Advanced AI Concepts, Search Algorithms, and Biometric Security

Posted on Jan 14, 2026 in Computer Engineering

Characteristics and Applications of Strong AI (AGI)

Characteristics of Strong AI

  1. Human-like Intelligence: Strong AI possesses reasoning, perception, and understanding comparable to humans.
  2. Self-awareness: It can think, understand emotions, and make independent decisions.
  3. Generalization: It can apply knowledge from one domain to another.
  4. Learning and Adaptation: It continuously improves through experience without explicit programming.
  5. Autonomous Decision-Making: Capable of performing complex tasks without human intervention.

Applications of Strong AI

Robotics: Fully autonomous robots for healthcare, defense, and industry.

  • AI Companions: Intelligent assistants like advanced humanoids.
  • Research & Development: Used in advanced scientific research and innovation.
  • Healthcare: Diagnosis systems that reason like human doctors.

AI’s Role in Enhancing Payment System Security

Artificial Intelligence enhances payment security through:

  1. Fraud Detection: AI models analyze real-time transaction data to detect unusual patterns or suspicious activity.
  2. Behavioral Biometrics: Recognizes user behavior patterns (typing speed, device use, etc.) to identify fraud.
  3. Face & Voice Recognition: Ensures secure authentication for digital payments.
  4. Risk Scoring: Machine learning assigns risk levels to transactions to prevent fraud.
  5. Anomaly Detection: Detects and blocks abnormal payment activities instantly.
  6. Data Encryption & Privacy: AI supports better encryption and cybersecurity algorithms.

Real-World Example

AI in platforms like Paytm, Google Pay, and banks detects unusual login or transfer behavior and instantly triggers a security alert or OTP verification.

Depth First Search (DFS) vs. Breadth First Search (BFS)

The following points highlight the key differences between these two fundamental graph search algorithms:

  • Full Form

    DFS stands for Depth First Search.
    BFS stands for Breadth First Search.

  • Approach

    DFS explores depth-wise, going deep into one branch before backtracking.
    BFS explores level-wise, visiting all nodes at the current level before moving deeper.

  • Data Structure Used

    DFS uses a Stack (either explicit or via recursion).
    BFS uses a Queue to store nodes to be visited next.

  • Path Finding

    DFS may not give the shortest path in an unweighted graph.
    BFS always gives the shortest path in an unweighted graph.

  • Memory Usage

    DFS uses less memory, as it only stores nodes in the current path.
    BFS uses more memory, as it stores all the neighboring nodes.

  • Backtracking

    DFS involves backtracking when a branch ends.
    BFS does not require backtracking.

  • Applications

    DFS is used in maze solving, topological sorting, and scheduling problems.
    BFS is used in shortest path finding, peer-to-peer networks, and social network analysis.

Pattern Recognition Flow: Fingerprint Algorithm

Flow of a Fingerprint Recognition System

  1. Image Acquisition: Capture the fingerprint image using a scanner or sensor.
  2. Preprocessing: Enhance image quality (noise removal, contrast adjustment).
  3. Feature Extraction: Identify key minutiae points (ridge endings, bifurcations).
  4. Template Creation: Store extracted features in a secure digital format.
  5. Pattern Matching: Compare the input fingerprint template with stored templates using AI/ML algorithms.
  6. Decision Making:
    • If match found → access granted.
    • If not → access denied.

Algorithms Used in Fingerprint Recognition

  • Minutiae-based Matching Algorithm: Compares ridge structures and minutiae points.
  • AI-based Models: Deep Learning models (e.g., CNNs) for improved accuracy and real-time recognition.