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Annals of Data Science OnlineFirst articles

Directional Dependence-Based SVR Modeling for Sentiment-Driven Forecasting of Indonesian Banking Stocks

  • Original Article

Following the establishment of Indonesia’s sovereign wealth fund, a move poised to reshape the nation’s investment environment, there has been an increasing need to interpret market sentiment and its influence on stock performance. This study …

Multiclass Prediction of Bitcoin Fear-Greed States Using Buy-and-Sell Pressure Induced Lagged Features in Machine Learning

  • Original Article

Trading decision-making is significantly influenced by psychological resistance that emerges under dynamic market conditions. Fear and greed states provide a quantifiable representation of these behavioral dynamics, serving as the basis for …

Cancelable Speaker Identification Based on Speech Deconvolution Methods

  • Review Article

Biometric authentication systems, which use unique biological traits for identification, have gained popularity in various fields as a replacement for traditional password- or token-based systems. While offering enhanced security, these systems …

Revisiting Computing Paradigm for Partitional Clustering Analysis and Recommendations

  • Review Article

The business world is concentric around the imperative of information extraction and analysis. The information extraction processes are blended with different data mining techniques like clustering, classification, etc. Clustering is an …

Adaptive Hierarchical Attention for Multivariate Time Series Anomaly Detection

  • Original Article

In multivariate time series (MTS) anomaly detection, existing graph neural network (GNN) methods often neglect multi-level feature representations, relying solely on the final layer output and fixed thresholds, which leads to information loss and …

A ML-Driven Framework for Phase Prediction in High-Entropy Alloys

  • Original Article

High-entropy alloys (HEAs) represent a state-of-the-art material system, exhibiting exceptional physical and chemical properties that hold great potential for engineering applications. Nevertheless, accurately identifying their complex phase …

Will Market Participants Affect the Volume-Price Relationship in the Chinese Stock Market? An Empirical Analysis Based on the DCC-MIDAS Model

  • Original Article

The connection between stock market prices and trading volume has long been a subject of extensive research interest among scholars. This study examines whether the effective utilization of exogenous information from market participants …

SHMADF: A Secure and Intelligent Framework for IoT-Enabled Healthcare Monitoring and Attack Detection

  • Original Article

The rapid integration of Internet of Things (IoT) devices in healthcare demands a robust framework to ensure secure patient monitoring and timely attack detection. This study proposes a Secure Healthcare Monitoring and Attack Detection Framework …

Depth-Based Nonparametric Tests for Comparing Mean Functions of Multiple Functional Samples

  • Original Article

Functional data analysis (FDA) continues to gain prominence in modern statistical research. Building on the concept of statistical depth, originally developed for multivariate data, this paper extends its application to functional settings and …

A Hybrid Model of Artificial Neural Network and SARIMA Models for Predicting Inflation Rate Change in Nigeria's Economy

  • Original Article

Accurate inflation forecasting is critical for effective economic planning and monetary policy formulation, especially in emerging economies such as Nigeria. Persistent inflation volatility driven by structural inefficiencies, external shocks, and …

Autism Spectrum Disorder Identification Using Dual-Branch Fusion Model with Privacy-Preserving

  • Original Article

Autism Spectrum Disorder (ASD) are neurodevelopmental disorders that severely impact daily life and social interactions. According to research, early diagnosis and intervention of autism is crucial to improve the overall quality of life of …

Automatic Music Generation with Multi-module Neural Networks for Chord, Rhythm, and Pitch Modeling

  • Original Article

Automatic music generation plays a crucial role in generating creative compositions autonomously, facilitating applications in various fields, including entertainment and education. The challenges faced by existing approaches include capturing …

Optimization Study of Electric-Hydrogen Hybrid Energy Storage Capacity in Warehouse Park for Smart Grid

  • Original Article

To address the seasonal energy imbalance resulting from the high penetration of renewable energy sources in power systems, this study leverages smart grid technologies to innovatively design a hybrid electric-hydrogen energy storage system. The …

COPD Scope: Smart Clinical Pathways for Respiratory Illness Diagnosis and Management

  • Open Access
  • Original Article

Internet of Things (IoT) is playing a vital role in healthcare by automating the real time monitoring of patients seamlessly with the help of a variety of sensors. In the current scenario of smart healthcare, IoT with the integration of machine …

Stock Return Predictability on S&P 500 ETF: A Deep Learning Approach

  • Original Article

Stock return predictability has been one of the most studied fields in finance. This research examines SPDR S&P500 ETF Trust—SPY’s—daily data from December 3, 2007, to May 20, 2025, employing machine learning (including Logistic Regression, Ridge …

Unit Rayleigh Half-Normal Distribution: Bayesian and Non-Bayesian Inference, Regression Model for Bounded Response Data and Application

  • Original Article

This paper introduces a novel one-parameter bounded distribution, called the Unit Rayleigh Half-Normal (URHN) distribution, designed for modeling data on the unit interval (0,1), which frequently arises in many fields such as economics, actuarial …

T-FWH: A Hybrid Feature Selection Method Combining Multi-Criteria Decision Making for Imbalanced Data

  • Original Article

In the presence of imbalanced data problem, existing feature selection methods focus on retaining features that are useful for majority class sample recognition, which leads to poor performance of recognizing minority class samples. However, in …

NoLAW: A Recursive Non-Linear Adaptive Wiener Filter for Time Series Smoothing

  • Original Article

Smoothing is a foundational operation in time series analysis, often used to mitigate the effects of noise and outliers prior to visualization, modeling, or classification. However, traditional filters such as Gaussian smoothing, exponential …