International Journal of Next-Generation Computing最新文献_第8页

Analytics of Epidemiological Data using Machine Learning Models 使用机器学习模型分析流行病学数据

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International Journal of Next-Generation Computing Pub Date : 2023-02-15 DOI: 10.47164/ijngc.v14i1.1014

Harshita Barapatre, Jatin Jangir, Sudhanshu Bajpai, Bhavesh Chawla, Gunjan Keswani

{"title":"Analytics of Epidemiological Data using Machine Learning Models","authors":"Harshita Barapatre, Jatin Jangir, Sudhanshu Bajpai, Bhavesh Chawla, Gunjan Keswani","doi":"10.47164/ijngc.v14i1.1014","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1014","url":null,"abstract":"Epidemiological data is the data obtained based on disease, injury or environmental hazard occurrence using the previous data on the epidemic situation. We can use it for analysis and find the trends and patterns. We can use different machine learning models to create a platform that can be used for different time series data. We can rely on the properties of time series data like trends and seasonality and use this for future prediction. Acquiring the dataset is the first step in data preprocessing in machine learning. We have collected the dataset from ourWorldIndia website which is a real-life dataset of covid-19. This paper presents the idea of a dedicated machine learning model to forecast the future using epidemiological data. We have taken a data-set of covid-19 for the prediction of the number of daily cases infected by the coronavirus. Our machine learning model can be applied on the dataset of any country in the world. We have applied it on the dataset of India in the experimentation. Our goal behind this research paper is to give the ML model which can be easily used on any epidemiological data for prediction by analysing the seasonality.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"57 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83367070","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

Outage Probability and Capacity Analysis for NOMA based 5G and B5G Cellular Communication 基于NOMA的5G和B5G蜂窝通信的中断概率和容量分析

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.912

Aditi Agrawal, Ishant Kohad, Mrunmayi Kinhikar, Dolly Tiwari, Prasheel Thakre, Sanjay Pokle

引用次数: 1

Machine Learning For Non- Invasive Diagnostics Of Glucose Metabolism Disorder 机器学习用于糖代谢紊乱的无创诊断

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.968

Suruchi Dive, Gopal Sakarkar

引用次数: 0

Optimal power allocation for NOMA-based Internet of things over OFDM sub bands OFDM子频带上基于noma的物联网最优功率分配

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.909

Prasheel Thakre, Sanjay Pokle

{"title":"Optimal power allocation for NOMA-based Internet of things over OFDM sub bands","authors":"Prasheel Thakre, Sanjay Pokle","doi":"10.47164/ijngc.v13i5.909","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.909","url":null,"abstract":"As a result of continued expansion of 5G technology, the density of IoT devices has increased dramatically.Increasing the throughput of 5G systems is now extremely important. Non-orthogonal multiple access technologiesand Ultra-dense networks have lately attracted a lot of attention in the context of Internet of Things networksbecause to their capacity to multiplex from the space domain and power domain. In order to boost systemthroughput, this article integrates non-orthogonal multiple access technology with ultra-dense network technology,taking into consideration orthogonal frequency division multiplexing non-orthogonal multiple access-based ultradensenetworks with several base stations. The network model and the channel model were created first. As aresult, under the condition of total power, the downlink transmission rate maximization problem is formulated.Then, the problem is divided into two subproblems to solve: device grouping and sub-band power distributionand built the best power allocation strategies by using convex optimization theory to these subproblems. Finally,numerical simulations are undertaken to validate the efficiency of proposed optimal downlink power distributionapproach and the total throughput of the system has substantially enhanced as compared to orthogonal Multipleaccess.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"17 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80177286","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

Plant leaves disease detection using Image Processing and Machine learning techniques 植物叶片病害检测使用图像处理和机器学习技术

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.926

Pratibha Kokardekar, Aman Shah, Arjun Thakur, Prachi Shahu, Rohan Raggad, Sudhanshu Keshaowar, Vineet Pashine

引用次数: 1

A Covid Outbreak Prediction using Machine Learning 使用机器学习的Covid爆发预测

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.925

Sakshi Saklani, Ashish Chandak, Purshottam J Assudani, Amrusha Rahangdale, Achal Loya

引用次数: 1

Feature Selection for Ranking using Heuristics based Learning to Rank using Machine Learning 使用启发式学习进行排序的特征选择使用机器学习进行排序

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.958

Sushilkumar Chavhan, Dr. R. C. Dharmik

{"title":"Feature Selection for Ranking using Heuristics based Learning to Rank using Machine Learning","authors":"Sushilkumar Chavhan, Dr. R. C. Dharmik","doi":"10.47164/ijngc.v13i5.958","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.958","url":null,"abstract":"Machine Learning based ranking is done every filed. Ranking is also solved by using (LTR i. e. learning to Rank)techniques. In this work, we propose a Heuristics LTR based models for information retrieval. Different newalgorithms are tackling the problem feature selection in ranking. In this proposed model try to makes use of thesimulated annealing and Principal Component analysis for document retrieval using learning to rank. A use ofsimulated annealing heuristics method used for the feature Selection to test the results improvement. The featureextraction technique helps to find the minimal subsets of features for better results. The core idea of the proposedframework is to make use of k-fold cross validation of training queries in the SA as well as the training queriesin the any feature selection method to extract features and only using training quires make use of validationand test quires to create a learning model with LTR. The standard evaluation measures are used to verify thesignificant improvement in the proposed model. Performance of proposed model are measured based on predictionon some selected benchmark datasets, Improvement in the results are compare on recent high performed pairwisealgorithms.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"22 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83954806","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 Model for Rainfall Forecasting using Distinct Machine Learning Algorithm 基于独特机器学习算法的降雨预报模型

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.949

Sachin Upadhye, Lalit Agrawal

{"title":"A Model for Rainfall Forecasting using Distinct Machine Learning Algorithm","authors":"Sachin Upadhye, Lalit Agrawal","doi":"10.47164/ijngc.v13i5.949","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.949","url":null,"abstract":"As Agriculture is the pivotal point of survival, rainfall is the important source of its cultivation. Rainfall prophecy has always been a major problem as a prophecy of downfall gives awareness to people and to know in advance about rain to take necessary precautions to cover their crops from rain. A particular dataset is taken from the Kaggle community and this design predicts whether it will rain henceforth or not by using the rainfall in the dataset. Cat Boost model is executed in this design as it’s an open-sourced machine knowledge algorithm, and features great quality without parameter tuning, categorical point support, bettered delicacy, and fast prophecy. Cat Boost model is a Grade boosting toolkit and two critical algorithms classical and innovative are introduced to produce a fight in prophecy shift present in presently being prosecutions of grade boosting algorithms. Cat Boostperformed truly well giving an AUC (Area under wind) score0.8 and a ROC (Receiver operating characteristic wind) score of 89. ROC is called an assessing wind whereas AUC presents a degree or measure of separability as the model is professed enough to distinguish between classes. An Exploratory data analysis is done to examine data distribution, and outliers and provides tools for imaging and understanding the data through graphical representation.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"15 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82428858","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

An Effective Framework for design of Dataset Using Twitter 利用Twitter设计数据集的有效框架

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.939

Monal R.Torney, Dr.K.H.Walse, Dr.V.M.Thakare

{"title":"An Effective Framework for design of Dataset Using Twitter","authors":"Monal R.Torney, Dr.K.H.Walse, Dr.V.M.Thakare","doi":"10.47164/ijngc.v13i5.939","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.939","url":null,"abstract":"The rapid expansion of internet usage and related services like social media and blogs has increased people's level of expressiveness in day-to-day life. Social media platforms like Twitter and Facebook facilitate people to interact and exchange opinions about people, products, and services. As a result, a vast amount of data is available online in the form of views, tweets, messages, audio, and videos. An interface is needed to collect knowledge and insights from the various tweets, ideas, and comments. Thus we have proposed the Twitter API-based Interface, able to perform Hashtag searches and extract tweets from Twitter along with the ample number of fields related to the Twitter object. Using the interface, the 55 properties of each tweet are collected and used for further investigations. The python-based library called Tweepy is used to interact with the Twitter API. Due to the availability of real-worlddata, various issues related to text analysis can be addressed. The problems such as Sentiment Analysis, Opinion Mining, Implicit and Explicit detection, genuineness of views, and Opinion Spam detection can be addressed using the dataset availability.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"45 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78627531","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

Identification and Evaluation of Factors Influencing Software Quality using Pythagorean Fuzzy DEMATEL Approach 应用毕达哥拉斯模糊DEMATEL方法识别和评价影响软件质量的因素

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.938

Mangesh Joshi, R. Mankar, Himanshu M. Shukla

引用次数: 0