As one of the main causes of morbidity and mortality, viral infections have a major impact on the well-being and economics of every nation in the globe. The ability to predictably diagnose viral infections improves the provision of good healthcare as well as the control and prevention of these conditions. Nanomaterials have gained widespread usage in the medical industry recently due to the rapid advancement of nanotechnology and their exceptional chemical and physical qualities, such as their small size and synthesized surface properties. The utilization of nanoparticles for illness detection, surveillance, control, preventive, and therapy, such as the treatment of bacterial infections, is referred to as nanomedicine. Nanomedicine is a comprehensive discipline that is founded on the usage of nanotechnology for clinical objectives. Nanoparticles, which have a nanoscale dimension and exhibit highly controllable optical and physical characteristics as well as the ability to bind to a large variety of chemicals, are among the most popular nanomaterials in nanomedicine. A deep learning framework of autoencoder for categorization study on viral infections is built based on actual hospital patient history of viral infections from August 2015 to August 2020. The information comprises of 10,950 cases, comprising outpatients and inpatients, encompassing the infectious diseases. Of such 10,950 instances, training set made up 70% or 7665 instances, and testing data made up 30% or 3285 instances. The data processing was done using the presented recurrent neural network-artificial bee colony (RNN-ABC) method. Sparse data densifying processes are done through the autoencoder to enhance the system learning outcome. The suggested autoencoder system was also evaluated to other widely used models, including support vector machine, logistic regression, random forest, and Naïve Bayes. In comparison to other approaches, the study’s findings demonstrate how well the suggested autoencoder model can predict viral diseases. The methods used for this research can aid in removing reported lags in current monitoring systems, hence reducing society’s expenses.
The range of diagnostic equipment has been widened and improved by the quick development of biomedical research technologies. The creation of multifunctional instruments that become essential for biomedical operations has been discovered by several research organizations to be made possible by optical imaging, acoustic image analysis, and magnetic resonance imaging. One of the most crucial tools is hyperspectral photoacoustic (PA) imaging, which combines optical and ultrasonic technology. In this study, the reconstruction of the PA pictures employs a new deployment of deep learning methods. This enabled us to train and evaluate our deep-learning approach under several imaging situations in addition to firmly establishing the contextual information. This study presents an optimization approach that blends multispectral optical acoustic imaging with detailed transfer learning-based diagnostic imaging. The particle swarm-convolutional neural network (PS-CNN) technique aims to reconstruct and categorize the presence of cancer using ultrasonic pictures. In image processing, the technique of bilateral filtration (BF) is commonly employed to remove noise. Additionally, the biological images are separated using portable LED Net frameworks. It is also possible to employ a feature extraction technique with the PS optimization methodology. Last but not least, biological images employ a CNN model to assign suitable classification. Using a standard dataset, the PS-CNN technology’s efficacy is confirmed, and testing findings revealed that it performs superior to other methods.
Biological tissues may be studied using photoacoustic (PA) spectroscopy, which can yield a wealth of physical and chemical data. However, it is really challenging to directly analyse these tissues because of a lot of data. Data mining techniques can get around this issue. In order to diagnose prostate cancer via PA spectrum assessment, this work describes the machine learning (ML) technique implementation, such as supervised classification and unsupervised hierarchical clustering. The collected PA signals were preprocessed using Pwelch method, and the features are extracted using two methods such as hierarchical cluster and correlation assessment. The extracted features are classified using four ML-methods, namely, Support Vector Machine (SVM), Naïve Bayes (NB), decision tree C4.5, and Linear Discriminant Analysis (LDA). Furthermore, as these components alter throughout the progression of prostate cancer, this study focuses on the composition and distribution of collagen, lipids, and haemoglobin. In diseased tissues compared to normal tissues, there is a stronger correlation between the various chemical components ultrasonic power spectra, suggesting that the microstructural dispersion in tumour tissues has been more uniform. The accuracy of several classifiers used in cancer tissue diagnosis was greater than 94% for all four methods, which is effective than that of benchmark medical methods. Thus, the method shows significant promise for the noninvasive, early detection of severe prostate cancer.
Wireless sensor networks (WMSNs) are becoming increasingly popular in many fields, from academia to transportation, environmental monitoring, wildlife preservation, and military espionage. Therefore, examining potential threats, power consumption, vulnerability recognition, and systemic vulnerability characteristics is essential to develop a reliable information security approach for WSNs. As a result, it is becoming increasingly crucial for the technical community to conduct intrusion recognition method evaluations. Since this is the case, using deep learning techniques in creating intrusion identification and mitigation systems for wireless multimedia sensor networks is essential. This article examines how well different machine learning and deep learning algorithms perform in attack identification systems. Testing the efficacy of different methods on the WMSN-DS database through experimentation is essential. In this work, we combine the power of a Convolutional Neural Network classifier with a Random forest. In order to accomplish this, a Convolutional Neural Network with a Random Forest Classifier is used. The intrusion detection system (IDS) is a crucial technique proposed in this study for WMSN. To address this issue, the current study proposal uses deep Learning with a Random Forest classifier to detect and prevent attacks and to promote efficient forwarding in WMSNs. Multiple WMSN assaults have been investigated, and the results of these investigations have been critically evaluated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.