Saadaldeen Rashid Ahmed

By informing and upholding legal guidelines, robotically figuring out motorcycle helmets through video surveillance contributes to improving road protection. It is difficult for current techniques to keep an eye fixed on motorbikes and inform riders from passengers. In order to tackle those problems, we recommend to reveal and recognize motorbikes using a CNN oriented multi- venture gaining knowledge of (MTL) approach, with a focal point on helmet- wearing motorcyclists. We offer the HELMET the dataset, that's made up of ninety one, 000 annotated frames of 10,006 motorbikes at 12 one-of-a-kind Myanmar observation web sites. This dataset may be used as a factor of reference for techniques of detection. Our technique, referred to as MTL, can provide higher accuracy and efficiency by means of combining helmet use categorization with similarity getting to know. Operating at a charge of greater than 8 frames for each 2d (FPS) on hardware, our technique attains a sixty seven.3% F degree in identifying cyclists and their helmet utilization. The effectiveness of learning in obtaining crucial expertise about avenue protection is highlighted by means of this study. In addition, we present a motorbike helmet prepared with earbuds, a charging module, an integrated computer unit, transceivers, and a photograph sensor. This helmet makes use of picture popularity modes in both daylight and midnight occasions to pick out automobiles coming near the wearer. According to experimental consequences, vehicles and buses' registration plates can be recognized with up to175% accuracy in the course of the1day & 70% accuracy at night. The new smart motorbike helmet is supposed to apprehend vehicles in real time inside a five-meter radius, increasing street protection.

Skin cancer is a highly frequent kind of cancer. Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities. Melanoma, basal, and squamous cell carcinomas are well-recognized cutaneous malignancies. Malignant We can differentiate Melanoma from non-pigmented carcinomas like basal and squamous cell carcinoma. The research on developing automated skin cancer detection systems has primarily focused on pigmented malignant type melanoma. The limited availability of datasets with a wide range of lesion categories has hindered in-depth exploration of non-pigmented malignant skin lesions. The present study investigates the feasibility of automated methods for detecting pigmented skin lesions with potential malignancy. To diagnose skin lesions, medical professionals employ a two-step approach. Before detecting malignant types with other deep learning (DL) models, a preliminary step involves using a DL model to identify the skin lesions as either pigmented or non-pigmented. The performance assessments accurately assessed four distinct DL models: Long short-term memory (LSTM), Visual Geometry Group (VGG19), Residual Blocks (ResNet50), and AlexNet. The LSTM model exhibited higher classification accuracy compared to the other models used. The accuracy of LSTM for pigmented and non-pigmented, pigmented tumours and benign classes, and melanomas and pigmented nevus classes was 0.9491, 0.9531, and 0.949, respectively. Automated computerized skin cancer detection promises to enhance diagnostic efficiency and precision significantly.