Research
Intelligent Ticket Assignment System: Leveraging Deep Machine Learning for Enhanced Customer Support
In the evolving customer support domain, traditional ticketing systems struggle to meet increasing demands for speed and accuracy. This study presents an intelligent ticket assignment system leveraging BERT, Graph Neural Networks (GNN), and Prototypical Networks to enhance classification and routing efficiency. The methodology includes comprehensive preprocessing of historical ticket data, feature extraction using natural language processing (NLP), and model evaluation based on accuracy, precision, recall, and F1-score. Results indicate that BERT achieves the highest accuracy (89.4%), precision (88.7%), recall (90.2%), and F1-score (89.4%), outperforming GNN (87.6%) and Prototypical Networks (86.8%) by notable margins. A comparative analysis with Random Forest (85.3%) further demonstrates a 4.1% improvement in accuracy. The analysis demonstrates both performance strengths and real-life practicality and scalability characteristics of the system when managing high traffic volumes. Stability and predictive accuracy improved through the application of noise filtering alongside SMOTE oversampling and weighted loss functions for addressing data quality problems and class imbalance and model integration complexities. The research demonstrates how machine learning changes the way customer service operations work while showing AI models can boost service quality and operational effectiveness
Advancing Real-Time Crime Weapon Detection and High-Risk Person Classification in Pre-Crime Scenes: A Comprehensive Machine Vision Approach Utilizing SSD Detector
The application of state-of-the-art in deep learning detection algorithms, such as You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD), presents a significant opportunity for enhancing crime prevention and control strategies. This research focuses on leveraging the SSD algorithm to detect common crime weapons on individuals in both pre-crime video scenes and real-world crime scenarios. By thoroughly understanding the operational principles of the SSD algorithm, we adapted it for the identification of dangerous weapons commonly associated with violent crimes. Our detection model, which targets both weapons and individuals, establishes a robust foundation for an artificial intelligence (AI) system that accurately predicts individuals at high risk. The model first identifies the presence of a person and subsequently checks for any of the specified weapons. If a weapon is detected, the system further analyzes the individual's movement and speed within the frame of reference. Should the individual exceed a predetermined movement threshold, the system flags them as high risk. For this study, the SSD model utilized a VGG16 backbone and was trained on a dataset comprising 3,317 images, featuring four distinct weapon categories: handgun, shotgun, rifle, and knife. The dataset was collected from UCF, via Kaggle and complimented with additional weapons from Google download all, all the sources are secondary, open sources and loyalty-free. We achieved a mean average precision of 84.19% across five classes after training for 59 epochs The findings of this research demonstrate the effectiveness of the SSD algorithm in crime prevention and control, contributing to the ongoing discourse surrounding the application of detection algorithms for crime prediction. This work aims to provide technological innovations that can assist local law enforcement agencies in their operational duties. Additionally, the insights gained from this study may enhance the detection of abnormal behavior within the broader field of artificial intelligence.
