Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is an important human-machine distinction technology for websites to prevent the automatic malicious program attack. CAPTCHA recognition studies can find security breaches in CAPTCHA technology By using the concept of deep learning and computer vision, the very purpose of the CAPTCHAs can be defeated. This test can be passed automatically with the help of Convolutional Neural networks(CNN). A CNN is an algorithm of deep learning which takes an image as input and then assigns some value to various features in the image which further helps to differentiate one feature from the other. Its main purpose is to transform the images into a form which is much easier to process, without losing features which are essential for getting an optimized prediction. The proposed system for this project is to expand this CAPTCHA recognition system for larger and more noisy CAPTCHA containing all the symbols possible, a method based on the Deep Convolutional Neural Network (DCNN) model to identify CAPTCHA and avoid the traditional image processing technology such as location and segmentation. The adaptive learning rate is introduced to accelerate the convergence rate of the model, and the problem of over fitting and local optimal solution has been solved. The multi task joint training model is used to improve the accuracy and generalization ability of model recognition. The experimental results show that the model has a good recognition effect on CAPTCHA with background noise and character adhesion distortion. The future scope of this project lies in technologies where more noisier images can be processed such as license plates, handwriting recognition etc.

The mining industry is very concerned about industrial safety. For workers to be safe and productive, communication and healthcare are essential. To monitor and react to potential dangers, reliable communication is essential, while medical personal protective equipment and examinations are crucial. The mining industry has to improve safety measures because the existing safety systems have flaws. To minimize dangers and safeguard workers, the mining industry uses safety systems like ventilation, emergency response plans, and gas monitoring. Fresh air is provided via ventilation, dangerous gasses are detected by gas monitoring, and accidents are reduced by emergency action procedures. These systems have drawbacks, including the inability to detect all gasses, insufficient airflow, and a limited ability to reduce accidents. Therefore, these systems need to be improved. By enhancing communication and utilizing the IoT (Internet of Things) to monitor air quality, toxicity, and workers’ vital signs, our solution increases safety in the mining industry. Real-time monitoring and reporting of dangers is made possible via sensors, an esp32 board, and the blynk software. Monitoring vital signs ensures workers’ health, while the method seeks to raise productivity in the mining sector. Our suggested system significantly contributes to improving safety in the mining industry by utilizing cutting-edge technology and creative solutions