Improved Very Deep Recurrent Convolutional Neural Network for Object Recognition

Brahimi, Sourour; Aoun, Najib Ben; Amar, Chokri Ben

doi:10.1109/smc.2018.00428

Cited by 12 publications

(4 citation statements)

References 14 publications

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“…The segmentation techniques play an essential role in the diagnosis, feature extraction, and classification accuracy of breast masses as benign and malignant. Different deep learning segmentation methods are used for breast cancer images such as FCN [ 8 ], U-Net [ 9 , 26 , 27 ], Segmentation Network (SegNet) [ 28 ], Full Resolution Convolutional Network (FrCN) [ 29 ], mask Region-Based Convolutional Neural Networks mask (RCNNs) [ 11 , 30 ], Attention guided dense up-sampling network(Aunet) [ 31 ], Residual attention U-Net model (RUNet) [ 32 ], conditional Generative Adversarial Networks (cGANs) [ 33 ], Densely connected U-Net and attention gates (AGs) [ 34 ], and Conditional random field model (CRF) [ 35 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

AlEisa

Touiti

Alhussan

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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In this paper, a new classification approach of breast cancer based on Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE) is presented. FCN, as a powerful image segmentation model, is used to extract the relevant information from mammography images. It will identify the relevant zones to model while WAE is used to model the extracted information for these zones. In fact, WAE has proven its superiority to the majority of the features extraction approaches. The fusion of these two techniques have improved the feature extraction phase and this by keeping and modeling only the relevant and useful features for the identification and description of breast masses. The experimental results showed the effectiveness of our proposed method which has given very encouraging results in comparison with the states of the art approaches on the same mammographic image base. A precision rate of 94% for benign and 93% for malignant was achieved with a recall rate of 92% for benign and 95% for malignant. For the normal case, we were able to reach a rate of 100%.

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Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

AlEisa

Touiti

Alhussan

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…A proper systematic process needs to analyze the relationship between frustrations, severity, and the adverse influence on students' future performance. e previous studies have various models that can be replicated to optimize the existing prediction systems [23][24][25][26][27][28][29]. Moreover, the instructor usually overcomes students' frustration via collaborative assignments and class activities to provide the best opportunities for learning [30].…”

Section: Literaturementioning

confidence: 99%

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

Ahmad

Aoun

ElAffendi

et al. 2022

Computational Intelligence and Neuroscience

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Recent articles reported a massive increase in frustration among weak students due to the outbreak of COVID-19 and Massive Open Online Courses (MOOCs). These students need to be evaluated to detect possible psychological counseling and extra attention. On the one hand, the literature reports many optimization techniques focusing on existing students’ performance prediction systems. On the other hand, psychological works provide insights into massive research findings focusing on various students’ emotions, including frustration. However, the synchronization among these contributions is still a black box, which delays the mathematical modeling of students’ frustration. Also, the literature is still limited in using insights of psychology and assumption-based datasets to provide an in-house iterative procedure for modeling students’ frustration severity. This paper proposes an optimization technique called the iterative model of frustration severity (IMFS) to explore the black box. It analyzes students’ performance via two modules. First, frustration is divided into four outer layers. Second, the students’ performance outcome is split into 34 inner layers. The prediction results are iteratively optimized under the umbrella of frustration severity layers through the outer and inner iterations. During validation, the IMFS achieves promising results with various evaluation measures.

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“…The exploitation of big data technology enables transportation systems to analyze a significant amount of stream data to prevent traffic jams, incidents, delays, or track given vehicles in real-time. Big data technology has been used for object detection Brahimi et al (2017;2018a), Event detection Aoun et al (2011;2014), and semantic segmentation Brahimi et al (2018b;2019) and had an impact on users' lifestyles. Indeed, the whole data of the traffic coming from several sources of information can be crossed for an effective decision-making aid concerning the mobility of the travelers.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Big Data Framework for Real-Time Traffic Monitoring System

Adoni¹,

Aoun²,

Nahhal³

et al. 2022

Journal of Computer Science

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In this study, a scalable and real-time intelligent transportation system based on a big data framework is presented. The proposed system allows for the use of existing data from road sensors to better understand traffic flow, and traveler behavior and increase road network performance. Our transportation system is designed to process large-scale stream data to analyze traffic events such as incidents, crashes, and congestion. The experiments performed on the public transportation modes of the city of Casablanca in Morocco reveal that the proposed system achieves a significant gain of time, gathers largescale data from many road sensors, and is not expensive in terms of hardware resource consumption.

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Improved Very Deep Recurrent Convolutional Neural Network for Object Recognition

Cited by 12 publications

References 14 publications

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

A Scalable Big Data Framework for Real-Time Traffic Monitoring System

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