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Motorcycle accidents increase due to high speed and traffic congestion. Riding motorcycles without wearing helmet is violating the rules and it may endanger the person's life. This paper aims to detect the motorcyclist failing to wear helmet and to recognize the vehicle license plate to display the number. The system is implemented using convention neural network (CNN) with you look only once (YOLO) architecture. [ABSTRACT FROM AUTHOR]

As we all know, women are being abused, molested, and raped in this day and age. Women are unable to leave their homes for these reasons. Only if women feel safe and protected can they come out and move freely, even at strange hours. If they have a computer, this is possible. They have a safety device with them. We suggest a gadget that ensures women's safety. There are push buttons on this device. A assist the woman in quickly contacting her family ones and sharing her present situation location. The device also includes a self-defence program that aids her in escaping. Situations that are critical. All of her information is updated in the cloud, and the cloud informs her family regarding the situation. This device enables a woman to quickly notify her family of her situation so that appropriate action can be taken to save herself. [ABSTRACT FROM AUTHOR]

Glaucoma disease causes the loss of eye sight if unnoticed. Earlier detection and the extent to which the disease is progressed may provide good suggestions to the physicians for giving treatment. In this work the glaucoma severity stage with its disease types are understood the image processing techniques. In this work, an image segmentation algorithm, namely, OTSU's segmentation method is chosen. It detects optic cup (OC) and optic disc (OD). Several researchers found only Cup-to-Disk ratio (CDR). CDR did not apply to categorize the images whether they are normal or affected by glaucoma. So it is decided that calculating rim-to-disk ratio (RDR) may give an expected solution in assessing the glaucoma disease. The classifiers are used to find the progression of disease. In this research Support vector machine (SVM) is chosen as the classifier. The abnormal images are of four stages. They are Inferior, Superior, Nasal, Temporal and the types are found out by ISNT method. Further this work concludes the disease type by indicating either diabetic retinopathy or glaucoma. [ABSTRACT FROM AUTHOR]