A comparative study of early stage Alzheimer's disease classification using various transfer learning CNN frameworks.

The current research explores the improvements in predictive performance and computational efficiency that machine learning and deep learning methods have made over time. Specifically, the application of transfer learning concepts within Convolutional Neural Networks (CNNs) has proved useful for diagnosing and classifying the various stages of Alzheimer's disease. Using base architectures such as Xception, InceptionResNetV2, DenseNet201, InceptionV3, ResNet50, and MobileNetV2, this study extends these models by adding batch normalization (BN), dropout, and dense layers.

Performance improvement in geographic routing for Vehicular Ad Hoc Networks.

Geographic routing is one of the most investigated themes by researchers for reliable and efficient dissemination of information in Vehicular Ad Hoc Networks (VANETs). Recently, different Geographic Distance Routing (GEDIR) protocols have been suggested in the literature. These protocols focus on reducing the forwarding region towards destination to select the Next Hop Vehicles (NHV).

Fault tolerant coverage and connectivity in presence of channel randomness.

Some applications of wireless sensor network require K-coverage and K-connectivity to ensure the system to be fault tolerance and to make it more reliable. Therefore, it makes coverage and connectivity an important issue in wireless sensor networks. In this paper, we proposed K-coverage and K-connectivity models for wireless sensor networks.

Sensing coverage prediction for wireless sensor networks in shadowed and multipath environment.

Sensing coverage problem in wireless sensor networks is a measure of quality of service (QoS). Coverage refers to how well a sensing field is monitored or tracked by the sensors. Aim of the paper is to have a priori estimate for number of sensors to be deployed in a harsh environment to achieve desired coverage.

miRNA prediction using computational approach.

The total number of miRNAs in any species even completely sequenced is still an open problem. However, researchers have been using limited techniques for miRNA prediction. Some of the prediction models are developed using different sets of features of pre-miRNA from model organisms.