A Hybrid Deep Learning Based Autonomous Vehicle Navigation and Obstacles Avoidance

Ibrahim, Habiba; Azar, Ahmad Taher; Ibrahim, Zahra Fathy; Ammar, Hossam Hassan

doi:10.1007/978-3-030-44289-7_28

Cited by 28 publications

(9 citation statements)

References 15 publications

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“…Deep learning tools have been widely used and successfully applied in a variety of related research areas (Ganesan et al, 2022;Boulmaiz et al, 2022;Yu et al, 2021;Amin et al, 2021Amin et al, , 2020aAmin et al, ,b,c, 2019Musallam et al, 2021;Altaheri et al, 2021;Ibrahim et al, 2020;Mohamed et al, 2020;Sayed et al, 2020;Elkholy et al, 2020a,b). Various deep learning algorithms have been used for cancer diagnosis and detection, as well as tumor segmentation.…”

Section: Related Workmentioning

confidence: 99%

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Azar

Tounsi

Fati

et al. 2023

International Journal of Sociotechnology and Knowledge Development

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Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.

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Section: Related Workmentioning

confidence: 99%

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Azar

Tounsi

Fati

et al. 2023

International Journal of Sociotechnology and Knowledge Development

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“…Artificial Intelligence (AI) is expanding at a rapid pace and finding new applications in a wide range of fields, including security, the environment, research and education, health, culture, and commerce (Khamis et al, 2022(Khamis et al, , 2021Ali et al, 2022a,b;Soliman et al, 2020;Aziz et al, 2013aAziz et al, ,b, 2012Azar et al, 2023Azar et al, , 2022Azar et al, , 2021cAzar et al, ,d,e, 2020aAzar et al, ,b,c,d,e,f,g,h,i,j,k,l,m,n, 2019aAzar et al, ,b,c, 2018aAzar et al, ,b, 2017Azar et al, , 2016aAzar et al, ,b, 2014Azar et al, , 2013aAzar et al, ,b, 2012Azar et al, , 2007Jothi et al, 2022Jothi et al, , 2020Jothi et al, , 2019aJothi et al, ,b, 2017Jothi et al, , 2016Jothi et al, , 2013Nasser et al, 2021;Hamida et al, 2022;Fouad et al, 2021;Azar & Serrano, 2019;Samanta et al, 2018, ElBedwehy et al, 2014Mukherjee et al, 2014). Deep learning technology, which evolved from Artificial Neural Networks (ANN), has become a major issue in the computer world and is widely used in fields such as healthcare, image identification, text analytics, cybersecurity, and many more (Dudekula et al, 2023;Fati et al, 2022;Boulmaiz et al, 2022, Zaidi et al 2022Ganesan et al, 2022;Abbas et al, 2022;Azar et al, 2021a,b;Ibrahim et al, 2020;…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Nivetha

Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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Coronavirus sickness (COVID-19) recently adversely disrupted the medical care system and the entire economy. Doctors, researchers, and specialists are working on new-fangled methods to detect COVID-19 relatively efficiently, such as constructing computerized COVID-19 detection systems. Medical imaging, such as Computed Tomography (CT), has a lot of opportunity as a solution to RT-PCR approaches for quantitative assessment and disease monitoring. COVID-19 diagnosis based on CT images can provide speedy and accurate results. A quantitative criterion for diagnosis is provided by an automated segmentation method of infection areas in the lungs. As an outcome, automatic image segmentation is in high demand as a clinical decision aid tool. To detect COVID-19, Computed Tomography images might be employed instead of the time-consuming RT-PCR assay. In this research, a unique technique is provided for segmenting infection areas in the lungs using CT scan images from COVID-19 patients. “Ground Glass Opacity (GGO)” regions were detected using Novel Adaptive Histogram Binning Based Lesion Segmentation (NAHBLS) method. Many metrics were also employed to evaluate the proposed method, including “Sorensen–Dice similarity”, “Sensitivity”, “Specificity”, “Precision”, and “Accuracy” measures. Experiments have shown that the proposed method can effectively separate the lung infections with good accuracy. The results show that the proposed Novel Adaptive Histogram Binning Based Lesion Segmentation based on automatic approach is effective at segmenting the lesion region of the image and calculated the Infection Rate (IR) over the lung region in Computed Tomography scan.

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“…In [12], the parameters of type-2 FL system were trained using extended Kalman filter. The RL proves to be one of the most powerful tools for optimizing a controller [13]. Another FL Q-learning method was tested on a complex cooperative multi-agent system [14].…”

Section: Related Workmentioning

confidence: 99%

A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms

Aksjonov¹,

Kyrki²

2022

Preprint

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While artificial-intelligence-based methods suffer from lack of transparency, rule-based methods dominate in safety-critical systems. Yet, the latter cannot compete with the first ones in robustness to multiple requirements, for instance, simultaneously addressing safety, comfort, and efficiency. Hence, to benefit from both methods they must be joined in a single system. This paper proposes a decision making and control framework, which profits from advantages of both the rule-and machine-learning-based techniques while compensating for their disadvantages. The proposed method embodies two controllers operating in parallel, called Safety and Learned. A rule-based switching logic selects one of the actions transmitted from both controllers. The Safety controller is prioritized every time, when the Learned one does not meet the safety constraint, and also directly participates in the safe Learned controller training. Decision making and control in autonomous driving is chosen as the system case study, where an autonomous vehicle learns a multi-task policy to safely cross an unprotected intersection. Multiple requirements (i.e., safety, efficiency, and comfort) are set for vehicle operation. A numerical simulation is performed for the proposed framework validation, where its ability to satisfy the requirements and robustness to changing environment is successfully demonstrated.

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A Hybrid Deep Learning Based Autonomous Vehicle Navigation and Obstacles Avoidance

Cited by 28 publications

References 15 publications

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms

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