Underwater Acoustic Network (UAN) is an emerging technology with attractive applications. In such type of networks, the control-overhead, redundant inner-network transmissions management, and data-similarity are still very challenging. The cluster-based frameworks manage the control-overhead and redundant inner-network transmissions persuasively. However, the current clustering protocols consume a big part of their energy resources in data-similarity as these protocols periodically sense and forward the same information. In this paper, we introduce a novel two-level Redundant Transmission Control (RTC) approach that ensures the data-similarity using some statistical tests with an appropriate degree of confidence. Later, the Cluster Head (CH) and the Region Head (RH) remove the data-similarity from the original data before forwarding it to the next level. We also introduce a new spatiotemporal and dynamic CH role rotation technique which is capable to adjust the drifted field nodes because of water current movements. The beauty of the proposed model is that the RH controls the communications and redundant transmission between the CH and Mobile Sink (MS), while the CH controls the redundant inner-network transmissions and data-similarity between the cluster members. We conduct simulations to evaluate the performance of our designed framework under different criteria such as average end-to-end delay, the packet delivery ratio, and energy consumption of the network with respect to the recent schemes. The presented results reveal that the proposed model outperforms the current approaches in terms of the selected metrics.

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.

Asteroseismology studies the physical structure of stars by analyzing their solar-type oscillations as seismic waves and frequency spectra. The physical processes in stars and oscillations are similar to the Sun, which is more evolved to the red-giant branch (RGB), representing the Sun’s future. In stellar astrophysics, the RGB is a crucial problem to determine. An RGB is formed when a star expands and fuses all the hydrogen in its core into helium which starts burning, resulting in helium burning (HeB). According to a recent state by NASA Kepler mission, 7000 HeB and RGB were observed. A study based on an advanced system needs to be implemented to classify RGB and HeB, which helps astronomers. The main aim of this research study is to classify the RGB and HeB in asteroseismology using a deep learning approach. Novel bidirectional-gated recurrent units and a recurrent neural network (BiGR)-based deep learning approach are proposed. The proposed model achieved a 93% accuracy score for asteroseismology classification. The proposed technique outperforms other state-of-the-art studies. The analyzed fundamental properties of RGB and HeB are based on the frequency separation of modes in consecutive order with the same degree, maximum oscillation power frequency, and mode location. Asteroseismology Exploratory Data Analysis (AEDA) is applied to find critical fundamental parameters and patterns that accurately infer from the asteroseismology dataset. Our key findings from the research are based on a novel classification model and analysis of root causes for the formation of HeB and RGB. The study analysis identified that the cause of HeB increases when the value of feature Numax is high and feature Epsilon is low. Our research study helps astronomers and space star oscillations analyzers meet their astronomy findings.