Neutrosophic Fuzzy Logic-Based Hybrid CNN- LSTM for Accurate Chest X-ray Classification in COVID-19 PredictionNeutrosophic Fuzzy Logic-Based Hybrid CNN- LSTM for Accurate Chest X-ray Classification in COVID-19 Prediction

The necessity for sophisticated and precise diagnostic instruments for the prompt recognition of COVID-19 patients has been highlighted by the continuing worldwide epidemic. In this regard, this study presents a unique method for accurately classifying X-ray images of chest in COVID-19 prediction by combining Neutrosophic Fuzzy Logic with a Hybrid CNN and LSTM architecture. Medical image analysis involves uncertainties and imprecise information, which is handled via Neutrosophic Fuzzy Logic. The suggested hybrid model offers a thorough comprehension of the spatial and temporal patterns in chest X-ray pictures by utilizing the advantages of CNN for feature extraction and LSTM for sequential information learning. Hybrid CNN-LSTM architecture based on Neutrosophic Fuzzy Logic is trained on an enormous set of various chest X-ray pictures, including both positive and negative instances of COVID-19 and other respiratory diseases. The proposed method is implemented using Python software. In addition to improving COVID-19 prediction accuracy, the combination of Neutrosophic Fuzzy Logic with a Hybrid CNN-LSTM structure creates a strong framework for managing uncertainty in medical image classification tasks. The proposed CNN-LSTM model with Neutrosophic Fuzzy logic shows better accuracy with 98.6% which is 4.4 % higher when compared with COVID CAPS , Bayesian CNN , Deep Feature + SVM and DCNN. This study represents a major advancement in the creation of sophisticated and trustworthy diagnostic instruments for effective healthcare administration during times of worldwide health emergencies

Antiglycation and antioxidant properties of Momordica charantia

The accumulation of advanced glycation endproducts (AGEs) and oxidative stress underlie the pathogenesis of diabetic complications. In many developing countries, diabetes treatment is unaffordable, and plants such as bitter gourd (or bitter melon; Momordica charantia) are used as traditional remedies because they exhibit hypoglycaemic properties. This study compared the antiglycation and antioxidant properties of aqueous extracts of M. charantia pulp (MCP), flesh (MCF) and charantin in vitro. Lysozyme was mixed with methylglyoxal and 0–15 mg/ml of M. charantia extracts in a pH 7.4 buffer and incubated at 37°C for 3 days. Crosslinked AGEs were assessed using gel electrophoresis, and the carboxymethyllysine (CML) content was analyzed by enzyme-linked immunosorbent assays. The antioxidant activities of the extracts were evaluated using assays to assess DPPH (1,1-diphenyl-2-picryl-hydrazyl) and hydroxyl radical scavenging activities, metal-chelating activity and reducing power of the extracts. The phenolic, flavonol and flavonoid content of the extracts were also determined. All extracts inhibited the formation of crosslinked AGEs and CML in a dose-dependent manner, with MCF being the most potent. The antioxidant activity of MCF was higher than that of MCP, but MCP showed the highest metal-chelating activity. MCF had the highest phenolic and flavonoid contents, whereas MCP had the highest flavonol content. M. charantia has hypoglycaemic effects, but this study shows that M. charantia extracts are also capable of preventing AGE formation in vitro. This activity may be due to the antioxidant properties, particularly the total phenolic content of the extracts. Thus, the use of M. charantia deserves more attention, as it may not only reduce hyperglycaemia but also protect against the build-up of tissue AGEs and reduce oxidative stress in patients with diabetes

The JNK Inhibitor XG-102 Protects against TNBS-Induced Colitis

The c-Jun N-terminal kinase (JNK)-inhibiting peptide D-JNKI-1, syn. XG-102 was tested for its therapeutic potential in acute inflammatory bowel disease (IBD) in mice. Rectal instillation of the chemical irritant trinitrobenzene sulfonic acid (TNBS) provoked a dramatic acute inflammation in the colon of 7–9 weeks old mice. Coincident subcutaneous application of 100 µg/kg XG-102 significantly reduced the loss of body weight, rectal bleeding and diarrhoea. After 72 h, the end of the study, the colon was removed and immuno-histochemically analysed. XG-102 significantly reduced (i) pathological changes such as ulceration or crypt deformation, (ii) immune cell pathology such as infiltration and presence of CD3- and CD68-positive cells, (iii) the production of tumor necrosis factor (TNF)-α in colon tissue cultures from TNBS-treated mice, (iv) expression of Bim, Bax, FasL, p53, and activation of caspase 3, (v) complexation of JNK2 and Bim, and (vi) expression and activation of the JNK substrate and transcription factor c-Jun. A single application of subcutaneous XG-102 was at least as effective or even better depending on the outcome parameter as the daily oral application of sulfasalazine used for treatment of IBD

Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer

It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind energy generation is a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In this paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We present an optimized dendritic neural regression (DNR) model for wind energy prediction. A new variant of the seagull optimization algorithm (SOA) is developed using the search operators of the Aquila optimizer (AO). The main idea is to apply the operators of the AO as a local search in the traditional SOA, which boosts the SOA’s search capability. The new method, called SOAAO, is employed to train and optimize the DNR parameters. We used four wind speed datasets to assess the performance of the presented time-series prediction model, called DNR-SOAAO, using different performance indicators. We also assessed the quality of the SOAAO with extensive comparisons to the original versions of the SOA and AO, as well as several other optimization methods. The developed model achieved excellent results in the evaluation. For example, the SOAAO achieved high R2 results of 0.95, 0.96, 0.95, and 0.91 on the four datasets

Implementation of African vulture optimization algorithm based on deep learning for cybersecurity intrusion detection

The smart grid is an innovation that employs two-way communications to give innovative services to end consumers. Due to the severe contradictions in this connection, this system may be the target of numerous cyber-attacks. Intelligent grid networks can be protected by employing intrusion detection systems. IDS increases smart grid security by identifying malicious activity in the networks. However, current detection methods have several areas for improvement, including a high false alarm rate and low detection accuracy. The paper proposes an innovative intrusion detection strategy for intelligent grids combining DL-based and feature-based techniques. For this, the dataset is pre-processed, and pre-processing is done by utilizing min–max normalization. Then, features including mean, median, mode, standard deviation, information gain, mutual information, correlation coefficient, data percentiles, and autoregressive data are extracted. Next, African Vulture Optimization Algorithm organizes feature selection. Finally, DBN-LSTM is utilized for categorization to identify normal and attack packets. The developed method attains higher performance when compared with other existing techniques. Hence, the outcomes demonstrate that the AVOA-DBN-LSTM technique has a reliable potential for cybersecurity intrusion detection

An Efficient Off-line Signature Identification Method Based On Fourier Descriptor and

Summary This paper presents a novel off-line signature identification method based on Fourier Descriptor ( FDs ) and Chain Codes features. Signature identification classified into two different problems: recognition and verification. In recognition process we used Principle Component Analysis. In verification process we designed a multilayer feed forward artificial neural network. The main steps of constructing a signature identification system are discussed and experiments on real data sets show that the average error rate can reach 3.8%

Leveraging Regression Analysis to Predict Overlapping Symptoms of Cardiovascular Diseases

In medical informatics, deep learning-based models are being used to predict and diagnose cardiovascular diseases (CVDs). These models can detect clinical signs, recognize phenotypes, and pick treatment methods for complicated illnesses. One approach to predicting CVDs is to collect a large dataset of patient medical records and use it to train a deep learning model. This study investigated CVDs for early prediction using deep learning-based regression analysis on a dataset of 2621 medical records from UAE hospitals, including age, symptoms, and CVD information. We propose a long short-term memory-based deep neural network for early prediction of CVDs by leveraging the regression analysis. It can be seen that the accuracy level of the diseases increased when they were simulated in pairs of one disease with another due to the overlapping symptoms. The study’s results suggest that coronary heart disease has been predicted with an 71.5% accuracy level, with 84.4% overlapping with Dyspnea; when accuracy measured with a combination of three conditions the accuracy was 86.7%, Dyspnea, Chest Pain, and Cyanosis, it has been increased up to 88.9%. Weakness, Fatigue, and Emptysis showed a value of 89.8%. In our proposed work, the combinations were Dyspnea, Chest Pain, Cyanosis, Weakness and Fatigue, Emptysis, and discomfort pressure in the chest have shown the ideal value of accuracy measured up to 90.6%, and with Fever, the accuracy is 91%. We show the effectiveness of our proposed method on several evaluation benchmarks