Artificial Intelligence is transforming modern software into intelligent systems capable of monitoring environments, personalizing education, assisting healthcare professionals, understanding human language, and delivering personalized recommendations. Today’s AI engineers must combine machine learning expertise with professional software engineering methodologies to create scalable, secure, and production-ready applications.

50 AI Projects: Practical Applications with Full Software Engineering Lifecycle (Vol-IV) continues this project-oriented series by introducing five advanced intermediate-level Artificial Intelligence projects that integrate Computer Vision, Deep Learning, Natural Language Processing (NLP), Recommendation Systems, Conversational AI, Educational Technology, and Intelligent Information Extraction within the framework of the complete Software Development Life Cycle (SDLC).

Rather than focusing solely on algorithms, every project demonstrates how professional AI software is designed—from initial problem identification and requirement analysis to architecture design, model development, testing, deployment, maintenance, and future scalability.

The projects in this volume address important real-world domains including public safety, digital education, mental healthcare, entertainment platforms, and intelligent recruitment, allowing readers to gain practical experience with applications that are increasingly adopted across industries.

Whether you are developing university projects, preparing for software engineering interviews, conducting AI research, or building commercial products, this volume provides a complete roadmap for designing intelligent software systems.

What Makes This Book Unique?

Every project is developed using the complete Software Development Life Cycle (SDLC) including:

✔ Problem Analysis

✔ Feasibility Study

✔ Requirement Engineering

✔ Software Requirement Specification (SRS)

✔ Functional & Non-Functional Requirements

✔ UML Modeling

✔ Use Case Diagrams

✔ Activity Diagrams

✔ Sequence Diagrams

✔ Data Flow Diagrams (DFD)

✔ Entity Relationship Diagrams (ERD)

✔ Database Design

✔ AI System Architecture

✔ Dataset Collection

✔ Data Annotation

✔ Data Preprocessing

✔ Feature Engineering

✔ Model Selection

✔ Model Training

✔ Hyperparameter Optimization

✔ Model Evaluation

✔ API Integration

✔ Frontend Development

✔ Backend Development

✔ Software Testing

✔ Deployment Planning

✔ User Documentation

✔ Maintenance and Future Enhancements

This structured approach prepares readers for professional software engineering and AI product development.

Projects Covered in Volume IV

Chapter 26 – Fire Detection System in Surveillance Videos

Develop an intelligent Computer Vision application capable of detecting fire and smoke from CCTV and surveillance videos using deep learning models for early warning, emergency response, and industrial safety monitoring.

Chapter 27 – E-learning Adaptive Content Generator

Build an AI-powered educational platform that automatically personalizes learning content, quizzes, and study recommendations based on student performance, learning pace, and knowledge gaps using Machine Learning and Learning Analytics.

Chapter 28 – AI-Powered Mental Health Chatbot

Design an intelligent conversational assistant capable of providing supportive conversations, mood tracking, wellness recommendations, and mental health resources using Natural Language Processing and conversational AI techniques.

Note: This project is educational in nature and emphasizes that AI chatbots are supportive tools and not substitutes for licensed mental health professionals.

Chapter 29 – Movie Recommendation System Using Collaborative Filtering

Implement a recommendation engine using collaborative filtering techniques that analyzes user preferences, viewing history, ratings, and behavioral patterns to generate personalized movie recommendations.

Chapter 30 – NLP-Based Resume Skill Extractor

Develop an intelligent recruitment application capable of extracting technical skills, certifications, education, work experience, and keywords from resumes using Natural Language Processing for automated recruitment and talent analytics.

What You Will Learn

✔ Artificial Intelligence Project Development

✔ Software Development Life Cycle (SDLC)

✔ Deep Learning

✔ Machine Learning

✔ Computer Vision

✔ Natural Language Processing (NLP)

✔ Conversational AI

✔ Recommendation Systems

✔ Educational AI

✔ Learning Analytics

✔ Intelligent Tutoring Systems

✔ Fire and Smoke Detection

✔ Video Analytics

✔ CCTV Monitoring

✔ Emergency Detection Systems

✔ Mental Health AI Applications

✔ Chatbot Development

✔ Collaborative Filtering

✔ Recommendation Engine Design

✔ Resume Parsing

✔ Skill Extraction

✔ Recruitment Analytics

✔ Information Extraction

✔ Text Processing

✔ Dataset Collection

✔ Data Annotation

✔ Data Cleaning

✔ Feature Engineering

✔ Model Training

✔ Model Evaluation

✔ Hyperparameter Optimization

✔ API Development

✔ Database Design

✔ Software Architecture

✔ UML Modeling

✔ Software Documentation

✔ Professional AI Engineering

Key Features

• Five complete advanced intermediate-level AI projects

• Complete Software Development Life Cycle (SDLC) implementation

• Practical Computer Vision and NLP applications

• Intelligent recommendation system development

• Conversational AI and educational technology projects

• Recruitment and HR analytics applications

• Public safety and surveillance AI solutions

• UML diagrams and software engineering documentation

• Portfolio-ready Artificial Intelligence projects

• Excellent preparation for internships, research, and technical interviews

• Suitable for university projects and industrial software development

• Industry-oriented software architecture and implementation

Who Should Read This Book?

This book is ideal for:

BCA Students
MCA Students
B.Tech (Computer Science & IT)
M.Tech Students
Artificial Intelligence Students
Machine Learning Students
Data Science Students
Computer Science Students
Software Engineering Students
Final Year Project Students
AI Researchers
Python Developers
Computer Vision Engineers
NLP Engineers
Machine Learning Engineers
Software Developers
Faculty Members
Technical Trainers
Startup Developers
Self-Learners

Why This Book?

Modern Artificial Intelligence applications increasingly combine Computer Vision, Natural Language Processing, Recommendation Systems, and Conversational AI to solve real-world challenges across education, healthcare, public safety, entertainment, and human resources.

50 AI Projects: Practical Applications with Full Software Engineering Lifecycle (Vol-IV) equips readers with practical knowledge for building intelligent software systems using professional software engineering methodologies and modern AI technologies.

Every project demonstrates how Artificial Intelligence models are integrated with scalable software architecture, database systems, APIs, testing frameworks, and deployment strategies to produce production-ready applications.

Whether preparing for academic projects, research, startup development, technical interviews, or careers in Artificial Intelligence and Software Engineering, this volume provides practical, hands-on experience with some of today’s most relevant AI application domains.

Suitable For

BCA
MCA
B.Tech (Computer Science & IT)
M.Tech
B.Sc. Computer Science
Artificial Intelligence Programs
Machine Learning Courses
Data Science Programs
Software Engineering Courses
Final Year Projects
Academic Research
AI Bootcamps
Technical Interviews
Capstone Projects
Professional AI Training

Book Details

Title: 50 AI Projects: Practical Applications with Full Software Engineering Lifecycle (Vol-IV)

Subtitle: A Complete Collection of Artificial Intelligence Projects with All Phases of SDLC

Author: Anshuman Mishra

Language: English

Subject: Artificial Intelligence, Machine Learning, Deep Learning, Software Engineering, Project Development

Level: Intermediate to Advanced

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