Aim:           To efficient RCNN based system for fire detection in videos captured in uncertain surveillance scenarios Synopsis:             Vision based fire detection framework has lately picked up popularity when contrasted with cus..

Abstract:             Due to a lack of knowledge about safety helmets, accidents and injuries on construction sites are now increasingly common. Worker supervision by hand is challenging and ineffective. Workers often take off the helmets because of weak security-conscious and discomfort, then ..

Aim:               To apply the Deep Learning techniques based on convolution neural network improving the face mask detector accuracy.Synopsis:              The corona virus disease 2019 (COVID-19) has globally infected over..

Aim:         To detect and identify the Alzheimers disease detection using Deep-Learning techniquesAbstract:      Alzheimer's disease is a neurodegenerative disorder that affects memory, thinking, and behavior. Detecting the disease early is crucial for effective management and treatment. ..

Aim:          To develop deep learning models to detect and track humans in aerial imagesAbstract:       Detecting humans in aerial images remains a tedious task for the application based on Search And Rescue operation (SAR). The prime goal of SAR is to detect and assist peo..

Aim:             The aim of this research is to enhance the detection of cardiovascular diseases in ECG images using advanced machine learning techniques. Abstract:            The accurate and timely detection of cardiovascular diseases thr..

Aim:             This paper aim to detect Real time driver's fatigue state using Convolutional Neural Network (CNN) Synopsis:           Accidents are more due to the driver’s drowsiness; it has been recorded that more than 40% of chances that acciden..

Aim:         The aim of this study is to address the escalating issue of wildfires on a global scale, particularly in regions like Brazil, where the Amazon forest and other forest biomes are significantly affected. The proposed aim is to introduce a novel and lightweight convolutional neural network (CNN) model fo..

Aim: To detect the diabetic retinopathy disease in the earlier stage using Deep learning method Synopsis: Diabetic Retinopathy is a disease that can lead to partial or complete blindness. Research shows that it contributes around 5 percent of the total cases of blindness. Usually it takes about two weeks for the diagnosis of disease; time and money..

Aim:          The aim of this paper is to develop an automatic white blood cell classification system using deep learning techniques. Specifically, the objective is to create a model capable of accurately classifying white blood cells (leucocytes) to aid in the diagnosis of various hematologic diseases, ..

Aim:          Housing prices keep changing day in and day out and sometimes are hyped rather than being based on valuation. Predicting housing prices with real factors is the main crux of our research project. Here we aim to make our evaluations based on every basic parameter that is considered while det..

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Aim:             To detect the plant leaf disease and to recommend the crop using Machine and Deep learning. Abstract:              India is one of the leading countries worldwide in terms of farm output. Even after being a leading ..

Aim: To create a robust and accurate diagnostic tool employing Convolutional Neural Networks (CNNs) for the analysis of CT scan images, aiming to enhance the efficiency, precision, and early detection of lung cancer, ultimately contributing to improved patient outcomes and healthcare practicesAbstract:The most deadly and life-threatening disea..

Aim:            To detect the plant leaf diseases using neural network for high accuracy detectionSynopsis:            To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classify..