Alzheimer’s disease (AD) is a disease that gradually develops and causes degeneration of the cells of the brain. The leading cause of AD is dementia that results in a person’s inability to work independently. In the early stages of AD, a person forgets recent conversations or the occurrence of an event. In the later stages, there could be severe loss of memory such that the person is not able to even perform everyday tasks. The medicines currently available for AD may improve its symptoms on a temporary basis in the early stage of the disease. Since no treatment is available for curing AD, its detection becomes extremely important. As the clinical treatments are very expensive, the need for automated diagnosis of AD is of critical importance. In this paper, a deep learning model based on a convolutional neural network has been used and applied to four classes of images of AD that is very mild demented, mild demented, average demented, and non-demented. It was found that the moderate demented class had the highest accuracy of 98.9%, a classification error rate of 0.01, and a specificity of 0.992. Also, the lowest false positive rate of 0.007 was obtained.

Huge swirling storms known as hurricanes are tropical storms appearing in the North Atlantic Ocean and Northeast Paci c that result in winds of 120 km/hour and higher. The winds occurring during hurricanes are catastrophic resulting in immense damage to human life and property. Rapid assessment of damage caused by hurricanes is extremely important for the rst responders. But this process is usually slow, expensive, labor intensive and prone to errors. The advancements in remote sensing and computer vision help in observing Earth at a different scale. In this paper, a Convolutional Neural Network model has been designed that assesses the damage caused to buildings of post hurricane satellite images. The images have been classi ed as Damaged and Undamaged. The model is composed of ve convolutional layers, ve pooling layers, one attening layer, one dropout layer and two dense layers. Hurricane Harvey dataset consisting of 23000 images of size 128 X 128 pixels has been used in this paper. The proposed model performed best at learning rate of 0.00001 and 30 epochs with the Adam optimizer obtaining an accuracy of 0.95, precision of 0.97, recall of 0.96 and F1-score of 0.96. It also achieved the best accuracy and minimum loss.

After the occurrence of a hurricane, assessing damage is extremely important for the emergency managers so that relief aid could be provided to afflicted people. One method of assessing the damage is to determine the damaged and the undamaged buildings post-hurricane. Normally, damage assessment is performed by conducting ground surveys, which are time-consuming and involve immense effort. In this paper, transfer learning techniques have been used for determining damaged and undamaged buildings in post-hurricane satellite images. Four different transfer learning techniques, which include VGG16, MobileNetV2, InceptionV3 and DenseNet121, have been applied to 23,000 Hurricane Harvey satellite images, which occurred in the Texas region. A comparative analysis of these models has been performed on the basis of the number of epochs and the optimizers used. The performance of the VGG16 pre-trained model was better than the other models and achieved an accuracy of 0.75, precision of 0.74, recall of 0.95 and F1-score of 0.83 when the Adam optimizer was used. When the comparison of the best performing models was performed in terms of various optimizers, VGG16 produced the best accuracy of 0.78 for the RMSprop optimizer.

A disaster is a devastating incident that causes a serious disruption of the functions of a community. It leads to loss of human life and environmental and financial losses. Natural disasters cause damage and privation that could last for months and even years. Immediate steps need to be taken and social media platforms like Twitter help to provide relief to the affected public. However, it is difficult to analyze high-volume data obtained from social media posts. Therefore, the efficiency and accuracy of useful data extracted from the enormous posts related to disaster are low. Satellite imagery is gaining popularity because of its ability to cover large temporal and spatial areas. But, both the social media and satellite imagery require the use of automated methods to avoid the errors caused by humans. Deep learning and machine learning have become extremely popular for text and image classification tasks. In this paper, a review has been done on natural disaster detection through information obtained from social media and satellite images using deep learning and machine learning.

Hurricane is one of the most disastrous natural disasters causing immense harm to the ecosystem and economic system worldwide. It is also known as a tropical cyclone. Heavy rainfall and high winds accompanying hurricane inflict damage to property as well as loss of human life. Hence, appropriate steps need to be taken to mitigate the damage caused by the disaster. Recently, social media platforms are used that help in providing immediate relief to the people affected by the disaster. Since, the difficulty arises in analysis of high volume data of social media, satellite imagery is also being used for damage detection due to its ability to cover large spatial and temporal areas. But manual damage detection is error prone. Therefore, machine learning and deep learning which automated methods are used for detection of damage. This paper includes the use of machine learning and deep learning for detection of damage caused by natural disasters with a special focus on hurricane damage.