A Traffic-Load-Based Algorithm for Wireless Sensor Networks’ Lifetime Extension

It has been shown in the literature that the lifetime of a wireless sensor network is heavily connected to the number of transmissions that network nodes have to undertake. Considering this finding, along with the effects of the energy hole problem where nodes closer to the sink node transmit more than the more distant ones, a node close to the sink node will be the one that transmits the most, while it will also be the node that will deplete its battery first. Taking into consideration that the failure of a single network node to operate, due to its battery being discharged, can lead to a network stopping its operation, the most energy-consuming node in the network will also be the one that will be responsible for the network’s termination. In this sense, the most energy-consuming node’s energy consumption optimization is the main case in this paper. More specifically, in this work, it is firstly shown that the energy consumption of a wireless sensor network is closely related to each network node’s traffic load, that is the transmissions of the packets that are created or forwarded by a node. The minimization of the most energy-consuming node’s energy consumption was studied here, while the implementation of a traffic-load-based algorithm is also proposed. Under the proposed algorithm, given a simple shortest path approach that assigns a parent (i.e., the next hop towards the sink node) in each network node and the knowledge it provides regarding the distance (in hops in this paper’s case) of network nodes from the sink node, the proposed algorithm exploits the shortest path’s results in order to discover, for all network nodes, neighbors that are of the same distance (from the sink node) with the initially assigned parent. Then, if such neighbors exist, all these neighbors are equally burdened with the parenting role. As a result, the traffic load is shared by all of them. To evaluate the proposed algorithm, simulation results are provided, showing that the goals set were achieved; thus, the network lifetime was prolonged. In addition, it is shown that under the algorithm, a fairer distribution of the traffic load takes place

Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%)

Modelling the COVID-19 pandemic: Focusing on the case of Greece

The SARS-CoV-2 infection (COVID-19) pandemic created an unprecedented chain of events at a global scale, with European counties initially following individual pathways on the confrontation of the global healthcare crisis, before organizing coordinated public vaccination campaigns, when proper vaccines became available. In the meantime, the viral infection outbreaks were determined by the inability of the immune system to retain a long-lasting protection as well as the appearance of SARS-CoV-2 variants with differential transmissibility and virulence. How do these different parameters regulate the domestic impact of the viral epidemic outbreak? We developed two versions of a mathematical model, an original and a revised one, able to capture multiple factors affecting the epidemic dynamics. We tested the original one on five European countries with different characteristics, and the revised one in one of them, Greece. For the development of the model, we used a modified version of the classical SEIR model, introducing various parameters related to the estimated epidemiology of the pathogen, governmental and societal responses, and the concept of quarantine. We estimated the temporal trajectories of the identified and overall active cases for Cyprus, Germany, Greece, Italy and Sweden, for the first 250 days. Finally, using the revised model, we estimated the temporal trajectories of the identified and overall active cases for Greece, for the duration of the 1230 days (until June 2023). As shown by the model, small initial numbers of exposed individuals are enough to threaten a large percentage of the population. This created an important political dilemma in most countries. Force the virus to extinction with extremely long and restrictive measures or merely delay its spread and aim for herd immunity. Most countries chose the former, which enabled the healthcare systems to absorb the societal pressure, caused by the increased numbers of patients, requiring hospitalization and intensive care

Grammatical Evolution-Based Feature Extraction for Hemiplegia Type Detection

Hemiplegia is a condition caused by brain injury and affects a significant percentage of the population. The effect of patients suffering from this condition is a varying degree of weakness, spasticity, and motor impairment to the left or right side of the body. This paper proposes an automatic feature selection and construction method based on grammatical evolution (GE) for radial basis function (RBF) networks that can classify the hemiplegia type between patients and healthy individuals. The proposed algorithm is tested in a dataset containing entries from the accelerometer sensors of the RehaGait mobile gait analysis system, which are placed in various patients’ body parts. The collected data were split into 2-second windows and underwent a manual pre-processing and feature extraction stage. Then, the extracted data are presented as input to the proposed GE-based method to create new, more efficient features, which are then introduced as input to an RBF network. The paper’s experimental part involved testing the proposed method with four classification methods: RBF network, multi-layer perceptron (MLP) trained with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) training algorithm, support vector machine (SVM), and a GE-based parallel tool for data classification (GenClass). The test results revealed that the proposed solution had the highest classification accuracy (90.07%) compared to the other four methods

Automatic Hemiplegia Type Detection (Right or Left) Using the Levenberg-Marquardt Backpropagation Method

Hemiplegia affects a significant portion of the human population. It is a condition that causes motor impairment and severely reduces the patient’s quality of life. This paper presents an automatic system for identifying the hemiplegia type (right or left part of the body is affected). The proposed system utilizes the data taken from patients and healthy subjects using the accelerometer sensor from the RehaGait mobile gait analysis system. The collected data undergo a pre-processing procedure followed by a feature extraction stage. The extracted features are then sent to a neural network trained by the Levenberg-Marquardt backpropagation (LM-BP) algorithm. The experimental part of this research involved creating a custom-created dataset containing entries taken from ten healthy and twenty non-healthy subjects. The data were taken from seven different sensors placed in specific areas of the subjects’ bodies. These sensors can capture a three-dimensional (3D) signal using the accelerometer, magnetometer, and gyroscope device types. The proposed system used the signals taken from the accelerometers, which were split into 2-sec windows. The proposed system achieved a classification accuracy of 95.12% and was compared with fourteen commonly used machine learning approaches

Ensemble Convolutional Neural Network Classification for Pancreatic Steatosis Assessment in Biopsy Images

Non-alcoholic fatty pancreas disease (NAFPD) is a common and at the same time not extensively examined pathological condition that is significantly associated with obesity, metabolic syndrome, and insulin resistance. These factors can lead to the development of critical pathogens such as type-2 diabetes mellitus (T2DM), atherosclerosis, acute pancreatitis, and pancreatic cancer. Until recently, the diagnosis of NAFPD was based on noninvasive medical imaging methods and visual evaluations of microscopic histological samples. The present study focuses on the quantification of steatosis prevalence in pancreatic biopsy specimens with varying degrees of NAFPD. All quantification results are extracted using a methodology consisting of digital image processing and transfer learning in pretrained convolutional neural networks for the detection of histological fat structures. The proposed method is applied to 20 digitized histological samples, producing an 0.08% mean fat quantification error thanks to an ensemble CNN voting system and 83.3% mean Dice fat segmentation similarity compared to the semi-quantitative estimates of specialist physicians