Human action recognition (HAR) by skeleton data is considered a potential research aspect in computer vision. Three-dimensional HAR with skeleton data has been used commonly because of its effective and efficient results. Several models have been developed for learning spatiotemporal parameters from skeleton sequences. However, two critical problems exist: (1) previous skeleton sequences were created by connecting different joints with a static order; (2) earlier methods were not efficient enough to focus on valuable joints. Specifically, this study aimed to (1) demonstrate the ability of convolutional neural networks to learn spatiotemporal parameters of skeleton sequences from different frames of human action, and (2) to combine the process of all frames created by different human actions and fit in the spatial structure information necessary for action recognition, using multi-task learning networks (MTLNs). The results were significantly improved compared with existing models by executing the proposed model on an NTU RGB+D dataset, an SYSU dataset, and an SBU Kinetic Interaction dataset. We further implemented our model on noisy expected poses from subgroups of the Kinetics dataset and the UCF101 dataset. The experimental results also showed significant improvement using our proposed model.

Modern technological advancements have made social media an essential component of daily life. Social media allow individuals to share thoughts, emotions, and ideas. Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive, negative, neutral, or any other personal emotion to understand the sentiment context of the text. Sentiment analysis is essential in business and society because it impacts strategic decision-making. Sentiment analysis involves challenges due to lexical variation, an unlabeled dataset, and text distance correlations. The execution time increases due to the sequential processing of the sequence models. However, the calculation times for the Transformer models are reduced because of the parallel processing. This study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their limitations. In particular, the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers (BERT) attention (DeBERTa) and the Gated Recurrent Unit (GRU) for sentiment analysis. Using the Decoding-enhanced BERT technique, the words are mapped into a compact, semantic word embedding space, and the Gated Recurrent Unit model can capture the distance contextual semantics correctly. The proposed hybrid model achieves F1-scores of 97% on the Twitter Large Language Model (LLM) dataset, which is much higher than the performance of new techniques.

Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers such as lung, breast, colorectal, liver, stomach, esophageal, cervical, thyroid, prostate, and skin cancers. The primary objective of this paper is to highlight the significant advancements that AI algorithms have brought to oncology within the medical industry. By enabling early cancer detection, improving diagnostic accuracy, and facilitating targeted treatment delivery, AI contributes to substantial improvements in patient outcomes. The integration of AI in medical imaging, genomic analysis, and pathology enhances diagnostic precision and introduces a novel, less invasive approach to cancer screening. This not only boosts the effectiveness of medical facilities but also reduces operational costs. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. Furthermore, it investigates the impact of AI on addressing healthcare challenges, particularly in underserved and remote regions. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures. By reviewing existing research and clinical studies, this paper underscores the pivotal role of AI in improving the overall cancer care system. It emphasizes how AI-enabled systems can enhance clinical decision-making and expand treatment options, thereby underscoring the importance of AI in advancing precision oncology

Human Action Recognition (HAR) using skeletons has become increasingly appealing to a growing number of researchers in recent years. It is particularly challenging to recognize actions when they are captured from different angles because there are so many variations in their representations. This paper proposes an automatic strategy for determining virtual observation viewpoints that are based on learning and data-driven to solve the problem of view variation throughout an act. Our VA-CNN and VA-RNN networks, which use convolutional and recurrent neural networks with the Long Short-term Memory (LSTM), offer an alternative to the conventional method of reorienting skeletons according to a human-defined earlier benchmark. Using the unique view adaption module, each network first identifies the best observation perspectives and then transforms the skeletons for end-to-end detection with the main classification network based on those viewpoints. Using the skeletons of different perspectives, the suggested view adaptive models can provide significantly more consistent virtual viewpoints. By removing views, the models allow networks to learn action-specific properties more efficiently. Furthermore, we developed a two-stream scheme (referred to as VA-fusion) that integrates the performance of two networks to obtain an improved prediction. Random rotation of skeletal sequences is used to avoid overfitting during training and improve the reliability of view adaption models. An extensive experiment demonstrates that our proposed view-adaptive networks outperform existing solutions on five challenging benchmarks.