One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in appearance. Hence, early detection of lesions (which form the base of skin cancer) is definitely critical and useful to completely cure the patients suffering from skin cancer. Significant progress has been made in developing automated tools for the diagnosis of skin cancer to assist dermatologists. The worldwide acceptance of artificial intelligence-supported tools has permitted usage of the enormous collection of images of lesions and benevolent sores approved by histopathology. This paper performs a comparative analysis of six different transfer learning nets for multi-class skin cancer classification by taking the HAM10000 dataset. We used replication of images of classes with low frequencies to counter the imbalance in the dataset. The transfer learning nets that were used in the analysis were VGG19, InceptionV3, InceptionResNetV2, ResNet50, Xception, and MobileNet. Results demonstrate that replication is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. It is inferred that Xception Net outperforms the rest of the transfer learning nets used for the study, with an accuracy of 90.48. It also has the highest recall, precision, and F-Measure values.

Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.

This chapter is mainly an advanced version of the previous version of the chapter named “An Insight to Deep Learning Architectures” in the encyclopedia. This chapter mainly focusses on giving the insights of information retrieval after the year 2014, as the earlier part has been discussed in the previous version. Deep learning plays an important role in today's era, and this chapter makes use of such deep learning architectures which have evolved over time and have proved to be efficient in image search/retrieval nowadays. In this chapter, various techniques to solve the problem of natural language processing to process text query are mentioned. Recurrent neural nets, deep restricted Boltzmann machines, general adversarial nets have been discussed seeing how they revolutionize the field of information retrieval.

We investigate flower species detection on a large number of classes. In this paper, we try to classify flower species using 102 flower species dataset offered by Oxford. Modern search engines provide methods to visually search for a query image that contains a flower, but it lacks robustness because of the intra-class variation among millions of flower species around the world. So, we use a Deep learning approach using Convolutional Neural Networks (CNN) to recognize flower species with high accuracy. We use the Oxford dataset which was made by the use of electronic items like a built-in camera in mobile phones and also a digital camera. Feature extraction of flower images is performed using a Transfer Learning approach (i.e. extraction of complex features from a pre-trained network). We also use image augmentation and image processing techniques to extract the flower images more efficiently. After the experimental analysis and using different pre-trained models, we achieve an accuracy of 85%. Further advancements can be made by using optimization parameters in the neural nets.