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Retraction Note: Artificial intelligence for a bio-sensored detection of tuberculosis
(Source: Network Modeling Analysis in Health Informatics and Bioinformatics)
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 15, 2022 Category: Bioinformatics Source Type: research

MTSE U-Net: an architecture for segmentation, and prediction of fetal brain and gestational age from MRI of brain
AbstractFetal brain segmentation and gestational age prediction have been under active research in the field of medical image processing for a long time. However, both these tasks are challenging due to factors like difficulty in acquiring a proper fetal brain image owing to the fetal movement during the scan. With the recent advancements in deep learning, many models have been proposed for performing both the tasks, individually, with good accuracy. In this paper, we present Multi-Tasking Single Encoder U-Net, MTSE U-Net, a deep learning architecture for performing three tasks on fetal brain images. The first task is the ...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 12, 2022 Category: Bioinformatics Source Type: research

Plectin as a putative novel biomarker for breast cancer: an in silico study
AbstractBreast cancer is a heterogenous disease accounting for about 0.68 million deaths among women in 2020. The drive to succumb the fatality in breast cancer, lies in the hands of rapid advancement of novel biomarker, which could render definitive information regarding the prognosis of breast cancer. To expedite the process, Plectin (PLEC), a cytoskeleton linker protein, was speculated as an effective biomarker for breast cancer, due to its implication  in various cancer tumorigenesis, but its involvement in breast cancer is still unexplored. To substantiate the claim, an exemplary bioinformatics approach was adopted....
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 7, 2022 Category: Bioinformatics Source Type: research

Machine learning-based intrusion detection for SCADA systems in healthcare
AbstractEnergy distribution systems and cyber-physical systems brought together information technology, electrical and mechanical engineering in an integrated manner. This cybernetic –mechatronics development has drawn the attention of both cybercriminals and cybersecurity researchers by expanding the attacks in critical infrastructures. With the development of information communication technology, supervisory control and data acquisition (SCADA) systems will turn into cloud -based systems that can communicate with IoT devices in the future. In addition, SCADA systems can be utilized in hospitals for various aspects and...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 7, 2022 Category: Bioinformatics Source Type: research

Retraction Note: An hybrid security framework using internet of things for healthcare system
(Source: Network Modeling Analysis in Health Informatics and Bioinformatics)
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 7, 2022 Category: Bioinformatics Source Type: research

GSO-CNN-based model for the identification and classification of thyroid nodule in medical USG images
AbstractThyroid ultrasonography is one of the widely used techniques for the detection and classification of thyroid nodules. In this paper, grid search optimization (GSO)-based convolutional neural network (CNN), i.e., GSO-CNN model is proposed for thyroid nodule identification and classification. A total of 295 public and 654 collected thyroid USG datasets are considered in this work. The increased datasets size for the proposed model becomes 1770 for the public dataset and 3924 for the collected dataset after applying data augmentation techniques. We experimentally determined the best optimized value using grid search o...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - November 4, 2022 Category: Bioinformatics Source Type: research

A comparison of Covid-19 cases and deaths in Turkey and in other countries
In this study, the characteristics of the Covid-19 pandemic in Turkey are examined in terms of the number of cases and deaths, and a characteristic prediction is made with an approach that employs artificial intelligence. The number of cases and deaths are estimated using the number of tests, the numbers of seriously ill and recovered patients as parameters. The machine learning methods used are linear regression, polynomial regression, support vector regression with different kernel functions, decision tree and artificial neural networks. The obtained results are compared by calculating the coefficient of determination (R...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 27, 2022 Category: Bioinformatics Source Type: research

Theoretical estimation of neck stiffness subjected to lateral dynamic striking
AbstractThe human neck is a resilient biological structure connecting the head to the body. It hosts different kinds of tissues including bones, muscles, skin, vertebra, discs, nerves and the spinal cord. These structures work together as one unit to provide stability and facilitate functionality to the head. However, accident or sport-related dynamic striking to the head is resisted entirely by the neck structure. Based on Machine Learning principles, the purpose of this paper is to attempt to derive a workable biomechanical –machine learning formula of the neck stiffness as represented by a single spring. Such a formul...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 26, 2022 Category: Bioinformatics Source Type: research

Impact of laboratory biomarkers on ALS disease progression: a probabilistic causation approach
In this study, we applied a causal learning model to the PRO-ACT data set to discover major biomarkers that have causal effects on the rate of ALS progression. This paper focuses on finding new biomarkers, which have causal impacts on ALS progression rate. In this study, the ALS patients were classified into three groups based on disease progression rate: fast, intermediate, and slow. Then, a causal graph for each group was constructed using the proposed model. Our findings revealed that nine biomarkers, especiallyAbsolute monocyte count andCreatine Kinase, have high causal impacts in all three types of patients, andTrigly...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 22, 2022 Category: Bioinformatics Source Type: research

An integrated simulation framework for the prevention and mitigation of pandemics caused by airborne pathogens
AbstractIn this work, we developed an integrated simulation framework for pandemic prevention and mitigation of pandemics caused by airborne pathogens, incorporating three sub-models, namely the spatial model, the mobility model, and the propagation model, to create a realistic simulation environment for the evaluation of the effectiveness of different countermeasures on the epidemic dynamics. The spatial model converts images of real cities obtained from Google Maps into undirected weighted graphs that capture the spatial arrangement of the streets utilized next for the mobility of individuals. The mobility model implemen...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 18, 2022 Category: Bioinformatics Source Type: research

Improving heart disease prediction using multi-tier ensemble model
AbstractHeart disease is the leading cause of morbidity and mortality around the globe. The only way to reduce these deaths is to diagnose them at the earliest. The aim of this paper is to evaluate the effect of ensemble learning methods in creating the model for effective and improved diagnosis of Heart disease (HD). In this paper, we propose a multi-tier ensemble (MTE) model with Random Forest feature selection for effective and improved diagnosis of HD. The tiers consist of different combinations of ML and ensemble learning techniques. The model is then validated using a dataset that is curated using five widely known h...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 16, 2022 Category: Bioinformatics Source Type: research

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics
AbstractThis systematic review aims to study and classify machine learning models that predict pandemics ’ evolution within affected regions or countries. The advantage of this systematic review is that it allows the health authorities to decide what prediction model fits best depending upon the region’s criticality and optimize hospitals’ approaches to preparing and anticipating patient care. We searched ACM Digital Library, Biomed Central, BioRxiv+MedRxiv, BMJ, Computers and Applied Sciences, IEEEXplore, JMIR Medical Informatics, Medline Daily Updates, Nature, Oxford Academic, PubMed, Sage Online, ScienceDirect, Sc...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 11, 2022 Category: Bioinformatics Source Type: research

Predicting pattern of coronavirus using X-ray and CT scan images
AbstractNovel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world ’s central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slo...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 5, 2022 Category: Bioinformatics Source Type: research

Network alignment and motif discovery in dynamic networks
We reported how several issues may be transferred from static to dynamic networks by taking into account the temporal information. Furthermore, we encountered a systematic convergence toward iterative strategies both for ne twork alignment and motif discovery, justified by the fact that a dynamic network is usually analyzed through the sub-analysis of its time points. (Source: Network Modeling Analysis in Health Informatics and Bioinformatics)
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 3, 2022 Category: Bioinformatics Source Type: research

Prediction of solubility of some dihydropyridine derivative drugs in supercritical fluid carbon dioxide by RBFNN
In this study, a radial basis function neural network (RBFNN) was used to predict the solubility of some 1,4-dihydropyridine derivative drugs in supercritical fluid carbon dioxide. The solubility of drugs was predicted based on the pressure, temperature, molecular weight, melting point, density, carbon number, and hydrogen number. The predicted solubility obtained by RBFNN was compared to experimental data. The root mean square error (RMSE), determination coefficient (R2), mean bias error, mean absolute error, modified agreement index (md), and modified Nash and Sutcliffe efficiency were determined. The square regression c...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - September 30, 2022 Category: Bioinformatics Source Type: research