Aspiring Cloud Computing Specialist +
AI/ML Engineer

The CrimeAlert System is an IT-enabled framework designed to empower individuals to report suspicious activities or crimes to the authorities efficiently and securely. Crime Alert serves as an anonymous crime reporting system, leverages blockchain technology for secure data storage and implements machine learning algorithms to detect potential false alarms. The system allows for anonymous reporting, ensuring user privacy, and includes features such as multimedia upload functionality, user feedback mechanisms, and support for user registration and authority login.

1. Anonymous Reporting:

Utilizes Blockchain technology to allow users to submit reports without requiring them to create an account or disclose personal information.

2. False Alarm Detection:

Implements machine learning algorithms to analyze submitted reports and identify potential false alarms, reducing unnecessary alerts for law enforcement.

3. Multi-media Upload Functionality:

Allows users to provide additional evidence or context when reporting incidents, enabling the upload of images related to the incidents.

4. User Feedback and Support:

Incorporates a feedback mechanism to gather user suggestions and improve the app continually. Provides a support system to address user queries or issues.

5. User Registration:

Generates random and unique IDs for user registration to ensure security and anonymity.

6. Authorities Login:

Implements a separate login system for authorities (e.g., law enforcement agencies) with appropriate credentials provided by the relevant organizations.

- Utilizes Blockchain technology to securely store each record as a Block/transaction, ensuring data integrity.
- Employs a Smart Contract, designed using Solidity code, to save and retrieve data from the Blockchain.
- Each tip is displayed in a tabular format, with ML predictions from CNN aiding in decision-making.
- Data tampering prevention is ensured by verifying the hash code of all previous blocks before storing new records.

The Document Management System is a comprehensive IT-enabled solution designed to facilitate efficient and secure management of documents, clients, and cases for law firms. This menu-based console application leverages Core Java, MySQL, and JDBC to provide robust functionalities that allow users to seamlessly upload, view, update, and delete documents, manage client information, and handle case details.

1. Document Management:

- Upload New Documents: Allows users to add new documents to the system with relevant details such as title, description, file path, and upload date.
- View Document Details: Provides the capability to view detailed information about any document stored in the system.
- Update Document Information: Enables users to update existing document details.
- Delete Documents: Allows users to delete documents from the system.

2. Client Management:

- Add New Clients: Facilitates the addition of new clients with necessary details like name, contact number, email, and address.
- View Client Details: Provides access to view detailed information about clients.
- Update Client Information: Enables updating of client details as needed.
- Delete Clients: Allows for the deletion of client records from the system.

3. Case Management:

- Create New Cases: Allows for the creation of new cases associated with clients, including case number, description, status, open date, and close date.
- View Case Details: Enables viewing detailed information about cases.
- Update Case Information: Facilitates updating of case details.
- Close Cases: Allows users to mark cases as closed.

1. Core Java:

Utilized to develop the menu-based console application, providing a robust and interactive user interface for managing documents, clients, and cases.

2. MySQL Database:

Employed for secure and efficient storage of all records. The database schema includes separate tables for documents, clients, and cases, ensuring organized data management.

3. JDBC (Java Database Connectivity):

Integrated to enable seamless interaction between the Java application and the MySQL database, facilitating CRUD (Create, Read, Update, Delete) operations.

4. Exception Handling:

Implemented comprehensive exception handling to ensure smooth operation and provide user-friendly error messages.

1. Setup Instructions:

- Clone the repository from GitHub.
- Set up the MySQL database using the provided SQL scripts.
- Configure the database connection by updating the 'db.properties' file with your MySQL credentials.

2. Running the Application:

- Compile the Java files using the provided instructions.
- Run the application and follow the menu options to manage documents, clients, and cases.

The Document Management System is a powerful tool designed to streamline the management of documents, clients, and cases for law firms. Its comprehensive features and robust implementation make it an essential system for efficient legal practice management.

Pioneering prompt engineering techniques to boost AI RhLF accuracy, ensuring models respond with higher precision.

Leading initiatives to optimize AI model responses, directly impacting their performance in real-world applications.

Creating and fostering a culture of innovation and meticulous attention to detail that supports continuous improvement and state-of-the-art solutions.

🔗 Passionate about driving the future of AI through advanced accuracy and reliability, I am keen to connect with professionals who are pushing the boundaries of technology in AI and machine learning.

Involved in training of Google Gemini - Bard AI, for further improvement of responses which need to be generated in 56 languages as part of Language training.

Rating prompts and responses on specific criteria to identify the best model responses out of two or more options until the content is high quality enough to be accepted in a review process.

Training models that generates and classify information from multiple modalities, including images, audio, or video and fine-tune models for specific tasks or industries, ensuring they meet performance benchmarks.

Developing and implementation of prompt engineering strategies to guide model outputs, improving response accuracy and contextual relevance.

Model Evaluation & Optimization: Continuously evaluation of model performance using various metrics and refine models to minimize errors, reduce biases, and enhance generalization capabilities.

Worked closely with cross-functional teams and domain experts across world, to integrate LLMs into applications and platforms.