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International Journal of Food and Nutritional Sciences最新文献

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Diagnosis and Grading of Diabetic Retinopathy using Deep Learning 基于深度学习的糖尿病视网膜病变的诊断和分级
International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/196
Mr. Shaik, Wasim Akram, K. V. Sathya, Sai Sri, Lekha Likitha, M. V. Suchitra, M. Manoj, G. D. Naga, Adithya Chowdary
{"title":"Diagnosis and Grading of Diabetic Retinopathy using Deep Learning","authors":"Mr. Shaik, Wasim Akram, K. V. Sathya, Sai Sri, Lekha Likitha, M. V. Suchitra, M. Manoj, G. D. Naga, Adithya Chowdary","doi":"10.48047/ijfans/v11/i12/196","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/196","url":null,"abstract":"Diabetic retinopathy (DR), which causes tissue on the eye that damages visibility, is a common complication of type-2 diabetes. If it is not discovered in time, total blindness might occur. DR is irreversible. DR is primarily among adults who are of working age. More than 150 million people are affected by diabetic retinopathy (DR), which accounts for 2.6% of blindness worldwide. Different indications of DR are vision distortion, bulging of the eye, and formation of irregular blood vessels. The traditional way is to use Computer-aided Diagnosis (CAD) systems during treatment. The dataset used is the APTOS blindness detection dataset that is accessible in Kaggle. The Convolutional Neural Networks (CNN) is the most effective way for classifying images. In this paper, the MobileNet architecture, a deep learning technique is utilized to automate the diagnosis of the disease and estimate the severity of the eye into several stages through which the accuracy obtained for training is 95% and validation is 82%.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"25 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":"122748520","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
Detection of Diabetic Retinopathy 糖尿病视网膜病变的检测
International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/181
Dr.P.Srinivas Rao, N.Sowmya, K.Naga Vasu, M. Sri, Naga Venkata Rupkanth, K.Siva, Naga Sirisha
{"title":"Detection of Diabetic Retinopathy","authors":"Dr.P.Srinivas Rao, N.Sowmya, K.Naga Vasu, M. Sri, Naga Venkata Rupkanth, K.Siva, Naga Sirisha","doi":"10.48047/ijfans/v11/i12/181","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/181","url":null,"abstract":".","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"18 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":"125587915","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: 7
Smart Grid Asset Monitoring and Management through Blockchain Technology 基于区块链技术的智能电网资产监控与管理
International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/195
Mr. J M Babu, Sk. Mohammad Waseem, Y. Divya, S. Sankeerthana, Y. Jahnavi
{"title":"Smart Grid Asset Monitoring and Management through Blockchain Technology","authors":"Mr. J M Babu, Sk. Mohammad Waseem, Y. Divya, S. Sankeerthana, Y. Jahnavi","doi":"10.48047/ijfans/v11/i12/195","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/195","url":null,"abstract":"1842","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"220 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":"130017648","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
Machine Learning Algorithm for Brain Stroke Detection 脑卒中检测的机器学习算法
International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/193
M. K. Babu, Sk. Reshma, V. B. Reddy, U. Jayanth, Sk. Naheda
{"title":"Machine Learning Algorithm for Brain Stroke Detection","authors":"M. K. Babu, Sk. Reshma, V. B. Reddy, U. Jayanth, Sk. Naheda","doi":"10.48047/ijfans/v11/i12/193","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/193","url":null,"abstract":"Insufficient blood flow to the brain results in a condition known as a stroke, which results in cell death. In worldwide it is currently the leading cause of death. Many risk factors that are suspected to be related to the stroke's origin have been identified through examination of the affected individuals. Using these risk factors, numerous research has been done to predict the disorders linked to stroke. Most models are built using machine learning techniques and data mining. In this study, we used data from medical reports and a person's physical condition to use five machine learning algorithms to identify strokes. We use a substantial number of hospital entries that we have collected. The classification outcome demonstrates that the result is satisfactory and can be applied to real-time medical records in order to address the issues. We think machine learning algorithms can aid in better understanding illnesses and make a useful healthcare partner.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"90 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":"124882777","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
Malicious URL Detection Using Machine Learning 使用机器学习的恶意URL检测
International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/218
Koteswara Rao Velpula, Kataru Gayathri Priya, Kushwanth Kumar Jammula, Krishna Sruthi Velaga, Praveen Kumar Kongara
{"title":"Malicious URL Detection Using Machine Learning","authors":"Koteswara Rao Velpula, Kataru Gayathri Priya, Kushwanth Kumar Jammula, Krishna Sruthi Velaga, Praveen Kumar Kongara","doi":"10.48047/ijfans/v11/i12/218","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/218","url":null,"abstract":"Currently, the risk of network information insecurity is increasing rapidly in number and level of danger. The methods mostly used by hackers today is to attack end-to-end technology and exploit human vulnerabilities. These techniques include social engineering, phishing, pharming, etc. One of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs). As a results, malicious URL detection is of great interest nowadays. There have been several scientific studies showing a number of methods to detect malicious URLs based on machine learning and deep learning techniques. In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behaviors and attributes.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"5 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":"126633012","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: 16
Intrusion Detection Using an Ensemble Deep Learning Approach 基于集成深度学习方法的入侵检测
International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/198
Dr. V. Ramachandran, Yamparala Anuhya, Vutukuri Venkata Lakshmi, Raga Pravallika, Vamsi Makke, Vasireddy Venkata, Leela Sai Srikar
{"title":"Intrusion Detection Using an Ensemble Deep Learning Approach","authors":"Dr. V. Ramachandran, Yamparala Anuhya, Vutukuri Venkata Lakshmi, Raga Pravallika, Vamsi Makke, Vasireddy Venkata, Leela Sai Srikar","doi":"10.48047/ijfans/v11/i12/198","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/198","url":null,"abstract":"Today's cyber society faces a serious intrusion detection security issue. Recent years have seen a sharp rise in network intrusion attacks, raising severe privacy and security concerns. The complexity of cyber-security threats is increasing due to technological improvement, making it impossible for the current detection methods to handle the problem. So, creating an intelligent and efficient network intrusion detection system would be crucial to resolving this problem. In this paper, we created an intelligent intrusion detection system that can detect different networking attacks using deep learning approaches, specifically Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN). We used an ensemble model of CNN and DNN which provides us with great accuracy. Before being used for model training and testing, the obtained data is analysed and pre-processed. Also, in order to choose the optimum model for the network intrusion detection system, we compared the outcomes of our proposed solution and evaluated the performance of the proposed solution using several evaluation matrices.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"46 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":"130364793","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
Alzheimers Disease Detection Using Cnn And Vision Transformation 基于Cnn和视觉变换的阿尔茨海默病检测
International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/211
Dr.P. Jeevana Jyothi, G. N. Sri, Ch. Annie Anuhya, B. Akhila, B. Bhanu
{"title":"Alzheimers Disease Detection Using Cnn And Vision Transformation","authors":"Dr.P. Jeevana Jyothi, G. N. Sri, Ch. Annie Anuhya, B. Akhila, B. Bhanu","doi":"10.48047/ijfans/v11/i12/211","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/211","url":null,"abstract":"Alzheimer’s disease is a brain related issue which effects the mental stability of a person. It degrades the thinking capability and targets the memory of a person.The person who is effected with the Alzheimer’s finds difficult to even remember simple daily things[8].Even the very recent or latest event is also difficult for them to remember or keep track of.TheAlzheimer’s disease is challenging one because there is no treatment for the disease. This disease is currently ranked as the seventh leading cause of death in the United States among older adults.There is no permanent cure or treatment for this. Thus, if the disease is predicted earlier, the progression or the symptoms of the disease can be slow down. In this paper we intend to create a model that detects Alzheimer disease using GAN and CNN.GAN can be adopted to fulfil the role of data augmentation. GANs are generative models: they create new data instances that resemble your training data. Classification process can be fulfilled by using the CNN model to the data for improving the efficiency and to ensure higher accuracy.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"67 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":"122401423","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 Systematic Approach to Detect Parkinson’s Disease using Traditional and Ensemble Machine Learning Techniques 使用传统和集成机器学习技术检测帕金森病的系统方法
International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/217
Dr. V. Rama Chandran, G. Hemanth, E. Jahnavi, A. Vasundhara, B. R. Kumar
{"title":"A Systematic Approach to Detect Parkinson’s Disease using Traditional and Ensemble Machine Learning Techniques","authors":"Dr. V. Rama Chandran, G. Hemanth, E. Jahnavi, A. Vasundhara, B. R. Kumar","doi":"10.48047/ijfans/v11/i12/217","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/217","url":null,"abstract":"Parkinson's disease (PD) is a neurodegenerative condition that worsens with time and affects both the neurological system and body components under the control of the nervous system. This condition causes slow movements, tremors, balance problems, and more. Currently, we have no proper cure or treatment available, but it can sometimes be cured with medication if it is diagnosed in its initial stages. Voice deterioration is also a common symptom, which often presents in the initial stages of the disease. As a result, the project 'A Systematic Approach to Detect Parkinson’s Disease Using Traditional and Ensemble Machine Learning Techniques' is used to detect PD using voice data. In order to create a model that is capable of accurately identifying the disease's existence in a person's body, this project makes use of a variety of machine learning techniques, ensemble learning approaches, and Python libraries. This work aims to compare various machine learning models in the successful prediction of PD and develop an effective and accurate model to help detect the disease at an earlier stage, which could help doctors assist in the cure and recovery of PD patients. This project showed 97% efficiency. For this purpose, we plan to use the Parkinson’s disease dataset in [5] , which is acquired from the UCIML repository.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"65 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":"115996734","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
Brain Tumor Detection Using Deep Learning 利用深度学习进行脑肿瘤检测
International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/197
Chirag Malik, Saad Rehman, S. Sushanth Kumar
{"title":"Brain Tumor Detection Using Deep Learning","authors":"Chirag Malik, Saad Rehman, S. Sushanth Kumar","doi":"10.48047/ijfans/v11/i12/197","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/197","url":null,"abstract":"Abstract: The Brain Tumor Detection using Deep Learning project is aimed at developing a deep learning based system that can accurately detect brain tumors from medical images such as MRI scans. The proposed system will use convolutional neural networks (CNNs) to analyze the medical images and output the probability of the presence of a tumor, along with its location, size, and type.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"88 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":"117278090","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: 2
Detection of Supra Ventricular arrhythmia using LSTM, BI-LSTM & GRU LSTM、BI-LSTM及GRU检测室上性心律失常
International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/199
D. N. L. Prasanna, T. S. Rekha, S. Vineela, V. Meenakshi, S. Veena
{"title":"Detection of Supra Ventricular arrhythmia using LSTM, BI-LSTM & GRU","authors":"D. N. L. Prasanna, T. S. Rekha, S. Vineela, V. Meenakshi, S. Veena","doi":"10.48047/ijfans/v11/i12/199","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/199","url":null,"abstract":"Deep learning techniques have made early strides in the analysis about complex ECG signals, particularly in the classification about heartbeats & the detection about arrhythmias. Nonetheless, there is still more work toward be done in terms about the analysis about health-related data. This study offers dual structured & bidirectional approaches for classifying arrhythmias that deal with the drawbacks about multilayered dilated models. The data is first preprocessed using the quicker Chebyshev Type II filtering method, which does not make use about statistical properties. Using the Daubechies wavelet, which may resolve fractal issues & signal discontinuities, noise from the preprocessed filter is additionally eliminated. In this paper, the proposed models LSTM, BI-LSTM, & GRU were employed toward provide fusion features. The signals are categorized by fully connected layer before. The suggested model is trained & validated using the dataset for supra-ventricular","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"56 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":"134041535","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
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