International Journal of Food and Nutritional Sciences最新文献_第10页

Resilient EDGE Detection Method for Significant IMPULSE Noise 显著脉冲噪声的弹性边缘检测方法

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/184

Dr. G. Krishna Mohan, P. M. Sai, N. H. Kumar, P. Raheema, K. S. Surendra

引用次数: 0

Sentiment Analysis on Multiple Indian Languages 多种印度语言的情感分析

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/209

B. Ramya, Asa Latha, Tagore Yuvaraj Singh, Sanam Venkata, Manoj Kumar, Turimella Deepthi, Sai Sri, Tella Welson Raju

{"title":"Sentiment Analysis on Multiple Indian Languages","authors":"B. Ramya, Asa Latha, Tagore Yuvaraj Singh, Sanam Venkata, Manoj Kumar, Turimella Deepthi, Sai Sri, Tella Welson Raju","doi":"10.48047/ijfans/v11/i12/209","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/209","url":null,"abstract":"Sentiment analysis has become popular in the computer science community as it is essential for moderating and analyzing information across the internet. There are various applications for sentiment analysis, such as opinion mining, social media monitoring, and market research. Sentiment analysis in Indian languages is gaining importance due to the growth of content on social media, news articles, and other online platforms in Indian languages. Since India is a diversified country it has many languages that are used by millions of people but many Indian languages do not have enough resources for moderation on the internet to analyze the sentiment in the text to use them for either eradicating hate speech or to improve the productivity of companies by the understanding of customer needs from reviews. This paper explains an approach that helps in analyzing the sentiment which helps in content moderation and avoiding negativity on the internet. This approach uses the BERT algorithm for sentiment analysis in English. All the text in other languages will be translated into English and their sentiment is then analyzed. In this approach, we use the BERT algorithm for sentiment analysis on translated English text. This approach works well because sentiment analysis using BERT gives higher accuracy and the translation of text from Indian languages is made easy by the advent of natural language processing. By combining both the above-discussed processes we can analyze the sentiment in multiple Indian languages.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128373551","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

A Modified Grey Wolf Optimizer algorithm for feature selection to predict heart diseases 一种改进的灰狼优化算法用于特征选择预测心脏病

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/180

S. K. Mohiddin, Susan Peteti, Tummala Swathi, Tambura Veera, Venkata Harshith, Vamshi Krishnamaneni, Vatluri Hanusha

{"title":"A Modified Grey Wolf Optimizer algorithm for feature selection to predict heart diseases","authors":"S. K. Mohiddin, Susan Peteti, Tummala Swathi, Tambura Veera, Venkata Harshith, Vamshi Krishnamaneni, Vatluri Hanusha","doi":"10.48047/ijfans/v11/i12/180","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/180","url":null,"abstract":"Globally, heart disease is a leading cause of illness and mortality. This impacts people from all around the world . Accurate prediction of the risk of heart disease is crucial for early detection and prevention.For this, large amounts of features/attributes need to be stored and analyzed to diagnose a patient. Storing many features can lead to substandard management of data. We need to store only the chief features. In this study, we proposed a modified grey wolf optimizer for feature selection. The resultant subset of features is then used to predict the risk of having a heart disease using machine learning model, Support Vector Machine (SVM). We compared the proposed algorithm with the existing GWO-SVM algorithm. We evaluated the effectiveness of the proposed algorithm using accuracy, sensitivity, and specificity metrics. Our results show that, using the modified grey wolf algorithm for feature selection and using SVM weobtained an accuracy of 95.82%, specificity of 94.64%, and sensitivity of 96.86%. The results show the proposed algorithm's capability for predicting the risk of heart disease and could contribute to the development of more accurate and efficient predictive models for heart disease risk","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133006493","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

Heart Disease Detection Using Machine Learning and Deep Learning 利用机器学习和深度学习进行心脏病检测

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/216

Mrs. B. Lalitha Rajeswari, M. Nandini, M. Venkata, Gopi Jayaram, P. Lokesh, P. D. Sri

引用次数: 0

A Siamese Network based Writer Independent Offline Signature Verification 基于暹罗网络的作家独立离线签名验证

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/204

P. R. K. Prasad, Kurri Renuka Reddy, Maddukuri Harshitha Sai, Lakshmisetty Thirumala Gopi, Kanneti Vedakshari

引用次数: 0

Paddy Crop Disease Detection Using Deep Learning 基于深度学习的水稻作物病害检测

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/219

Dr. T. Kameswara Rao, M. Nandini, N. Bharadwaj, P. Susmitha, N. Mounika

{"title":"Paddy Crop Disease Detection Using Deep Learning","authors":"Dr. T. Kameswara Rao, M. Nandini, N. Bharadwaj, P. Susmitha, N. Mounika","doi":"10.48047/ijfans/v11/i12/219","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/219","url":null,"abstract":"Agriculture plays a crucial role in human life, with approximately 60% of the population directly or indirectly involved in agricultural activities. Paddy is one of the essential food crops globally and is particularly significant in the Asian subcontinent. As a result of excessive use of chemicals and unpredictable weather patterns, there has been a significant increase in crop diseases. Sometimes an expert may be unavailable to identify the disease. Due to mistaken conclusions of experts, there is an unnecessary use of pesticides which will affect the yield badly, hence, it is essential to know which disease has affected the Paddy crop. Early detection of these diseases is essential to minimize the losses . To address this issue, Deep Learning models, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and ResNet101, were employed to detect three types of paddy crop diseases including Leaf Blast (LB), Brown Spot (BS), and Hispa along with the healthy category. The dataset consisted of 6,061 images of three types of disease affected and healthy paddy crops, collected from Paddy Doctor Website and IRRI. ANN Model achieved an accuracy of 66.1%, CNN Model 94.3% and ResNet101 Model 98.2%.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114866291","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

Drug Combination Therapy for Malaria using Deep Learning 基于深度学习的疟疾药物联合治疗

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/192

Siva Prasad Pinnamaneni, G. K. tej, D. Kartheek, B. Dayamani, Ch. Geya, A. Surendra

引用次数: 0

Detection of COVID-19 Using Deep Learning 利用深度学习检测COVID-19

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/174

Dr. N. Sri, M. R. Sri, N. S. Harshitha, M. VenkataNaga, Sai Kumar

{"title":"Detection of COVID-19 Using Deep Learning","authors":"Dr. N. Sri, M. R. Sri, N. S. Harshitha, M. VenkataNaga, Sai Kumar","doi":"10.48047/ijfans/v11/i12/174","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/174","url":null,"abstract":"The world health organization states that the coronavirus epidemic has created a daily threat to the global healthcare system. After numerous deaths around the world, the pandemic unlocked a new threat making people ready for something which is similar and unpredictable. There were many challenges including the shortage of medical staff, beds, diagnosis centres, and intensive care units. Correct detection of disease is also crucial in surviving the pandemic. So, with a growing need for accurate and rapid diagnosis, there are many alternatives that are derived to identify the disease with the help of Radiology and Computed Tomography (CT) scans. This paper proposes a deep-learning-based approach for the detection of COVID-19 from X-ray and CT-scan images and is based on Predefined CNN architectures such as DenseNet201 and ResNet152, which are fine-tuned to classify images as COVID-19 positive or negative. The results obtained demonstrate that the proposed methods achieve high accuracy in detecting COVID-19 cases from X-ray and CT scan images. Hence, this project can be used as a valuable tool for frontline healthcare workers and public health officials to fight against the COVID-19","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122111725","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

Multi Disease Detection Using Machine Learning 基于机器学习的多种疾病检测

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/176

Dr. N. Sri, P. Vanaja, M. A. Kumar, M.D.V.S. Akash, K. Sivaiah

引用次数: 1

AI Based Identification of Gender from Images Based on Facial Features using CNN and OPENCV 基于CNN和OPENCV的基于人脸特征的图像性别识别

International Journal of Food and Nutritional Sciences Pub Date : 2023-01-10 DOI: 10.48047/ijfans/issue4/001

{"title":"AI Based Identification of Gender from Images Based on Facial Features using CNN and OPENCV","authors":"","doi":"10.48047/ijfans/issue4/001","DOIUrl":"https://doi.org/10.48047/ijfans/issue4/001","url":null,"abstract":"The main objective of this paper is to classify the gender based on different facial features such as eyes, nose, mouth, overall features such as face contour, head shape, hair line etc. The gender classification algorithm uses machine learning technique (supervised learning). In this case the algorithm is trained on a set of male and female faces and then used to classify new data. In this paper, face detection and gender classification methods are combined. The face detection acts as a pre- processing operation to the gender classifier that determines the gender. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from a given image with faces within a database. It is also described as a Biometric Artificial Intelligence based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. Automated gender recognition plays an important role in many application areas such as human computer interaction, biometric, surveillance, demographic statistics etc. Existing systems has a disadvantage in accuracy. Though there are many algorithms in Present system are being developed and implemented to achieve accuracy in identifying gender the results arestill unsatisfactory. Proposed system has an advantage of accuracy. The accuracy achieved in this system is impressive compared to the existing system. CNN algorithm gives better accuracy compared to other algorithms","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122512601","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