Person identification is one of the most vital tasks for network security. People are more concerned about their security due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprints and faces have been widely used for person identification, which has the risk of information leakage as a result of reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiable pattern, which will not be reproducible falsely by capturing psychological and behavioral information of a person using vision and sensor-based techniques. In existing studies, most of the researchers used very complex patterns in this direction, which need special training and attention to remember the patterns and failed to capture the psychological and behavioral information of a person properly. To overcome these problems, this research devised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. This study developed two hand gesture-based pattern datasets for performing the experiments, which contained more than 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the hand geometry. Random forest was used to measure feature importance using the Gini Index. Finally, the support vector machine was implemented for person identification and evaluate its performance using identification accuracy. The experimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitrary hand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicated that the proposed system can be used for person identification in the field of security.

Sentiment Analysis, a significant domain within Natural Language Processing (NLP), focuses on extracting and interpreting subjective information—such as emotions, opinions, and attitudes—from textual data. With the increasing volume of user-generated content on social media and digital platforms, sentiment analysis has become essential for deriving actionable insights across various sectors. This study presents a systematic literature review of sentiment analysis methodologies, encompassing traditional machine learning algorithms, lexicon-based approaches, and recent advancements in deep learning techniques. The review follows a structured protocol comprising three phases: planning, execution, and analysis/reporting. During the execution phase, 67 peer-reviewed articles were initially retrieved, with 25 meeting predefined inclusion and exclusion criteria. The analysis phase involved a detailed examination of each study’s methodology, experimental setup, and key contributions. Among the deep learning models evaluated, Long Short-Term Memory (LSTM) networks were identified as the most frequently adopted architecture for sentiment classification tasks. This review highlights current trends, technical challenges, and emerging opportunities in the field, providing valuable guidance for future research and development in applications such as market analysis, public health monitoring, financial forecasting, and crisis management.

Activity recognition is a challenging topic in the field of computer vision that has various applications, including surveillance systems, industrial automation, and human-computer interaction. Today, the demand for automation has greatly increased across industries worldwide. Real-time detection requires edge devices with limited computational time. This study proposes a novel hybrid deep learning system for human activity recognition (HAR), aiming to enhance the recognition accuracy and reduce the computational time. The proposed system combines a pre-trained image classification model with a sequence analysis model. First, the dataset was divided into a training set (70%), validation set (10%), and test set (20%). Second, all the videos were converted into frames and deep-based features were extracted from each frame using convolutional neural networks (CNNs) with a vision transformer. Following that, bidirectional long short-term memory (BiLSTM)- and temporal convolutional network (TCN)-based models were trained using the training set, and their performances were evaluated using the validation set and test set. Four benchmark datasets (UCF11, UCF50, UCF101, and JHMDB) were used to evaluate the performance of the proposed HAR-based system. The experimental results showed that the combination of ConvNeXt and the TCN-based model achieved a recognition accuracy of 97.73% for UCF11, 98.81% for UCF50, 98.46% for UCF101, and 83.38% for JHMDB, respectively. This represents improvements in the recognition accuracy of 4%, 2.67%, 3.67%, and 7.08% for the UCF11, UCF50, UCF101, and JHMDB datasets, respectively, over existing models. Moreover, the proposed HAR-based system obtained superior recognition accuracy, shorter computational times, and minimal memory usage compared to the existing models.