International Journal of Food and Nutritional Sciences最新文献_第8页

An Approach for Early Prediction of Diabetes using Firefly Optimization Algorithm 基于萤火虫优化算法的糖尿病早期预测方法

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/183

S. K. Mohiddin, Sk. Heena Kousar, V. S. Krishna, S. Anupriya

{"title":"An Approach for Early Prediction of Diabetes using Firefly Optimization Algorithm","authors":"S. K. Mohiddin, Sk. Heena Kousar, V. S. Krishna, S. Anupriya","doi":"10.48047/ijfans/v11/i12/183","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/183","url":null,"abstract":"The prediction of diabetes is a challenging task due to the complex and multifactorial nature of the disease. In recent years, machinelearning algorithms have been applied to predict the onset of diabetes using various sets of predictors, such as demographic, clinical, and laboratory data. In this study, we propose a firefly algorithm to identify diabetes and compare its performance with other algorithms. We evaluate the performance of the firefly algorithm using four widemetrics for evaluation:accuracy, precision, recall, and F-score. Our experiments were conducted on a real-world dataset consisting of 768 individuals, of which 268 had diabetes. The training and testing sets were randomly divided into two groups with an 80:20 ratio. We performed the firefly algorithm for feature selection. It is one of the Nature-Inspired Algorithms (NIA). It is used to optimize the parameters using the firefly algorithm. Then the optimized parameters were then used to train the firefly algorithm on the entire training set.The experimental results demonstrate that the firefly algorithm achieves competitive performance compared to other machine learning algorithms in terms of precision, accuracy, F-score, and recall, the firefly method outperforms other algorithms.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"22 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":"121992698","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: 1

Predicting Fake Job Posts with a Voting Classifier of Multiple Classification Models 基于多分类模型的投票分类器预测虚假职位

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/202

Ch.Vijayananda Ratnam, B.Nithya, Kranthi Sri, D.Dhanwanth Sai, A.Preetham Paul, Ch.Leela Aditya

引用次数: 0

Speech Emotion Recognition 语音情感识别

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/203

Mr. M China, Pentu Saheb, P. S. Srujana, P. Lalitha, Siva Jyothi

引用次数: 0

Semantic Segmentation and Augmentation of salt in seismic images using Deep Learning 基于深度学习的地震图像中盐的语义分割与增强

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/215

Ch.Vijayananda Ratnam, G.Meghana, D.Sri Lekha, Ch.Sai Harsha, A.Yasaswini

引用次数: 0

Plane Delay Simulator 飞机延迟模拟器

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/206

Dr. G. Sanjay Gandhi, Bolla Yasaswi, G. A. Devi, Gangineni Dinesh, Doredla Rakesh

引用次数: 0

A Comparative Analysis of Convolutional Neural Networkbased Transfer Learning Models for Plant Disease Detection 基于卷积神经网络的植物病害检测迁移学习模型比较分析

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/201

引用次数: 0

Human Personality Prediction by Text Analysis Using CNN 基于CNN的文本分析预测人类性格

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/205

引用次数: 0

Ovarain Cancer Prediction in Early Stage Using Machine Learning Approaches 使用机器学习方法进行早期卵巢癌预测

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/186

Dr. K. Lohitha Lakshmi, P. H. Chandana, P. H. Sri, N. N. Kumar, N. Hemanth

{"title":"Ovarain Cancer Prediction in Early Stage Using Machine Learning Approaches","authors":"Dr. K. Lohitha Lakshmi, P. H. Chandana, P. H. Sri, N. N. Kumar, N. Hemanth","doi":"10.48047/ijfans/v11/i12/186","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/186","url":null,"abstract":"Ovarian cancer is a disorder of ovarian cell growth that is triggered by series of acquired mutations affecting a single cell or its clonal progeny. It is purposeless prey on host and virtually autonomous. It is usually diagnosed at a late stage because of poor sensitivity of screening test. There are still no effective cures for this illness. Still early detection might lower the mortality rate. Our project's major goal is to conduct predictive analytics for early detection by using machine learning models and statistical techniques on clinical data collected from specific patients. Mutual information testing is crucial in statistical analysis for identifying indicative biomarkers. A collection of machine learning models, such as the Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM), and Light Gradient Boosting Machine (LGBM)are utilized in the classificationof ovarian tumors as benign or malignant. By using proposed system, it can significantly identify the class of benign and malignant patients. The data collected is analyzed and pre-processed before it is used for model training and testing.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"353 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":"120838345","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: 1

A Systematic Approach to Detect Spliced and Forged Images using Deep Learning Technique 一种利用深度学习技术检测拼接和伪造图像的系统方法

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/191

N. B. Naidu, E. Harish, G. V. Kumar, B. Sai, Ravi Teja, B.Naveen

引用次数: 0

Object Detection and Screen Presence Time Estimation Using Opencv and Yolo Alogrithm 基于Opencv和Yolo算法的目标检测和屏幕存在时间估计

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/190

K. M. Krishna, K. A. Chowdari, J. Anusha, S. L. Sravani, Sreya Chowdary

{"title":"Object Detection and Screen Presence Time Estimation Using Opencv and Yolo Alogrithm","authors":"K. M. Krishna, K. A. Chowdari, J. Anusha, S. L. Sravani, Sreya Chowdary","doi":"10.48047/ijfans/v11/i12/190","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/190","url":null,"abstract":"This paper describes about the object detection and object’s screen presence time estimating model which will run on any computer easily. Currently there are only models that will only detect the motion of the object alongside estimating screen time. But here we tried to propose a model that will detect the object that is present in the camera view and also estimate the amount of time the object is present in the camera view. For object detection there are many algorithms such as CNN (Convolutional Neural Network), R-CNN, Fast R-CNN, Faster R-CNN, SSD (Single Shot Detector), YOLO (You Only Look Once) etc. In the current model we gave preference to the speed of detecting the object along with the accuracy. So, we preferred using the Yolo algorithm among all the existing algorithms that can be used for the object detection. But yolo is only designed for using in the GPU based computers. So, in order to implement yolo algorithm in our normal CPU based computers we used OpenCV library such that real time object detection is also possible in Non-GPU computers. Our model estimates the time of screen presence of each object using python libraries such as pandas, time etc. As we used yolo as our object detection algorithm our model detects objects with 80-99% confidence.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"11 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":"116652745","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