Comparison of different backpropagation training algorithms using robust M-estimators performance functions

Ellah, Ali R. Abd; Essai, Mohamed H.; Yahya, Ahmed

doi:10.1109/icces.2015.7393080

Cited by 14 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dataset is then contaminated in the x-y axis by Gaussian noise with a mean of zero and a standard deviation of 0.1, G2~N (0, 0.1). A variable percentage, ε, of data was randomly selected and then replaced with probability, ε, by background noise uniformly distributed in the specific range [24,26,29,31,32].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The DL algorithm has addressed detecting an anomaly in 5G networks regarding network latency [25]. In [26], the robustness of different ANNs training algorithms has been investigated using the robust M-estimators as a loss function in order to robustize learning in the presence of outliers. Maimó et al [27] studied DL's performance for abnormality discovery in 5G networks.…”

Section: Relevant Workmentioning

confidence: 99%

“…When gross errors or outliers contaminate the data, it leads to an unreliable method. To overcome the problem of outliers, several robust statistical estimators that are not severely affected by deviations from observations, such as M-estimators [20,21,23,24,26,29,31], R-estimators, L-estimators, and LTS [30], and LMedS [37], have been used in recent research papers. In this paper, we address the robust training of DNN based on using M-estimators as robust loss functions to substitute the conventional MSE loss function, which loses robustness in the presence of outliers in the V2X dataset.…”

Section: Robust Learning and Outliersmentioning

confidence: 99%

See 2 more Smart Citations

Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers

et al. 2021

View full text Add to dashboard Cite

Recently, 5G networks have emerged as a new technology that can control the advancement of telecommunication networks and transportation systems. Furthermore, 5G networks provide better network performance while reducing network traffic and complexity compared to current networks. Machine-learning techniques (ML) will help symmetric IoT applications become a significant new data source in the future. Symmetry is a widely studied pattern in various research areas, especially in wireless network traffic. The study of symmetric and asymmetric faults and outliers (anomalies) in network traffic is an important topic. Nowadays, deep learning (DL) is an advanced approach in challenging wireless networks such as network management and optimization, anomaly detection, predictive analysis, lifetime value prediction, etc. However, its performance depends on the efficiency of training samples. DL is designed to work with large datasets and uses complex algorithms to train the model. The occurrence of outliers in the raw data reduces the reliability of the training models. In this paper, the performance of Vehicle-to-Everything (V2X) traffic was estimated using the DL algorithm. A set of robust statistical estimators, called M-estimators, have been proposed as robust loss functions as an alternative to the traditional MSE loss function, to improve the training process and robustize DL in the presence of outliers. We demonstrate their robustness in the presence of outliers on V2X traffic datasets.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Relevant Workmentioning

confidence: 99%

Section: Robust Learning and Outliersmentioning

confidence: 99%

See 1 more Smart Citation

Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The conjugate gradient algorithms are usually much faster than the variable learning rate backpropagation. However, they require more storage than simple algorithms, so they are often a good choice for networks with a large number of weights [40].…”

Section: Training the Nfs With Cgfmentioning

confidence: 99%

Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

Atlam

Azad

Fadhel

2022

Sensors

View full text Add to dashboard Cite

Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg–Marquardt (LM), Conjugate Gradient with Fletcher–Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children’s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.

show abstract

“…While the traditional approach, the teaching of lectures and the use of textbooks contribute to ineffective learning around the world. To avoid the boredom in learning that only uses chalk and talk, it was inspired to develop AR (Abd Ellah, Essai & Yahya, 2015). Students believe that introducing technology will aid them in their learning process.…”

Section: Trainingmentioning

confidence: 99%

Implementation of Distance and Digital Learning During Pandemic Covid-19 in Malaysia

Ismail¹,

Syakir²,

Bahrudin³

et al. 2022

OJTP

View full text Add to dashboard Cite

Due to physical constraints preventing Covid-19 spread, TVET institutions are unable to develop and offer their courses effectively. Almost every country in the world has been affected by the pandemic Covid-19. Many students are having difficulty, limiting their access to a high-quality education. This scenario shines a light on how stakeholders can guarantee that the curriculum continues to operate even if students are unable to attend school. Many initiatives have been conducted, including curricular innovation and digitalization. The learning materials are transformed to digital form, and the curriculum delivery is done online. This involves the use of Open Educational Resources (OER). Many institutions are also implementing cutting-edge technology, such as Augmented Reality (AR) and Virtual Reality (VR), to improve student’s learning. However, challenges and issues occurred in terms of institutions' preparation to transition from traditional learning, and students' readiness in terms of physical equipmentand connectivity. The objective of this paper is to highlight the initiatives and efforts made by the TVET stakeholders in relation to distance and digital learning. This article will discuss the strategies and practices taken by the Malaysian Government and the TVET institutions to deliver quality education including the issues and challenges that most TVET institutions confront particularly the teachers and lecturers. The findings of this article elaborate the best practices initiated during pandemic. Furthermore, based on the online survey of 242 respondents from various TVET institutions, the student’s perception on distance learning is at moderate level with mean=2.875, SD=0.95. There is a need to enhance the student’s experience while taking distance and online learning. The outcome of this paper may be used as a guideline in the development and planning of TVET curriculum and delivery particularly on distance and online learning.

show abstract

Comparison of different backpropagation training algorithms using robust M-estimators performance functions

Cited by 14 publications

References 6 publications

Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers

Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers

Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

Implementation of Distance and Digital Learning During Pandemic Covid-19 in Malaysia

Contact Info

Product

Resources

About