Revolutionizing Customer Interaction: NLP-Powered Virtual Shopping Assistants and Sentiment Analysis in E-commerce

Dattatray G. Takale — 2024-03-26

The title of the research article investigates the revolutionary influence that Natural Language Processing (NLP) and sentiment analysis have had on the interactions that customers have with businesses that are involved in e-commerce. Among the primary goals are the investigation of the applications of natural language processing (NLP) in the powering of virtual shopping assistants and the evaluation of the relevance of sentiment analysis in comprehending the level of customer pleasure. An in-depth assessment of the current literature, case studies of successful implementations, and an analysis of customer evaluations and attitudes are all components of the technique. The results highlight the critical role that natural language processing plays in the enhancement of virtual shopping assistants, the improvement of product suggestions, and the simplification of the process of making a purchase choice. Additionally, sentiment analysis is essential for determining the feelings and feedback of customers, which in turn influences the strategic decisions that are made by organisations that deal in e-commerce enterprises.

Detection and Analysis of Anomalous Behavior in On-Orbit Satellites Using AI Algorithms

Iqtiar Md Siddique — 2024-08-29

The increasing deployment of satellites for essential applications necessitates robust anomaly detection to maintain their operational integrity. Traditional methods, which depend on manual monitoring and predefined thresholds, often prove inadequate in the complex space environment. This paper investigates the application of Artificial Intelligence (AI) algorithms to improve the detection and analysis of anomalous behavior in on-orbit satellites. AI, especially through machine learning (ML) and deep learning (DL), provides advanced capabilities for processing extensive telemetry data and identifying intricate patterns. By leveraging historical data, AI systems can establish normal operational parameters and detect deviations indicating potential anomalies. Techniques such as supervised and unsupervised learning are employed to develop models with high predictive accuracy. Furthermore, AI facilitates root cause analysis by correlating anomalies with operational conditions or external factors, enabling effective corrective measures. The integration of AI also promotes autonomous satellite operations, which are crucial for deep-space missions. This advancement enhances satellite reliability and safety, supporting sustainable and progressive space exploration. In this research, machine learning algorithms were employed to develop the proposed anomaly detection system. The system aims to detect subtle failures in the spacecraft’s attitude dynamics system, particularly in the reaction wheel subsystem, by learning solely from the spacecraft's nominal behavioral data. The system was developed from a small satellite's attitude dynamics control system, which may exhibit bearing failures in the reaction wheels. Two types of anomaly detection systems were introduced: a two-sided learning anomaly detection system and a one-sided learning anomaly detection system. For this study, a two-sided learning anomaly detection system was developed using the Logistic Regression (LR) method. This provided a foundation for the training process using a machine learning approach. By learning from both nominal and failure behaviors of the satellite, the system was designed to detect small reaction wheel friction failures effectively.

Journal of Firewall Software‎ and Networking (e-ISSN: 2584-1750)

Revolutionizing Customer Interaction: NLP-Powered Virtual Shopping Assistants and Sentiment Analysis in E-commerce

Detection and Analysis of Anomalous Behavior in On-Orbit Satellites Using AI Algorithms