According to the most recent estimates from global cancer statistics for 2020, liver cancer is the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting the tumor from the liver adds some difficulty. After a sample of liver tissue is taken, imaging tests, such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US), are used to segment the liver and liver tumor. Due to overlapping intensity and variability in the position and shape of soft tissues, segmentation of the liver and tumor from computed abdominal tomography images based on shade gray or shapes is undesirable. This study proposed a more efficient method for segmenting liver and tumors from CT image volumes using a hybrid ResUNet model, combining the ResNet and UNet models to address this gap. The two overlapping models were primarily used in this study to segment the liver and for region of interest (ROI) assessment. Segmentation of the liver is done to examine the liver with an abdominal CT image volume. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01. Based on the experimental analysis, the true value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16%. The authentication rate of the dice coefficient also increased, indicating that the experiment went well and that the model is ready to use for the detection of liver tumors.

Image-based detection of human actions has recently emerged as a hot research area in the fields of computer vision and pattern recognition. It is concerned with detecting a person's actions or behavior from a static image. In this article, we are using a decision tree to develop an action recognition technique. In order to enhance the clarity of the video frames, the proposed method begins by implementing the HSI color transformation in the initial stage. Subsequently, it utilizes filters to minimize noise. The silhouette is extracted using a statistical method. We use SIFT and ORB for feature extraction. Next, using a parallel process, extract the shape and texture features needed for fusion applying name length control features. Additionally, the best high-dimensional data for classification is explored using vectors and the t-distributed stochastic neighbour embedding (t-SNE).The final step involves the features to be input into a decision tree, where they will be sorted into relevant human actions based on those final characteristics. The recognition rate of the UT interaction data used in the experimental process is 94.6%.

In computer vision and pattern recognition, image-based human activity detection has gained popularity as a research topic. The process of recognizing human behavior from an image is called activity recognition. Here, we put an XGBoost into practice to create an activity recognition system. The suggested method applies an HSV colour transformation in its initial phases to improve video frame clarity. Subsequently, it utilizes filters to minimize noise. Multiple object tracking (MOT) and VIBE techniques extract the silhouette. For feature extraction, we employ both Textone maps and FAST. In the next step, In order to conduct feature discrimination, Independent Component Analysis (ICA) is used to identify the most informative independent components that capture the data’s underlying structure. Subsequently, the features are inputted into XGBoost and categorized into pertinent human activities according to their final features. The experimental procedure makes use of the SBUInteraction dataset to attain a 91% recognition rate.

One of the most active subfields of computer vision and pattern recognition, human action recognition seeks to identify specific actions in still photos. The wide range of possible uses in this area has attracted a lot of interest from researchers in fields like human-computer interaction, video analysis, and surveillance. Researchers have developed a variety of algorithms and methods to accurately identify and classify the various actions that individuals performed while they were captured in photographs. An embedded HMM is going to be utilised in the process of developing an action recognition technique that will be presented in this article. It is recommended that the HSV colour transformation be carried out first in order to enhance the clarity of the video frames. The MOT and Vibe methods are used to get the silhouette. Feature extraction is accomplished through the utilisation of Texton Map and ORB. The fuzzy optimisation process is then utilised for the purpose of feature discrimination. After that, the features are input into an embedded HMM, where they are categorised into specific human actions based on the final qualities that they possess. The UT interaction data that was used in the experiment was recognised 93.8%.