| S.No. | Paper ID | Paper Title & Authors | Pages | Paper |
|---|---|---|---|---|
| 1 | IJTMSR250807001 |
Parametric Optimization of End Milling Process for Aluminum Alloys through Taguchi Design of Experiments
|
001-009 | Download |
| 2 | IJTMSR250807002 |
A Dataset-Based Machine Learning Analysis of Solar-Powered Electric Vehicle Charging Stations for Intelligent Load Balancing
|
010-017 | Download |
| 3 | IJTMSR250807003 |
Al-Based Power Flow Optimization in Electrical Networks with Solar PV and EV Integration
|
018-023 | Download |
| 4 | IJTMSR250807004 |
Modeling and Hardware Validation of Fast Charging Strategies for EV Batteries Using Electrochemical and Data-Driven Techniques
|
024-032 | Download |
| 5 | IJTMSR250807005 |
A Smart Solar-Powered EV Charging Station with LSTM-Based Demand Forecasting and Real-Time Data Analytics
|
033-039 | Download |
| 6 | IJTMSR250807006 |
Deep Learning - Based Landmine Detection Using Thermal Drone Imaging
|
040-049 | Download |
| 7 | IJTMSR250807007 |
Random Forest-Based Smart Load Sharing Control for a Hybrid Solar-Wind Renewable Energy System with Battery Storage
|
050-058 | Download |
| 8 | IJTMSR250807008 |
Multi-Objective Optimization in Manufacturing: From Evolutionary Algorithms to Quantum Variational Methods
|
059-068 | Download |
| 9 | IJTMSR250807009 |
AI-Based Solar Power Forecasting Using Machine Learning Techniques
|
069-074 | Download |
| 10 | IJTMSR250807010 |
An ML/DL Based dual Stage Stacked Ensemble Model for Accurate Heart Disease Prediction
|
075-082 | Download |
| 11 | IJTMSR250807011 |
Optimization Of Material Flow In Manufacturing Plant For Improved Productivity And Cost Reduction
|
083-092 | Download |
| 12 | IJTMSR250807012 |
Integration of Lean Manufacturing and Industry 4.0 Technologies for Aggregate Production Planning and Cost Optimization in a Steel Manufacturing Industry
|
093-100 | Download |
| 13 | IJTMSR250807013 |
AI Road Damage Detection And Complaint Systemy
|
101-108 | Download |
| 14 | IJTMSR250807014 |
Medical Insurance Cost Prediction Using Machine learningg
|
109-118 | Download |