Sumit S. Mohite, Amarnath B. Munde, Jai A. Lagad, Trupti Shaha
Traditional dosage forms, such as pills and capsules, are now used to treat conditions like dysphagia, which increases the risk of non-compliance and makes therapy ineffective. Mouth dissolving tablets were created to solve the problems associated with traditional dosage forms. They offer good hardness, dose uniformity, and convenience of administration. For pediatric, geriatric, and travel patients, they are the preferred dose form. Providing the MDTs with enough hardness, integrity, and disintegration speed without the requirement for water was the aim of their development. For fast-dissolving tablets to dissolve in saliva quickly, water is not needed. Some tablets are designed to dissolve in saliva very quickly in just a few seconds and are therefore referred to as "fast-dissolving" tablets. Because they contain substances that accelerate the pace at which the tablet dissolves in the mouth otherwise, it might take up to a minute some tablets are best referred to as fast-disintegrating tablets. This tablet type is designed to make it possible to administer an oral solid dose form in situations when no water or fluids are taken. These tablets typically dissolve in less than 60 seconds when dissolved in saliva.
{"title":"A Review on Mouth Dissolving Tablets","authors":"Sumit S. Mohite, Amarnath B. Munde, Jai A. Lagad, Trupti Shaha","doi":"10.32628/ijsrst52411276","DOIUrl":"https://doi.org/10.32628/ijsrst52411276","url":null,"abstract":"Traditional dosage forms, such as pills and capsules, are now used to treat conditions like dysphagia, which increases the risk of non-compliance and makes therapy ineffective. Mouth dissolving tablets were created to solve the problems associated with traditional dosage forms. They offer good hardness, dose uniformity, and convenience of administration. For pediatric, geriatric, and travel patients, they are the preferred dose form. Providing the MDTs with enough hardness, integrity, and disintegration speed without the requirement for water was the aim of their development. For fast-dissolving tablets to dissolve in saliva quickly, water is not needed. Some tablets are designed to dissolve in saliva very quickly in just a few seconds and are therefore referred to as \"fast-dissolving\" tablets. Because they contain substances that accelerate the pace at which the tablet dissolves in the mouth otherwise, it might take up to a minute some tablets are best referred to as fast-disintegrating tablets. This tablet type is designed to make it possible to administer an oral solid dose form in situations when no water or fluids are taken. These tablets typically dissolve in less than 60 seconds when dissolved in saliva.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"51 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736764","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}
The present study aimed to compare the elemental composition of leaf extracts from three different plant species of Mulberry, Morus alba L., Morus nigra L., and Morus indica L. The results showed that ten essential elements of biological importance for human metabolism, such as Copper, Chromium, Iron, Manganese, Sodium, Zinc, Calcium, Nickel, Lead, and Cadmium, were present in varying concentrations, well below the World Health Organization, (WHO's) daily intake limits. Mulberry leaves are an economical and readily available source of essential mineral elements vital for human health, which can be used to fortify functional foods, treat various diseases, and act as nutraceuticals. The information obtained could be used to determine the efficacy and dosage of herbal drugs manufactured from the leaf extracts of the Mulberry plant, making it a new contributor to food supplements and nutraceutical products.
{"title":"Elemental Characterization of Leaf Extracts of Three Different Species of Mulberry: Morus. alba L., Morus. nigra L. and Morus. indica L., Using Inductively Coupled Plasma -Atomic Emission Spectroscopy (ICP-AES)","authors":"Nikki Huria, Aparna Saraf","doi":"10.32628/ijsrst52411277","DOIUrl":"https://doi.org/10.32628/ijsrst52411277","url":null,"abstract":"The present study aimed to compare the elemental composition of leaf extracts from three different plant species of Mulberry, Morus alba L., Morus nigra L., and Morus indica L. The results showed that ten essential elements of biological importance for human metabolism, such as Copper, Chromium, Iron, Manganese, Sodium, Zinc, Calcium, Nickel, Lead, and Cadmium, were present in varying concentrations, well below the World Health Organization, (WHO's) daily intake limits. Mulberry leaves are an economical and readily available source of essential mineral elements vital for human health, which can be used to fortify functional foods, treat various diseases, and act as nutraceuticals. The information obtained could be used to determine the efficacy and dosage of herbal drugs manufactured from the leaf extracts of the Mulberry plant, making it a new contributor to food supplements and nutraceutical products.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"26 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140744876","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}
Pub Date : 2024-04-02DOI: 10.32628/ijsrst524111104
Mrs. Neeta Bajpai, Kajal Kotangale, Asmita Bhaire
Occupational structure is the distribution or division of working population in different sectors based on occupations. It is one of the key elements in manifestation of population composition of a region. The economy is segregated based on its occupational structure namely, the agriculture sector, industrial or manufacturing sector and service sector. Occupational structure provides clear picture of working and non-working population in an area. The growth and prosperity of a region largely depends on the size of working population and proportion of productive workers engaged in various economic activities. The objective of this study is to find out the shift in occupation from primary sector to other sectors and the rate of change in last two decades from 2001 to 2011 in Kerala state with district as spatial unit. The study is based upon the secondary data collected from Census 2001 and 2011.By using GIS techniques thematic maps were prepared to show the variation in occupational structure in the study area. To find out the changes in occupational structure, percentage of workers engaged in four categories are to be calculated. There is a remarkable shift of workers from agriculture to non-agricultural sector in Kerala.
{"title":"Advanced Ambiance Sensing Device using ESP32 and BME680","authors":"Mrs. Neeta Bajpai, Kajal Kotangale, Asmita Bhaire","doi":"10.32628/ijsrst524111104","DOIUrl":"https://doi.org/10.32628/ijsrst524111104","url":null,"abstract":"Occupational structure is the distribution or division of working population in different sectors based on occupations. It is one of the key elements in manifestation of population composition of a region. The economy is segregated based on its occupational structure namely, the agriculture sector, industrial or manufacturing sector and service sector. Occupational structure provides clear picture of working and non-working population in an area. The growth and prosperity of a region largely depends on the size of working population and proportion of productive workers engaged in various economic activities. The objective of this study is to find out the shift in occupation from primary sector to other sectors and the rate of change in last two decades from 2001 to 2011 in Kerala state with district as spatial unit. The study is based upon the secondary data collected from Census 2001 and 2011.By using GIS techniques thematic maps were prepared to show the variation in occupational structure in the study area. To find out the changes in occupational structure, percentage of workers engaged in four categories are to be calculated. There is a remarkable shift of workers from agriculture to non-agricultural sector in Kerala. ","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"154 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140754944","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}
A Samuvel, Dr. G Manikandan, Ms. Vilma Veronica, Ms. S. Hemalatha
Agriculture stands as a crucial sector, making significant contributions to the economies of many countries. Nevertheless, it encounters various challenges, one of which is animal disruption. This poses a considerable threat to crops, leading to financial losses for farmers. In response to this concern, we have engineered an animal disruption warning system for agricultural settings based on YOLOv6 technology.The system operates by analyzing live video feeds from strategically placed cameras. Utilizing deep learning algorithms, it can detect and classify animals in real-time. The computer vision algorithms enable tracking and prediction of animal movements. Upon detection, the system promptly sends alerts, enabling timely and appropriate actions.In this paper, we periodically monitor the entire farm through a camera that continuously records its surroundings. The identification of animal entry is achieved using a deep learning model, and alarm systems serve as a deterrent, notifying forest officials. This report provides details on the libraries and convolutional neural networks employed in constructing the model.This research focuses on the implementation of a robust animal detection system in agricultural environments, leveraging the capabilities of deep learning. The project utilizes state-of-the-art deep neural networks and computer vision algorithms to analyze live video feeds from strategically positioned cameras across the farm. The deep learning model is trained to detect and classify various animals in real-time, contributing to the early identification of potential threats to crops.The system employs sophisticated computer vision techniques, enabling accurate tracking and prediction of animal movements within the monitored areas. Upon detection, the system triggers timely alerts, providing farmers with the necessary information to take swift and appropriate actions, thereby mitigating potential damage to crops.To achieve these objectives, the project involves periodic monitoring of the entire farm through a camera that continuously records its surroundings. The deep learning model, supported by alarm systems, effectively identifies animal entries, serving as a proactive deterrent. This research report outlines the libraries, frameworks, and convolutional neural networks employed in the development of the animal detection model, shedding light on the technical aspects of its implementation.The integration of deep learning and computer vision in agriculture not only enhances crop protection but also contributes to the sustainable and efficient management of farming practices. This research offers insights into the potential of advanced technologies to address challenges in agriculture and opens avenues for further exploration in the intersection of technology and agriculture.
{"title":"Smart Agriculture: Enhancing Security Through Animal Detection Via Deep Learning and Computer Vision","authors":"A Samuvel, Dr. G Manikandan, Ms. Vilma Veronica, Ms. S. Hemalatha","doi":"10.32628/ijsrst52411226","DOIUrl":"https://doi.org/10.32628/ijsrst52411226","url":null,"abstract":"Agriculture stands as a crucial sector, making significant contributions to the economies of many countries. Nevertheless, it encounters various challenges, one of which is animal disruption. This poses a considerable threat to crops, leading to financial losses for farmers. In response to this concern, we have engineered an animal disruption warning system for agricultural settings based on YOLOv6 technology.The system operates by analyzing live video feeds from strategically placed cameras. Utilizing deep learning algorithms, it can detect and classify animals in real-time. The computer vision algorithms enable tracking and prediction of animal movements. Upon detection, the system promptly sends alerts, enabling timely and appropriate actions.In this paper, we periodically monitor the entire farm through a camera that continuously records its surroundings. The identification of animal entry is achieved using a deep learning model, and alarm systems serve as a deterrent, notifying forest officials. This report provides details on the libraries and convolutional neural networks employed in constructing the model.This research focuses on the implementation of a robust animal detection system in agricultural environments, leveraging the capabilities of deep learning. The project utilizes state-of-the-art deep neural networks and computer vision algorithms to analyze live video feeds from strategically positioned cameras across the farm. The deep learning model is trained to detect and classify various animals in real-time, contributing to the early identification of potential threats to crops.The system employs sophisticated computer vision techniques, enabling accurate tracking and prediction of animal movements within the monitored areas. Upon detection, the system triggers timely alerts, providing farmers with the necessary information to take swift and appropriate actions, thereby mitigating potential damage to crops.To achieve these objectives, the project involves periodic monitoring of the entire farm through a camera that continuously records its surroundings. The deep learning model, supported by alarm systems, effectively identifies animal entries, serving as a proactive deterrent. This research report outlines the libraries, frameworks, and convolutional neural networks employed in the development of the animal detection model, shedding light on the technical aspects of its implementation.The integration of deep learning and computer vision in agriculture not only enhances crop protection but also contributes to the sustainable and efficient management of farming practices. This research offers insights into the potential of advanced technologies to address challenges in agriculture and opens avenues for further exploration in the intersection of technology and agriculture. ","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"14 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140753871","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}
N. Ezhil Arasi, Dr. G Manikandan, Ms. S. Hemalatha, Ms. Vilma Veronica
Malicious social bots generate fake tweets and automate their social relationships either by pretending to be a followers or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweets to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes’ theorem, and the indirect trust is derived from the Dempster– Shafer theory (DST) to determine the trustworthiness of each participant accurately. Finally, we shown the user tweet data in terms of graph visualization of bar chart and pie chart of the system. Experimental results shown the better performance of the system.
{"title":"Malicious Social Bot Using Twitter Network Analysis in Django","authors":"N. Ezhil Arasi, Dr. G Manikandan, Ms. S. Hemalatha, Ms. Vilma Veronica","doi":"10.32628/ijsrst52411222","DOIUrl":"https://doi.org/10.32628/ijsrst52411222","url":null,"abstract":"Malicious social bots generate fake tweets and automate their social relationships either by pretending to be a followers or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweets to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes’ theorem, and the indirect trust is derived from the Dempster– Shafer theory (DST) to determine the trustworthiness of each participant accurately. Finally, we shown the user tweet data in terms of graph visualization of bar chart and pie chart of the system. Experimental results shown the better performance of the system. ","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"22 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140754018","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}
Ms. K Jebima Jessy, Dr. G Manikandan, Ms. S. Hemalatha, Ms. Vilma Veronica
One of the most prominent tools for detecting cardiovascular problems is the electrocardiogram (ECG). The electrocardiogram (ECG or EKG) is a diagnostic tool that is used to routinely assess the electrical and muscular functions of the heart. Even though it is a comparatively simple test to perform, the interpretation of the ECG charts requires considerable amounts of training. Till recently, the majority of ECG records were kept on paper. Thus, manually examining and re-examining the ECG paper records often can be a time-consuming and daunting process. If we digitize such paper ECG records, we can perform automated diagnosis and analysis. The main goal of this project is to use machine learning to convert ECG paper records into a 1-D signal. This can be achieved by extracting the P, QRS, and T waves that exist in ECG signals to demonstrate the electrical activity of the heart using various techniques. The techniques include splitting the original ECG report into 13 Leads, extracting and converting into the signal, smoothing, converting them to binary images using threshold and scaling. Post-feature-extraction, dimension reduction techniques like Principal Component Analysis are applied to understand the data. Multiple classifiers like k-nearest neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), and Voting Based Ensemble Classifier are implemented, and based on the acceptable criteria on the accuracy, precision, recall, f1-score, and support, the model will be finalized. This final model will aid in the diagnosing of cardiac diseases, to detect whether a patient has/had Myocardial Infarction, Abnormal Heartbeat, or the patient is hale and healthy by inferring the ECG reports
心电图（ECG）是检测心血管问题的最重要工具之一。心电图（ECG 或 EKG）是一种诊断工具，用于常规评估心脏的电气和肌肉功能。尽管心电图是一项相对简单的检查，但解读心电图却需要大量的培训。直到最近，大多数心电图记录都保存在纸上。因此，手动检查和重新检查心电图纸质记录往往是一个耗时且令人生畏的过程。如果我们将这些纸质心电图记录数字化，就可以进行自动诊断和分析。本项目的主要目标是利用机器学习将心电图纸质记录转换为一维信号。这可以通过提取心电图信号中存在的 P 波、QRS 波和 T 波来实现，从而利用各种技术展示心脏的电活动。这些技术包括将原始心电图报告分割成 13 个导联、提取并转换成信号、平滑处理、使用阈值和缩放将它们转换成二进制图像。特征提取后，应用主成分分析等降维技术来理解数据。根据准确率、精确度、召回率、f1-分数和支持率等可接受的标准，最终确定模型。这一最终模型将有助于诊断心脏疾病，通过推断心电图报告检测病人是否患有心肌梗塞、心跳异常或病人是否健康。
{"title":"Detection of Cardiovascular Disease Using ECG Images in Machine Learning and Deep Learning","authors":"Ms. K Jebima Jessy, Dr. G Manikandan, Ms. S. Hemalatha, Ms. Vilma Veronica","doi":"10.32628/ijsrst52411224","DOIUrl":"https://doi.org/10.32628/ijsrst52411224","url":null,"abstract":"One of the most prominent tools for detecting cardiovascular problems is the electrocardiogram (ECG). The electrocardiogram (ECG or EKG) is a diagnostic tool that is used to routinely assess the electrical and muscular functions of the heart. Even though it is a comparatively simple test to perform, the interpretation of the ECG charts requires considerable amounts of training. Till recently, the majority of ECG records were kept on paper. Thus, manually examining and re-examining the ECG paper records often can be a time-consuming and daunting process. If we digitize such paper ECG records, we can perform automated diagnosis and analysis. The main goal of this project is to use machine learning to convert ECG paper records into a 1-D signal. This can be achieved by extracting the P, QRS, and T waves that exist in ECG signals to demonstrate the electrical activity of the heart using various techniques. The techniques include splitting the original ECG report into 13 Leads, extracting and converting into the signal, smoothing, converting them to binary images using threshold and scaling. Post-feature-extraction, dimension reduction techniques like Principal Component Analysis are applied to understand the data. Multiple classifiers like k-nearest neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), and Voting Based Ensemble Classifier are implemented, and based on the acceptable criteria on the accuracy, precision, recall, f1-score, and support, the model will be finalized. This final model will aid in the diagnosing of cardiac diseases, to detect whether a patient has/had Myocardial Infarction, Abnormal Heartbeat, or the patient is hale and healthy by inferring the ECG reports ","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140751785","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}
S Dennis Emmanuel, Dr. G Manikandan, Vilma Veronica, S. Hemalatha
The successful development of amyloid-based biomarkers and tests for Alzheimer’s disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers.
基于淀粉样蛋白的阿尔茨海默病（AD）生物标记物和检测方法的成功开发是阿尔茨海默病诊断领域的一个重要里程碑。然而，目前仍存在两大局限性。基于淀粉样蛋白的诊断生物标记物和检测方法只能提供有关疾病过程的有限信息，而且它们无法在淀粉样蛋白-β在大脑中大量积聚之前识别出患病个体。本研究的目的是开发一种方法，以确定潜在的基于血液的非淀粉样蛋白生物标志物，用于早期AD检测。使用血液很有吸引力，因为它容易获得且相对便宜。我们的方法主要基于机器学习（ML）技术（尤其是支持向量机），因为它们能够通过从复杂数据中学习模式来创建多变量模型。利用新颖的特征选择和评估模式，我们确定了 5 组新的非淀粉样蛋白，它们有可能成为早期 AD 的生物标记物。特别是，我们发现 A2M、ApoE、BNP、Eot3、RAGE 和 SGOT 的组合可能是早期疾病的关键生物标志物特征。基于已识别面板的疾病检测模型在疾病前驱期（后期表现更佳）的灵敏度（SN）> 80%，特异度（SP）> 70%，接收器工作曲线下面积（AUC）至少为 0.80。相比之下，现有的 ML 模型在疾病的这一阶段表现不佳，这表明基础蛋白质面板可能不适合疾病的早期检测。我们的研究结果证明了使用非淀粉样蛋白生物标记物早期检测AD的可行性。
{"title":"Alzheimer Disease Using Machine Learning","authors":"S Dennis Emmanuel, Dr. G Manikandan, Vilma Veronica, S. Hemalatha","doi":"10.32628/ijsrst52411221","DOIUrl":"https://doi.org/10.32628/ijsrst52411221","url":null,"abstract":"The successful development of amyloid-based biomarkers and tests for Alzheimer’s disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers. ","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"170 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140754912","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}
M. Sandhya Rani, Guda Ankitha, Polasani Harini, G Ravi
In an era of escalating cyber threats, the need for robust defenses against malicious activities is paramount. In this project, we propose a novel approach to leverage honeypots in conju nction with Canary Tokens to accurately pinpoint the geographical locations of attackers. By strategically deploying these decoy resources across diverse network environments, we capture valuable data on unauthorized access attempts and malicious behavior. Through the analysis of Canary Tokens, which act as unique identifiers triggered upon interaction, we can trace the origin of these attacks to specific IP addresses. Utilizing this information, security professionals gain insights into the geographical distribution of attackers, aiding in threat intelligence, incident response, and the implementation of targeted security measures. This integration of project honeypots and Canary Tokens enhances network defense strategies, providing organizations with a proactive stance against cyber threats.
在网络威胁不断升级的时代，最需要的是针对恶意活动的强大防御。在本项目中，我们提出了一种利用 "蜜罐 "和 "金丝雀令牌 "来精确定位攻击者地理位置的新方法。通过在不同的网络环境中战略性地部署这些诱饵资源，我们可以捕捉到有关未经授权的访问尝试和恶意行为的宝贵数据。金丝雀令牌是互动时触发的唯一标识符，通过分析金丝雀令牌，我们可以将这些攻击的源头追溯到特定的 IP 地址。利用这些信息，安全专业人员可以深入了解攻击者的地理分布，有助于威胁情报、事件响应和有针对性的安全措施的实施。项目 "巢穴 "和金丝雀令牌的整合增强了网络防御策略，为企业提供了应对网络威胁的前瞻性措施。
{"title":"Cyber Honeypot","authors":"M. Sandhya Rani, Guda Ankitha, Polasani Harini, G Ravi","doi":"10.32628/ijsrst52411168","DOIUrl":"https://doi.org/10.32628/ijsrst52411168","url":null,"abstract":"In an era of escalating cyber threats, the need for robust defenses against malicious activities is paramount. In this project, we propose a novel approach to leverage honeypots in conju nction with Canary Tokens to accurately pinpoint the geographical locations of attackers. By strategically deploying these decoy resources across diverse network environments, we capture valuable data on unauthorized access attempts and malicious behavior. Through the analysis of Canary Tokens, which act as unique identifiers triggered upon interaction, we can trace the origin of these attacks to specific IP addresses. Utilizing this information, security professionals gain insights into the geographical distribution of attackers, aiding in threat intelligence, incident response, and the implementation of targeted security measures. This integration of project honeypots and Canary Tokens enhances network defense strategies, providing organizations with a proactive stance against cyber threats.","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"145 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140752213","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}
Ms. J Seetha, Dr. G Manikandan, Ms. S. Hemalatha, Ms. Vilma Veronica
To solve the QR code recognition problem caused by ordinary camera collection, the recognition algorithm based on image processing is put forward in this paper. The whole process including image binarization, image tilt correction, image orientation, image geometric correction and image normalization allows images collected on different illumination conditions. Experiments show that the improved method can enhance the recognition speed of two-dimensional code and accuracy. QR i.e. “Quick Response” code is a 2D matrix code that is designed by keeping two points under consideration, i.e. it must store large amount of data as compared to 1D barcodes and it must be decoded at high speed using any handheld device like phones. QR code provides high data storage capacity, fast scanning, omnidirectional readability, and many other advantages including, error-correction (so that damaged code can also be read successfully) and different type of versions. Different varieties of QR code symbols like logo QR code, encrypted QR code, QR Code are also available so that user can choose among them according to their need. Now these days, a QR code is applied in different application streams related to marketing, security, academics etc. and gain popularity at a really high pace. Day by day more people are getting aware of this technology and use it accordingly. The popularity of QR code grows rapidly with the growth of smartphone users and thus the QR code is rapidly arriving at high levels of acceptance worldwide.
{"title":"QR Code Recognition Based on Image Processing","authors":"Ms. J Seetha, Dr. G Manikandan, Ms. S. Hemalatha, Ms. Vilma Veronica","doi":"10.32628/ijsrst52411227","DOIUrl":"https://doi.org/10.32628/ijsrst52411227","url":null,"abstract":"To solve the QR code recognition problem caused by ordinary camera collection, the recognition algorithm based on image processing is put forward in this paper. The whole process including image binarization, image tilt correction, image orientation, image geometric correction and image normalization allows images collected on different illumination conditions. Experiments show that the improved method can enhance the recognition speed of two-dimensional code and accuracy. QR i.e. “Quick Response” code is a 2D matrix code that is designed by keeping two points under consideration, i.e. it must store large amount of data as compared to 1D barcodes and it must be decoded at high speed using any handheld device like phones. QR code provides high data storage capacity, fast scanning, omnidirectional readability, and many other advantages including, error-correction (so that damaged code can also be read successfully) and different type of versions. Different varieties of QR code symbols like logo QR code, encrypted QR code, QR Code are also available so that user can choose among them according to their need. Now these days, a QR code is applied in different application streams related to marketing, security, academics etc. and gain popularity at a really high pace. Day by day more people are getting aware of this technology and use it accordingly. The popularity of QR code grows rapidly with the growth of smartphone users and thus the QR code is rapidly arriving at high levels of acceptance worldwide. ","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"91 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140752645","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}
Pub Date : 2024-04-02DOI: 10.32628/ijsrst524111103
Vairagade S. P.
Zooplanktons are diverse organisms, are found more or less in all water bodies. The plankton research is a highly helpful technique for determining the biotic potential of water bodies and adds to the overall calculation of their biotic nature and general economic potential. Zooplanktons are microscopic, free-floating organisms that are essential to the functioning of aquatic ecosystems. As the most crucial link in the energy transfer between phytoplankton and higher aquatic animals, zooplanktons are significant biotic components and play a significant role in the aquatic environment. The functioning of an aquatic ecosystem's food chains, food webs, energy flow, and nutrient cycling are all influenced by zooplankton. Zooplankton populations are excellent indicators of the stability of the food chain. Numerous environmental parameters, including pH, temperature, salinity, oxygen, and others, have an impact on zooplankton. The food chain and the flow of energy between the primary and tertiary trophic levels are both significantly influenced by zooplankton. They serve as indicators of the physical, chemical, and biological processes occurring in aquatic systems due to their high densities. Because they are highly sensitive to environmental change, changes in the abundance of certain species or in the makeup of certain communities can be used to gauge the health of the environment. An assessment of the literature on zooplanktons in Indian lentic water has been done in the current work, which has long been deemed necessary in this subject.
{"title":"A Review on Zooplankton Diversity with Reference to Physico-Chemical Parameters of Lentic Ecosystems in Maharashtra","authors":"Vairagade S. P.","doi":"10.32628/ijsrst524111103","DOIUrl":"https://doi.org/10.32628/ijsrst524111103","url":null,"abstract":"Zooplanktons are diverse organisms, are found more or less in all water bodies. The plankton research is a highly helpful technique for determining the biotic potential of water bodies and adds to the overall calculation of their biotic nature and general economic potential. Zooplanktons are microscopic, free-floating organisms that are essential to the functioning of aquatic ecosystems. As the most crucial link in the energy transfer between phytoplankton and higher aquatic animals, zooplanktons are significant biotic components and play a significant role in the aquatic environment. The functioning of an aquatic ecosystem's food chains, food webs, energy flow, and nutrient cycling are all influenced by zooplankton. Zooplankton populations are excellent indicators of the stability of the food chain. Numerous environmental parameters, including pH, temperature, salinity, oxygen, and others, have an impact on zooplankton. The food chain and the flow of energy between the primary and tertiary trophic levels are both significantly influenced by zooplankton. They serve as indicators of the physical, chemical, and biological processes occurring in aquatic systems due to their high densities. Because they are highly sensitive to environmental change, changes in the abundance of certain species or in the makeup of certain communities can be used to gauge the health of the environment. An assessment of the literature on zooplanktons in Indian lentic water has been done in the current work, which has long been deemed necessary in this subject. ","PeriodicalId":14387,"journal":{"name":"International Journal of Scientific Research in Science and Technology","volume":"312 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140751249","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：