Continuous Latent Space (CLS) and Discrete Latent Space (DLS) models, like AttnUNet and VQUNet, have excelled in medical image segmentation. In contrast, Synergistic Continuous and Discrete Latent Space (CDLS) models ...
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Given an n by n matrix A and an n-vector b, along with a rational function R(z):= D(z)−1N (z), we show how to find the optimal approximation to R(A)b from the Krylov space, span(b, Ab, . . ., Ak−1b), using the basis v...
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INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents....
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INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents. These hereditary diseases of blood involve abnormal haemoglobin (Hb) or anemia which are major public health issues. Sickle Cell Disease (SCD) is one of the common non-communicable disease and genetic disorder due to changes in hematological conditions of the RBCs which often causes the inheritance of mutant Hb genes by the patient.. OBJECTIVES: The process of manual valuation, predictions and diagnosis of SCD necessitate for a passionate time spending and if not done properly can lead to wrong predictions and diagnosis. Machine Learning (ML), a branch of AI which emphases on building systems that improve performance based on the data they consume is appropriate. Despite previous research efforts in predicting with single ML algorithm, the existing systems still suffer from high false and wrong predictions. METHODS: Thus, this paper aimed at performing comparative analysis of individual ML algorithms and their ensemble models for effective predictions of SCD (elongated shapes) in erythrocytes blood cells. Three ML algorithms were selected, and ensemble models were developed to perform the predictions and metrics were used to evaluate the performance of the model using accuracy, sensitivity, Receiver Operating Characteristics-Area under Curve (ROC-AUC) and F1 score metrics. The results were compared with existing literature for model(s) with the best prediction metrics performance.. RESULTS: The analysis was carried out using Python programming language. Individual ML algorithms reveals that their accuracies show MLR=87%, XGBoost=90%, and RF=93%, while hybridized RF-MLR=92% and RF-XGBoost=99%. The accuracy of RF-XGBoost of 99% outperformed other individual ML algorithms and Hybrid models. CONCLUSION: Thus, the study concluded that involving hybridized M
Free space optics (FSO) is gradually being seen as a potential technique for supporting high-speed communication. Nonetheless, FSO meets challenges caused by the air medium through which the laser beam travels. This r...
Free space optics (FSO) is gradually being seen as a potential technique for supporting high-speed communication. Nonetheless, FSO meets challenges caused by the air medium through which the laser beam travels. This research overcomes these obstacles by utilizing spatial diversity tactics. In particular, a two-way relay (TWR) is used to build a two-way communication link between two remote places. The TWR extends the range of FSO gearbox and mitigates the effects of fading, which vary with distance. Extensive research is conducted on the many applications of TWR-supported FSO communication, and a mathematical methodology for analyzing performance indicators at the physical layer is introduced. Furthermore, various channel impediments such as route loss, pointing mistakes, and atmospheric turbulence are addressed in detail. To account for changing amounts of atmospheric turbulence, the use of log-normal, Gamma-Gamma, and M-distributions is done to replicate the fading effects caused by turbulence. There is an urgent need to broaden the application of TWR protocols into a multiuser scenario, especially in today's context when several pairs of users' demand data interchange among themselves. Thorough analysis on the use of a single TWR that serves many user-pairs via the implementation of a scheduleris explored.
The paper proposes an interdisciplinary approach including methods from disciplines such as history of concepts, linguistics, natural language processing (NLP) and Semantic Web, to create a comparative framework for d...
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We study the propagation of a domain wall (kink) of the ϕ4 model in a radially symmetric environment defined by a gravity source. This source introduces a Schwarzschild-like geometry. We introduce an effective model t...
We study the propagation of a domain wall (kink) of the ϕ4 model in a radially symmetric environment defined by a gravity source. This source introduces a Schwarzschild-like geometry. We introduce an effective model that accurately describes the dynamics of the kink center. This description works well even outside the perturbation region, i.e., even for large masses of the gravitating object. We observed that such a spherical domain wall surrounding a star-type object inevitably “collapses,” i.e., shrinks in radius toward the origin and offer an understanding of the latter phenomenology. The relevant analysis is presented for a circular domain wall and a spherical one.
A novel approach for modeling the nonlinear dynamics of cable slabs using Koopman operator theory is presented. Cable slab dynamics are a critical challenge in precision motion systems, as the cables can induce undesi...
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Constructing confidence intervals that are simultaneously valid across a class of estimates is central for tasks such as multiple mean estimation, bounding generalization error in machine learning, and adaptive experi...
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This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, Departmen...
This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE.
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