88 research outputs found
Survey and Analysis of Communication and Routing Mechanisms for Uavs in Wireless Sensor Networks
No full textUnmanned aerial vehicles (UAVs), commonly called drones, have seen much relevance in most disciplines because of their multiple uses such as in military operations, spying, monitoring the impact of climate change, and delivery. Emerging UAV communication technologies addressing the applications in next-generation wireless networks have been explored in this paper. Specifically, it investigates networking technologies that communicate successfully between UAVs, and routing strategies in UAV-assisted wireless sensor networks (WSNs). Networking and common communication technologies for UAV communication systems have been discussed in this survey paper. Drawing insights from recent academic and industrial literature, we have provided a comprehensive overview of UAV communication technologies, including IoT-enabled communication, ultra-reliable low-latency communication (URLLC), navigation strategies, and advancements driven by machine learning and artificial intelligence
Data analytics for novel coronavirus disease
No full textThis paper describes different aspects of novel coronavirus disease (COVID-19), presents visualization of the
spread of the infection, and discusses the potential applications of data analytics on this viral infection. Firstly, a
literature survey is done on COVID-19 highlighting a number of factors including its origin, its similarity with
previous coronaviruses, its transmission capacity, its symptoms, etc. Secondly, data analytics is applied on a
dataset of Johns Hopkins University to find out the spread of the viral infection. It is shown here that although
the disease started in China in December 2019, the highest number of confirmed cases up to June 04, 2020 is in
the USA. Thirdly, the worldwide increase in the number of confirmed cases over time is modelled here using a
polynomial regression algorithm with degree 2. Fourthly, classification algorithms are applied on a dataset of
5644 samples provided by Hospital Israelita Albert Einstein of Brazil in order to diagnose COVID-19. It is shown
here that multilayer perceptron (MLP), XGBoost and logistic regression can classify COVID-19 patients at an
accuracy above 91%. Finally, a discussion is presented on the potential applications of data analytics in several
important factors of COVID-19
Iris feature extraction using three-level Haar wavelet transform and modified local binary pattern
No full textIn this chapter, a novel feature extraction method is proposed for faster iris recognition. This new method is a hybrid process combining three-level Haar wavelet transform (HWT) and modified local binary pattern (MLBP). In this hybrid method, firstly, HWT is applied to the normalized iris image, resulting in four output images including the approximation image known as LL subband. This LL subband is then further decomposed using HWT into four subimages. The resultant second-level LL is decomposed using HWT into the third-level LL subband. The application of repeated HWT extracts the major information-containing region, reducing the information size. Next, MLBP is applied to the obtained LL, where MLBP includes local binary pattern and Exclusive OR operations. The output of MLBP is a binary iris template. The effectiveness of this proposed hybrid HWT-MLBP method is experimentally evaluated using three different datasets, namely CASIA-IRIS-V4, CASIA-IRIS-V1, and MMU. The proposed HWT-MLBP method can obtain a reduced feature vector length of 1×64. For instance, when applied to the CASIA-IRIS-V1 dataset, HWT-MLBP can obtain an average correct recognition rate of 98.30% and a false acceptance rate of 0.003%. Results indicate that the proposed HWT-MLBP outperforms existing methods in terms of reduced feature length, which ensures faster iris recognition. © 2022 Elsevier Inc. All rights reserved
Deep Learning for UAV Detection and Classification Via Radio Frequency Signal Analysis
No full textUnmanned Aerial Vehicles (UAVs) are advertised as great tool that benefits society and humanity. However, UAVs also pose significant security threats ranging from privacy invasions, to interfering with commercial aircraft landing and takeoff, to accidently crashing into vehicles or people, to military or terrorist attacks. Consequently, there is a pressing need to detect and identify UAVs to mitigate such potential risks. While image-based methods are crucial for UAV detection, radio frequency (RF) emissions offer additional valuable insights. Analyzing RF signals, such as those used in UAV-ground station communications, can provide information about UAV types based on distinct frequency usage or communication patterns. This work introduces a deep-learning-based approach for recognizing and identifying UAVs using their RF emissions. Captured RF signals are transformed into spectrograms, which are subsequently analyzed using deep neural networks. Existing methods achieve low identification accuracy, for instance the ResNet-50V2 model achieves an accuracy of 85.39% even in controlled, laboratory, noise-free conditions. Moreover, in outdoor environments at distances of 50m and 100m, the accuracy drops to 68.90% and 56.88%, respectively. To improve classification accuracy in outdoors, a CNN model was developed, yielding an accuracy of 78.12%. Leveraging the ResNet 50 V2 architecture, remarkable accuracy of 95.08% was attained in binary classification tasks involving a dataset comprising 195 mixed UAV images and 290 non-mix UAV images
Weighted Rank Difference Ensemble: A New Form of Ensemble Feature Selection Method for Medical Datasets
No full textBackground: Feature selection (FS), a crucial preprocessing step in machine learning, greatly reduces the dimension of data and improves model performance. This paper focuses on selecting features for medical data classification. Methods: In this work, a new form of ensemble FS method called weighted rank difference ensemble (WRD-Ensemble) has been put forth. It combines three FS methods to produce a stable and diverse subset of features. The three base FS approaches are Pearson’s correlation coefficient (PCC), reliefF, and gain ratio (GR). These three FS approaches produce three distinct lists of features, and then they order each feature by importance or weight. The final subset of features in this study is chosen using the average weight of each feature and the rank difference of a feature across three ranked lists. Using the average weight and rank difference of each feature, unstable and less significant features are eliminated from the feature space. The WRD-Ensemble method is applied to three medical datasets: chronic kidney disease (CKD), lung cancer, and heart disease. These data samples are classified using logistic regression (LR). Results: The experimental results show that compared to the base FS methods and other ensemble FS methods, the proposed WRD-Ensemble method leads to obtaining the highest accuracy value of 98.97% for CKD, 93.24% for lung cancer, and 83.84% for heart disease. Conclusion: The results indicate that the proposed WRD-Ensemble method can potentially improve the accuracy of disease diagnosis models, contributing to advances in clinical decision-making
Deep Learning and Federated Learning for Screening COVID-19: A Review
No full textSince December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated
EEG Eye State Prediction and Classification in order to Investigate Human Cognitive State
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