function analyzeSymptoms() {
const neuralNet = new NeuralNetwork();
const patterns = await scanDatabase(symptoms);
const diagnosis = await processPatterns(patterns);
const confidence = calculateConfidence(diagnosis);
return { diagnosis, confidence, patterns };
}
def ai_diagnostic_engine():
model = load_model('sophia_xt_v7.h5')
symptoms_vector = preprocess_symptoms(input_data)
prediction = model.predict(symptoms_vector)
confidence_score = calculate_confidence(prediction)
return {
'diagnosis': decode_prediction(prediction),
'confidence': confidence_score,
'processing_time': time.process_time()
}
interface AIDiagnostic {
symptoms: Symptom[];
confidence: number;
processing: boolean;
}
class SophiaXT implements DiagnosticEngine {
private neuralNetwork: NeuralNetwork;
private database: SymptomDatabase;
async diagnose(appliance: Appliance): Promise {
this.neuralNetwork.process(appliance.symptoms);
const patterns = await this.database.query(appliance.type);
const result = this.analyzePatterns(patterns);
return this.generateReport(result);
}
}
fn sophia_xt_analyze(symptoms: &Vec) -> Result {
let neural_net = NeuralNetwork::load("sophia_xt_model.bin")?;
let input_tensor = Tensor::from_symptoms(symptoms)?;
let output = neural_net.forward(input_tensor)?;
let confidence = calculate_confidence(&output);
Ok(Diagnosis {
result: decode_output(output),
confidence,
processing_time: timing::elapsed(),
})
}
func (ai *SophiaXT) ProcessDiagnostic(ctx context.Context, req *DiagnosticRequest) (*DiagnosticResponse, error) {
patterns, err := ai.database.QueryPatterns(ctx, req.ApplianceType)
if err != nil { return nil, err }
input := ai.preprocessSymptoms(req.Symptoms)
output := ai.neuralNetwork.Predict(input)
confidence := ai.calculateConfidence(output)
return &DiagnosticResponse{
Diagnosis: ai.decodeResult(output),
Confidence: confidence,
ProcessingTime: time.Since(start),
}, nil
}
class SophiaXT {
private:
NeuralNetwork* neural_net;
SymptomDatabase* database;
public:
DiagnosticResult analyze(SymptomVector symptoms) {
auto patterns = database->query(symptoms.appliance_type);
auto input = preprocess(symptoms);
auto output = neural_net->predict(input);
auto confidence = calculate_confidence(output);
return DiagnosticResult{
.diagnosis = decode_result(output),
.confidence = confidence,
.processing_time = timer.elapsed()
};
}
};
public class SophiaXT {
private NeuralNetwork neuralNetwork;
private SymptomDatabase database;
public DiagnosticResult analyzeSymptoms(Appliance appliance) {
List
patterns = database.queryPatterns(appliance.getType());
double[] input = preprocessSymptoms(appliance.getSymptoms());
double[] output = neuralNetwork.predict(input);
double confidence = calculateConfidence(output);
return new DiagnosticResult(
decodeResult(output),
confidence,
System.currentTimeMillis() - startTime
);
}
}WITH symptom_analysis AS (
SELECT
appliance_type,
symptom_name,
COUNT(*) as frequency,
AVG(confidence_score) as avg_confidence,
STDDEV(confidence_score) as confidence_stddev
FROM diagnostic_cases
WHERE created_at >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY appliance_type, symptom_name
),
neural_patterns AS (
SELECT
sa.*,
np.pattern_vector,
np.neural_activation
FROM symptom_analysis sa
JOIN neural_patterns np ON sa.symptom_name = np.symptom
ORDER BY sa.frequency DESC, sa.avg_confidence DESC
)
SELECT * FROM neural_patterns WHERE avg_confidence > 0.85;