An Efficient Person Re-Identification Model Based on New Regularization Technique

Albahar, Marwan Ali; Albahr, Abdulaziz; Binsawad, Muhammad

doi:10.1109/access.2020.3024120

Cited by 5 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach prevents the learning model from using a wide range of weight space by multiplying the standard deviation of the weight matrix by its parameter to obtain its regularization term. The standard deviation measures the spread of the weight values within the matrix and is computed by taking the square root of the variance of the weights [ [31] , [32] , [33] , [34] , [35] ].

…”

Section: Methodsmentioning

confidence: 99%

Exploring Hajj pilgrim satisfaction with hospitality services through expectation-confirmation theory and deep learning

Albahar,

Gazzawe,

Thanoon

et al. 2023

Heliyon

Self Cite

View full text Add to dashboard Cite

…”

Section: Methodsmentioning

confidence: 99%

Exploring Hajj pilgrim satisfaction with hospitality services through expectation-confirmation theory and deep learning

Albahar,

Gazzawe,

Thanoon

et al. 2023

Heliyon

Self Cite

View full text Add to dashboard Cite

“…us, the regularizer prevents the learning model from taking values from the weight space that are too widely distributed. In fact, the new regularizer has been extensively tested on various tasks with different datasets and proved to be more effective than other regularization methods [57,[76][77][78][79][80].…”

Section: Proposed Modelmentioning

confidence: 99%

A Modified ResNeXt for Android Malware Identification and Classification

Albahar

Elsayed

Jurcut

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

It is critical to successfully identify, mitigate, and fight against Android malware assaults, since Android malware has long been a significant threat to the security of Android applications. Identifying and categorizing dangerous applications into categories that are similar to one another are especially important in the development of a safe Android app ecosystem. The categorization of malware families may be used to improve the efficiency of the malware detection process as well as to systematically identify malicious trends. In this study, we proposed a modified ResNeXt model by embedding a new regularization technique to improve the classification task. In addition, we present a comprehensive evaluation of the Android malware classification and detection using our modified ResNeXt. The nonintuitive malware’s features are converted into fingerprint images in order to extract the rich information from the input data. In addition, we applied fine-tuned deep learning (DL) based on the convolutional neural network (CNN) on the visualized malware samples to automatically obtain the discriminatory features that separate normal from malicious data. Using DL techniques not only avoids the domain expert costs but also eliminates the frequent need for the feature engineering methods. Furthermore, we evaluated the effectiveness of the modified ResNeXt model in the classification process by testing a total of fifteen different combinations of the Android malware image sections on the Drebin dataset. In this study, we only use grayscale malware images from a modified ResNeXt to analyze the malware samples. The experimental results show that the modified ResNeXt successfully achieved an accuracy of 98.25% using Android certificates only. Furthermore, we undertook extensive trials on the dataset in order to confirm the efficacy of our methodology, and we compared our approach with several existing methods. Finally, this article reveals the evaluation of different models and a much more precise option for malware identification.

show abstract

“…Inspired by this, we advocate the use of Wasserstein loss to minimize the Wasserstein distance between the data distribution of low-coupling binary codes and the feature distribution of the derived binary local descriptors. Although the Wasserstein loss has been successfully employed in applications like person re-identification [37], [38], it has never been employed to learn binary local descriptors yet.…”

Section: Low-coupling Binary Local Descriptorsmentioning

confidence: 99%

Learning Transformation-Invariant Local Descriptors With Low-Coupling Binary Codes

Miao

Lin

et al. 2021

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Despite the great success achieved by prevailing binary local descriptors, they are still suffering from two problems: 1) vulnerable to the geometric transformations; 2) lack of an effective treatment to the highly-correlated bits that are generated by directly applying the scheme of image hashing. To tackle both limitations, we propose an unsupervised Transformationinvariant Binary Local Descriptor learning method (TBLD). Specifically, the transformation invariance of binary local descriptors is ensured by projecting the original patches and their transformed counterparts into an identical high-dimensional feature space and an identical low-dimensional descriptor space simultaneously. Meanwhile, it enforces the dissimilar image patches to have distinctive binary local descriptors. Moreover, to reduce high correlations between bits, we propose a bottom-up learning strategy, termed Adversarial Constraint Module, where low-coupling binary codes are introduced externally to guide the learning of binary local descriptors. With the aid of the Wasserstein loss, the framework is optimized to encourage the distribution of the generated binary local descriptors to mimic that of the introduced low-coupling binary codes, eventually making the former more low-coupling. Experimental results on three benchmark datasets well demonstrate the superiority of the proposed method over the state-of-the-art methods. The project page is available at https://github.com/yoqim/TBLD.

show abstract

An Efficient Person Re-Identification Model Based on New Regularization Technique

Cited by 5 publications

References 47 publications

Exploring Hajj pilgrim satisfaction with hospitality services through expectation-confirmation theory and deep learning

Exploring Hajj pilgrim satisfaction with hospitality services through expectation-confirmation theory and deep learning

A Modified ResNeXt for Android Malware Identification and Classification

Learning Transformation-Invariant Local Descriptors With Low-Coupling Binary Codes

Contact Info

Product

Resources

About