International Journal of Advanced Research in Science, Communication and Technology最新文献_第3页

Human Centred Design in Engineering 以人为本的工程设计

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-24 DOI: 10.48175/ijarsct-19228

Prof. Shivprasad B J, Premkumar, Hanumanth, Pavanraj, Nikhith Naik, Sai Sankarsh

引用次数: 0

Formulation and Evaluation of Diuretic Activity of Polyherbal Drug in Rats 多草药的配制和对大鼠利尿活性的评价

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-24 DOI: 10.48175/ijarsct-19229

Archana Ingle, Sourabh Jain, Ragini Bundela, Dr. Karunakar Shukla, Dr. Rakesh Patel, Prajakta Shelke Maskawade

{"title":"Formulation and Evaluation of Diuretic Activity of Polyherbal Drug in Rats","authors":"Archana Ingle, Sourabh Jain, Ragini Bundela, Dr. Karunakar Shukla, Dr. Rakesh Patel, Prajakta Shelke Maskawade","doi":"10.48175/ijarsct-19229","DOIUrl":"https://doi.org/10.48175/ijarsct-19229","url":null,"abstract":"An attempt was made in this study to investigate the diuretic activity of an ethanolic extract of a polyherbal formulation containing four drugs: seeds of Coriandrum sativum, buds of Syzigiumaromaticum, leaves of Ocimum sanctum, and curcumin (Curcuma longa), as well as leaves of Syzigiumaromaticum. According to the findings of this investigation, the extract of a polyherbal formulation (including seeds of Coriandrum sativum, buds and leaves of Syzigiumaromaticum& leaves of Ocimum sanctum, and Curcuma longa) possessed substantial activity. Various quantities of Polyherbal formulations (200 and 400 mg/kg), furosemide (10 mg/kg), and vehicle were given orally to rats (n = 6 animals per group), and the urine output was collected after 24 hours. All concentrations of Polyherbal formulation exhibited a dose-dependent relationship when compared to the control animals in the study. According to the findings of this study, the Polyherbal formulations have significant diuretic effect in rats when tested in the above-mentioned experimental model. That polyherbal formulation extract has such potent effect may be due to their ability to stimulate the excretion of Na+, K+, and Cl- concentrations in urine while also increasing the amount of urine excreted by the body. As diuretics, medicinal herbs are a key source of supply. When it comes to diuretics, both mono- and poly-herbal formulations have been employed successfully","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"24 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generating Descriptive Text From Images - Image Caption Generator 从图像生成描述性文本 - 图像标题生成器

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-23 DOI: 10.48175/ijarsct-19225

Avdhi Pagariya, Riddhi Jain

{"title":"Generating Descriptive Text From Images - Image Caption Generator","authors":"Avdhi Pagariya, Riddhi Jain","doi":"10.48175/ijarsct-19225","DOIUrl":"https://doi.org/10.48175/ijarsct-19225","url":null,"abstract":"In the modern era, image captioning has become one of the most widely required tools. Moreover, there are inbuilt applications that generate and provide a caption for a certain image, all these things are done with the help of deep neural network models. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. It generates syntactically and semantically correct sentences. In this paper, we present a deep learning model to describe images and generate captions using computer vision and machine translation. This paper aims to detect different objects found in an image, recognize the relationships between those objects and generate captions. The dataset used is Flickr8k and the programming language used was Python3, and an ML technique called Transfer Learning will be implemented with the help of the caption model, to demonstrate the proposed experiment. This paper will also elaborate on the functions and structure of the various Neural networks involved. Generating image captions is an important aspect of Computer Vision and Natural language processing. Image caption generators can find applications in Image segmentation as used by Facebook and Google Photos, and even more so, its use can be extended to video frames. They will easily automate the job of a person who has to interpret images. Not to mention it has immense scope in helping visually impaired people","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"129 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phytochemical and Phrmacological Review on Butea Monosperma (PALASH) 单叶菠萝（PALASH）的植物化学和药理学综述

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-23 DOI: 10.48175/ijarsct-19226

Snehal Shelke, Sanika Gogawale, Prof. Sharvari Chavan

引用次数: 0

Deep Learning based Apple Fruit Disease Detection using Dense Net Recursive Convolutional Neural Network (DNRCNN) 使用密集网递归卷积神经网络（DNRCNN）进行基于深度学习的苹果果实病害检测

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-23 DOI: 10.48175/ijarsct-19227

V. Subha, Dr. K. Kasturi

{"title":"Deep Learning based Apple Fruit Disease Detection using Dense Net Recursive Convolutional Neural Network (DNRCNN)","authors":"V. Subha, Dr. K. Kasturi","doi":"10.48175/ijarsct-19227","DOIUrl":"https://doi.org/10.48175/ijarsct-19227","url":null,"abstract":"Every year, fruit diseases cost the apple industry a lot of money. It can be challenging for growers to identify various apple infections because the symptoms of various illnesses are often similar and may overlap. In this study, we suggest a deep learning-based method for identifying and categorizing apple diseases. Dataset generation, which includes data collection and data labelling, is the first stage of the investigation. On the prepared dataset, we then trained a deep learning-based Dense Net Recurrent Convolutional Neural Network (DNRCNN) model for automatically classifying apple diseases. The end-to-end learning algorithm DNRCNN is appropriate for a range of tasks including image classification, object detection, and segmentation because it automatically extracts complex features from source images and learns them directly. Initialize the parameters of the proposed deep model using transfer learning. To avoid over-fitting, data augmentation techniques like rotation, translation, reflection, and scaling are also used. On the prepared dataset, the proposed Dense Net Recursive Convolutional Neural Network (DNRCNN) model achieves promising results, with an accuracy of about 96%. Some of the intricate and helpful image characteristics for detection are captured by the suggested model for classification. The model can learn the higher-order features of two adjacent layers that are not in the same channel but have a high correlation more effectively than existing techniques. High training and validation accuracy have been achieved when training and validating the suggested model. The findings support the method's usefulness in categorizing different apple diseases and show that it can be useful for farmers","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"137 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CHATBOT: A Comprehensive Review of AI 聊天机器人人工智能全面回顾

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-22 DOI: 10.48175/ijarsct-19219

Sowjanya S, Prof. Jayant Kumar Rathod, Suraj, Tejesvin R, Thanvi S Sanil, Thirumala

引用次数: 0

Predictive Modelling for Diabetes and Insulin Dosage Using Machine Learning 利用机器学习建立糖尿病和胰岛素剂量预测模型

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-22 DOI: 10.48175/ijarsct-19216

Harshitha R, Hemanth Kumar

引用次数: 0

Green Marketing: Opportunity for Innovation and Sustainable Development 绿色营销：创新与可持续发展的机遇

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-22 DOI: 10.48175/ijarsct-19217

Mrs. Pooja Joshi

引用次数: 0

Prediction of Student Depression State of Mind Using Machine Learning Technique 利用机器学习技术预测学生的抑郁心理状态

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-22 DOI: 10.48175/ijarsct-19214

Harshitha S, Hemanth Kumar

引用次数: 0

Identifying Missing Individual using AI 利用人工智能识别失踪个体

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-07-22 DOI: 10.48175/ijarsct-19215

Sharath BG, Hemanth Kumar

引用次数: 0