Human activity recognition (HAR) is an emerging challenge among researchers. HAR has many possible uses in various fields, including healthcare, sports, and security. Furthermore, there are only a few publicly accessible datasets for classifying and recognizing physical activity in the literature, and these datasets comprise fewer activities. We created and compared our dataset with available datasets, that is, NTU-RGBD, UP-FALL, UR-Fall, WISDM, and UCI HAR. The proposed dataset consists of seven activities: eating, exercise, handshake, situps, vomiting, headache, and walking. The activities were collected from 20 people between the ages of 25 and 40 years using Kinect V2 sensor at 30 FPS. For classification, we use deep learning architectures based on convolutional neural network (CNN) and long short-term memory (LSTM). Additionally, we developed a novel hybrid deep learning model by combining a CNN, a bidirectional LSTM unit, and a fully connected layer for activity identification. The suggested model builds unique guided features using the preprocessed skeleton coordinates and their distinctive geometrical and kinematic aspects. Results from the experiment are contrasted with the performance of stand-alone CNNs, LSTMs, and ConvLSTM. The proposed model’s accuracy of 99.5% surpasses that of CNN, LSTM, and ConvLSTM, which have accuracy rates of 95.76%, 97%, and 98.89%, respectively. Our proposed technique is invariant of stance, speed, individual, clothes, etc. The proposed dataset sample is accessible to the general public.

Wines are usually evaluated by wine experts and enthusiasts who give numeric ratings as well as text reviews. While most wine classification studies have been based on conventional statistical models using numeric variables, there has been very limited work on implementing neural network models using wine reviews. In this paper, we apply neural network models (CNN, BiLSTM, and BERT) to extract useful information from wine reviews and classify wines according to different rating classes. Using a large collection of wine reviews from Wine Spectator, the study shows that BERT, a neural network framework recently developed by Google, has the best performance. In the two-class classification (90–100 and 80–89), BERT achieves an accuracy of 89.12%, followed by BiLSTM (88.69%) and CNN (88.02%). In the four-class classification (95–100, 90–94, 85–89, and 80–84), BERT yields an 81.57% accuracy, while the other two produce an 80% accuracy. The neural network models in the paper are independent of domain knowledge and thus can be easily extended to other kinds of text analysis. Expanding the limited work on wine text review classification studies, these models are up-to-date and provide valuable additions to wine data analysis. (JEL Classifications: C45, C88, D83)