Recent Articles

Open access

ISSN: 2666-7649
CN: 61-1530/TN
p-ISSN: 2097-3187

ESG performance and executive compensation levels: an empirical study

As global environmental concerns grow, corporate environmental, social, and governance (ESG) performance has become an essential indicator for measuring organizational value and sustainability. Considering...

Prediction of retail commodity hot-spots: a machine learning approach

The accurate prediction of hot-spot commodities in the retail market is crucial for inventory management and market strategy formulation. Traditional methods focus on analyzing the sales trends of listed...

Counterfactual synthetic minority oversampling technique: solving healthcare's imbalanced learning challenge

The application of machine learning in the healthcare domain has groundbreaking potential across a wide range of scenarios. However, this potential is often stalled by data-related challenges, such...

Automatic method for identification of cycles in COVID-19 time-series data

All previous methods identify cycles in COVID-19 daily and weekly data based on a subjective interpretation of the results. This poses difficulties for researchers interested in conducting comprehensive...

Particle swarm optimization-enhanced machine learning and deep learning techniques for Internet of Things intrusion detection

The exponential escalation of cyber threats and attacks targeting Internet of Things (IoT) devices in this decade necessitates the development of effective intrusion detection methods. This paper presents...

Public sentiment analysis of roadway work zones using social media data and machine learning models

The construction and maintenance of roadway infrastructure contribute positively to social and economic development and improve traffic safety. However, roadway work zones (WZs) present safety issues...

Enhancing imbalanced text classification: an overlap-based refinement approach

The inherent class imbalance within textual data poses a significant challenge for machine learning-based techniques, as the available data often fails to adequately represent all classes. This scarcity...

AI-driven innovation in emerging markets: extending the technology acceptance model–technology-organization-environment framework in small- and medium-sized enterprises

This study examined the impact of artificial intelligence (AI) adoption on innovation outcomes in Pakistani manufacturing small- and medium-sized enterprises (SMEs), addressing a critical gap in the...

Artificial intelligence in corporate boards: a dual-dimensional framework for integration across autonomy and structural levels

This study examines the integration of artificial intelligence (AI) into corporate governance, with a specific focus on board-level decision-making. It critically evaluates five progressive stages of...

InGPT we trust: perceptions of the future of work with artificial intelligence on online forums

This study analyzes 10 years of Reddit discussions on artificial intelligence (AI) labor’s market impact using a state-of-the-art pre-trained robust optimized bidirectional encoder representations from...

Meta-synthesis of supply chain modeling tools: leveraging natural language processing for optimal selection

The modeling frameworks used for supply chains and operations are often selected without rigorous justification. This meta-synthesis addresses this gap by systematically examining the published literature...

Multi-source heterogeneous data fusion using vector concatenation for quality fluctuation analysis

Big data mining is an important driver of quality control in intelligent manufacturing, however, conventional approaches often rely on a single data source, limiting their accuracy and applicability....

Exploring artificial intelligence for sustainable business development: a review

Artificial intelligence (AI) has revolutionized business practices and enabled relevant techniques in various sectors to drive efficiency and innovation. This study examines the impact of AI across...

Trust or distrust? AIGC trustworthiness and an extended analysis within nomology framework

Given the dizzying advancements in artificial intelligence (AI) applications such as ChatGPT and DeepSeek, AI-generated content (AIGC) has attracted considerable attention from scholars, practitioners,...

Investors’ behavior-based nonlinear goal programming models for financial portfolio selection considering future stock market trends

Financial markets are ever-changing, and portfolio selection (PS) problems frequently involve multiple optimization objectives in an imprecise investment environment. Real-world investors attempt to...

Identifying accounting control issues from online employee reviews

This paper presents and describes an approach to generate innovative labeled datasets that enable automated text classifiers to automatically detect online employee reviews referring to accounting control...

Toward accurate credit evaluation: an efficient imputation approach for financial data

Missing instances and mixed data types, including discrete and ordered (e.g., continuous and ordinal) variables, are widespread in many datasets in the finance sector. In this domain, estimating missing...

Probabilistic oil price forecasting with a variational mode decomposition-gated recurrent unit model incorporating pinball loss

Prediction methods have garnered significant attention in intelligent decision-making. Most existing approaches to predicting crude oil prices prioritize accuracy and stability while providing precise...

Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction

Accelerating urbanization and the rapid development of intelligent transportation systems have rendered short-term traffic flow prediction an important research field. Accurate prediction of traffic...

Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms

Public data serves as a fundamental pillar in the advancement of the digital economy. Its importance for unlocking the value associated with information asymmetry has attracted substantial attention...

Exploration of salience theory to deep learning: evidence from Chinese new energy market high-frequency trading

Salience theory has been proposed as a new stock trading strategy. To assess the validity of this proposal, a complex decision trading system was constructed based on salience theory, a variational...

Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation

The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues. Identifiability can be divided into two: subjective identifiability, which...

The effect of green mergers and acquisitions on the green transformation of heavily polluting enterprises: empirical evidence from China

Against the backdrop of increasingly prominent global environmental issues, heavily polluting enterprises (HPPs) urgently need to find a path to green transformation that achieves sustainable development...

L2R-MLP: a multilabel classification scheme for the detection of DNS tunneling

Domain name system (DNS) tunneling attacks can bypass firewalls, which typically “trust” DNS transmissions by concealing malicious traffic in the packets trusted to convey legitimate ones, thereby making...

The impact of mHealth apps’ affordance on consumers’ novel food purchasing decisions

Mobile health apps (MHAs) provide users with exercise support and dietary recommendations. They also suggest a wide range of novel foods. However, some users may face food neophobia, a reluctance to...

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