Effect of Gymnema montanum Leaves on Serum and Tissue Lipids in Alloxan Diabetic Rats

The effect of Gymnema montanum leaves on alloxaninduced hyperlipidemia was studied in male Wistar rats. Ethanolic extract of G. montanum leaves was administered orally and different doses of the extract on blood glucose, serum and tissue lipids, hexokinase, glucose-6-phosphatase, thiobarbituric acid–reactive substances (TBARS), hydroperoxides, and glutathione in alloxan-induced diabetic rats were studied. G. montanum leaf extract (GLEt) at doses of 50, 100, 200 mg/kg body weight for 3 weeks suppressed the elevated blood glucose and lipid levels in diabetic rats. GLEt at 200 mg/kg body weight was found to be comparable to glibenclamide, a reference drug. These data indicate that G. montanum represents an effective antihyperglycemic and antihyperlipidemic adjunct for the treatment of diabetes and a potential source of discovery of new orally active agent for future therapy

A HYBRID NETWORK FOR AUTOMATIC GREENHOUSE MANAGEMENT

ABSTRACT A greenhouse is a building in which plants are grown in closed environment. Greenhouse management is controlling of several greenhouse. The wireless section is located in the indoor environment where great flexibility is needed, particularly in the production area of greenhouse. Instead, the wired section is mainly used in the outside area as a control backbone, to interconnect the greenhouse with the control room. An integrated wired/wireless solution is to use the advantages of both technologies by improving performances. In the wired section, a controller area network (CAN) type network has been chosen on the account of its simplicity, strongest, cheapness, and good performances. for the wireless part, a Zigbee type network has been chosen. The SCADA system is to monitor and control data in a simple way. To maintain the optimal conditions of the environment, greenhouse management requires data acquisition using the SCADA (supervisory control and data acquisition)

Breast cancer diagnosis model using stacked autoencoder with particle swarm optimization

Breast cancer (BrC) stands as the most prevalent cancer affecting women globally, comprising 24.5% of all female cancer diagnoses and contributing to 15.0% of total cancer-related fatalities. The timely detection and precise categorization of breast cancer play pivotal roles in enhancing patient prognosis and treatment outcomes. The main goal is to enhance the precision of classifying mammogram images, thus offering vital support to radiology experts in diagnosing BrCs. The proposed model encompasses several pivotal stages, including pre-processing, feature extraction, segmentation, and classification. To assess the model's efficacy, we employed the INBreast dataset. During pre-processing, mammogram images were enhanced through a customized contrast-limited adaptive histogram equalization (mCLAHE) technique coupled with data augmentation. Segmentation was executed utilizing the Res-SegNet model, and feature extraction employing the VGG-19 model. The classification was conducted via a stacked autoencoder (SAE) with particle swarm optimization (PSO). Our proposed model exhibited notably high performance compared to alternative models such as CNN, Yolo-v4, and Inception-v3. The results unveiled an accuracy of 98.33%, precision of 99.39%, recall of 98.78%, specificity of 93.75%, an F1-score of 99.08%, and an MCC score of 90.04%

HLASwin-T-ACoat-Net Based Underwater Object Detection

Due to the limited light penetration in underwater environments, sonar equipment plays a crucial role in various commercial and military operations. However, underwater images often suffer from degradation due to scattering and absorption phenomena, resulting in poor visibility of submerged objects. To address this challenge, image enhancement techniques are essential for enhancing the appearance and visibility of underwater objects. This research proposes a novel approach called HLAST-ACNet, which combines the advantages of a hybrid Local Acuity Swin Transformer and an Adapted Coat-Net for Underwater Object Detection (UOD). The HLASwin-T-ACoat-Net leverages Contrast Limited Adaptive Histogram Equalization (CLAHE) to increase the quality of images. Additionally, it incorporates a path aggregation network to integrate deep and shallow feature maps and utilizes online complicated example mining to improve training efficiency. Furthermore, the algorithm improves Region of Interest (ROI) pooling by introducing ROI alignment, which mitigates quantization errors and enhances object detection accuracy. Compared to existing algorithms, the algorithms based on HLASTACNet demonstrate significant improvements in the URPC2018 and OUC datasets, achieving precision rates of 91.25% and 92.36%, respectively. The research model has a higher computational complexity than four existing methods, as evidenced by its GFLOPs, per-image processing time with a speed of 20ms, and the FPS measures for average processed frames per second reaching 2.28s. The research model effectively addressed the challenges and false detection with varying sizes of objects in complicated underwater environments

Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence

Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%

Maternal Nutritional Understanding, Attitudes, and Practices: Implications for Children's Eating Habits

Purpose: This study delves into the maternal understanding, attitudes, and practices related to nutrition and their potential implications for children's eating habits in India. Through a comprehensive examination, we aim to uncover valuable insights into the dynamics of maternal influence on children's dietary patterns in the specific cultural and contextual setting of India. The findings from this research contribute to a nuanced understanding of the interplay between maternal nutritional knowledge, attitudes, and practices, shedding light on potential avenues for targeted interventions to promote healthier eating habits among children in the Indian context. The structured questionnaire, encompassing dimensions of maternal nutritional knowledge, attitudes toward dietary practices, and specific behaviors influencing children's eating habits, serves as the primary tool for data collection. Anthropometric measurements of both mothers and children augment the dataset, offering a nuanced perspective on the nutritional status of participants. Limitations, such as reliance on self-reported data and the cross-sectional nature of the study, are acknowledged. Despite these constraints, this research aspires to contribute valuable insights into the intricate web of factors influencing children's eating habits within the unique socio-cultural landscape of India. The findings are expected to inform targeted interventions and policy recommendations, fostering healthier nutritional practices among children in this diverse and dynamic setting