Enhancing Digital Image Forgery Detection Using Transfer Learning

 Nowadays, digital images are a main source of shared information in social media. Meanwhile, malicious software can forge such images for fake information. So, it’s crucial to identify these forgeries. This problem was tackled in the literature by various digital image forgery detection techniques. But most of these techniques are tied to detec...

Digital Image Forgery Transfer Learning MobileNetV2 Convolutional neural network (CNN) ResNet VGG

ENRICHING THE TRANSFER LEARNING WITH PRE-TRAINED LEXICON EMBEDDINGFOR LOW-RESOURCE NEURAL MACHINE TRANSLATION

 Most State-Of-The-Art (SOTA) Neural Machine Translation (NMT) systems today achieve outstanding results based only on large parallel corpora. The large-scale parallel corpora for high-resource languages is easily obtainable. However, the translation quality of NMT for morphologically rich languages is still unsatisfactory, mainly because of the dat...

MACHINE LEARNING ENRICHING THE TRANSFER LEARNING

AN ONLINE TRANSFER LEARNING FRAMEWORK WITH EXTREME LEARNING MACHINFOR AUTOMATED CREDIT SCORINGE

 Automated Credit Scoring (ACS) is the process of predicting user credit based on historical data. It involves analyzing and predicting the association between the data and particular credit values based on similar data. Recently, ACS has been handled as a machine learning problem, and numerous models were developed to address it. In this paper, we ...

MACHINE LEARNING AN ONLINE TRANSFER LEARNING

META-TRANSFER LEARNING THROUGH HARD TASKS

 Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples o...

MACHINE LEARNING META-TRANSFER

A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING

 A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING...

DEEP LEARNING TRANSFER LEARNING

A Transfer Learning Approach for Securing Resource-Constrained IoT Devices

 In recent years, Internet of Things (IoT) security has attracted significant interest by researchers due to new characteristics of IoT such as heterogeneity of devices, resource constraints, and new types of attacks targeting IoT. Intrusion detection, which is an indispensable part of a security system, is also included in these studies. In order t...

Transfer Learning Promotes 6G Wireless Communications Recent Advances and Future Challenges

 In the coming 6G communications, network densification, high throughput, positioning accuracy, energy efficiency, and many other key performance indicator requirements are becoming increasingly strict. In the future, how to improve work efficiency while saving costs is one of the foremost research directions in wireless communications. Being able t...