proposed methodology is solid and efficient, and specialists can adopt it. However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.

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Section: ) Al-biruni Earth Radius (Ber) Optimizationmentioning

confidence: 99%

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Hadjouni

Abdelhamid

El-kenawy³

et al. 2023

IEEE Access

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“…The metrics used to assess the proposed methodology and their corresponding formulas are presented in Table 1. These metrics are: root mean square error (RMSE), normalized RMSE (NRMSE), Nash-Sutcliffe model efficiency (NSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R 2 metrics [42][43][44][45][46][47][48]. In these formulas, F i is the forecast daily COVID-19 value, A i is the actual daily COVID-19 value, y i is the observed daily COVID-19, x i is the model's simulated daily COVID-19, and n is the number of data points.…”

Section: Key Performance Indicatorsmentioning

confidence: 99%

Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

El-kenawy¹,

Abdelhamid²,

Ibrahim³

et al. 2023

Computer Systems Science and Engineering

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Several instances of pneumonia with no clear etiology were recorded in Wuhan, China, on December 31, 2019. The world health organization (WHO) called it COVID-19 that stands for "Coronavirus Disease 2019," which is the second version of the previously known severe acute respiratory syndrome (SARS) Coronavirus and identified in short as (SARSCoV-2). There have been regular restrictions to avoid the infection spread in all countries, including Saudi Arabia. The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread. Methodology: Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory (LSTM). The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius (BER) algorithm. Results: To evaluate the effectiveness of the proposed methodology, a dataset is collected based on the recorded cases in Saudi Arabia between March 7 th , 2020 and July 13 th , 2022. In addition, six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach. The achieved results show that the proposed approach could reduce the mean square error (MSE), mean absolute error (MAE), and R 2 by 5.92%, 3.66%, and 39.44%, respectively, when compared with the six base models. On the other hand, a statistical analysis is performed to measure the significance of the proposed approach. Conclusions: The achieved results confirm the effectiveness, superiority, and significance of the proposed approach in predicting the infection cases of COVID-19.

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“…Figure 2 illustrates the LSTM cell architecture. Based on the following formulas, the memory cell (m t ) is defined [37,38].…”

Section: Unidirectional Long Short-term Memory (Lstm)mentioning

confidence: 99%

Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

et al. 2022

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One of the relevant factors in smart energy management is the ability to predict the consumption of energy in smart households and use the resulting data for planning and operating energy generation. For the utility to save money on energy generation, it must be able to forecast electrical demands and schedule generation resources to meet the demand. In this paper, we propose an optimized deep network model for predicting future consumption of energy in smart households based on the Dipper Throated Optimization (DTO) algorithm and Long Short-Term Memory (LSTM). The proposed deep network consists of three parts, the first part contains a single layer of bidirectional LSTM, the second part contains a set of stacked unidirectional LSTM, and the third part contains a single layer of fully connected neurons. The design of the proposed deep network targets represents the temporal dependencies of energy consumption for boosting prediction accuracy. The parameters of the proposed deep network are optimized using the DTO algorithm. The proposed model is validated using the publicly available UCI household energy dataset. In comparison to the other competing machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Sequence-to-Sequence (Seq2Seq), and standard LSTM, the performance of the proposed model shows promising effectiveness and superiority when evaluated using eight evaluation criteria including Root Mean Square Error (RMSE) and R2. Experimental results show that the proposed optimized deep model achieved an RMSE of (0.0047) and R2 of (0.998), which outperform those values achieved by the other models. In addition, a sensitivity analysis is performed to study the stability and significance of the proposed approach. The recorded results confirm the effectiveness, superiority, and stability of the proposed approach in predicting the future consumption of energy in smart households.

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Pothole and Plain Road Classification Using Adaptive Mutation Dipper Throated Optimization and Transfer Learning for Self Driving Cars

Cited by 45 publications

References 59 publications

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

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