Distributed Network Coding-Aware Routing Protocol Incorporating Fuzzy-Logic-Based Forwarders in Wireless Ad hoc Networks

Rafiee, Parisa; Mirjalily, Ghasem

doi:10.1007/s10922-020-09537-w

Cited by 23 publications

(14 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For staging results, DETECT-LC average performance outperforms the next higher model with 0.03 of Acc, 0.08 of Sn and 0.14 of F1-Score. In Figure 14 To sum up, our proposed model manifested acceptable performance compared to the state of the art, which illustrates its success in handling the problem considered here, as well as other engineering problems [43][44][45][46].…”

Section: Lung Cancer Stagingmentioning

confidence: 85%

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

2022

View full text Add to dashboard Cite

Lung Cancer is one of the primary causes of cancer-related deaths worldwide. Timely diagnosis and precise staging are pivotal for treatment planning, and thus can lead to increased survival rates. The application of advanced machine learning techniques helps in effective diagnosis and staging. In this study, a multistage neurobased computational model is proposed, DETECT-LC learning. DETECT-LC handles the challenge of choosing discriminative CT slices for constructing 3D volumes, using Haralick, histogram-based radiomics, and unsupervised clustering. ALT-CNN-DENSE Net architecture is introduced as part of DETECT-LC for voxel-based classification. DETECT-LC offers an automatic threshold-based segmentation approach instead of the manual procedure, to help mitigate this burden for radiologists and clinicians. Also, DETECT-LC presents a slice selection approach and a newly proposed relatively light weight 3D CNN architecture to improve existing studies performance. The proposed pipeline is employed for tumor phenotyping and staging. DETECT-LC performance is assessed through a range of experiments, in which DETECT-LC attains outstanding performance surpassing its counterparts in terms of accuracy, sensitivity, F1-score and Area under Curve (AuC). For histopathology classification, DETECT-LC average performance achieved an improvement of 20% in overall accuracy, 0.19 in sensitivity, 0.16 in F1-Score and 0.16 in AuC over the state of the art. A similar enhancement is reached for staging, where higher overall accuracy, sensitivity and F1-score are attained with differences of 8%, 0.08 and 0.14.

show abstract

Section: Lung Cancer Stagingmentioning

confidence: 85%

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

2022

View full text Add to dashboard Cite

show abstract

“…In addition to the mentioned advantages, machine learning has different applications in various engineering fields (e.g., [42][43][44][45]). Increasing interest in machine learning is because of various data, better computational tools and processing that make computation cheaper and more powerful.…”

Section: Discussionmentioning

confidence: 99%

Applying Machine Learning to Explore Feelings about Sharing the Road with Autonomous Vehicles as a Bicyclist or as a Pedestrian

2022

View full text Add to dashboard Cite

The current literature on public perceptions of autonomous vehicles focuses on potential users and the target market. However, autonomous vehicles need to operate in a mixed traffic condition, and it is essential to consider the perceptions of road users, especially vulnerable road users. This paper builds explicitly on the limitations of previous studies that did not include a wide range of road users, especially vulnerable road users who often receive less priority. Therefore, this paper considers the perceptions of vulnerable road users towards sharing roads with autonomous vehicles. The data were collected from 795 people. Extreme gradient boosting (XGBoost) and random forests are used to select the most influential independent variables. Then, a decision tree-based model is used to explore the effects of the selected most effective variables on the respondents who approve the use of public streets as a proving ground for autonomous vehicles. The results show that the effect of autonomous vehicles on traffic injuries and fatalities, being safe to share the road with autonomous vehicles, the Elaine Herzberg accident and its outcome, and maximum speed when operating in autonomous are the most influential variables. The results can be used by authorities, companies, policymakers, planners, and other stakeholders.

show abstract

“…Prediction and/or classification tasks are frequently found in many scientific and engineering fields with a large amount of potential artificial intelligence related techniques. The specific topics covered are rather diverse, including weather forecasts [21], plane flight time deviation [22], distributed networks [23], and many others [24][25][26]. One frequently used set of techniques are artificial neural networks.…”

Section: Forecasting and Classification Modelsmentioning

confidence: 99%

“…It is assumed, for simplicity, without loss of generality that S 1 max < S 1 max−1 . If that it is not the case then the interval needs to be switched ({S Iter t ≥ Iter max (23) or until the desire hit rate (HR desired ) is reached HR(S) ≤ HR desired (24) or until no further HR improvement is achieved. Select S t max .…”

Section: Main Algorithmmentioning

confidence: 99%

Alzheimer Identification through DNA Methylation and Artificial Intelligence Techniques

Perez

Caballero-Villarraso

2021

Mathematics

View full text Add to dashboard Cite

A nonlinear approach to identifying combinations of CpGs DNA methylation data, as biomarkers for Alzheimer (AD) disease, is presented in this paper. It will be shown that the presented algorithm can substantially reduce the amount of CpGs used while generating forecasts that are more accurate than using all the CpGs available. It is assumed that the process, in principle, can be non-linear; hence, a non-linear approach might be more appropriate. The proposed algorithm selects which CpGs to use as input data in a classification problem that tries to distinguish between patients suffering from AD and healthy control individuals. This type of classification problem is suitable for techniques, such as support vector machines. The algorithm was used both at a single dataset level, as well as using multiple datasets. Developing robust algorithms for multi-datasets is challenging, due to the impact that small differences in laboratory procedures have in the obtained data. The approach that was followed in the paper can be expanded to multiple datasets, allowing for a gradual more granular understanding of the underlying process. A 92% successful classification rate was obtained, using the proposed method, which is a higher value than the result obtained using all the CpGs available. This is likely due to the reduction in the dimensionality of the data obtained by the algorithm that, in turn, helps to reduce the risk of reaching a local minima.

show abstract

Distributed Network Coding-Aware Routing Protocol Incorporating Fuzzy-Logic-Based Forwarders in Wireless Ad hoc Networks

Cited by 23 publications

References 40 publications

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

Applying Machine Learning to Explore Feelings about Sharing the Road with Autonomous Vehicles as a Bicyclist or as a Pedestrian

Alzheimer Identification through DNA Methylation and Artificial Intelligence Techniques

Contact Info

Product

Resources

About