@astermind/astermind-pro/docs/features/ELM_VARIANTS_BUSINESS_EXAMPLES.md

Astermind Pro - Premium ML Toolkit with Advanced RAG, Reranking, Summarization, and Information Flow Analysis

# Advanced ELM Variants - Real-World Business Examples

Practical business applications of Astermind Pro's advanced ELM variants across finance, healthcare, retail, manufacturing, and other industries.

---

## Table of Contents

1. [Finance & Banking](#finance--banking)
2. [Healthcare & Life Sciences](#healthcare--life-sciences)
3. [Retail & E-commerce](#retail--e-commerce)
4. [Manufacturing & Supply Chain](#manufacturing--supply-chain)
5. [Legal & Compliance](#legal--compliance)
6. [Marketing & Advertising](#marketing--advertising)
7. [Insurance](#insurance)
8. [Real Estate](#real-estate)
9. [Energy & Utilities](#energy--utilities)
10. [Telecommunications](#telecommunications)

---

## Finance & Banking

### 1. Fraud Detection with Online Kernel ELM

**Business Problem:** Detect fraudulent credit card transactions in real-time as they occur.

**Solution:** Use Online Kernel ELM to continuously learn from streaming transaction data and adapt to new fraud patterns.

**Implementation:**

```typescript
import { OnlineKernelELM } from '@astermind/astermind-pro';

// Real-time fraud detection system
const fraudDetector = new OnlineKernelELM({
  kernel: { type: 'rbf', gamma: 0.1 },
  categories: ['legitimate', 'fraud'],
  windowSize: 10000, // Keep last 10,000 transactions
  decayFactor: 0.99, // Gradual decay for pattern adaptation
  ridgeLambda: 0.001,
});

// Initial training on historical data
const historicalTransactions = [
  {
    amount: 150.00,
    merchant: 'grocery_store',
    timeOfDay: 14,
    location: 'US',
    cardType: 'credit',
    // ... more features
  },
  // ... more transactions
];

const historicalLabels = [0, 0, 1, 0, ...]; // 1 = fraud

fraudDetector.fit(
  historicalTransactions.map(t => extractFeatures(t)),
  historicalLabels
);

// Real-time fraud detection
function processTransaction(transaction: Transaction) {
  const features = extractFeatures(transaction);
  
  // Update model with new transaction (labeled by fraud team)
  if (transaction.reviewedByFraudTeam) {
    fraudDetector.update(features, transaction.isFraud ? 1 : 0);
  }
  
  // Predict in real-time
  const prediction = fraudDetector.predict(features, 1);
  
  if (prediction[0].label === 'fraud' && prediction[0].prob > 0.85) {
    // Block transaction and alert fraud team
    blockTransaction(transaction);
    alertFraudTeam(transaction, prediction[0].prob);
  }
}

// Extract features from transaction
function extractFeatures(t: Transaction): number[] {
  return [
    t.amount / 1000, // Normalized amount
    t.timeOfDay / 24,
    encodeMerchant(t.merchant),
    encodeLocation(t.location),
    // ... more features
  ];
}
```

**Business Impact:**
- Real-time fraud detection (milliseconds)
- Adapts to new fraud patterns automatically
- Reduces false positives through continuous learning
- **ROI:** Prevents $X million in fraudulent transactions annually

---

### 2. Credit Risk Assessment with Multi-Task ELM

**Business Problem:** Assess multiple risk factors simultaneously: default probability, credit limit recommendation, and loan approval decision.

**Solution:** Use Multi-Task ELM to jointly predict default risk, optimal credit limit, and approval recommendation.

**Implementation:**

```typescript
import { MultiTaskELM } from '@astermind/astermind-pro';

// Credit risk assessment system
const creditRiskModel = new MultiTaskELM({
  tasks: [
    {
      name: 'default_risk',
      categories: ['low', 'medium', 'high', 'very_high'],
      weight: 2.0, // Most important
    },
    {
      name: 'credit_limit',
      categories: ['$500', '$1000', '$5000', '$10000', '$20000+'],
      weight: 1.5,
    },
    {
      name: 'approval',
      categories: ['approve', 'reject', 'manual_review'],
      weight: 1.0,
    },
  ],
  sharedHiddenUnits: 512,
  taskSpecificHiddenUnits: [256, 256, 128],
  activation: 'relu',
  useTokenizer: false,
});

// Customer application data
const applications = [
  {
    creditScore: 720,
    income: 75000,
    debtToIncome: 0.25,
    employmentYears: 5,
    // ... more features
  },
  // ... more applications
];

// Prepare task labels
const defaultRiskLabels = [0, 1, 2, 3, ...]; // low, medium, high, very_high
const creditLimitLabels = [1, 2, 3, 4, ...]; // $1000, $5000, $10000, $20000+
const approvalLabels = [0, 0, 1, ...]; // approve, reject, manual_review

const yTaskData = new Map([
  ['default_risk', defaultRiskLabels],
  ['credit_limit', creditLimitLabels],
  ['approval', approvalLabels],
]);

creditRiskModel.train(
  applications.map(a => extractFeatures(a)),
  yTaskData
);

// Assess new application
function assessCreditApplication(application: Application) {
  const features = extractFeatures(application);
  const predictions = creditRiskModel.predict(features, 1);
  
  const defaultRisk = predictions.get('default_risk')?.[0];
  const creditLimit = predictions.get('credit_limit')?.[0];
  const approval = predictions.get('approval')?.[0];
  
  return {
    defaultRisk: defaultRisk?.label,
    defaultRiskProbability: defaultRisk?.prob,
    recommendedCreditLimit: creditLimit?.label,
    approvalDecision: approval?.label,
    confidence: approval?.prob,
  };
}

// Example output:
// {
//   defaultRisk: 'low',
//   defaultRiskProbability: 0.92,
//   recommendedCreditLimit: '$10000',
//   approvalDecision: 'approve',
//   confidence: 0.88
// }
```

**Business Impact:**
- Faster loan processing (automated decisions)
- Better risk assessment accuracy
- Optimized credit limits (maximize revenue, minimize defaults)
- **ROI:** Reduces default rate by X%, increases loan volume by Y%

---

### 3. Stock Market Sentiment Analysis with Multi-Kernel ELM

**Business Problem:** Analyze financial news and social media to predict stock price movements by combining multiple information sources.

**Solution:** Use Multi-Kernel ELM to combine linear patterns (direct correlations) and non-linear patterns (complex relationships) from financial text data.

**Implementation:**

```typescript
import { MultiKernelELM } from '@astermind/astermind-pro';
import { tokenize } from '@astermind/astermind-pro';

// Financial sentiment analysis
const sentimentAnalyzer = new MultiKernelELM(['bullish', 'bearish', 'neutral'], {
  kernels: [
    { type: 'rbf', params: { gamma: 0.01 } }, // Non-linear patterns
    { type: 'linear' }, // Linear patterns
  ],
  learnWeights: true, // Automatically learn optimal combination
  ridgeLambda: 0.001,
});

// Financial news and social media data
const financialTexts = [
  'Company XYZ reports record quarterly earnings, stock surges',
  'Market crash fears as inflation rises',
  'Analysts downgrade tech sector outlook',
  // ... more texts
];

// Historical stock price movements (labels)
const priceMovements = [
  1, // bullish (price went up)
  2, // bearish (price went down)
  2, // bearish
  // ... more labels
];

// Convert texts to features
const X = financialTexts.map(text => {
  const tokens = tokenize(text, true);
  // Use TF-IDF or embeddings in practice
  return tokensToFeatures(tokens);
});

sentimentAnalyzer.fit(X, priceMovements);

// Analyze new financial text
function analyzeFinancialSentiment(text: string) {
  const features = tokensToFeatures(tokenize(text, true));
  const prediction = sentimentAnalyzer.predict(features, 1);
  
  return {
    sentiment: prediction[0].label,
    confidence: prediction[0].prob,
    // Get learned kernel weights to understand which patterns matter
    kernelWeights: sentimentAnalyzer.getKernelWeights(),
  };
}

// Example: Analyze earnings call transcript
const earningsCall = 'We expect strong growth in Q4...';
const analysis = analyzeFinancialSentiment(earningsCall);
// Output: { sentiment: 'bullish', confidence: 0.87, kernelWeights: [0.65, 0.35] }
```

**Business Impact:**
- Real-time market sentiment tracking
- Better trading signal generation
- Automated news monitoring
- **ROI:** Improves trading strategy returns by X%

---

### 4. Anti-Money Laundering (AML) Detection with Sparse ELM

**Business Problem:** Detect suspicious transactions from millions of daily transactions with high-dimensional features (1000+ features per transaction).

**Solution:** Use Sparse ELM to identify the most important features for AML detection and create an interpretable, efficient model.

**Implementation:**

```typescript
import { SparseELM } from '@astermind/astermind-pro';

// AML detection system
const amlDetector = new SparseELM({
  categories: ['normal', 'suspicious', 'high_risk'],
  hiddenUnits: 512,
  regularization: {
    type: 'l1', // L1 for feature selection
    lambda: 0.01,
  },
  sparsityTarget: 0.8, // 80% sparsity for efficiency
  pruneThreshold: 1e-6,
});

// Transaction features (1000+ dimensions)
// Features include: amount, frequency, geographic patterns, 
// network analysis, time patterns, entity relationships, etc.
const transactions = [
  [0.1, 0.0, 0.0, 0.3, 0.0, 0.0, 0.2, ...], // 1000+ features
  [0.0, 0.2, 0.0, 0.0, 0.1, 0.0, 0.0, ...],
  // ... millions of transactions
];

const labels = [0, 1, 2, ...]; // normal, suspicious, high_risk

amlDetector.train(transactions, labels);

// Get feature importance to understand what signals matter
const importance = amlDetector.getFeatureImportance();
const topRiskFeatures = importance
  .map((imp, idx) => ({ feature: featureNames[idx], importance: imp }))
  .filter(f => f.importance > 0.1)
  .sort((a, b) => b.importance - a.importance);

console.log('Top AML risk indicators:');
topRiskFeatures.forEach(({ feature, importance }) => {
  console.log(`  ${feature}: ${(importance * 100).toFixed(1)}%`);
});

// Detect suspicious transactions
function detectAML(transaction: Transaction) {
  const features = extractTransactionFeatures(transaction);
  const prediction = amlDetector.predict(features, 1);
  
  if (prediction[0].label !== 'normal') {
    // Flag for compliance review
    flagForReview(transaction, {
      riskLevel: prediction[0].label,
      confidence: prediction[0].prob,
      topFeatures: topRiskFeatures.slice(0, 5), // Explainable AI
    });
  }
}
```

**Business Impact:**
- Efficient processing of millions of transactions
- Interpretable results (compliance teams can understand why flagged)
- Reduced false positives through feature selection
- **ROI:** Meets regulatory requirements, avoids fines ($X million)

---

### 5. Algorithmic Trading with DeepELMPro

**Business Problem:** Predict stock price movements using deep feature hierarchies from multiple data sources (price, volume, news, social media).

**Solution:** Use DeepELMPro with pretraining to learn hierarchical patterns in financial data.

**Implementation:**

```typescript
import { DeepELMPro } from '@astermind/astermind-pro';

// Algorithmic trading prediction model
const tradingModel = new DeepELMPro({
  layers: [512, 256, 128, 64], // Deep network for complex patterns
  categories: ['buy', 'hold', 'sell'],
  activation: 'relu',
  pretraining: true, // Pretrain layers to learn financial patterns
  layerWiseTraining: true, // Sequential training for stability
  regularization: {
    type: 'l2',
    lambda: 0.0001,
  },
  useBatchNorm: true, // Stabilize training
  useDropout: true,
  dropoutRate: 0.2,
  maxLen: 1000,
});

// Multi-source financial data
// Features: price history, volume, technical indicators, 
// news sentiment, social media sentiment, market indicators
const marketData = [
  {
    priceHistory: [100, 101, 102, ...],
    volume: [1000000, 1200000, ...],
    technicalIndicators: [0.5, 0.6, ...],
    newsSentiment: 0.7,
    socialSentiment: 0.65,
    marketIndicators: [0.3, 0.4, ...],
  },
  // ... more data
];

const tradingSignals = [0, 1, 2, ...]; // buy, hold, sell

await tradingModel.train(
  marketData.map(d => flattenFeatures(d)),
  tradingSignals
);

// Generate trading signal
function generateTradingSignal(currentMarketData: MarketData) {
  const features = flattenFeatures(currentMarketData);
  const prediction = tradingModel.predict(features, 1);
  
  return {
    action: prediction[0].label,
    confidence: prediction[0].prob,
    // Use for automated trading or trader alerts
  };
}
```

**Business Impact:**
- Better prediction accuracy through deep learning
- Handles complex multi-source data
- Reduces overfitting with regularization
- **ROI:** Improves trading strategy Sharpe ratio by X%

---

## Healthcare & Life Sciences

### 1. Disease Diagnosis with Multi-Task ELM

**Business Problem:** Diagnose patients by predicting disease type, severity, and recommended treatment simultaneously.

**Solution:** Use Multi-Task ELM to leverage shared patient features across multiple diagnostic tasks.

**Implementation:**

```typescript
import { MultiTaskELM } from '@astermind/astermind-pro';

// Medical diagnosis system
const diagnosisModel = new MultiTaskELM({
  tasks: [
    {
      name: 'disease',
      categories: ['diabetes', 'hypertension', 'heart_disease', 'none'],
      weight: 2.0, // Most critical
    },
    {
      name: 'severity',
      categories: ['mild', 'moderate', 'severe', 'critical'],
      weight: 1.5,
    },
    {
      name: 'treatment',
      categories: ['medication', 'lifestyle', 'surgery', 'monitoring'],
      weight: 1.0,
    },
  ],
  sharedHiddenUnits: 512,
  taskSpecificHiddenUnits: [256, 256, 128],
});

// Patient data
const patients = [
  {
    age: 55,
    bmi: 28.5,
    bloodPressure: [140, 90],
    glucose: 110,
    cholesterol: 220,
    // ... more vitals
  },
  // ... more patients
];

const diseaseLabels = [0, 1, 2, 0, ...];
const severityLabels = [1, 2, 1, 0, ...];
const treatmentLabels = [0, 1, 0, 3, ...];

const yTaskData = new Map([
  ['disease', diseaseLabels],
  ['severity', severityLabels],
  ['treatment', treatmentLabels],
]);

diagnosisModel.train(
  patients.map(p => extractPatientFeatures(p)),
  yTaskData
);

// Diagnose new patient
function diagnosePatient(patient: Patient) {
  const features = extractPatientFeatures(patient);
  const predictions = diagnosisModel.predict(features, 1);
  
  return {
    disease: predictions.get('disease')?.[0].label,
    severity: predictions.get('severity')?.[0].label,
    recommendedTreatment: predictions.get('treatment')?.[0].label,
    confidence: predictions.get('disease')?.[0].prob,
  };
}
```

**Business Impact:**
- Faster diagnosis (reduces time from hours to minutes)
- More accurate multi-faceted diagnosis
- Better treatment recommendations
- **ROI:** Reduces misdiagnosis rate by X%, improves patient outcomes

---

### 2. Drug Discovery with Sparse ELM

**Business Problem:** Identify promising drug compounds from millions of molecular features (genes, proteins, pathways).

**Solution:** Use Sparse ELM to identify the most important molecular features for drug efficacy.

**Implementation:**

```typescript
import { SparseELM } from '@astermind/astermind-pro';

// Drug discovery system
const drugDiscoveryModel = new SparseELM({
  categories: ['effective', 'ineffective', 'toxic'],
  hiddenUnits: 1024,
  regularization: {
    type: 'l1',
    lambda: 0.1, // Strong L1 for feature selection
  },
  sparsityTarget: 0.9, // Very sparse - only key features matter
});

// Molecular compound data (10,000+ features)
// Features: gene expressions, protein interactions, 
// pathway activations, chemical properties, etc.
const compounds = [
  [0.1, 0.0, 0.0, 0.3, 0.0, ...], // 10,000+ features
  [0.0, 0.2, 0.0, 0.0, 0.1, ...],
  // ... thousands of compounds
];

const efficacyLabels = [0, 1, 2, ...]; // effective, ineffective, toxic

drugDiscoveryModel.train(compounds, efficacyLabels);

// Identify important biomarkers
const importance = drugDiscoveryModel.getFeatureImportance();
const keyBiomarkers = importance
  .map((imp, idx) => ({ biomarker: biomarkerNames[idx], importance: imp }))
  .filter(b => b.importance > 0.15)
  .sort((a, b) => b.importance - a.importance);

console.log('Key biomarkers for drug efficacy:');
keyBiomarkers.forEach(({ biomarker, importance }) => {
  console.log(`  ${biomarker}: ${(importance * 100).toFixed(1)}%`);
});

// Screen new compounds
function screenCompound(compound: Compound) {
  const features = extractCompoundFeatures(compound);
  const prediction = drugDiscoveryModel.predict(features, 1);
  
  if (prediction[0].label === 'effective' && prediction[0].prob > 0.8) {
    // Prioritize for further testing
    prioritizeForTesting(compound, {
      efficacy: prediction[0].label,
      confidence: prediction[0].prob,
      keyBiomarkers: keyBiomarkers.slice(0, 10),
    });
  }
}
```

**Business Impact:**
- Faster drug candidate identification
- Reduced R&D costs (focus on promising compounds)
- Interpretable results (scientists understand why)
- **ROI:** Reduces drug discovery time from 10 years to X years, saves $X billion

---

### 3. Patient Monitoring with Online Kernel ELM

**Business Problem:** Monitor ICU patients in real-time and detect deterioration early.

**Solution:** Use Online Kernel ELM to continuously learn from streaming patient vitals and adapt to individual patient patterns.

**Implementation:**

```typescript
import { OnlineKernelELM } from '@astermind/astermind-pro';

// Real-time patient monitoring
const patientMonitor = new OnlineKernelELM({
  kernel: { type: 'rbf', gamma: 0.1 },
  categories: ['stable', 'deteriorating', 'critical'],
  windowSize: 1000, // Last 1000 readings
  decayFactor: 0.95, // Fast adaptation
});

// Initial training on historical patient data
const historicalVitals = [
  { heartRate: 72, bloodPressure: [120, 80], oxygen: 98, ... },
  // ... more readings
];

patientMonitor.fit(
  historicalVitals.map(v => extractVitalFeatures(v)),
  historicalLabels
);

// Real-time monitoring
function monitorPatient(patientId: string, vitals: Vitals) {
  const features = extractVitalFeatures(vitals);
  
  // Update model with new reading (labeled by medical staff)
  if (vitals.reviewedByStaff) {
    patientMonitor.update(features, vitals.status);
  }
  
  // Predict patient status
  const prediction = patientMonitor.predict(features, 1);
  
  if (prediction[0].label === 'deteriorating' || prediction[0].label === 'critical') {
    // Alert medical staff
    alertMedicalStaff(patientId, {
      status: prediction[0].label,
      confidence: prediction[0].prob,
      vitals: vitals,
    });
  }
}

// Extract features from vitals
function extractVitalFeatures(v: Vitals): number[] {
  return [
    v.heartRate / 200, // Normalized
    v.bloodPressure[0] / 200,
    v.bloodPressure[1] / 150,
    v.oxygen / 100,
    // ... more features
  ];
}
```

**Business Impact:**
- Early detection of patient deterioration
- Reduced ICU mortality rates
- Real-time alerts to medical staff
- **ROI:** Reduces ICU mortality by X%, saves lives and reduces costs

---

## Retail & E-commerce

### 1. Product Recommendation with Multi-Kernel ELM

**Business Problem:** Recommend products to customers by combining purchase history (linear patterns) and browsing behavior (non-linear patterns).

**Solution:** Use Multi-Kernel ELM to combine different types of customer behavior patterns.

**Implementation:**

```typescript
import { MultiKernelELM } from '@astermind/astermind-pro';

// Product recommendation system
const recommender = new MultiKernelELM(['product1', 'product2', 'product3', ...], {
  kernels: [
    { type: 'rbf', params: { gamma: 0.01 } }, // Non-linear browsing patterns
    { type: 'linear' }, // Linear purchase patterns
  ],
  learnWeights: true,
});

// Customer features: purchase history, browsing behavior, 
// demographics, preferences, etc.
const customers = [
  {
    purchaseHistory: [1, 0, 1, 0, ...], // Products purchased
    browsingTime: [120, 45, 300, ...], // Time spent on each category
    demographics: [35, 1, 0, ...], // Age, gender, location
    preferences: [0.8, 0.2, 0.9, ...], // Preference scores
  },
  // ... more customers
];

const recommendedProducts = [0, 1, 2, ...]; // Product IDs

recommender.fit(
  customers.map(c => flattenCustomerFeatures(c)),
  recommendedProducts
);

// Recommend products for new customer
function recommendProducts(customer: Customer) {
  const features = flattenCustomerFeatures(customer);
  const recommendations = recommender.predict(features, 10); // Top 10
  
  return recommendations.map(r => ({
    productId: r.label,
    relevanceScore: r.prob,
  }));
}
```

**Business Impact:**
- Increased conversion rates
- Better personalization
- Higher average order value
- **ROI:** Increases revenue by X% through better recommendations

---

### 2. Inventory Demand Forecasting with Online Kernel ELM

**Business Problem:** Forecast product demand in real-time as sales data streams in, adapting to seasonal changes and trends.

**Solution:** Use Online Kernel ELM to continuously update demand forecasts from streaming sales data.

**Implementation:**

```typescript
import { OnlineKernelELM } from '@astermind/astermind-pro';

// Demand forecasting system
const demandForecaster = new OnlineKernelELM({
  kernel: { type: 'rbf', gamma: 0.01 },
  categories: ['low', 'medium', 'high', 'very_high'],
  windowSize: 2000, // Last 2000 sales records
  decayFactor: 0.98, // Gradual decay for trend adaptation
});

// Initial training on historical sales
const historicalSales = [
  {
    productId: 'P001',
    date: '2024-01-01',
    sales: 100,
    season: 'winter',
    promotions: true,
    // ... more features
  },
  // ... more records
];

demandForecaster.fit(
  historicalSales.map(s => extractSalesFeatures(s)),
  historicalDemandLabels
);

// Real-time demand forecasting
function forecastDemand(product: Product, currentContext: Context) {
  const features = extractSalesFeatures({
    productId: product.id,
    date: new Date(),
    season: getCurrentSeason(),
    promotions: currentContext.hasPromotions,
    // ... more features
  });
  
  // Update with latest sales data
  if (currentContext.latestSales) {
    demandForecaster.update(features, currentContext.latestSales.demandLevel);
  }
  
  // Forecast demand
  const forecast = demandForecaster.predict(features, 1);
  
  return {
    demandLevel: forecast[0].label,
    confidence: forecast[0].prob,
    // Use for inventory management
  };
}
```

**Business Impact:**
- Reduced stockouts and overstock
- Better inventory optimization
- Adapts to trends automatically
- **ROI:** Reduces inventory costs by X%, increases sales by Y%

---

### 3. Customer Churn Prediction with DeepELMPro

**Business Problem:** Predict which customers will churn using complex patterns from transaction history, support interactions, and usage data.

**Solution:** Use DeepELMPro to learn hierarchical patterns in customer behavior.

**Implementation:**

```typescript
import { DeepELMPro } from '@astermind/astermind-pro';

// Customer churn prediction
const churnPredictor = new DeepELMPro({
  layers: [256, 128, 64],
  categories: ['retain', 'at_risk', 'churn'],
  activation: 'relu',
  pretraining: true,
  regularization: {
    type: 'l2',
    lambda: 0.0001,
  },
  useDropout: true,
  dropoutRate: 0.2,
});

// Customer data: transaction history, support tickets,
// usage patterns, demographics, etc.
const customers = [
  {
    transactionHistory: [100, 150, 200, ...],
    supportTickets: 2,
    usageFrequency: 0.8,
    lastPurchaseDays: 5,
    // ... more features
  },
  // ... more customers
];

const churnLabels = [0, 1, 2, ...]; // retain, at_risk, churn

await churnPredictor.train(
  customers.map(c => extractCustomerFeatures(c)),
  churnLabels
);

// Predict churn risk
function predictChurn(customer: Customer) {
  const features = extractCustomerFeatures(customer);
  const prediction = churnPredictor.predict(features, 1);
  
  if (prediction[0].label === 'at_risk' || prediction[0].label === 'churn') {
    // Trigger retention campaign
    triggerRetentionCampaign(customer, {
      riskLevel: prediction[0].label,
      confidence: prediction[0].prob,
    });
  }
}
```

**Business Impact:**
- Proactive customer retention
- Reduced churn rate
- Better customer lifetime value
- **ROI:** Reduces churn by X%, saves $Y million in lost revenue

---

## Manufacturing & Supply Chain

### 1. Quality Control with Multi-Task ELM

**Business Problem:** Inspect products for multiple quality issues simultaneously: defects, dimensions, and material quality.

**Solution:** Use Multi-Task ELM to jointly predict multiple quality attributes.

**Implementation:

```typescript
import { MultiTaskELM } from '@astermind/astermind-pro';

// Quality control system
const qualityInspector = new MultiTaskELM({
  tasks: [
    {
      name: 'defects',
      categories: ['none', 'minor', 'major', 'critical'],
      weight: 2.0,
    },
    {
      name: 'dimensions',
      categories: ['within_spec', 'out_of_spec'],
      weight: 1.5,
    },
    {
      name: 'material',
      categories: ['good', 'acceptable', 'poor'],
      weight: 1.0,
    },
  ],
  sharedHiddenUnits: 256,
  taskSpecificHiddenUnits: [128, 128, 128],
});

// Product inspection data: images, sensor readings, measurements
const products = [
  {
    imageFeatures: [0.1, 0.2, 0.3, ...],
    sensorReadings: [45.2, 23.1, 67.8, ...],
    measurements: [10.0, 20.0, 15.0, ...],
  },
  // ... more products
];

const defectLabels = [0, 1, 2, 3, ...];
const dimensionLabels = [0, 1, 0, ...];
const materialLabels = [0, 1, 2, ...];

const yTaskData = new Map([
  ['defects', defectLabels],
  ['dimensions', dimensionLabels],
  ['material', materialLabels],
]);

qualityInspector.train(
  products.map(p => extractProductFeatures(p)),
  yTaskData
);

// Inspect product
function inspectProduct(product: Product) {
  const features = extractProductFeatures(product);
  const inspection = qualityInspector.predict(features, 1);
  
  const defects = inspection.get('defects')?.[0];
  const dimensions = inspection.get('dimensions')?.[0];
  const material = inspection.get('material')?.[0];
  
  if (defects?.label !== 'none' || dimensions?.label === 'out_of_spec' || material?.label === 'poor') {
    // Reject product
    rejectProduct(product, {
      defects: defects?.label,
      dimensions: dimensions?.label,
      material: material?.label,
    });
  }
}
```

**Business Impact:**
- Faster quality inspection
- More comprehensive quality checks
- Reduced defective products reaching customers
- **ROI:** Reduces defect rate by X%, saves $Y in warranty costs

---

### 2. Predictive Maintenance with Online Kernel ELM

**Business Problem:** Predict equipment failures in real-time from streaming sensor data.

**Solution:** Use Online Kernel ELM to continuously monitor equipment and adapt to changing conditions.

**Implementation:**

```typescript
import { OnlineKernelELM } from '@astermind/astermind-pro';

// Predictive maintenance system
const maintenancePredictor = new OnlineKernelELM({
  kernel: { type: 'rbf', gamma: 0.1 },
  categories: ['normal', 'warning', 'critical', 'failure'],
  windowSize: 5000, // Last 5000 sensor readings
  decayFactor: 0.99,
});

// Initial training on historical sensor data
const historicalSensorData = [
  {
    temperature: 75.2,
    vibration: 0.5,
    pressure: 100.5,
    // ... more sensors
  },
  // ... more readings
];

maintenancePredictor.fit(
  historicalSensorData.map(d => extractSensorFeatures(d)),
  historicalStatusLabels
);

// Real-time monitoring
function monitorEquipment(equipmentId: string, sensorData: SensorData) {
  const features = extractSensorFeatures(sensorData);
  
  // Update with latest reading (labeled by maintenance team)
  if (sensorData.reviewedByMaintenance) {
    maintenancePredictor.update(features, sensorData.status);
  }
  
  // Predict equipment status
  const prediction = maintenancePredictor.predict(features, 1);
  
  if (prediction[0].label === 'warning' || prediction[0].label === 'critical') {
    // Schedule maintenance
    scheduleMaintenance(equipmentId, {
      status: prediction[0].label,
      confidence: prediction[0].prob,
      sensorData: sensorData,
    });
  }
}
```

**Business Impact:**
- Prevents unexpected equipment failures
- Optimized maintenance scheduling
- Reduced downtime
- **ROI:** Reduces unplanned downtime by X%, saves $Y in production losses

---

## Legal & Compliance

### 1. Contract Analysis with Multi-Kernel ELM

**Business Problem:** Classify legal contracts by type, risk level, and required actions by analyzing both structured (clauses) and unstructured (language patterns) content.

**Solution:** Use Multi-Kernel ELM to combine linear clause patterns and non-linear language patterns.

**Implementation:**

```typescript
import { MultiKernelELM } from '@astermind/astermind-pro';
import { tokenize } from '@astermind/astermind-pro';

// Contract analysis system
const contractAnalyzer = new MultiKernelELM([
  'employment',
  'nda',
  'service_agreement',
  'purchase_order',
  'other'
], {
  kernels: [
    { type: 'rbf', params: { gamma: 0.01 } }, // Language patterns
    { type: 'linear' }, // Clause patterns
  ],
  learnWeights: true,
});

// Contract texts
const contracts = [
  'This Employment Agreement is entered into...',
  'This Non-Disclosure Agreement...',
  // ... more contracts
];

const contractTypes = [0, 1, 2, ...];

// Convert to features
const X = contracts.map(contract => {
  const tokens = tokenize(contract, true);
  // Extract clause features and language features
  return combineFeatures(
    extractClauseFeatures(contract),
    extractLanguageFeatures(tokens)
  );
});

contractAnalyzer.fit(X, contractTypes);

// Analyze new contract
function analyzeContract(contractText: string) {
  const tokens = tokenize(contractText, true);
  const features = combineFeatures(
    extractClauseFeatures(contractText),
    extractLanguageFeatures(tokens)
  );
  
  const prediction = contractAnalyzer.predict(features, 1);
  
  return {
    contractType: prediction[0].label,
    confidence: prediction[0].prob,
    // Route to appropriate legal team
  };
}
```

**Business Impact:**
- Faster contract processing
- Automated contract routing
- Better risk assessment
- **ROI:** Reduces legal review time by X%, saves $Y in legal costs

---

### 2. Regulatory Compliance Monitoring with Sparse ELM

**Business Problem:** Monitor transactions and activities for compliance violations across thousands of regulatory features.

**Solution:** Use Sparse ELM to identify the most important compliance signals and create an interpretable model.

**Implementation:**

```typescript
import { SparseELM } from '@astermind/astermind-pro';

// Compliance monitoring system
const complianceMonitor = new SparseELM({
  categories: ['compliant', 'review_required', 'violation'],
  hiddenUnits: 512,
  regularization: {
    type: 'l1',
    lambda: 0.01,
  },
  sparsityTarget: 0.85, // Very sparse for interpretability
});

// Transaction/activity data (1000+ compliance features)
const activities = [
  [0.1, 0.0, 0.0, 0.3, 0.0, ...], // 1000+ features
  // ... more activities
];

const complianceLabels = [0, 1, 2, ...];

complianceMonitor.train(activities, complianceLabels);

// Get important compliance signals
const importance = complianceMonitor.getFeatureImportance();
const keySignals = importance
  .map((imp, idx) => ({ signal: complianceFeatureNames[idx], importance: imp }))
  .filter(s => s.importance > 0.1)
  .sort((a, b) => b.importance - a.importance);

// Monitor new activity
function monitorCompliance(activity: Activity) {
  const features = extractComplianceFeatures(activity);
  const prediction = complianceMonitor.predict(features, 1);
  
  if (prediction[0].label !== 'compliant') {
    // Flag for compliance review
    flagForReview(activity, {
      status: prediction[0].label,
      confidence: prediction[0].prob,
      keySignals: keySignals.slice(0, 5), // Explainable
    });
  }
}
```

**Business Impact:**
- Automated compliance monitoring
- Interpretable results (auditors can understand)
- Reduced compliance violations
- **ROI:** Prevents regulatory fines ($X million), reduces audit costs

---

## Marketing & Advertising

### 1. Customer Segmentation with DeepELMPro

**Business Problem:** Segment customers into detailed personas using complex behavioral patterns from multiple data sources.

**Solution:** Use DeepELMPro to learn hierarchical customer patterns.

**Implementation:**

```typescript
import { DeepELMPro } from '@astermind/astermind-pro';

// Customer segmentation
const segmenter = new DeepELMPro({
  layers: [256, 128, 64],
  categories: ['price_sensitive', 'quality_seeker', 'brand_loyal', 'bargain_hunter', 'impulse_buyer'],
  activation: 'relu',
  pretraining: true,
  regularization: {
    type: 'l2',
    lambda: 0.0001,
  },
});

// Customer data: purchase history, browsing, demographics, 
// social media, preferences, etc.
const customers = [
  {
    purchaseHistory: [100, 150, 200, ...],
    browsingBehavior: [0.8, 0.2, 0.9, ...],
    demographics: [35, 1, 0, ...],
    socialMediaActivity: [50, 30, 20, ...],
  },
  // ... more customers
];

const segmentLabels = [0, 1, 2, 3, 4, ...];

await segmenter.train(
  customers.map(c => extractCustomerFeatures(c)),
  segmentLabels
);

// Segment new customer
function segmentCustomer(customer: Customer) {
  const features = extractCustomerFeatures(customer);
  const prediction = segmenter.predict(features, 1);
  
  return {
    segment: prediction[0].label,
    confidence: prediction[0].prob,
    // Use for targeted marketing campaigns
  };
}
```

**Business Impact:**
- Better customer targeting
- Improved marketing ROI
- Personalized campaigns
- **ROI:** Increases marketing conversion by X%, improves ROI by Y%

---

### 2. Ad Performance Prediction with Multi-Kernel ELM

**Business Problem:** Predict ad performance by combining click-through patterns (linear) and engagement patterns (non-linear).

**Solution:** Use Multi-Kernel ELM to combine different types of ad performance signals.

**Implementation:**

```typescript
import { MultiKernelELM } from '@astermind/astermind-pro';

// Ad performance prediction
const adPredictor = new MultiKernelELM(['low', 'medium', 'high', 'very_high'], {
  kernels: [
    { type: 'rbf', params: { gamma: 0.01 } }, // Engagement patterns
    { type: 'linear' }, // Click patterns
  ],
  learnWeights: true,
});

// Ad features: creative, targeting, placement, timing, etc.
const ads = [
  {
    creativeFeatures: [0.8, 0.2, 0.9, ...],
    targetingFeatures: [0.5, 0.7, 0.3, ...],
    placementFeatures: [0.6, 0.4, 0.8, ...],
  },
  // ... more ads
];

const performanceLabels = [0, 1, 2, 3, ...];

adPredictor.fit(
  ads.map(a => flattenAdFeatures(a)),
  performanceLabels
);

// Predict ad performance
function predictAdPerformance(ad: Ad) {
  const features = flattenAdFeatures(ad);
  const prediction = adPredictor.predict(features, 1);
  
  return {
    expectedPerformance: prediction[0].label,
    confidence: prediction[0].prob,
    // Use for ad budget allocation
  };
}
```

**Business Impact:**
- Better ad budget allocation
- Improved ad performance
- Reduced wasted ad spend
- **ROI:** Increases ad ROI by X%, reduces wasted spend by $Y

---

## Insurance

### 1. Claims Fraud Detection with Sparse ELM

**Business Problem:** Detect fraudulent insurance claims from high-dimensional claim data (1000+ features per claim).

**Solution:** Use Sparse ELM to identify key fraud indicators and create an interpretable model.

**Implementation:**

```typescript
import { SparseELM } from '@astermind/astermind-pro';

// Claims fraud detection
const fraudDetector = new SparseELM({
  categories: ['legitimate', 'suspicious', 'fraud'],
  hiddenUnits: 512,
  regularization: {
    type: 'l1',
    lambda: 0.01,
  },
  sparsityTarget: 0.8,
});

// Claim data (1000+ features)
const claims = [
  [0.1, 0.0, 0.0, 0.3, 0.0, ...], // 1000+ features
  // ... more claims
];

const fraudLabels = [0, 1, 2, ...];

fraudDetector.train(claims, fraudLabels);

// Get key fraud indicators
const importance = fraudDetector.getFeatureImportance();
const fraudIndicators = importance
  .map((imp, idx) => ({ indicator: claimFeatureNames[idx], importance: imp }))
  .filter(i => i.importance > 0.1)
  .sort((a, b) => b.importance - a.importance);

// Detect fraud
function detectFraud(claim: Claim) {
  const features = extractClaimFeatures(claim);
  const prediction = fraudDetector.predict(features, 1);
  
  if (prediction[0].label !== 'legitimate') {
    flagForInvestigation(claim, {
      riskLevel: prediction[0].label,
      confidence: prediction[0].prob,
      indicators: fraudIndicators.slice(0, 5),
    });
  }
}
```

**Business Impact:**
- Reduced fraudulent claims
- Interpretable fraud detection
- Faster claim processing
- **ROI:** Prevents $X million in fraudulent claims annually

---

### 2. Risk Assessment with Multi-Task ELM

**Business Problem:** Assess insurance risk across multiple dimensions: claim probability, claim severity, and premium recommendation.

**Solution:** Use Multi-Task ELM to jointly predict multiple risk factors.

**Implementation:**

```typescript
import { MultiTaskELM } from '@astermind/astermind-pro';

// Insurance risk assessment
const riskAssessor = new MultiTaskELM({
  tasks: [
    {
      name: 'claim_probability',
      categories: ['low', 'medium', 'high', 'very_high'],
      weight: 2.0,
    },
    {
      name: 'claim_severity',
      categories: ['low', 'medium', 'high'],
      weight: 1.5,
    },
    {
      name: 'premium',
      categories: ['standard', 'increased', 'high', 'decline'],
      weight: 1.0,
    },
  ],
  sharedHiddenUnits: 256,
  taskSpecificHiddenUnits: [128, 128, 128],
});

// Policyholder data
const policyholders = [
  {
    age: 35,
    drivingRecord: [0, 1, 0, ...], // Accidents, violations
    vehicleType: 'sedan',
    location: 'urban',
    // ... more features
  },
  // ... more policyholders
];

const claimProbLabels = [0, 1, 2, 3, ...];
const severityLabels = [0, 1, 2, ...];
const premiumLabels = [0, 1, 2, 3, ...];

const yTaskData = new Map([
  ['claim_probability', claimProbLabels],
  ['claim_severity', severityLabels],
  ['premium', premiumLabels],
]);

riskAssessor.train(
  policyholders.map(p => extractPolicyholderFeatures(p)),
  yTaskData
);

// Assess risk
function assessRisk(policyholder: Policyholder) {
  const features = extractPolicyholderFeatures(policyholder);
  const assessment = riskAssessor.predict(features, 1);
  
  return {
    claimProbability: assessment.get('claim_probability')?.[0].label,
    claimSeverity: assessment.get('claim_severity')?.[0].label,
    recommendedPremium: assessment.get('premium')?.[0].label,
  };
}
```

**Business Impact:**
- Better risk pricing
- Reduced underwriting losses
- Optimized premium recommendations
- **ROI:** Improves underwriting profitability by X%

---

## Real Estate

### 1. Property Valuation with Multi-Kernel ELM

**Business Problem:** Estimate property values by combining location features (linear) and property characteristics (non-linear).

**Solution:** Use Multi-Kernel ELM to combine different types of property features.

**Implementation:**

```typescript
import { MultiKernelELM } from '@astermind/astermind-pro';

// Property valuation
const propertyValuer = new MultiKernelELM([
  '$100k-$200k',
  '$200k-$300k',
  '$300k-$500k',
  '$500k-$750k',
  '$750k+'
], {
  kernels: [
    { type: 'rbf', params: { gamma: 0.01 } }, // Property characteristics
    { type: 'linear' }, // Location features
  ],
  learnWeights: true,
});

// Property data
const properties = [
  {
    locationFeatures: [0.8, 0.2, 0.9, ...], // School ratings, crime, amenities
    propertyFeatures: [3, 2, 1500, ...], // Bedrooms, bathrooms, sqft
    marketFeatures: [0.7, 0.5, ...], // Market trends, inventory
  },
  // ... more properties
];

const priceRangeLabels = [0, 1, 2, 3, 4, ...];

propertyValuer.fit(
  properties.map(p => flattenPropertyFeatures(p)),
  priceRangeLabels
);

// Estimate property value
function estimateValue(property: Property) {
  const features = flattenPropertyFeatures(property);
  const prediction = propertyValuer.predict(features, 1);
  
  return {
    priceRange: prediction[0].label,
    confidence: prediction[0].prob,
  };
}
```

**Business Impact:**
- Faster property valuations
- More accurate pricing
- Better investment decisions
- **ROI:** Improves pricing accuracy by X%, increases sales velocity

---

## Energy & Utilities

### 1. Energy Demand Forecasting with Online Kernel ELM

**Business Problem:** Forecast energy demand in real-time from streaming consumption data, adapting to weather and events.

**Solution:** Use Online Kernel ELM to continuously update demand forecasts.

**Implementation:**

```typescript
import { OnlineKernelELM } from '@astermind/astermind-pro';

// Energy demand forecasting
const demandForecaster = new OnlineKernelELM({
  kernel: { type: 'rbf', gamma: 0.01 },
  categories: ['low', 'medium', 'high', 'peak'],
  windowSize: 2000,
  decayFactor: 0.98,
});

// Historical consumption data
const consumptionData = [
  {
    time: '2024-01-01 10:00',
    temperature: 72,
    weather: 'sunny',
    dayOfWeek: 1,
    consumption: 1000,
  },
  // ... more data
];

demandForecaster.fit(
  consumptionData.map(d => extractConsumptionFeatures(d)),
  consumptionLabels
);

// Forecast demand
function forecastDemand(currentContext: Context) {
  const features = extractConsumptionFeatures({
    time: new Date(),
    temperature: currentContext.temperature,
    weather: currentContext.weather,
    dayOfWeek: new Date().getDay(),
  });
  
  // Update with latest consumption
  if (currentContext.latestConsumption) {
    demandForecaster.update(features, currentContext.latestConsumption.level);
  }
  
  const forecast = demandForecaster.predict(features, 1);
  
  return {
    demandLevel: forecast[0].label,
    confidence: forecast[0].prob,
    // Use for grid management
  };
}
```

**Business Impact:**
- Better grid management
- Reduced energy waste
- Optimized power generation
- **ROI:** Reduces energy costs by X%, improves grid stability

---

## Telecommunications

### 1. Network Anomaly Detection with Online Kernel ELM

**Business Problem:** Detect network anomalies in real-time from streaming network metrics.

**Solution:** Use Online Kernel ELM to continuously monitor network health.

**Implementation:**

```typescript
import { OnlineKernelELM } from '@astermind/astermind-pro';

// Network monitoring
const networkMonitor = new OnlineKernelELM({
  kernel: { type: 'rbf', gamma: 0.1 },
  categories: ['normal', 'degraded', 'critical', 'outage'],
  windowSize: 5000,
  decayFactor: 0.99,
});

// Network metrics
const networkMetrics = [
  {
    latency: 50,
    packetLoss: 0.01,
    bandwidth: 1000,
    errorRate: 0.001,
  },
  // ... more metrics
];

networkMonitor.fit(
  networkMetrics.map(m => extractNetworkFeatures(m)),
  networkStatusLabels
);

// Monitor network
function monitorNetwork(metrics: NetworkMetrics) {
  const features = extractNetworkFeatures(metrics);
  
  if (metrics.reviewedByOps) {
    networkMonitor.update(features, metrics.status);
  }
  
  const prediction = networkMonitor.predict(features, 1);
  
  if (prediction[0].label !== 'normal') {
    alertNetworkOps({
      status: prediction[0].label,
      confidence: prediction[0].prob,
      metrics: metrics,
    });
  }
}
```

**Business Impact:**
- Proactive network issue detection
- Reduced downtime
- Better service quality
- **ROI:** Reduces network downtime by X%, improves customer satisfaction

---

## Summary: Choosing the Right Variant

### Use **Multi-Kernel ELM** when:
- You have heterogeneous data patterns
- Combining linear and non-linear patterns
- **Examples:** Financial sentiment, product recommendations, contract analysis

### Use **DeepELMPro** when:
- Complex hierarchical patterns
- Need better generalization
- **Examples:** Algorithmic trading, customer segmentation, churn prediction

### Use **Online Kernel ELM** when:
- Streaming/real-time data
- Data distribution changes over time
- **Examples:** Fraud detection, patient monitoring, demand forecasting

### Use **Multi-Task ELM** when:
- Multiple related predictions needed
- Tasks share underlying features
- **Examples:** Credit risk, disease diagnosis, quality control

### Use **Sparse ELM** when:
- High-dimensional data (1000+ features)
- Need interpretability
- Need efficiency
- **Examples:** Drug discovery, AML detection, compliance monitoring

---

## Business ROI Summary

| Industry | Use Case | ELM Variant | Key Benefit | Typical ROI |
|----------|----------|-------------|-------------|-------------|
| Finance | Fraud Detection | Online Kernel | Real-time adaptation | Prevents $X million/year |
| Finance | Credit Risk | Multi-Task | Multi-faceted assessment | Reduces defaults by X% |
| Healthcare | Disease Diagnosis | Multi-Task | Comprehensive diagnosis | Reduces misdiagnosis by X% |
| Healthcare | Drug Discovery | Sparse | Feature identification | Saves $X billion, 10→X years |
| Retail | Recommendations | Multi-Kernel | Better personalization | Increases revenue by X% |
| Retail | Demand Forecasting | Online Kernel | Real-time adaptation | Reduces inventory costs by X% |
| Manufacturing | Quality Control | Multi-Task | Comprehensive inspection | Reduces defects by X% |
| Legal | Contract Analysis | Multi-Kernel | Faster processing | Saves $Y in legal costs |
| Insurance | Fraud Detection | Sparse | Interpretable detection | Prevents $X million/year |
| Energy | Demand Forecasting | Online Kernel | Grid optimization | Reduces costs by X% |

---

## Next Steps

- See [ELM_VARIANTS_EXAMPLES.md](./ELM_VARIANTS_EXAMPLES.md) for technical implementation details
- Review [DEVELOPER_GUIDE.md](../guides/DEVELOPER_GUIDE.md) for advanced patterns
- Check [PREMIUM_FEATURES.md](./PREMIUM_FEATURES.md) for complete feature documentation