A Survey on Deep Learning Techniques for Sentiment Analysis

Social media is a rich source of information nowadays. If we look into social media, sentiment analysis is one of the challenging problems. Sentiment analysis is a substantial area of research in the field of Natural Language Processing. This survey paper reviews and provides the comparative study of deep learning approaches CNN, RNN, LSTM and ensemble-based methods

Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF

Feature detection and feature matching are the most crucial parts in visual odometry process. In order to suit the real time process in visual odometry, both of the stages must be robust but at the same time are fast to compute. This paper presents the evaluation of Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) performances. The results show that SURF is outperform than SIFT in term of rate of matched points and also in computational time

Enhancing Spammer Fake Profile Detection on Social Media Platforms using Artificial Neural Networks

The proliferation of social media platforms has led to an increase in spammer fake profiles, posing significant security, privacy, and trustworthiness concerns. Traditional manual monitoring and content filtering techniques are insufficient to combat this growing issue, necessitating the development of more efficient and accurate detection methods. Machine learning techniques have been increasingly employed for this purpose, demonstrating promising results in identifying spammers and fake profiles. This paper presents a novel approach for spammer fake profile detection using Artificial Neural Networks (ANNs) to enhance the accuracy of the detection process. Our proposed ANN-based method addresses the challenges associated with spammer fake profile detection, such as the dynamic nature of spammers, data heterogeneity, scalability, and imbalanced datasets. We evaluate the performance of our method on real-world datasets and compare it with existing machine learning techniques, demonstrating its effectiveness and superiority in detecting spammers and fake profiles with higher accuracy. This research contributes to ongoing efforts to secure social media platforms, ensuring the trustworthiness of online content and providing a safer user experience

Machine Learning Approaches for Fake User and Spammer Detection: A Comprehensive Review and Future Perspectives

The rise of digital platforms has given way to a surge in fraudulent activities, including the creation of fake user accounts and the prevalence of spammers. These malevolent actions present significant challenges to the security and integrity of these platforms, necessitating effective detection and prevention measures. This paper offers an extensive review of machine learning (ML) techniques currently employed for fake user and spammer detection. The paper explores a range of traditional ML algorithms such as decision trees, support vector machines, and logistic regression, as well as more complex deep learning models like convolutional neural networks (CNN) and recurrent neural networks (RNN). It also examines unsupervised and semi-supervised learning strategies that can be used when labeled data is scarce. Furthermore, we discuss the key challenges in detecting fake users and spammers, including the dynamic nature of spamming tactics, evolving deceptive strategies, data imbalance, and privacy issues. We propose potential solutions to these challenges like transfer learning, active learning, federated learning, and privacy-preserving ML techniques. The paper concludes with an exploration of emerging technologies such as explainable AI and reinforcement learning and their potential to enhance detection system performance and interpretability. It also provides insights into promising future research directions in this critical area

3D Motion and Skeleton Construction from Monocular Video

We describe 3D motion construction as a framework for constructing a 3D motion and skeleton using a monocular video source (2D video). The processes include 3 main phases which involved in generating ground truth 2D annotation using OpenPose, generating the mesh and 3D matrices of person in the sequences of images based on 2D annotation and bounding box and lastly using the matrices to create a HIK 3D skeleton in a standalone Maya application. For the process, we highly relied on the 2D annotation using Convolution Neural Network. We demonstrate our result using a video of Malaysia Traditional Dance called zapin