Monkeypox Detection Using CNN with Transfer Learning.

Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the public. As a result of COVID reaching the pandemic dimension, the development and implementation of rapid detection methods have become important. In this context, our study aims to detect monkeypox disease in case of a possible pandemic through skin lesions with deep-learning methods in a fast and safe way. Deep-learning methods were supported with transfer learning tools and hyperparameter optimization was provided. In the CNN structure, a hybrid function learning model was developed by customizing the transfer learning model together with hyperparameters. Implemented on the custom model MobileNetV3-s, EfficientNetV2, ResNET50, Vgg19, DenseNet121, and Xception models. In our study, AUC, accuracy, recall, loss, and F1-score metrics were used for evaluation and comparison. The optimized hybrid MobileNetV3-s model achieved the best score, with an average F1-score of 0.98, AUC of 0.99, accuracy of 0.96, and recall of 0.97. In this study, convolutional neural networks were used in conjunction with optimization of hyperparameters and a customized hybrid function transfer learning model to achieve striking results when a custom CNN model was developed. The custom CNN model design we have proposed is proof of how successfully and quickly the deep learning methods can achieve results in classification and discrimination.

Historical Text Image Enhancement Using Image Scaling and Generative Adversarial Networks.

Historical documents such as newspapers, invoices, contract papers are often difficult to read due to degraded text quality. These documents may be damaged or degraded due to a variety of factors such as aging, distortion, stamps, watermarks, ink stains, and so on. Text image enhancement is essential for several document recognition and analysis tasks. In this era of technology, it is important to enhance these degraded text documents for proper use. To address these issues, a new bi-cubic interpolation of Lifting Wavelet Transform (LWT) and Stationary Wavelet Transform (SWT) is proposed to enhance image resolution. Then a generative adversarial network (GAN) is used to extract the spectral and spatial features in historical text images. The proposed method consists of two parts. In the first part, the transformation method is used to de-noise and de-blur the images, and to increase the resolution effects, whereas in the second part, the GAN architecture is used to fuse the original and the resulting image obtained from part one in order to improve the spectral and spatial features of a historical text image. Experiment results show that the proposed model outperforms the current deep learning methods.

IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning.

The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.

Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration.

Traffic management is a critical task in software-defined IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic management approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and multi-arm bandit algorithms to dynamically optimize traffic management policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a multi-arm bandit algorithm to optimize traffic management policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic management methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic management in SDNs, enabling efficient resource management and QoS assurance.

CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway.

As a result of vehicle platooning, advantages including decreased traffic congestion and improved fuel economy are expected. Vehicles in a platoon move in a single line, closely spaced, and at a constant speed. Vehicle-to-vehicle communications and sensor data help keep the platoon formation in place, and the CACC system is responsible for maintaining it. In reality, V2V transmissions are essential for reducing platooning distances while still ensuring their safety and security. It is far more difficult to confirm the veracity of a V2V message's content than it is to verify its integrity and source authentication. Only platoon members can send and receive V2V communications by implementing a practical access control mechanism. The goal is to link a prospective platoon member's digital identification to their actual location inside the unit. A physical challenge-response interaction is used in the CAVVPM process to verify that a prospective platoon member respects the rules. The applicant is asked to perform a series of random longitudinal movements, thus, the protocol's name. Remote attackers cannot join the platoon or send bogus CACC communications because CAVVPM blocks them. CAVVPM is more resistant to pre-recording assaults than previous work, and it can validate that the candidate is precisely behind the verifier in the same lane compared to previous studies.

Moderating Effect of Burnout on the Relationship between Self-Efficacy and Job Performance among Psychiatric Nurses for COVID-19 in National Hospitals.

Background and Objective: The unprecedented spread of infectious diseases, such as the COVID-19 pandemic, in psychiatric units has affected the self-efficacy, burnout, and job performances of psychiatric nurses. We conducted a survey to investigate the moderating effect of burnout on the relationship between the self-efficacy and job performances of psychiatric nurses. Materials and Methods: Validated and structured questionnaires were used to collect data from 186 nurses in psychiatric units for COVID-19. The data were analyzed using descriptive statistics, Pearson's correlation coefficient, and a series of multiple linear regression analyses based on Baron and Kenny's method using the SPSS 26.0 program. Results: Job performance was positively correlated with self-efficacy (r = 0.75, p < 0.001) but had no significant correlation with burnout (r = -0.11, p = 0.150). Self-efficacy was negatively correlated with burnout (r = -0.22, p = 0.002). Burnout among psychiatric nurses had significant moderating effects on self-efficacy and job performance (ß = -0.11, p = 0.024). Conclusions: These findings indicate a need to prevent burnout and to enhance self-efficacy in psychiatric nurses to increase their job performances and serve as a basis for establishing strategies to deploy medical staff in the future.

Korean red ginseng extract exhibits neuroprotective effects through inhibition of apoptotic cell death.

Red ginseng has long been used as a traditional medicine in many East Asian countries including Korea. It is known to exhibit various pharmacological effects, including anti-oxidant, anti-cancer, anti-stress and anti-diabetes activities. To further explore its actions, the present study evaluated effects of Korean red ginseng (KRG) extract on neuronal injury induced by various types of insults using primary cultured rat cortical cells. KRG extract inhibited neuronal damage and generation of intracellular reactive oxygen species (ROS) induced by excitatory amino acids, such as glutamate and N-methyl-D-aspartate (NMDA), or by Aß(25-35). To elucidate possible mechanism(s) by which KRG extract exerts neuroprotective action, its effects on apoptosis and apoptosis-related signaling molecules in neurons were assessed. KRG extract markedly increased phosphorylation of Bad at Ser 112 and inhibited Bax expression and caspase 3 activity. It also inhibited DNA fragmentation induced by NMDA or Aß(25-35). These results indicate that KRG extract protects cultured neurons from excitotoxicity and Aß(25-35)-induced toxicity through inhibition of ROS generation and apoptotic cell death. In addition, KRG extract inhibited ß-secretase activity, implying that it may reduce Aß peptide formation. Taken together, these findings suggest that KRG extract may be beneficial for the prevention and/or treatment of neurodegenerative disorders including Alzheimer's disease.

One-pot treatment of Saccharophagus degradans for polyhydroxyalkanoate production from brown seaweed.

The conventional production of polyhydroxyalkanoate (PHA) from waste biomass requires a pretreatment step (acid or alkali) for reducing sugar extraction, followed by bacterial fermentation. This study aims to find a greener approach for PHA production from brown seaweed. Saccharophagus degradans can be a promising bacterium for simultaneous reducing sugar and PHA production, bypassing the need for a pretreatment step. Cell retention cultures of S. degradans in membrane bioreactor resulted in approximately 4- and 3-fold higher PHA concentrations than batch cultures using glucose and seaweed as carbon sources, respectively. X-ray diffraction, Fourier transform infrared spectroscopy, and nuclear magnetic resonance results revealed identical peaks for the resulting PHA and standard poly(3-hydroxybutyrate). The developed one step process using cell retention culture of S. degradans could be a beneficial process for scalable and sustainable PHA production.

DSM-IV psychiatric comorbidity according to symptoms of insomnia: a nationwide sample of Korean adults.

PURPOSE: The diagnosis of insomnia is based on the presence of four different symptoms: difficulty in initiating sleep (DIS), difficulty in maintaining sleep (DMS), early morning awakening (EMA), and non-restorative sleep (NRS). This study investigated the differences in sociodemographic correlates and psychiatric comorbidity between the four symptoms of insomnia in the general population of South Korea. METHODS: A sample of the population aged 18-64 (N = 6,510) was questioned using a face-to-face interview. Insomnia was defined as having at least one of the four following symptoms three or more times per week: DIS, DMS, EMA, and NRS. Psychiatric disorders were evaluated using the Korean version of Composite International Diagnostic Interview. Logistic regression analysis was used to test each of the sleep outcomes (DIS, DMS, EMA, or NRS) for an association with sociodemographic and clinical variables. RESULTS: The prevalence of DIS, DMS, EMA, and NRS were 7.9 % (95 % CI 6.6-9.5 %), 7.9 % (95 % CI 6.5-9.6 %), 4.9 % (95 % CI 3.9-6.0 %), and 14.8 % (95 % CI 12.6-17.4 %), respectively. The overall prevalence of insomnia was 19.0 % (95 % CI 16.1-22.2 %). Being separated, divorced, or widowed, being single, having a part-time job, having a psychiatric illness, and having a physical illness were all significantly related to insomnia. Older age also increased the risk of DMS and EMA, and younger age was a risk factor for NRS. The presence of most psychiatric disorders was significantly related to insomnia. However, the relationship between the psychiatric illness and each insomnia symptom varied and was dependent on the insomnia symptom. CONCLUSIONS: Most psychiatric disorders were significantly associated with each insomnia symptom in different ways. Differences in sociodemographic and clinical correlates between the four insomnia symptoms implied the heterogeneous characteristics of insomnia as defined by the current diagnostic criteria.