Annals of Data Science最新文献_第2页

Transmuted Shifted Lindley Distribution: Characterizations, Classical and Bayesian Estimation with Applications 变换的移位林德利分布：特征、经典和贝叶斯估计及其应用

Annals of Data Science Pub Date : 2024-07-16 DOI: 10.1007/s40745-024-00562-z

A. Chakraborty, S. Rana, S. I. Maiti

引用次数: 0

Apple Leaf Disease Detection Using Transfer Learning

Annals of Data Science Pub Date : 2024-07-13 DOI: 10.1007/s40745-024-00555-y

Ozair Ahmad Wani, Umer Zahoor, Syed Zubair Ahmad Shah, Rijwan Khan

引用次数: 0

A Review of Anonymization Algorithms and Methods in Big Data 大数据中的匿名算法和方法综述

Annals of Data Science Pub Date : 2024-07-13 DOI: 10.1007/s40745-024-00557-w

Elham Shamsinejad, Touraj Banirostam, Mir Mohsen Pedram, Amir Masoud Rahmani

{"title":"A Review of Anonymization Algorithms and Methods in Big Data","authors":"Elham Shamsinejad, Touraj Banirostam, Mir Mohsen Pedram, Amir Masoud Rahmani","doi":"10.1007/s40745-024-00557-w","DOIUrl":"10.1007/s40745-024-00557-w","url":null,"abstract":"<div><p>In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution to this challenge. In this study, we examined various data anonymization methods, including differential privacy, advanced encryption, and strong access controls. In addition, the operation, advantages, disadvantages, and use of these methods, the challenges of adapting these methods to big data, and possible solutions for them were also examined. Our results show that traditional data anonymization methods lack scalability, leading to privacy breaches and data loss. When faced with large volumes of data, these methods may not be able to fully process the data. Also, these methods may be ineffective against re-identification attacks, linkage attacks, and inference attacks. We introduced emerging methods that are capable of providing improved privacy with minimal data loss. These methods have scalability for big data. Finally, we examined future research works and raised important questions that can help improve existing algorithms or develop new methods, better manage the complexity and scale of unstructured data.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 1","pages":"253 - 279"},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650932","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

Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing 使用内存处理来表示大数据流匿名化模型

Annals of Data Science Pub Date : 2024-07-13 DOI: 10.1007/s40745-024-00556-x

Elham Shamsinejad, Touraj Banirostam, Mir Mohsen Pedram, Amir Masoud Rahmani

引用次数: 0

Analyzing Insurance Data with an Alpha Power Transformed Exponential Poisson Model 用阿尔法幂变换指数泊松模型分析保险数据

Annals of Data Science Pub Date : 2024-07-10 DOI: 10.1007/s40745-024-00554-z

M. Meraou, M. Z. Raqab, Fatmah B. Almathkour

引用次数: 0

Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects 开启在线洞察力：LSTM 探索与迁移学习的前景

Annals of Data Science Pub Date : 2024-07-08 DOI: 10.1007/s40745-024-00551-2

Muhammad Tahir, Sufyan Ali, Ayesha Sohail, Ying Zhang, Xiaohua Jin

{"title":"Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects","authors":"Muhammad Tahir, Sufyan Ali, Ayesha Sohail, Ying Zhang, Xiaohua Jin","doi":"10.1007/s40745-024-00551-2","DOIUrl":"10.1007/s40745-024-00551-2","url":null,"abstract":"<div><p>Machine learning algorithms can improve the time series data analysis as compared to the traditional methods such as moving averages or auto-regressive approaches. This advancement has helped to unlock several challenging problems since machine learning not only helps to forecast the overall trend of the data, but it also helps to keep the historical track of changes in factors, influencing this trend. These predictions play a pivotal role in almost all areas of research where the observations are time dependent, such as problems ranging from challenges of finance to public health, environmental and climate change challenges. A key challenge of these domains is the higher number of attributes and predictors since managing and manipulating data from many attributes is itself a significant challenge for future forecasting. Addressing these challenges is possible with Recursive Long Short-Term Memory models. The application of such models is crucial, and their efficacy is further amplified when considering transfer learning. During this research, a detailed and comprehensive description of such models is addressed. Practical application is illustrated through an example, emphasizing that these models, when transferred to complex and large datasets using transfer learning, hold great promise.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1421 - 1434"},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-024-00551-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141667526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Drinkers Voice Recognition Intelligent System: An Ensemble Stacking Machine Learning Approach 饮酒者语音识别智能系统：集合堆叠机器学习方法

Annals of Data Science Pub Date : 2024-07-07 DOI: 10.1007/s40745-024-00559-8

P. Terlapu

引用次数: 0

A New Kernel Density Estimation-Based Entropic Isometric Feature Mapping for Unsupervised Metric Learning 用于无监督度量学习的基于核密度估计的新熵等距特征映射法

Annals of Data Science Pub Date : 2024-07-06 DOI: 10.1007/s40745-024-00548-x

Alaor Cervati Neto, A. Levada, Michel Ferreira Cardia Haddad

引用次数: 0

Power Evaluation of Some Tests for Inverse Rayleigh Distribution 反瑞利分布某些测试的功率评估

Annals of Data Science Pub Date : 2024-07-05 DOI: 10.1007/s40745-024-00536-1

Vahideh Ahrari, Parisa Hasanalipour

引用次数: 0

A Comprehensive Survey of Image Generation Models Based on Deep Learning

Annals of Data Science Pub Date : 2024-06-20 DOI: 10.1007/s40745-024-00544-1

Jun Li, Chenyang Zhang, Wei Zhu, Yawei Ren

{"title":"A Comprehensive Survey of Image Generation Models Based on Deep Learning","authors":"Jun Li, Chenyang Zhang, Wei Zhu, Yawei Ren","doi":"10.1007/s40745-024-00544-1","DOIUrl":"10.1007/s40745-024-00544-1","url":null,"abstract":"<div><p>In recent years, generative artificial intelligence has been developing rapidly. In the image domain, image generation models based on deep learning have made remarkable achievements. Early frameworks for image generation models were dominated by generative adversarial networks (GANs) and variational autoencoders (VAEs). Nowadays, large-scale generative models based on diffusion models have become mainstream, and the quality of their generated images is significantly improved. We will review the research and development of image generation models and delve into the significant progress made in the field in recent years. Initially, we revisit the development of traditional image generation models like GANs and VAEs, emphasizing their contributions and challenges. We also introduce diffusion models, which have received much attention in the field of image generation due to their unique generative process and excellent generative performance. Subsequently, we emphasized the large vision models with SAM as the focal point. We also pay special attention to large-scale generative models like Stable Diffusion, which have demonstrated unprecedented capabilities in high-quality image generation tasks. Additionally, we explore target models and respective fine-tuning methods for domain-oriented image generation tasks, predicts future directions in image generation, and proposes potential research focuses and challenges.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 1","pages":"141 - 170"},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521779","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