This literature review attempts to compile and compare the most recent advancements in Machine Learning-based techniques for the detection and classification of bots on five primary social media platforms namely Facebook, Instagram, LinkedIn, Twitter, and Weibo. We bring forth a concise overview of a...
How does it work? Instead of learning about a topic in class, students use digital information and occasionalmicrolearning techniques, for example, watching short videos on a specific topic at home. The content of this homework is then explored and discussed in more detail in the classroom. In...
The potential of deep learning-based techniques to address imaging bottlenecks in the field of low-field MRI was demonstrated by Koonjoo et al.42 They use an end-to-end deep convolutional neural network approach to boost SNR in low-field MR images acquired from highly noise-corrupted data. We...
In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we...
Tips and Techniques for Incorporating Team-Based Learning (TBL) Methods into a College ClassroomTeam-based learningcritical thinkinggroup dynamicsstudent engagementlearning satisfactionTeam-based learning (TBL) methods provide instructors with an avenue to create student engagement and enhance learning ...
Learn about all the programming techniques in the GCSE Computer Science curriculum using MicroPython to program a micro:bit. Learners apply these techniques across several projects, mirroring real-world product development. pythoncomputer-scienceprogrammingmicrobitpblcomputinggcsephysical-computingmicro-pythonpro...
To obtain a high performance of person identification and authentication, deep-learning techniques are presented to learn and model the gait biometrics from the walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space ...
Deep learning-based feature extraction or classification techniques achieved high accuracy for AD classification but short of the lack of interpretability. Deep learning-based feature extraction methods need a large scale of data, which is hard to precisely define and varies on a different scale of ...
the implementation and use of many of these unsupervised behavior recognition approaches is out of reach of many basic science labs that lack the necessary programming and machine learning know-how. Therefore, widespread use/dissemination of new cutting-edge techniques will likely depend on their comme...
Extracting useful features at multiple scales is a crucial task in computer vision. The emergence of deep-learning techniques and the advancements in convolutional neural networks (CNNs) have facilitated effective multiscale feature extraction that resul