Annals of Data Science最新文献_第3页

Farm-Level Smart Crop Recommendation Framework Using Machine Learning 利用机器学习的农场级智能作物推荐框架

Annals of Data Science Pub Date : 2024-07-20 DOI: 10.1007/s40745-024-00534-3

Amit Bhola, Prabhat Kumar

{"title":"Farm-Level Smart Crop Recommendation Framework Using Machine Learning","authors":"Amit Bhola, Prabhat Kumar","doi":"10.1007/s40745-024-00534-3","DOIUrl":"10.1007/s40745-024-00534-3","url":null,"abstract":"<div><p>Agriculture is the primary source of food, fuel, and raw materials and is vital to any country’s economy. Farmers, the backbone of agriculture, primarily rely on instinct to determine what crops to plant in any given season. They are comfortable following customary farming practices and standards and are oblivious to the fact that crop yield is highly dependent on current environmental and soil conditions. Crop recommendations involve multifaceted factors such as weather, soil quality, crop production, market demand, and prices, making it crucial for farmers to make well-informed decisions. An improper or imprudent crop recommendation can affect them, their families, and the entire agricultural sector. Modern technologies like artificial intelligence, machine learning, and data science have emerged as efficient solutions to combat issues like declining crop production and lower profits. This research proposes a Smart Crop Recommendation framework that leverages machine learning to empower farmers to make informed decisions about optimal crop selection. The framework consists of two phases: crop filtration and yield prediction. Crops are filtered in the first phase using an artificial neural network based on local input parameters. The second phase estimates yield for filtered crops, considering the season, farm area, and location data. The final recommendation provides farmers with crops aimed at maximizing profit. The remarkable 99.10% accuracy of the framework is demonstrated through experimentation using artificial neural networks and the 0.99 <span>(text {R}^{text {2}})</span> error metric for the random forest. The uniqueness of this framework lies in its distinctive focus on the farm level and its consideration of the challenges and various agricultural features that change over time. The experimental results affirm the effectiveness of the framework, and its lightweight nature enhances its practicality, making it an efficient real-time recommendation solution.\u0000</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 1","pages":"117 - 140"},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819448","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

Reaction Function for Financial Market Reacting to Events or Information 金融市场对事件或信息的反应函数

Annals of Data Science Pub Date : 2024-07-17 DOI: 10.1007/s40745-024-00565-w

Bo Li, Guangle Du

{"title":"Reaction Function for Financial Market Reacting to Events or Information","authors":"Bo Li, Guangle Du","doi":"10.1007/s40745-024-00565-w","DOIUrl":"10.1007/s40745-024-00565-w","url":null,"abstract":"<div><p>Observations indicate that the distributions of stock returns in financial markets usually do not conform to normal distributions, but rather exhibit characteristics of high peaks, fat tails and biases. In this work, we assume that the effects of events or information on prices obey normal distribution, while financial markets often overreact or underreact to events or information, resulting in non normal distributions of stock returns. Based on the above assumptions, we for the first time propose a reaction function for a financial market reacting to events or information, and a model based on it to describe the distribution of real stock returns. Our analysis of the returns of China Securities Index 300 (CSI 300), the Standard & Poor’s 500 Index (SPX or S &P 500) and the Nikkei 225 Index (N225) at different time scales shows that financial markets often underreact to events or information with minor impacts, overreact to events or information with relatively significant impacts, and react slightly stronger to positive events or information than to negative ones. In addition, differences in financial markets and time scales of returns can also affect the shapes of the reaction functions.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1265 - 1290"},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830830","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

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 利用迁移学习技术检测苹果叶病

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