তথ্য যখন ভয়ংকর, ইত্তেফাক

 শিরোনামঃ তথ্য যখন ভয়ংকর, 

পত্রিকাঃ ইত্তেফাক  

তারিখঃ ২৩ জানুয়ারী, ২০২৫

লিঙ্কঃ https://epaper.ittefaq.com.bd/edition/1818/2nd-edition/page/9

#তথ্য

#ইত্তেফাক 

 Article Title; 

Enhancing Marine Radar Security Through Semi-Supervised Learning: A Self-Training Approach

Conference: 2024 2nd International Conference on Information and Communication Technology (ICICT)

#security

#AlamgirHossain

#MarineRadarSecurity 

A novel feature selection-driven ensemble learning approach for accurate botnet attack detection

 Publication of new article: 

Title: "A novel feature selection-driven ensemble learning approach for accurate botnet attack detection". 

Journal: Alexandria Engineering Journal

Volume: 118

Link: https://www.sciencedirect.com/science/article/pii/S1110016825000602

DoI: https://doi.org/10.1016/j.aej.2025.01.042

#security

#botnet

#AlamgirHossain 

Sample project proposal for software engineering lab, AI-Powered Mental Health Support Chatbot

 1. Title Page

Project Title: AI-Powered Mental Health Support Chatbot

Team Members:

            a)

            b)

            c)

            d)

Course: Software Engineering Lab

Date: January, 2025

2. Abstract/Executive Summary

This project proposes the development of an AI-powered chatbot to provide mental health support. The system will use natural language processing (NLP) to understand users’ concerns and provide empathetic, research-backed responses. It includes a mood tracker, daily affirmations, and recommendations for professional resources if needed. This chatbot aims to make mental health support accessible, private, and stigma-free.

3. Introduction

Background:

Mental health is a growing concern globally, with limited access to affordable and timely support. Many individuals feel reluctant to seek professional help due to stigma or lack of resources.

Relevance:

Technology can bridge the gap by providing an accessible, private, and always-available solution to users who need support.

Target Audience:

·         Students and young professionals

·         Individuals with mild to moderate mental health concerns

4. Problem Statement

What is the problem?

Mental health support is often expensive, inaccessible, or stigmatized, leaving many without the help they need.

Why does it matter?

Unaddressed mental health issues can lead to severe consequences, such as decreased productivity, strained relationships, and poor quality of life.

 

Who is affected?

Individuals facing mental health challenges but lacking resources or motivation to seek traditional help.

5. Objectives and Scope

Objectives:

Develop a chatbot that can respond empathetically to users’ mental health queries.

Incorporate a mood-tracking feature to monitor user emotions over time.

Provide scientifically-backed coping strategies and resources.

Scope:

Included: Chatbot interface, mood tracker, data encryption for user privacy.

Excluded: Professional diagnosis or therapy sessions.

6. Methodology/Approach

Technology Stack:

·         Frontend: React.js

·         Backend: Python (Flask)

·         AI/NLP: OpenAI GPT API or Hugging Face Transformers

·         Database: Firebase (real-time database)

·         Deployment: Google Cloud

Development Steps:

·         Requirement Analysis: Collect requirements through research and stakeholder interviews.

·         Design: Create UI mockups and chatbot architecture.

Development:

·         Implement chatbot logic using NLP.

·         Develop frontend for user interaction.

·         Integrate mood tracker and database.

Testing: Perform usability and functional testing with a focus group.

Deployment: Launch on both web and mobile platforms.

7. Timeline

Phase	Task	Duration
Requirement Analysis	Research and requirement gathering	Week 1, 2
Design	UI design and architecture planning	Week 3, 4
Development	AI/NLP chatbot logic	Weeks 5–10
Testing	Functional and usability testing	Weeks 11–12
Deployment	Launch and final documentation	Week 13

 

8. Resources

·         Development laptops

·         Internet connection

·         Access to mental health datasets for training the chatbot

9. Risk Management

Potential Risks:

·         Inaccurate chatbot responses due to limited training data.

·         Data privacy concerns if user data is not securely handled.

·         Misuse of the chatbot for purposes other than intended.

Mitigation Strategies:

·         Use pre-trained, well-documented NLP models.

·         Encrypt all user data and comply with GDPR standards.

·         Include disclaimers about the chatbot’s limitations.

10. Conclusion

The AI-Powered Mental Health Support Chatbot aims to address the growing need for accessible mental health solutions. Its innovative use of NLP, mood tracking, and resource recommendations provides a private and convenient support system. This project demonstrates how technology can positively impact society by improving mental health accessibility.

 

Sample project proposal for software engineering lab, Smart Library Management System

 1. Title Page

Project Title: Smart Library Management System

Team Members:

Date: January, 2025

2. Abstract/Executive Summary

This project proposes the development of a Smart Library Management System to enhance the traditional library experience. The system will support text-based and voice-based search functionalities, provide book suggestions based on keywords, and offer other intelligent features such as personalized recommendations, real-time availability tracking, and overdue reminders. The project aims to make library operations more efficient while improving the user experience through advanced technologies like natural language processing (NLP) and machine learning.

3. Introduction

Background:
Traditional library systems often lack modern features that make searching for and managing books convenient [1]. As libraries cater to users with diverse needs, integrating smart features can improve their accessibility and usability [2].

Relevance:
By leveraging AI, this system can provide users with personalized recommendations, improve search accuracy, and streamline library operations.

Target Audience:

• Students and researchers
• Library staff and administrators
• General readers

4. Problem Statement

What is the problem?

Conventional library systems rely on basic search and manual processes, which are time-consuming and lack intelligent features to assist users effectively.

Why does it matter?

Inefficient search and management can discourage library usage, reducing the library’s role as a valuable resource.

Who is affected?

Library users, including students, faculty, and researchers, as well as librarians managing operations.

5. Objectives and Scope

Objectives:

• Implement text-based and voice-based search for locating books.
• Provide intelligent book suggestions based on search keywords and user preferences.
• Enable real-time availability tracking for books.
• Send automated reminders for overdue books.
• Support personalized dashboards for users and administrators.

Scope:

• Included: User and admin portals, text and voice search, recommendation engine, notifications.
• Excluded: Integration with third-party e-book platforms.

6. Methodology/Approach

Technology Stack:

• Frontend: React.js
• Backend: Python (Django/Flask)
• Database: MySQL
• Voice Search: Google Speech-to-Text API
• Recommendation Engine: Machine Learning using scikit-learn
• Deployment: AWS

Development Steps:

1. Requirement Analysis: Identify key features and functionalities.
2. Design: Develop UI wireframes and backend architecture.
3. Development:
• Implement text and voice search functionality.
• Develop the recommendation engine using user behavior data.
• Build user and admin portals.
4. Testing: Perform unit, integration, and user acceptance testing.
5. Deployment: Launch the system on AWS.

System Architecture Diagram:

(Include a diagram depicting system components and their interactions, e.g., user interfaces, database, search algorithms, and APIs.)

7. Timeline and Milestones

Phase	Task	Duration
Requirement Analysis	Requirements gathering and analysis	Week 1-3
Design	Wireframe and database design	Week 4-6
Development	Coding	Week 7-10
Testing	Functional and usability testing	Week 11-12
Deployment		Week 13

8. Budget and Resources

Estimated Costs:

• Google Speech-to-Text API:  
• Cloud Hosting (AWS):  
• Development Tools: Free (Open Source)

Resources Required:

• Development laptops
• Internet connection
• Access to library datasets for training

9. Risk Management

Potential Risks:

• Challenges in integrating voice-based search with the database.
• Inaccurate recommendations due to insufficient training data.
• User adoption issues for advanced features.

Mitigation Strategies:

• Use pre-trained NLP models to improve voice search accuracy.
• Collect and curate high-quality datasets for the recommendation engine.
• Provide user training and clear documentation.

10. Conclusion

The Smart Library Management System revolutionizes library operations by integrating intelligent features like text and voice search, recommendation engines, and real-time tracking. These enhancements aim to make libraries more user-friendly, efficient, and adaptive to modern needs, ensuring an enriched experience for both users and administrators.

11. References

[1]        A. Ozeer, Y. Sungkur, and S. D. Nagowah, “Turning a Traditional Library into a Smart Library,” in 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates: IEEE, Dec. 2019, pp. 352–358. doi: 10.1109/ICCIKE47802.2019.9004242.

[2]        F. Farkhari, M. CheshmehSohrabi, and H. Karshenas, “Smart library: Reflections on concepts, aspects and technologies,” J. Inf. Sci., p. 01655515241260715, Aug. 2024, doi: 10.1177/01655515241260715.

 

গুরু এবং শিষ্যের রম্য

 গুরু এবং শিষ্যদের মাঝে আলাপ হচ্ছেঃ 

গুরু: আমি রবিবার কক্সবাজার যাবো। 

এক শিষ্য: আচ্ছা গুরু আপনি হোটেল বুক করেছেন?

গুরু: হ্যাঁ, করেছি। সোমবার। 

ওই শিষ্য: কবে যাবেন? 

গুরু: রবিবার। 

ওই শিষ্য কিছুক্ষণ নিরব থেকে: আচ্ছা রবিবার যাবেন, সোমবার হোটেলে উঠবেন। কিন্তু রবিবার রাতে আপনি কোথায় থাকবেন!!

গুরু: মানে!!

ওই শিষ্য: মানে রবিবার রাতে আপনি কোথায় ঘুমাবেন!! 

গুরু সাত দিন পরে ফিরে আসলেন । শিষ্য আবার জিজ্ঞেস করলোঃ 

রবিবার রাতে আপনি কোথায় ঘুমিয়েছিলেন!! 

গুরু এবং অন্য শিষ্যরা: হা, হা, হা.....।। (অট্টহাসি)

#রম্য