JournalTOCs

Annals of Data Science
Number of Followers: 15

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2198-5804 - ISSN (Online) 2198-5812
Published by Springer-Verlag

[2468 journals]

A Transformation Tree Based on Extension Set Theory
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  Abstract: In the current era of information abundance, data mining and strategy generation have become crucial. Although decision trees are widely used across various domains, their direct application to obtain optimal solutions often lacks the flexibility and precision needed to adapt update themselves under specific conditions. This paper introduces an enhanced data mining algorithm, the Transformation Tree, which addresses these limitations by systematically comparing parameter variables under diverse conditions. The transformation tree algorithm builds on the traditional decision tree methodology to identify optimal transformation schemes, delivering precise and adaptable solutions. The algorithm employs basic-element theory to extract data features, evaluates them using information gain ratio and the comprehensive gain ratio metrics, and constructs a transformation tree structure. This structure facilitates the generation and refinement of transformation schemes and strategies through iterative testing. To validate its effectiveness, we applied the algorithm to a hypertension case study. The results indicate that while the transformation tree algorithm exhibits slightly lower efficiency in classification tasks, it excels in discovering multiple transformation strategies, enabling intelligent scheme generation, and providing flexible and accurate decision support.
  PubDate: 2025-07-07
Development of Customized Strategies for Emergency Language Teaching
Content Based on Big Data
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  Abstract: This paper studies the application and effect of customized content strategy for emergency language teaching driven by big data. Using quantitative research methods, 300 college students and teachers were surveyed and standardized. Descriptive statistics, correlation analysis and regression analysis were used to evaluate the practical impact of big data technology on emergency language education. With the support of big data technology, customized teaching content can improve students’ learning performance, learning attitude, course satisfaction and teaching feedback evaluation, and improve teachers’ teaching strategies. This study proposes four strategies: real-time data collection and analysis, personalized learning path design, dynamic adjustment of teaching content, and intelligent learning feedback and evaluation. Use big data analytics and instructional design to dynamically adjust content based on students’ learning behaviors and needs to provide a highly personalized learning experience. The study highlights the unique role of big data technology in improving classroom interaction, fostering student self-directed learning, and optimizing instructional feedback. This paper verifies the significant impact of big data technology on emergency language teaching and provides theoretical and empirical support for its wide application in the field of education. The research results provide specific guidance for the integration of big data technology in education practice in the future to improve education quality, promote personalized learning, and enhance emergency education response ability.
  PubDate: 2025-07-02
From pixels to profits: a novel approach to identify rare events for a
group of US equities
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  Abstract: Investors are interested in the growth of their portfolios. The investment strategy focuses primarily on investing in low-risk and low-volatility companies. Nevertheless, it is important to acknowledge that every investment portfolio carries an inherent danger of experiencing financial losses due to negative progression and market crashes. This paper proposes an innovative methodology for forecasting rare events in financial time series based on the analysis of “images”. In addition, this paper deals with an enhancement to the current image formulation techniques, with the objective of addressing specific limitations that hinder their practical application in the financial analysis. In contrast to prior studies, which impose limitations on the “image-based-examination” of multiple securities, the approach presented in this paper enables the evaluation of various US Large Cap Securities, hence expanding its breadth and significance. Furthermore, the increased scope of applicability enhances the underlying algorithm’s adaptability and pertinence. One notable advancement is the incorporation of CVaR as a key measure for distinguishing between rare-event and non-rare-event scenarios. Expanding upon the limitations identified in the preceding studies, this paper presents a comprehensive enhancement in the study of financial data based on image analysis techniques.
  PubDate: 2025-06-29
Stock Prices Forecasting by Using a Novel Hybrid Method Based on the
MFO-Optimized GRU Network
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  Abstract: With the social economy growing at a quick pace and the stock market seeing constant developments, more and more people are voicing concerns about investing in stocks. The importance of forecasting stock values has increased in the domain of engineering's use of cognitive computing. Utilizing data-driven tactics for forecasting stock prices, investors can effectively mitigate risks and enhance profits. Investors can use projections based on historical values and textual data to make well-informed judgments about future patterns in stock prices. Stock price anticipation is a pivotal undertaking in the financial sector that has substantial consequences for traders and investors. This article presents an in-depth comparison analysis of machine learning tactics for forecasting price fluctuations in stocks. The research deploys historical stock data and diverse technical indicators. This paper presents the Gated Recurrent Unit (GRU) model for Nasdaq stock index anticipation, which is optimized by Particle swarm optimization (PSO), Biogeography-based optimization (BBO), and Moth flame optimization (MFO). Among these optimizers, MFO has the best outcomes. Compared to the GRU scheme the optimized PSO-GRU, BBO-GRU, and MFO-optimized GRU for stock forecasting has the outcomes of 0.9807, 0.9824, and 0.9904 in coefficient of determination ($${R}^{2}$$) which shows the improvement of the presented scheme as a result of its development. The criteria used to evaluate this model are mean absolute error, root mean absolute error, and $${R}^{2}$$.
  PubDate: 2025-06-18
Optimization of Oil and Gas Pipeline Leakage Data and Defect
Identification Based on Graph Neural Processing
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  Abstract: With the increasing complexity of oil and gas pipeline networks, early identification of leaks and defects is crucial to ensure the safe operation of pipelines. This study proposes a graph neural network (GNN) method for data processing and defect identification aimed at optimizing monitoring and maintenance strategies for oil and gas pipelines. Through the analysis of historical leakage data, we constructed a graph database containing 5000 samples, each containing 10 features such as pressure, flow, temperature, etc. Using graph convolutional network and graph attention network (GAT) to perform feature extraction and pattern recognition on nodes in pipeline network, our model achieves 92% accuracy in defect recognition, which is 15% higher than traditional methods. In addition, we have developed a leakage prediction model based on time series analysis, which is able to predict potential leakage risks 24 h in advance with an accuracy of 85%. The results of this study not only improve the safety management level of oil and gas pipelines, but also provide a new technical path for future intelligent pipeline maintenance.
  PubDate: 2025-06-16
Modeling Economics and Finance Data with the Arctan Marshall-Olkin Weibull
Distribution
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  Abstract: The Arctan Marshall-Olkin family is introduced as an innovative and adaptable class of heavy-tailed distributions for modeling extreme events in financial and economic sciences. This family emerges from the combination of the Marshall-Olkin framework with the Arctan-X approach, which leverages the arctangent inverse trigonometric function. A particular case, the Arctan-Marshall-Olkin-Weibull (ATMOW) distribution, is explored in detail. Unlike the conventional two-parameter Weibull distribution, ATMOW incorporates an additional parameter, enhancing its adaptability to heavy-tailed data. Monte Carlo simulations confirm the efficiency of the maximum likelihood estimation for parameter inference. Furthermore, closed-form expressions for key actuarial risk measures, including value at risk and tail value at risk, are derived to assess extreme financial risks. Empirical applications to real financial and economic datasets demonstrate better performance of ATMOW compared to several competing multiparameter distributions.
  PubDate: 2025-06-12
A Study on Quadri-Partitioned Interval-Valued Pythagorean Neutrosophic
Fuzzy MCDM
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  Abstract: We present two methods for solving multicriteria fuzzy decision-making based on a Quadri partitioned interval-valued Pythagorean neutrosophic set. Firstly, we deduce the alternatives with different weights usi...
  PubDate: 2025-06-06
A New Regression Model for Analysis of Count Response and Other Related
Patient Outcomes
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  Abstract: This study introduces a novel statistical model, termed New Poisson Generalized Lindley (NP-GLindley) distribution, for analysing the count response variable and various associated patient outcomes. The model ...
  PubDate: 2025-06-04
An Explainable Machine Learning-Based Employee Attrition Predictive System
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  Abstract: As human capital forms the backbone of any organization, managing and minimizing employee attrition is of paramount importance. Attrition prediction is essential because attrition disrupts projects advancement...
  PubDate: 2025-06-03
Improving Predictive Accuracy in Writing Assessment Through Advanced
Machine Learning Techniques
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  Abstract: This research investigates the application of the Machine Learning (ML) model for effective and equitable essay scoring in education. Unlike their human counterpart, ML models have the capacity to rapidly anal...
  PubDate: 2025-06-03
Diabetic Retinopathy: An Exploration of Retinal Blood Vessel Segmentation
Using Multilayered Thresholding
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  Abstract: Diabetic retinopathy is the leading cause of blindness worldwide; it is a consequence of diabetes that affects the retina’s blood vessels. Consequently, correct segmentation of the retinal arteries is essentia...
  PubDate: 2025-06-02
Hybrid Learning Based Visual Facial Emotion Recognition in Speech Videos
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  Abstract: Facial Emotion Recognition (FER) plays a crucial role in understanding human behavior and interactions, with applications in human-computer interaction, healthcare, surveillance, and multimedia content analysi...
  PubDate: 2025-06-02
Evaluation of Path-Planning Algorithms for Autonomous Navigation in
Unstructured Indian Road Environments Considering the Effects of Weather
and Road Curvatures
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  Abstract: This study presents the development and comprehensive evaluation of an autonomous ground vehicle prototype designed specifically for the diverse and complex road conditions prevalent in India. The prototype incorporates a multi-sensor suite, including RP LIDAR A1M8, Intel RealSense Depth Camera, ultrasonic distance sensor HC-SR04, GPS sensor neo6m, and 1D LIDAR TF LUNA micro, integrated with advanced control algorithms. Steering control utilizes a path-tracking algorithm based on a kinematic bicycle model, while acceleration is managed by a PID controller. The system’s performance was rigorously tested across various scenarios, encompassing different weather conditions, road types, curvatures, and obstacle densities. Results indicate a maximum accuracy of 98.2% under optimal conditions, decreasing to 80% in rainy weather and 79.1% on roads with potholes and uneven surfaces. The prototype demonstrated varying levels of accuracy with increasing road curvature and obstacle density. This research provides valuable insights into the challenges and potential solutions for autonomous vehicle operation in the unique context of Indian road environments, highlighting areas for future improvement in sensor technology and control algorithms to enhance safety and reliability in diverse conditions.
  PubDate: 2025-06-01
Exponentiated Odd Lindley-X Power Series Class of Distributions:
Properties and Applications
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  Abstract: In this article, we introduce a new family of probability distributions, the Exponentiated Odd Lindley-X Power Series (EOL-XPS) class, which is derived by integrating the power series distribution with the exponentiated odd Lindley-X family. We establish several statistical properties of this new class, including moments, the moment-generating function, the quantile function, mean deviations, order statistics, and Rényi entropy. As a special case, we derive the probability density function and cumulative distribution function of the Exponentiated Odd Lindley-Weibull Poisson (EOL-WP) distribution, using the Weibull-Poisson distribution as the baseline. To assess the robustness of the proposed model, we conduct a Monte Carlo simulation study to evaluate the performance of maximum likelihood estimation for parameter estimation. Furthermore, we apply the EOL-WP model to COVID-19 and Kevlar datasets, demonstrating its flexibility and practical relevance in modeling complex data.
  PubDate: 2025-05-30
Variable Selection and Variable Integration for Categorical Dummy
Variables in Regression Analysis
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  Abstract: Mode selection or variable selection in regression analysis is considered one of the most popular problems to study in empirical research and various theoretical and simulation studies have been conducted. When dealing with categorical data, coding with multiple dummy variables is usually used. However, with this approach, variable selection criteria cannot be systematically applied when some categories are integrated into one category. Most researchers see variables with coefficients near size and integrate them and select variables by minimizing information criteria or other model selection criteria. The underlying reason for the implementation of such cumbersome procedures is related to the lack of systematic variable selection procedures. Finding a way to list all the candidates of the combinations of explanatory variables and their integrated versions remains a major problem. Even if all of them can be listed, estimating the regression models for all the variations of combinations takes too much time, which is another practical problem. To effectively address these issues, in this work, we examined the possibility of utilizing variable selection criteria for ordered categorical data by estimated ordering. We also conducted a simulation study to check the performance of some information criteria.
  PubDate: 2025-05-17
New Algorithms for Constructing Magic Squares with Ancient Chinese Wisdoms
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  Abstract: The construction of magic squares is an ancient mathematical problem with numerous applications in areas such as information transmission in network security. While there have been various approaches to constructing high-order magic squares, they are typically computationally intensive. We explore and adapt approaches with roots in Chinese classics to develop a set of simple yet comprehensive methods for constructing magic squares of any order. For generating odd-order magic squares, the traditional "Siamese method" (The Siamese Method(or De la Loubère Method): starting from the central box of the first row with the number 1 (or the first number of any arithmetic progression), the fundamental movement for filling the boxes is diagonally up and right (↗), one step at a time. When a move would leave the square, it is wrapped around to the last row or first column, respectively. If a filled box is encountered, one moves vertically down one box (↓) instead, then continuing as before.) was introduced over 300 years ago by Simon de la Loubère, a French ambassador to Thailand in later seventeenth century. While workable, the Siamese method is cumbersome and lacks an underlying logic (Benjamin et al. in Coll Math J 45:92–100, 2014). We employ a concept derived from the circular structure of Five Elements (The Five Elements (Wuxing) is one of the two fundamental Theories define Chinese civilization. It is characterized essentially by five components to interpret everything. In endless cycles, Five Elements are mutually generative, and at the same time mutually restrictive. For the generation relationship, Metal generates Water generates Wood generates Fire generates Earth generates Metal generates Water generates Wood……(see Fig. 1B below); For the restraint relationship, Metal destructs Wood destructs Earth destructs Water destructs Fire destructs Metal destructs Wood destructs Earth generates Water …… This key concept of dynamic and constant balancing, unbalancing, rebalancing process applies universally to explaining natural phenomena, cosmology, medicine, politics, and human affairs.) in Chinese culture to establish a prototype for constructing odd-order magic squares. This approach significantly simplifies the algorithm, and reveals the underlying mathematical logic and mechanism of the Siamese method. We also adapt the composition of the Bagua (The Yin-Yang is another fundamental theory of Chinese thoughts, originating from Yijing. It is characterized with the Yin-Yang duality and interdependence of two for all things in the universe. Where Yin represents the passive, dark, cold, feminine, and receptive aspects of nature; Yang represents the active, bright, hot, masculine, and expansive aspects of nature. Yin and Yang are contrast yet complementary. The Yin Sphere and Yang Sphere are in opposition, eternally embracing together, moving toward and transforming into each other [6].) (eight trigrams in Yijing) from the ancient wisdom in Chinese classics to develop a prototype for constructing magic squares of 2 k order as reported (Zongxi in Yixue Xiangshulun, Jiuzhou Publishing House, 2007; Zhu in A history of Yi philosophy, Peking University Press, 1986). This in turn enables us to establish a comprehensive algorithm to cover the construction of magic squares of all orders. Our comprehensive algorithm is capable of constructing extremely high-order magic squares, which are of a high degree of symmetricity and ready for generating countless variations (McCranie in Math Teach 81:674–678, 1988). It therefore holds promise to be applied in network security for data transmission, and other applications, such as password encryption, etc.
  PubDate: 2025-05-16
Uncovering University Application Patterns Through Graph Representation
Learning
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  Abstract: In university admissions, interaction networks naturally emerge between prospective students and available majors. Understanding hidden patterns in such a vast network is crucial for decision-making but poses technical challenges due to its complexity and data limitations. Many existing models rely heavily on user profiling, raising privacy concerns and making data collection difficult. Instead, this work extracts meaningful insights using only the adjacency information of the network, avoiding the need for personal data. We leverage Graph Convolutional Networks (GCN) to generate compact representations for major recommendation and clustering tasks. Our GCN-based approach outperforms classical methods such as popularity-based and Non-negative Matrix Factorization (NMF), as well as the neural Generalized Matrix Factorization (GMF) model, achieving up to 61.06% and 12.17% improvements in smaller (dimension 40) and larger (dimension 80) embeddings, respectively. Furthermore, hierarchical clustering on these embeddings reveals implicit patterns in student preferences, particularly regarding fields of study and geographic locations, even without explicit data on these attributes. These findings demonstrate that meaningful insights can be derived from interaction networks while mitigating privacy concerns associated with user profiling.
  PubDate: 2025-05-08
A Hybrid Approach for Stock Market Price Forecasting Using Long Short-Term
Memory and Seahorse Optimization Algorithm
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  Abstract: Stock market price forecasting is a challenging task due to the complexity and volatility of market dynamics. This paper proposes a novel approach that combines the strengths of Long Short-Term Memory (LSTM) networks and the Seahorse Optimization (SHO) algorithm for stock market price forecasting. The LSTM-SHO model is compared with other LSTM models optimized using Genetic Algorithm (GA) and different dimensionalities (1D, 2D, 3D), as well as an Artificial Neural Network (ANN) model. The results show that the LSTM-SHO model outperforms the other models in terms of mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The hyperparameter tuning process using SHO significantly improves the forecasting accuracy of the LSTM model. The proposed approach demonstrates its potential in developing more accurate and robust stock market forecasting models, which can aid investors and analysts in making informed decisions. The findings of this study have important implications for investors, analysts, and policymakers, and contribute to the existing body of literature on stock market forecasting.
  PubDate: 2025-05-05
Rumor Governance Under Uncertain Conditions: An Evolutionary Game Theory
Analysis
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  Abstract: In the rapidly evolving landscape of online information dissemination, managing rumors has become an imperative challenge for governments worldwide. This study employs a tripartite evolutionary game model to examine the behavior evolution of the government, online media, and netizens in the process of rumor propagation under uncertain conditions. The innovation of the model lies in considering the probability of successful rumor detection under government regulation, the uncertainty of rumor dissemination by online media and netizens, and introducing a dynamic government penalty mechanism. Through simulation and analysis, we identify the evolutionarily stable strategies of each participant under different scenarios and provide specific governance strategies for each party involved. The results reveal that appropriate government penalties, proactive regulation by online media, and rational choices by netizens can effectively curb rumor spreading. In uncertain environments, adopting flexible policies and dynamic adjustment mechanisms is crucial for effective rumor governance. The results reveal that appropriate government penalties, proactive regulation by online media, and rational choices by netizens can effectively curb rumor spreading. In uncertain environments, adopting flexible policies and dynamic adjustment mechanisms is crucial for effective rumor governance. This study not only enriches the application of evolutionary game theory but also offers practical strategic recommendations for policymakers to address the challenges of rumor propagation.
  PubDate: 2025-05-04
Optimal Individual Selection Algorithm Based on Layer Proximity and Branch
Distance Functions
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  Abstract: Automatic generation of test cases using heuristic methods is a hot research topic nowadays. Although its advantages are obvious, it is slightly insufficient in the selection of optimal individuals. Aiming at the existing problems in the evaluation and selection of the optimal individual, this paper proposes a test case evaluation algorithm based on the comprehensive analysis of the characteristics of layer proximity and branch distance function, which is a joint structure of “layer proximity and branch distance function”. The basic idea of this algorithm is that when selecting pilot individuals in the evolutionary process, we first select the individuals with high proximity between the actual execution path and the target path, and then select the individuals with the smallest branching distances among these individuals, so as to obtain the individuals with the optimal piloting ability. Experiments show that the proposed algorithm can quickly find the optimal test cases, especially for the test case generation of multi-layer nested programs.
  PubDate: 2025-05-04

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