[FEATURE] SMART: Semantic Multi-Objective Adversarial Training

[BhakktiGautam]

##Research Evidence: SMART framework integrates semantic enrichment, multi-objective adversarial training, and reinforcement learning for dynamic spam detection .

##Labels: feature, SSoC26, hard, machine-learning

##Description
SMART improves detection by 2.41% over EGOAMLPs, 3.36% over MFO, 4.51% over OPTICS, 6.16% over STM, and 6.89% over MOBGOA .

##Key Components
Semantic enrichment: BERT for contextual word replacements
Multi-objective adversarial training: Optimizes multiple objectives
Reinforcement learning: Adapts detection strategies dynamically
K-means clustering: Semantic enhancement of features

##Suggested Fix
python
class SMARTDetector:
def init(self):
self.bert = BertModel()
self.reinforcement = RLAgent()

def detect(self, text):
    enriched = self.bert.enhance(text)
    decision = self.multi_objective_predict(enriched)
    if decision.confidence < 0.7:
        decision = self.reinforcement.reassess(text)
    return decision

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[github-actions]

The SMART framework is proposed as an advanced approach for dynamic spam detection, integrating semantic enrichment (using BERT), multi-objective adversarial training, reinforcement learning, and K-means clustering. It reportedly improves detection accuracy significantly compared to various existing methods, leveraging these techniques to enhance feature representation and adapt detection strategies dynamically.