• Cloud computing has emerged as a fundamental technology in todays digital landscape, transforming how businesses and individuals manage, process, and store data. Google Cloud Platform (GCP), one of the leading cloud providers, offers a vast array of services and tools that enable developers and organizations to build, deploy, and scale applications efficiently. Leveraging Google Cloud in final year projects allows students to work with state-of-the-art technology, solve complex problems, and implement scalable solutions, preparing them for the demands of modern IT environments.
  
  Google Cloud provides a robust infrastructure for building applications across various domains such as artificial intelligence (AI), machine learning (ML), big data analytics, IoT, serverless computing, and more. GCPs integration with powerful APIs, advanced machine learning tools, and secure data storage makes it a versatile choice for cloud-based development.
  
  this project is to develop an end-to-end cloud-based solution using Google Cloud Platform, incorporating modern cloud technologies to solve a real-world problem. By leveraging GCP, the project will demonstrate how cloud-native architectures can provide scalability, high availability, and cost-effectiveness while maintaining security and performance. The project may span across various domains such as machine learning, data analytics, IoT, or web application development.

Software Tools and Technologies

• • Operating System: Ubuntu 20.04 LTS 64bit / Windows 10
• • Development Tools: Netbeans IDE 22,Google Compute Engine V1,AutoML 3.46.0.5
• • Language Version: JAVA SDK 21.0.2
• Google Cloud Open Source Tools for Cloud Computing

List Of Final Year Projects in Google Cloud

• • Multimodal ML for Image, Text, and Sensor Data Fusion Using Google Cloud Vertex AI.
• • Explainable AI (XAI) Models for Decision Support Using Google Cloud AI Platform.
• • GAN-Based Synthetic Data Generation for Training Deep Learning Models on Google Cloud.
• • Energy Consumption Forecasting Using LSTM Networks on Google Cloud ML Engine.
• • IoT Sensor Data Analytics for Predictive Maintenance Using Google Cloud Dataflow and BigQuery ML.
• • Resource-Aware Workflow Optimization for Big Data Jobs Using Cloud Composer and Dataflow.
• • AI-Powered Anomaly Detection in IoT Networks Using Google Cloud ML and Pub/Sub.
• • Edge AI Deployment for Low-Latency IoT Applications Using Google Cloud IoT Edge.
• • Developing a Federated Learning Framework for Edge IoT Devices using Google Cloud IoT Core and TensorFlow Lite.
• • A Cost-Benefit Analysis of Edge Computing vs. Cloud Computing for Latency-Sensitive Industrial IoT Applications.
• • QoE-Aware Video Streaming with Adaptive Bitrate.
• • Real-Time Fraud Detection with Streaming ML.
• • Smart Traffic Management with Edge + Cloud AI.
• • Energy-Efficient IoT Sensor Networks for Smart Cities.
• • Privacy-Preserving Healthcare Data Analytics.
• • Optimizing Geospatial Analytics: Processing Large Satellite Imagery Datasets using Spark on Dataproc and Visualizing in BigQuery GeoViz.
• • Data Lakehouse Architecture on GCP: Performance Evaluation of Querying Iceberg Tables with Spark on Dataproc vs. BigQuery.
• • Real-time Sentiment Analysis of Social Feeds: A Complete Pipeline with Pub/Sub, Natural Language API, and Dataflow.
• • Serverless Real-time Analytics: Designing a Clickstream Analysis Pipeline with Pub/Sub, BigQuery, and Dataflow for Sub-Second Latency.
• • Orchestrating a Multi-Tool Batch Pipeline: Integrating Cloud Storage, Dataproc, and BigQuery with Cloud Composer (Apache Airflow).