International Journal of Data Science and Analysis最新文献

Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification 用于胸部 X 光片分类的增强型深度卷积神经网络

International Journal of Data Science and Analysis Pub Date : 2024-03-19 DOI: 10.11648/j.ijdsa.20241001.12

Hannah Kariuki, Samuel Mwalili, Anthony Waititu

{"title":"Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification","authors":"Hannah Kariuki, Samuel Mwalili, Anthony Waititu","doi":"10.11648/j.ijdsa.20241001.12","DOIUrl":"https://doi.org/10.11648/j.ijdsa.20241001.12","url":null,"abstract":"The recent release of large amounts of Chest radiographs (CXR) has prompted the research of automated analysis of Chest X-rays to improve health care services. DCNNs are well suited for image classification because they can learn to extract features from images that are relevant to the task at hand. However, class imbalance is a common problem in chest X-ray imaging, where the number of samples for some disease category is much lower than the number of samples in other categories. This can occur as a result of rarity of some diseases being studied or the fact that only a subset of patients with a particular disease may undergo imaging. Class imbalance can make it difficult for Deep Convolutional Neural networks (DCNNs) to learn and make accurate predictions on the minority classes. Obtaining more data for minority groups is not feasible in medical research. Therefore, there is a need for a suitable method that can address class imbalance. To address class imbalance in DCNNs, this study proposes, Deep Convolutional Neural Networks with Augmentation. The results show that data augmentation can be applied to imbalanced dataset to increase the representation of the minority class by generating new images that are a slight variation of the original CXR images. This study further evaluates identifiability and consistency of the proposed model.\u0000","PeriodicalId":181499,"journal":{"name":"International Journal of Data Science and Analysis","volume":"56 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140230916","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

Bayesian Spatio-Temporal Models for the Incidence of Malaria Using Time Dependent Covariates 使用时间相关变量的疟疾发病率贝叶斯时空模型

International Journal of Data Science and Analysis Pub Date : 2023-11-11 DOI: 10.11648/j.ijdsa.20230903.12

Evalyne Nduvi Musyoka, Samuel Mwalili, Boniface Malenje

引用次数: 0

Copula Analysis of Dependencies Between Extreme Exchange Rates and NSE20 Price Index 极端汇率与 NSE20 价格指数之间的相关性 Copula 分析

International Journal of Data Science and Analysis Pub Date : 2023-11-01 DOI: 10.11648/j.ijdsa.20230903.11

Rosemary Wanjiru Ng’ethe, Thomas Mageto, Joseph Mungatu

引用次数: 0

Bayesian Kernel Machine Analysis of Communicable Diseases Distribution in Machakos County, Kenya 肯尼亚马查科斯县传染病分布的贝叶斯核机分析

International Journal of Data Science and Analysis Pub Date : 2023-10-31 DOI: 10.11648/j.ijdsa.20230902.13

Cecilia Mbithe Titus, A. Wanjoya, Thomas Mageto

引用次数: 0

Forecasting Stock Prices Using Heston-Artificial Neural Network Model

使用 Heston-Artificial Neural Network 模型预测股票价格

International Journal of Data Science and Analysis Pub Date : 2023-10-28 DOI: 10.11648/j.ijdsa.20230902.11

Ann Maina, Samuel Mwalili, Bonface Malenje

引用次数: 0

Modeling and Forecasting the Domestic Retail Price of Teff in Ethiopia 埃塞俄比亚 Teff 国内零售价的建模和预测

International Journal of Data Science and Analysis Pub Date : 2023-10-28 DOI: 10.11648/j.ijdsa.20230902.12

Sisay Yohannes Gagabo

引用次数: 0

Detection and Estimation of Change Point in Volatility Function of Foreign Exchange Rate Returns 外汇收益率波动函数变化点的检测与估计

International Journal of Data Science and Analysis Pub Date : 2023-04-06 DOI: 10.11648/j.ijdsa.20230901.11

Josephine Njeri Ngure, Anthony Gichuhi Waititu, Simon Maina Mundia

引用次数: 0

Racial Filtering Classification Model Through Data Analysis of Racial Contents in Twitter 基于Twitter种族内容数据分析的种族过滤分类模型

International Journal of Data Science and Analysis Pub Date : 2021-11-10 DOI: 10.11648/J.IJDSA.20210706.11

Jung-hun Baeck, Teresa Hyoju Chang, Jaden Chunho Chyu, Bryan Chunwoo Chyu, Chaehyun Lim

{"title":"Racial Filtering Classification Model Through Data Analysis of Racial Contents in Twitter","authors":"Jung-hun Baeck, Teresa Hyoju Chang, Jaden Chunho Chyu, Bryan Chunwoo Chyu, Chaehyun Lim","doi":"10.11648/J.IJDSA.20210706.11","DOIUrl":"https://doi.org/10.11648/J.IJDSA.20210706.11","url":null,"abstract":"Stop Asian Hate or Stop Asian American Pacific Islanders (AAPI) Hate refers to the national movement against racially-motivated attacks on Asians. This protest was initiated in line with the Black Lives Matter (BLM) movement, to dismantle the ongoing hate and targeted crimes against Asians, and to educate people of such threats. Hate crimes targeting Asians have been occurring steadily across the U.S, but with the effect of COVID-19, these crimes started increasing in number. For the Stop Asian Hate movement, the matter was exacerbated with people accusing certain Asian countries as the source for COVID-19. In 2021, Asian Americans reported a single biggest increase in serious incidents of online hate and harassment with racist and xenophobic slurs blaming people of Asian descent for the spread of COVID-19. To specifically assess the impacts and measures of each movement, research was conducted to examine the racial slurs used towards Asians on social media, specifically Twitter. For analysis of the data on social media, Python programming was used to collect and analyze the ratio of racial slurs and Anti-Asian hate. In doing so, the data set was modeled through data labeling, which classified each social media tweet into one of three sub-categories. Data were classified into two types: type 1 that contains racial contents or information against Asians and type 0 that has non-racial contents. The data collection was done through Twint, a Python scraping tool for Twitter, gathering a total of over 2,000 recent tweets for keywords relevant to the movement. Then, a preprocessing step was taken through Python, involving the process of decapitalizing, lemmatizing, and tokenizing. These data were then represented by graphs and word clouds, displaying some of the most commonly used terms targeting Asians on social media. Lastly, the data went through a design of a binary classification model for filtering tweets with racial content. We compared the accuracy of classification models with three different algorithms: logistic regression, random forest, and SVM. The model created would be able to safeguard users from exposures to racist terms vastly pervaded on the internet.","PeriodicalId":181499,"journal":{"name":"International Journal of Data Science and Analysis","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127207318","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

Seismic Detection Model Using Machine Learning to Protect the Public from Landslide and Earthquake Disasters in Kenya 使用机器学习的地震检测模型保护肯尼亚公众免受滑坡和地震灾害的影响

International Journal of Data Science and Analysis Pub Date : 2021-10-15 DOI: 10.11648/J.IJDSA.20210705.11

Moses Kung'u Githu, E. Kagereki, Serah Munyua

{"title":"Seismic Detection Model Using Machine Learning to Protect the Public from Landslide and Earthquake Disasters in Kenya","authors":"Moses Kung'u Githu, E. Kagereki, Serah Munyua","doi":"10.11648/J.IJDSA.20210705.11","DOIUrl":"https://doi.org/10.11648/J.IJDSA.20210705.11","url":null,"abstract":"Earthquakes and tremors are a common occurrence throughout the world, mostly in China, Japan and Indonesia. In Kenya, we experience a lot of tremors and landslides during the rainy seasons that have extensive negative social, economic, and environmental impacts. These damages include loss of human life, financial loss and destruction of infrastructure. This becomes a lagging factor towards achieving the Vision 2030 and Sustainable Development Goals (SDGs). This study used secondary data, obtained from World Wide Standardized Seismograph Station (WWSSSN) in Kilimambogo. Stochastic artificial neural network was adopted to identify prone areas to the said natural disasters, measure the socioeconomic impacts and build a predictive model for landslides, tremor and earthquakes in Kenya. It was evident that landslides are destructive in nature through observable measurable impacts on people. They increase the social and economic burden on the affected people. 64.76% of the measurable impacts affect human beings directly while the rest affect cattle and crops. Along the Great rift valley, most earthquakes and landslides took place. This is attributed to the active seismic activities. Kenya experiences earthquakes of magnitude m < 4. Our model achieved root mean square of 0.435. Furthermore, we got R2=0.80 for testing dataset. This implied that 80% of data was trainable by the model. Therefore, the predictive neural network model is efficient and accurate in forecasting, and more importantly is a good fit model.","PeriodicalId":181499,"journal":{"name":"International Journal of Data Science and Analysis","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129127240","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 Logical Clock Based Discovery of Patterns 基于逻辑时钟的模式发现

International Journal of Data Science and Analysis Pub Date : 2021-08-11 DOI: 10.11648/J.IJDSA.20210704.11

Friedemann Schwenkreis

{"title":"A Logical Clock Based Discovery of Patterns","authors":"Friedemann Schwenkreis","doi":"10.11648/J.IJDSA.20210704.11","DOIUrl":"https://doi.org/10.11648/J.IJDSA.20210704.11","url":null,"abstract":"This paper focusses on aspects of applied data mining in the context of team handball. It presents an approach to transform the collected data of team handball matches into formats that allow the use of classification and methods to search for association rules. To be able to search for patterns at arbitrary times of matches a concept of a logical clock is introduced, which becomes an essential part of the data preparation. The applied data mining methods are described in detail using RapidMiner processes and their settings. However, the approach is independent of the used data mining tool. Based on the results of the data mining processes, the applicability of data mining techniques in the given context will be discussed. Particularly it will be shown that rule-based results have significant advantages compared to approaches using support vector machines in the given context. The results are also compared based on the logical clock which will show how patterns evolve over time in case of team handball. We will show that the overall prediction accuracy of a model is not the primary concern in the chosen application area. It is rather to discover rules which clearly help to identify the need for action. The concept of time is crucial in this context because rules are less helpful if they are detected when the game is over, and we are at the end of a slippery slope which could have been prevented long before.","PeriodicalId":181499,"journal":{"name":"International Journal of Data Science and Analysis","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128124088","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