Key Applications of AI: Transforming Healthcare, Finance, Transportation, and More

import numpy as np
import torch
import torch.nn as nn
from torchvision import models, transforms
 
class MedicalImageDiagnostics:
    def __init__(self, num_classes=5):
        # Use pre-trained ResNet for transfer learning
        self.model = models.resnet50(pretrained=True)
        self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)
        
        self.transform = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                               std=[0.229, 0.224, 0.225])
        ])
        
        self.disease_classes = [
            'Normal', 'Pneumonia', 'COVID-19', 
            'Lung Cancer', 'Tuberculosis'
        ]
        
    def preprocess_xray(self, image):
        """Preprocess X-ray image for analysis"""
        # Apply CLAHE for better contrast
        # In practice, use specialized medical imaging preprocessing
        return self.transform(image)
    
    def diagnose(self, xray_image):
        """Analyze X-ray and provide diagnosis"""
        self.model.eval()
        
        # Preprocess image
        input_tensor = self.preprocess_xray(xray_image).unsqueeze(0)
        
        with torch.no_grad():
            outputs = self.model(input_tensor)
            probabilities = torch.softmax(outputs, dim=1)
            
        # Get top predictions
        top_probs, top_classes = torch.topk(probabilities, k=3)
        
        results = {
            'primary_diagnosis': self.disease_classes[top_classes[0][0]],
            'confidence': float(top_probs[0][0]),
            'differential_diagnosis': [
                {
                    'condition': self.disease_classes[top_classes[0][i]],
                    'probability': float(top_probs[0][i])
                }
                for i in range(3)
            ]
        }
        
        return results
    
    def generate_heatmap(self, xray_image):
        """Generate attention heatmap showing areas of interest"""
        # Grad-CAM implementation for interpretability
        # Shows which parts of the image influenced the diagnosis
        pass
 
# Example usage
diagnostic_ai = MedicalImageDiagnostics()
# result = diagnostic_ai.diagnose(xray_image)
# print(f"Diagnosis: {result['primary_diagnosis']} ({result['confidence']:.1%} confidence)")

from rdkit import Chem
from rdkit.Chem import Descriptors
import pandas as pd
 
class DrugDiscoveryAI:
    def __init__(self):
        self.property_models = {}
        self.toxicity_model = None
        self.interaction_model = None
        
    def analyze_molecule(self, smiles):
        """Analyze molecular properties from SMILES notation"""
        mol = Chem.MolFromSmiles(smiles)
        
        if mol is None:
            return {"error": "Invalid molecular structure"}
        
        properties = {
            'molecular_weight': Descriptors.MolWt(mol),
            'logp': Descriptors.MolLogP(mol),
            'num_h_donors': Descriptors.NumHDonors(mol),
            'num_h_acceptors': Descriptors.NumHAcceptors(mol),
            'num_rotatable_bonds': Descriptors.NumRotatableBonds(mol),
            'tpsa': Descriptors.TPSA(mol),
            'num_aromatic_rings': Descriptors.NumAromaticRings(mol)
        }
        
        # Check Lipinski's Rule of Five
        properties['lipinski_compliant'] = (
            properties['molecular_weight'] <= 500 and
            properties['logp'] <= 5 and
            properties['num_h_donors'] <= 5 and
            properties['num_h_acceptors'] <= 10
        )
        
        return properties
    
    def predict_drug_target_interaction(self, drug_features, protein_features):
        """Predict probability of drug-protein interaction"""
        # In practice, use deep learning models trained on interaction data
        # This is a simplified example
        interaction_score = np.random.random()  # Placeholder
        
        return {
            'interaction_probability': interaction_score,
            'binding_affinity': -8.5 + np.random.randn(),  # kcal/mol
            'interaction_type': 'competitive' if interaction_score > 0.7 else 'non-competitive'
        }
    
    def virtual_screening(self, compound_library, target_protein):
        """Screen large libraries of compounds"""
        results = []
        
        for compound in compound_library:
            properties = self.analyze_molecule(compound['smiles'])
            if properties.get('lipinski_compliant'):
                interaction = self.predict_drug_target_interaction(
                    properties, target_protein
                )
                
                if interaction['interaction_probability'] > 0.8:
                    results.append({
                        'compound': compound,
                        'properties': properties,
                        'interaction': interaction,
                        'score': interaction['interaction_probability']
                    })
        
        return sorted(results, key=lambda x: x['score'], reverse=True)

class PersonalizedMedicineAI:
    def __init__(self):
        self.genomic_model = None
        self.treatment_response_model = None
        
    def analyze_patient_profile(self, patient_data):
        """Comprehensive patient analysis"""
        risk_factors = self.assess_risk_factors(patient_data)
        genetic_markers = self.analyze_genetic_markers(patient_data.get('genomic_data', {}))
        
        return {
            'risk_profile': risk_factors,
            'genetic_predispositions': genetic_markers,
            'recommended_screenings': self.recommend_screenings(risk_factors, genetic_markers),
            'lifestyle_recommendations': self.generate_lifestyle_plan(patient_data)
        }
    
    def predict_treatment_response(self, patient_profile, treatment_options):
        """Predict how patient will respond to different treatments"""
        predictions = []
        
        for treatment in treatment_options:
            # Simulate prediction based on patient features
            response_probability = self.calculate_response_probability(
                patient_profile, treatment
            )
            
            side_effect_risk = self.predict_side_effects(
                patient_profile, treatment
            )
            
            predictions.append({
                'treatment': treatment['name'],
                'efficacy_probability': response_probability,
                'side_effect_risk': side_effect_risk,
                'recommendation_score': response_probability * (1 - side_effect_risk)
            })
        
        return sorted(predictions, key=lambda x: x['recommendation_score'], reverse=True)
    
    def calculate_response_probability(self, patient, treatment):
        # Simplified calculation - in reality, use ML models
        base_probability = 0.7
        
        # Adjust based on genetic markers
        if patient.get('genetic_markers', {}).get(treatment['target_gene']):
            base_probability += 0.15
            
        # Adjust based on previous treatments
        if treatment['class'] in patient.get('failed_treatments', []):
            base_probability -= 0.3
            
        return max(0, min(1, base_probability))

import pandas as pd
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
import numpy as np
 
class FraudDetectionSystem:
    def __init__(self):
        self.isolation_forest = IsolationForest(
            contamination=0.001,  # Expected fraud rate
            random_state=42
        )
        self.scaler = StandardScaler()
        self.feature_history = {}
        
    def extract_transaction_features(self, transaction):
        """Extract features for fraud detection"""
        user_id = transaction['user_id']
        
        # Get user's transaction history
        user_history = self.feature_history.get(user_id, {
            'avg_amount': 0,
            'avg_daily_transactions': 1,
            'common_merchants': set(),
            'common_locations': set()
        })
        
        features = {
            # Transaction features
            'amount': transaction['amount'],
            'hour_of_day': transaction['timestamp'].hour,
            'day_of_week': transaction['timestamp'].dayofweek,
            'is_weekend': transaction['timestamp'].dayofweek >= 5,
            
            # Relative features
            'amount_vs_average': transaction['amount'] / (user_history['avg_amount'] + 1),
            'is_new_merchant': transaction['merchant_id'] not in user_history['common_merchants'],
            'is_new_location': transaction['location'] not in user_history['common_locations'],
            
            # Velocity features
            'transactions_last_hour': self.get_recent_transaction_count(user_id, hours=1),
            'amount_last_24h': self.get_recent_transaction_amount(user_id, hours=24),
            
            # Risk indicators
            'is_high_risk_category': transaction['category'] in ['cryptocurrency', 'gambling', 'wire_transfer'],
            'is_foreign_transaction': transaction['country'] != user_history.get('home_country', 'US')
        }
        
        return features
    
    def assess_transaction(self, transaction):
        """Real-time fraud assessment"""
        features = self.extract_transaction_features(transaction)
        feature_vector = np.array(list(features.values())).reshape(1, -1)
        
        # Scale features
        feature_vector_scaled = self.scaler.transform(feature_vector)
        
        # Predict
        is_anomaly = self.isolation_forest.predict(feature_vector_scaled)[0] == -1
        anomaly_score = self.isolation_forest.score_samples(feature_vector_scaled)[0]
        
        # Calculate risk score (0-100)
        risk_score = int((1 - (anomaly_score + 1) / 2) * 100)
        
        # Determine action
        if risk_score > 80:
            action = "BLOCK"
            reason = "High risk transaction detected"
        elif risk_score > 60:
            action = "VERIFY"
            reason = "Medium risk - additional verification required"
        else:
            action = "APPROVE"
            reason = "Transaction appears legitimate"
        
        return {
            'risk_score': risk_score,
            'action': action,
            'reason': reason,
            'risk_factors': self.explain_risk_factors(features, risk_score)
        }
    
    def explain_risk_factors(self, features, risk_score):
        """Explain what contributed to the risk score"""
        risk_factors = []
        
        if features['amount_vs_average'] > 10:
            risk_factors.append("Transaction amount significantly higher than usual")
        
        if features['is_new_merchant'] and features['amount'] > 500:
            risk_factors.append("Large transaction with new merchant")
        
        if features['transactions_last_hour'] > 5:
            risk_factors.append("Unusually high transaction frequency")
        
        if features['is_foreign_transaction'] and features['is_high_risk_category']:
            risk_factors.append("Foreign transaction in high-risk category")
        
        return risk_factors

import numpy as np
import pandas as pd
from collections import deque
 
class AlgorithmicTradingBot:
    def __init__(self, initial_capital=100000):
        self.capital = initial_capital
        self.positions = {}
        self.trade_history = []
        self.market_data_buffer = deque(maxlen=1000)
        
    def analyze_market_sentiment(self, news_data, social_media_data):
        """Analyze sentiment from multiple sources"""
        # In practice, use NLP models for sentiment analysis
        news_sentiment = self.analyze_news_sentiment(news_data)
        social_sentiment = self.analyze_social_sentiment(social_media_data)
        
        combined_sentiment = {
            'overall': (news_sentiment['score'] * 0.6 + social_sentiment['score'] * 0.4),
            'confidence': min(news_sentiment['confidence'], social_sentiment['confidence']),
            'trending_topics': news_sentiment['topics'] + social_sentiment['topics']
        }
        
        return combined_sentiment
    
    def predict_price_movement(self, symbol, timeframe='1h'):
        """Predict future price movement"""
        # Get historical data
        historical_data = self.get_historical_data(symbol, timeframe)
        
        # Calculate technical indicators
        indicators = {
            'rsi': self.calculate_rsi(historical_data['close']),
            'macd': self.calculate_macd(historical_data['close']),
            'bollinger_bands': self.calculate_bollinger_bands(historical_data['close']),
            'volume_trend': self.analyze_volume_trend(historical_data['volume'])
        }
        
        # ML prediction (simplified)
        features = self.extract_features(historical_data, indicators)
        prediction = self.ml_model_predict(features)
        
        return {
            'direction': 'UP' if prediction > 0 else 'DOWN',
            'confidence': abs(prediction),
            'expected_change': prediction,
            'timeframe': timeframe,
            'indicators': indicators
        }
    
    def execute_trading_strategy(self, market_data):
        """Execute trading decisions based on strategy"""
        decisions = []
        
        for symbol in self.watchlist:
            # Get prediction
            prediction = self.predict_price_movement(symbol)
            sentiment = self.analyze_market_sentiment(
                self.get_news(symbol), 
                self.get_social_data(symbol)
            )
            
            # Risk management
            position_size = self.calculate_position_size(
                symbol, prediction['confidence'], sentiment['confidence']
            )
            
            # Make trading decision
            if prediction['direction'] == 'UP' and prediction['confidence'] > 0.7:
                if symbol not in self.positions:
                    decision = {
                        'action': 'BUY',
                        'symbol': symbol,
                        'quantity': position_size,
                        'reason': f"Bullish signal: {prediction['confidence']:.1%} confidence"
                    }
                    decisions.append(decision)
                    
            elif prediction['direction'] == 'DOWN' and symbol in self.positions:
                decision = {
                    'action': 'SELL',
                    'symbol': symbol,
                    'quantity': self.positions[symbol],
                    'reason': f"Bearish signal: {prediction['confidence']:.1%} confidence"
                }
                decisions.append(decision)
        
        # Execute decisions
        for decision in decisions:
            self.execute_trade(decision)
            
        return decisions
    
    def calculate_position_size(self, symbol, prediction_confidence, sentiment_confidence):
        """Calculate position size based on Kelly Criterion and risk management"""
        # Simplified Kelly Criterion
        win_probability = (prediction_confidence + sentiment_confidence) / 2
        win_loss_ratio = 1.5  # Expected profit/loss ratio
        
        kelly_percentage = (win_probability * win_loss_ratio - (1 - win_probability)) / win_loss_ratio
        
        # Apply risk management constraints
        max_position_size = self.capital * 0.02  # Max 2% per position
        kelly_size = self.capital * kelly_percentage
        
        return min(kelly_size, max_position_size)

class CreditScoringAI:
    def __init__(self):
        self.traditional_features = [
            'credit_history_length', 'payment_history', 'credit_utilization',
            'credit_mix', 'recent_inquiries'
        ]
        self.alternative_features = [
            'social_media_presence', 'education_level', 'employment_stability',
            'digital_footprint', 'behavioral_patterns'
        ]
        
    def calculate_credit_score(self, applicant_data):
        """Calculate comprehensive credit score"""
        # Traditional credit score components
        traditional_score = self.calculate_traditional_score(applicant_data)
        
        # Alternative data score
        alternative_score = self.calculate_alternative_score(applicant_data)
        
        # Combine scores with weights
        if traditional_score > 0:
            final_score = traditional_score * 0.7 + alternative_score * 0.3
        else:
            # No traditional credit history - rely more on alternative data
            final_score = alternative_score
            
        # Risk categorization
        risk_category = self.categorize_risk(final_score)
        
        return {
            'credit_score': int(final_score),
            'risk_category': risk_category,
            'traditional_score': int(traditional_score),
            'alternative_score': int(alternative_score),
            'loan_recommendations': self.generate_loan_recommendations(final_score, applicant_data)
        }
    
    def calculate_alternative_score(self, data):
        """Score based on alternative data sources"""
        score = 500  # Base score
        
        # Education factor
        education_scores = {
            'high_school': 10, 'bachelors': 30, 
            'masters': 40, 'phd': 50
        }
        score += education_scores.get(data.get('education'), 0)
        
        # Employment stability
        if data.get('employment_months', 0) > 24:
            score += 50
        elif data.get('employment_months', 0) > 12:
            score += 25
            
        # Digital behavior patterns
        if data.get('regular_bill_payments_online', False):
            score += 30
            
        # Social network analysis (simplified)
        if data.get('professional_network_size', 0) > 100:
            score += 20
            
        return min(850, max(300, score))

import cv2
import numpy as np
from collections import defaultdict
 
class AutonomousVisionSystem:
    def __init__(self):
        self.object_detector = self.load_yolo_model()
        self.lane_detector = self.load_lane_detection_model()
        self.depth_estimator = self.load_depth_estimation_model()
        self.tracked_objects = defaultdict(dict)
        
    def process_frame(self, frame, sensor_data):
        """Process single frame from vehicle cameras"""
        # Detect objects (cars, pedestrians, signs, etc.)
        objects = self.detect_objects(frame)
        
        # Detect lanes
        lanes = self.detect_lanes(frame)
        
        # Estimate depth/distance
        depth_map = self.estimate_depth(frame)
        
        # Combine with sensor data
        fused_perception = self.sensor_fusion(
            objects, lanes, depth_map, sensor_data
        )
        
        # Make driving decisions
        driving_commands = self.make_driving_decisions(fused_perception)
        
        return {
            'objects': objects,
            'lanes': lanes,
            'depth_map': depth_map,
            'driving_commands': driving_commands,
            'safety_alerts': self.check_safety_conditions(fused_perception)
        }
    
    def detect_objects(self, frame):
        """Detect and classify objects in the frame"""
        # Simplified YOLO-style detection
        detections = []
        
        # In practice, use actual YOLO or similar model
        # This is a conceptual example
        detected_objects = self.object_detector.detect(frame)
        
        for obj in detected_objects:
            detection = {
                'class': obj['class'],
                'confidence': obj['confidence'],
                'bbox': obj['bbox'],
                'distance': self.estimate_distance(obj, frame),
                'velocity': self.estimate_velocity(obj),
                'trajectory': self.predict_trajectory(obj)
            }
            detections.append(detection)
            
        return detections
    
    def detect_lanes(self, frame):
        """Detect lane markings and road boundaries"""
        # Preprocess image
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray, 50, 150)
        
        # Region of interest
        height, width = frame.shape[:2]
        roi_vertices = np.array([
            [(0, height), (width/2, height/2), 
             (width, height)]
        ], dtype=np.int32)
        
        roi_edges = self.region_of_interest(edges, roi_vertices)
        
        # Detect lines using Hough transform
        lines = cv2.HoughLinesP(roi_edges, 1, np.pi/180, 100, 
                               minLineLength=100, maxLineGap=50)
        
        # Classify lanes
        left_lane, right_lane = self.classify_lanes(lines)
        
        return {
            'left_lane': left_lane,
            'right_lane': right_lane,
            'lane_center': self.calculate_lane_center(left_lane, right_lane),
            'curvature': self.estimate_road_curvature(left_lane, right_lane)
        }
    
    def make_driving_decisions(self, perception_data):
        """Make high-level driving decisions"""
        commands = {
            'steering_angle': 0,
            'throttle': 0,
            'brake': 0,
            'signal': None
        }
        
        # Lane keeping
        lane_center = perception_data['lanes']['lane_center']
        vehicle_position = perception_data['vehicle_position']
        lateral_offset = lane_center - vehicle_position
        
        # PID controller for steering
        commands['steering_angle'] = self.pid_controller(lateral_offset)
        
        # Obstacle avoidance
        front_obstacles = [
            obj for obj in perception_data['objects']
            if obj['class'] in ['car', 'truck', 'pedestrian'] and
            obj['distance'] < 50  # meters
        ]
        
        if front_obstacles:
            closest_obstacle = min(front_obstacles, key=lambda x: x['distance'])
            
            # Emergency braking
            if closest_obstacle['distance'] < 10:
                commands['brake'] = 1.0
                commands['throttle'] = 0
            # Gradual slowing
            elif closest_obstacle['distance'] < 30:
                commands['brake'] = 0.3
                commands['throttle'] = 0
            else:
                # Maintain safe following distance
                commands['throttle'] = 0.5
                
        # Traffic sign recognition
        traffic_signs = [
            obj for obj in perception_data['objects']
            if obj['class'] in ['stop_sign', 'traffic_light', 'speed_limit']
        ]
        
        for sign in traffic_signs:
            if sign['class'] == 'stop_sign' and sign['distance'] < 20:
                commands['brake'] = 0.8
            elif sign['class'] == 'traffic_light' and sign['state'] == 'red':
                commands['brake'] = 0.6
                
        return commands

class PathPlanningAI:
    def __init__(self):
        self.map_data = None
        self.current_route = None
        
    def plan_route(self, start, destination, preferences):
        """Plan optimal route considering multiple factors"""
        # Generate possible routes
        possible_routes = self.generate_routes(start, destination)
        
        # Score each route
        scored_routes = []
        for route in possible_routes:
            score = self.score_route(route, preferences)
            scored_routes.append((route, score))
            
        # Select best route
        best_route = max(scored_routes, key=lambda x: x[1])
        
        return {
            'primary_route': best_route[0],
            'alternative_routes': [r[0] for r in scored_routes[1:3]],
            'estimated_time': self.estimate_travel_time(best_route[0]),
            'distance': self.calculate_distance(best_route[0])
        }
    
    def score_route(self, route, preferences):
        """Score route based on multiple factors"""
        score = 100
        
        # Traffic conditions
        traffic_delay = self.get_traffic_conditions(route)
        score -= traffic_delay * 2
        
        # Safety score (accident history, road conditions)
        safety_score = self.calculate_safety_score(route)
        score += safety_score * preferences.get('safety_weight', 1.0)
        
        # Comfort (fewer turns, highway preference)
        comfort_score = self.calculate_comfort_score(route)
        score += comfort_score * preferences.get('comfort_weight', 0.5)
        
        # Fuel efficiency
        if preferences.get('eco_mode', False):
            efficiency_score = self.calculate_efficiency_score(route)
            score += efficiency_score * 1.5
            
        return score
    
    def dynamic_replanning(self, current_position, destination, obstacles):
        """Replan route in real-time based on new information"""
        # Check if current route is still valid
        if self.is_route_blocked(self.current_route, obstacles):
            # Generate new route avoiding obstacles
            new_route = self.plan_route(
                current_position, 
                destination,
                {'avoid': obstacles}
            )
            self.current_route = new_route['primary_route']
            
        return self.current_route

class IntelligentAssistant:
    def __init__(self):
        self.context_memory = []
        self.user_preferences = {}
        self.knowledge_base = self.load_knowledge_base()
        
    def process_query(self, user_input, context=None):
        """Process natural language query"""
        # Intent recognition
        intent = self.recognize_intent(user_input)
        
        # Entity extraction
        entities = self.extract_entities(user_input)
        
        # Context understanding
        full_context = self.build_context(user_input, context, self.context_memory)
        
        # Generate response
        response = self.generate_response(intent, entities, full_context)
        
        # Update context memory
        self.context_memory.append({
            'input': user_input,
            'intent': intent,
            'entities': entities,
            'response': response
        })
        
        return response
    
    def recognize_intent(self, text):
        """Identify user's intent"""
        # In practice, use trained NLU models
        intents = {
            'weather': ['weather', 'temperature', 'rain', 'sunny'],
            'calendar': ['meeting', 'schedule', 'appointment', 'event'],
            'reminder': ['remind', 'remember', 'forget', 'later'],
            'information': ['what is', 'who is', 'define', 'explain']
        }
        
        text_lower = text.lower()
        for intent, keywords in intents.items():
            if any(keyword in text_lower for keyword in keywords):
                return intent
                
        return 'general'
    
    def generate_response(self, intent, entities, context):
        """Generate appropriate response"""
        if intent == 'weather':
            location = entities.get('location', 'your location')
            # Mock weather data
            return f"The weather in {location} is sunny with a high of 75°F."
            
        elif intent == 'calendar':
            # Check calendar
            meetings = self.check_calendar(entities.get('date', 'today'))
            if meetings:
                return f"You have {len(meetings)} meetings scheduled: " + \
                       ", ".join([m['title'] for m in meetings])
            else:
                return "Your calendar is clear."
                
        elif intent == 'reminder':
            # Set reminder
            reminder_text = entities.get('reminder_text', 'your task')
            reminder_time = entities.get('time', 'later')
            self.set_reminder(reminder_text, reminder_time)
            return f"I'll remind you to {reminder_text} {reminder_time}."
            
        else:
            # Use knowledge base or general response
            return self.search_knowledge_base(entities, context)

class NeuralTranslator:
    def __init__(self):
        self.encoder = self.build_encoder()
        self.decoder = self.build_decoder()
        self.attention_mechanism = self.build_attention()
        
    def translate(self, text, source_lang, target_lang):
        """Translate text between languages"""
        # Tokenize and encode source text
        source_tokens = self.tokenize(text, source_lang)
        encoded = self.encode(source_tokens)
        
        # Apply attention mechanism
        context_vectors = self.attention_mechanism(encoded)
        
        # Decode to target language
        translated_tokens = self.decode(context_vectors, target_lang)
        
        # Convert tokens back to text
        translated_text = self.detokenize(translated_tokens, target_lang)
        
        # Post-processing
        translated_text = self.post_process(translated_text, target_lang)
        
        return {
            'translation': translated_text,
            'confidence': self.calculate_confidence(context_vectors),
            'alternative_translations': self.get_alternatives(context_vectors, target_lang)
        }
    
    def handle_idioms_and_context(self, text, source_lang, target_lang):
        """Handle idiomatic expressions and cultural context"""
        idioms = self.detect_idioms(text, source_lang)
        
        for idiom in idioms:
            # Look up culturally equivalent expression
            target_expression = self.idiom_database.get(
                (idiom, source_lang, target_lang)
            )
            if target_expression:
                text = text.replace(idiom, target_expression)
                
        return text

class RecommendationEngine:
    def __init__(self):
        self.user_embeddings = {}
        self.item_embeddings = {}
        self.interaction_matrix = None
        
    def hybrid_recommendations(self, user_id, context=None):
        """Generate recommendations using multiple approaches"""
        # Collaborative filtering
        collab_recs = self.collaborative_filtering(user_id)
        
        # Content-based filtering
        content_recs = self.content_based_filtering(user_id)
        
        # Knowledge-based recommendations
        knowledge_recs = self.knowledge_based_filtering(user_id, context)
        
        # Combine recommendations
        final_recommendations = self.ensemble_recommendations(
            collab_recs, content_recs, knowledge_recs,
            weights=[0.5, 0.3, 0.2]
        )
        
        # Apply business rules
        final_recommendations = self.apply_business_rules(final_recommendations)
        
        # Explain recommendations
        explanations = self.generate_explanations(final_recommendations, user_id)
        
        return {
            'recommendations': final_recommendations,
            'explanations': explanations,
            'confidence_scores': [rec['score'] for rec in final_recommendations]
        }
    
    def real_time_personalization(self, user_session):
        """Personalize in real-time based on session behavior"""
        # Extract session features
        session_features = {
            'viewed_items': user_session['viewed_items'],
            'time_on_items': user_session['time_spent'],
            'search_queries': user_session['searches'],
            'cart_items': user_session['cart'],
            'device_type': user_session['device']
        }
        
        # Update user profile dynamically
        user_profile = self.update_user_profile(
            user_session['user_id'], 
            session_features
        )
        
        # Generate real-time recommendations
        recommendations = self.generate_session_recommendations(
            user_profile, 
            session_features
        )
        
        return recommendations

class SupplyChainAI:
    def __init__(self):
        self.demand_forecaster = self.build_demand_model()
        self.inventory_optimizer = self.build_inventory_model()
        
    def optimize_inventory(self, current_inventory, historical_data):
        """Optimize inventory levels across locations"""
        # Forecast demand
        demand_forecast = self.forecast_demand(historical_data)
        
        # Calculate optimal stock levels
        optimal_levels = {}
        for product in current_inventory:
            optimal = self.calculate_optimal_stock(
                product,
                demand_forecast[product['id']],
                product['lead_time'],
                product['holding_cost']
            )
            optimal_levels[product['id']] = optimal
            
        # Generate replenishment orders
        orders = self.generate_replenishment_orders(
            current_inventory, 
            optimal_levels
        )
        
        return {
            'optimal_levels': optimal_levels,
            'replenishment_orders': orders,
            'estimated_savings': self.calculate_savings(current_inventory, optimal_levels)
        }
    
    def dynamic_pricing(self, product, market_conditions):
        """AI-driven dynamic pricing"""
        features = {
            'current_inventory': product['inventory'],
            'competitor_prices': market_conditions['competitor_prices'],
            'demand_elasticity': self.calculate_elasticity(product),
            'seasonality_factor': self.get_seasonality_factor(product),
            'time_to_expiry': product.get('expiry_days', float('inf'))
        }
        
        # Calculate optimal price
        base_price = product['base_price']
        price_multiplier = self.pricing_model.predict(features)
        
        optimal_price = base_price * price_multiplier
        
        # Apply constraints
        optimal_price = max(product['min_price'], 
                          min(optimal_price, product['max_price']))
        
        return {
            'recommended_price': optimal_price,
            'expected_demand': self.predict_demand_at_price(product, optimal_price),
            'revenue_impact': self.calculate_revenue_impact(product, optimal_price)
        }

class AdaptiveLearningAI:
    def __init__(self):
        self.student_models = {}
        self.content_difficulty_estimator = self.build_difficulty_model()
        
    def personalize_learning_path(self, student_id):
        """Create personalized learning path for student"""
        # Get student profile
        student = self.get_student_model(student_id)
        
        # Assess current knowledge state
        knowledge_state = self.assess_knowledge(student)
        
        # Identify learning gaps
        gaps = self.identify_knowledge_gaps(knowledge_state, student['target_skills'])
        
        # Generate personalized curriculum
        curriculum = self.generate_curriculum(
            gaps, 
            student['learning_style'],
            student['pace']
        )
        
        # Adapt difficulty
        for lesson in curriculum:
            lesson['difficulty'] = self.adapt_difficulty(
                lesson, 
                student['performance_history']
            )
            
        return {
            'learning_path': curriculum,
            'estimated_completion': self.estimate_completion_time(curriculum, student),
            'focus_areas': gaps,
            'recommended_resources': self.recommend_resources(student, curriculum)
        }
    
    def real_time_hint_generation(self, student_id, problem, attempt_history):
        """Generate contextual hints based on student's attempts"""
        student = self.get_student_model(student_id)
        
        # Analyze error patterns
        errors = self.analyze_errors(attempt_history)
        
        # Generate appropriate hint
        hint_level = len(attempt_history)  # Progressive hints
        
        if hint_level == 1:
            hint = self.generate_conceptual_hint(problem, errors)
        elif hint_level == 2:
            hint = self.generate_procedural_hint(problem, errors)
        else:
            hint = self.generate_specific_hint(problem, errors)
            
        return {
            'hint': hint,
            'hint_type': ['conceptual', 'procedural', 'specific'][hint_level - 1],
            'related_concepts': self.identify_related_concepts(errors)
        }

class PredictiveMaintenanceAI:
    def __init__(self):
        self.sensor_models = {}
        self.failure_predictor = self.build_failure_model()
        
    def analyze_equipment_health(self, equipment_id, sensor_data):
        """Analyze equipment health and predict failures"""
        # Extract features from sensor data
        features = self.extract_sensor_features(sensor_data)
        
        # Detect anomalies
        anomalies = self.detect_anomalies(features)
        
        # Predict remaining useful life (RUL)
        rul_prediction = self.predict_rul(equipment_id, features)
        
        # Identify failure modes
        failure_risks = self.identify_failure_modes(features, anomalies)
        
        # Generate maintenance recommendations
        recommendations = self.generate_maintenance_plan(
            equipment_id, 
            rul_prediction, 
            failure_risks
        )
        
        return {
            'health_score': self.calculate_health_score(features, anomalies),
            'remaining_useful_life': rul_prediction,
            'failure_risks': failure_risks,
            'maintenance_recommendations': recommendations,
            'cost_savings': self.estimate_cost_savings(recommendations)
        }
    
    def optimize_maintenance_schedule(self, equipment_fleet, constraints):
        """Optimize maintenance schedule for entire fleet"""
        # Predict maintenance needs for all equipment
        maintenance_needs = []
        for equipment in equipment_fleet:
            health = self.analyze_equipment_health(
                equipment['id'], 
                equipment['sensor_data']
            )
            maintenance_needs.append({
                'equipment': equipment,
                'health': health,
                'priority': self.calculate_priority(health)
            })
            
        # Optimize schedule considering constraints
        schedule = self.optimization_algorithm(
            maintenance_needs,
            constraints['technician_availability'],
            constraints['budget'],
            constraints['production_schedule']
        )
        
        return schedule