The ACID properties ensure reliable transactions in databases:

1. Atomicity: Ensures that a transaction is indivisible; it either completes entirely or has no effect. If any part of the transaction fails, the entire transaction is rolled back, maintaining data integrity.
2. Consistency: Guarantees that a transaction brings the database from one valid state to another, adhering to all defined rules, constraints, and relationships. This ensures that any transaction only results in valid database states.
3. Isolation: Ensures that transactions are executed independently, without interference. Intermediate transaction results are not visible to other transactions until the transaction is committed, preventing conflicts and ensuring data consistency.
4. Durability: Ensures that once a transaction is committed, its changes are permanent and survive system failures. The data changes are written to non-volatile storage, guaranteeing that committed transactions remain intact even after crashes or restarts.

These properties collectively maintain the integrity, consistency, and reliability of database transactions.

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Convolutional Neural Network (CNN)

Convolutional Neural Networks (CNNs) are a class of deep learning algorithms specifically designed for processing grid-like data, such as images. They are particularly effective in tasks where spatial hierarchies and patterns are important.

Key Components:

• Convolutional Layers: Apply convolution operations to detect features such as edges and textures. They use filters or kernels that slide over the input data.
• Activation Functions: Non-linear functions (e.g., ReLU) that introduce non-linearity into the model.
• Pooling Layers: Reduce the spatial dimensions of feature maps, helping to reduce computation and control overfitting.
• Fully Connected Layers: Classify the data based on extracted features.

Applications:

• Image Classification: Identifying objects or categories within an image (e.g., classifying images of animals).
• Object Detection: Locating objects within an image and classifying them (e.g., detecting faces in photos).
• Image Segmentation: Dividing an image into regions or segments for detailed analysis (e.g., medical imaging).
• Facial Recognition: Identifying or verifying individuals based on facial features.
• Video Analysis: Recognizing patterns or actions in video frames (e.g., surveillance).

CNNs are crucial for many modern AI applications due to their ability to learn and extract complex features from data.

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Difference between Compiled and Interpreted Languages are mentioned below :

Compiled Languages:

• Translation: Convert the entire source code into machine code before execution.
• Performance: Generally faster since code is directly executed by the computer’s CPU.
• Error Detection: Errors are caught during the compilation process.
• Examples: C, C++, Rust.

Interpreted Languages:

• Translation: Convert source code into intermediate code, which is then executed line-by-line.
• Performance: Generally slower due to the overhead of interpretation.
• Error Detection: Errors are caught at runtime.
• Examples: Python, JavaScript, Ruby.

Both types have their use cases and choosing between them depends on the specific requirements of a project.

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Memory management in Python uses a private heap containing all objects and data structures, managed internally by the Python memory manager. It consists of several components for dynamic storage management like sharing, segmentation, preallocation, or caching. At the lowest level, a raw memory allocator ensures sufficient room in the private heap by interacting with the OS memory manager. Object-specific allocators operate on top of this, implementing distinct policies for different object types, such as integers and strings.

The Python memory manager manages heap allocation through the Python/C API functions. Users should not manipulate Python objects using C library functions like malloc(), calloc(), realloc(), and free() to avoid memory corruption. Extension writers should allocate memory through the Python memory manager to ensure accurate memory footprint monitoring and proper garbage collection.

Python’s memory allocators belong to three domains: Raw, Mem, and Object. The Raw domain interfaces with the system allocator, while the Mem and Object domains allocate memory within the Python heap. Each domain’s functions must be used consistently to avoid errors.

Debug hooks, available in debug and release modes, fill memory blocks with recognizable bit patterns to detect memory errors, ensuring robust memory management.

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Ransomware is a type of malicious software designed to block access to a computer system or its data until a ransom is paid. It typically encrypts the victim’s files, making them inaccessible. The attackers then demand payment, often in cryptocurrency, to provide the decryption key needed to restore access to the files.

Ransomware spreads through various methods. Commonly, it is distributed via phishing emails that contain malicious attachments or links. When the recipient opens the attachment or clicks the link, the ransomware is downloaded and executed. It can also spread through exploit kits, which take advantage of vulnerabilities in software or operating systems to install the ransomware without the user’s knowledge. Additionally, ransomware can propagate through network vulnerabilities, allowing it to move laterally across connected devices and systems. Another method of spread is through malicious advertisements (malvertising) that automatically download ransomware when a user visits an infected website.

Preventing ransomware infections involves maintaining updated software, using robust security measures like firewalls and antivirus programs, regularly backing up data, and educating users about recognizing phishing attempts and avoiding suspicious links and attachments.