Enterprise AI Analysis
Reinforcement learning-driven feature selection enhanced by an evolutionary approach tuning for criminal suspect identification
This study presents a novel deep learning model for criminal suspect identification, integrating an Off-policy Proximal Policy Optimization (PPO) strategy for dynamic feature selection (FS) and class imbalance management, enhanced by a Differential Evolution (DE) algorithm for hyperparameter tuning. It addresses key limitations of traditional CNN-based methods, such as fixed feature sets, biased predictions from imbalanced datasets, and manual hyperparameter tuning. The model, harnessing CNNs and MLPs, uses a tailored reward function in Off-policy PPO to prioritize crucial features and minority classes. A novel k-means clustering-based mutation strategy within the DE algorithm further refines hyperparameter optimization. Evaluated on CelebA, LFW, CASIA-WebFace, and VGGFace2 datasets, the model achieves F-measures of 89.409%, 91.152%, 92.184%, and 92.202% respectively, significantly outperforming existing methods and advancing early suspect detection and investigative strategies.
Executive Impact: Quantifiable Results
The proposed AI model delivers significant, measurable improvements in criminal suspect identification across key performance indicators.
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Average F-measure
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CelebA F-measure
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VGGFace2 F-measure
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Performance Improvement vs. ML
Deep Analysis & Enterprise Applications
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Reinforcement Learning (RL) Framework
The core of our approach is an Off-policy Proximal Policy Optimization (PPO) algorithm. Unlike traditional supervised methods, RL dynamically adjusts feature selection based on contextual requirements, pinpointing the most vital features for criminal suspect identification. This adaptive mechanism is crucial for handling complex, high-dimensional, and often imbalanced datasets, ensuring higher accuracy and efficiency.
Dynamic Feature Selection and Class Imbalance Management
Off-policy PPO is strategically employed to address feature selection and class imbalance. It adaptively focuses on informative features while filtering irrelevant data, capturing complex non-linear facial patterns often overlooked by static methods. By assigning higher rewards to correctly identified minority samples, the model increases sensitivity to underrepresented classes without overfitting or data duplication, enhancing robustness in real-world scenarios.
Enhanced Hyperparameter Optimization
To overcome the challenges of manual hyperparameter tuning, a sophisticated Differential Evolution (DE) algorithm is integrated. This enhanced DE uses a novel mutation strategy based on k-means clustering to effectively identify optimal parameter clusters. This automation reduces manual effort, boosts model stability, and ensures optimal performance, providing a systematic and comprehensive search of the parameter space for better model configurations.
Deep Learning for Facial Recognition
The model harnesses Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs) as its policy network. CNNs are powerful in capturing visual patterns, while MLPs aid in classification. This integration allows the model to learn complex patterns automatically, providing advanced feature extraction and robust classification methods critical for accurate criminal suspect recognition.
Enterprise Process Flow
Input Image
→
CNN Feature Extraction
→
Off-policy PPO (FS & Imbalance)
→
DE (Hyperparameter Tuning)
→
Suspect Classification
91.237%
Average F-measure across all datasets, demonstrating superior balanced accuracy.
| Model | Accuracy | F-measure | G-means | AUC |
|---|---|---|---|---|
| Proposed | 87.951±0.096 | 89.409±0.087 | 90.193±0.030 | 0.829±0.073 |
| GAN-DSAEAN47 | 82.650±0.010 | 83.961±0.009 | 84.509±0.054 | 0.786±0.032 |
| DNVPT4 | 82.443±0.008 | 85.048±0.033 | 85.765±0.046 | 0.773±0.014 |
| Proposed w/o FS | 81.251±0.010 | 83.067±0.097 | 83.846±0.057 | 0.820±0.044 |
| Proposed w/o Off-policy PPO | 82.600±0.070 | 87.180±0.046 | 87.942±0.068 | 0.808±0.032 |
| Proposed w/o HO | 83.742±0.024 | 88.251±0.015 | 89.007±0.066 | 0.816±0.076 |
Enhanced Hyperparameter Optimization with K-means Clustering
The proposed Differential Evolution (DE) algorithm significantly enhances model performance by integrating a novel mutation strategy based on k-means clustering. This approach efficiently identifies and optimizes key parameters, avoiding local optima and accelerating convergence across diverse datasets. For instance, on CelebA, the proposed DE achieves a 9.7% higher accuracy than the original DE, 7.8% higher than ABC, and an 18.2% improvement over Hyperband, demonstrating robust and accurate hyperparameter tuning for criminal suspect identification.
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Your AI Implementation Roadmap
A strategic overview of how our solution integrates into your enterprise, driving continuous innovation.
Integrate Streaming Data & Real-time Analysis
Deliver immediate suspect identification and predictive insights during ongoing investigations, handling low-latency inference and mobile deployment.
Incorporate Multimodal Data
Use witness descriptions, behavioral patterns, and audio input for richer, context-aware suspect profiles.
Explore Domain Adaptation & Transfer Learning
Improve generalizability to low-quality CCTV, body-worn camera footage, and unfamiliar data types.
Continuous Model Evolution & Adaptability
Implement online/incremental learning, proactive maintenance, and real-time feedback for sustained operational performance against evolving criminal behaviors.
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