Expert Systems最新文献

In this paper, we introduce Traversal Learning (TL), a novel approach designed to address the problem of decreased quality encountered in popular distributed learning (DL) paradigms such as Federated Learning (FL), Split Learning (SL) and SplitFed Learning (SFL). Traditional FL often suffers an accuracy drop during aggregation due to its averaging function, while SL and SFL face increased loss due to the independent gradient updates on each split network. TL adopts a unique strategy where the model traverses the nodes during forward propagation (FP) and performs backward propagation (BP) at the orchestrator, effectively implementing centralised learning (CL) principles within a distributed environment. The orchestrator is tasked with generating virtual batches and planning the model's sequential node visits during FP, aligning them with the ordered index of the data within these batches. We conducted experiments on six datasets representing diverse characteristics across various domains. Our evaluation demonstrates that TL is on par with classic CL approaches in terms of accurate inference, thereby offering a viable and robust solution for DL tasks. TL outperformed other DL methods and improved accuracy by 7.85% for independent and identically distributed (IID) datasets, macro F1-score by 1.06% for non-IID datasets, accuracy by 2.05% for text classification and AUC by 1.41% and 2.82% for medical and financial datasets, respectively. By effectively preserving data privacy while maintaining performance, TL represents a significant advancement in DL methodologies.

Nowadays, automated intelligent systems play an increasingly vital role in aiding decision-making processes across various fields. Firefighting represents a crucial area where accurate information gathering is paramount for efficient resource allocation. Social media platforms as Twitter (or X) have emerged as valuable sources of real-time data, often referred to as ‘citizen science’, offering additional insights alongside traditional data sources. In this work, we introduce a novel pipeline that leverages Natural Language Processing (NLP) techniques and Twitter data, utilising transformer models to identify and monitor wildfire incidents. Expanding on this approach, we incorporate sentiment analysis to provide deeper insights into public perceptions and emotions related to fire events. Additionally, we present visual representations of geographic data through heat mapping, potentially aiding firefighters in making informed decisions. By integrating advanced NLP techniques with social media data, our approach presents a promising strategy for enhancing wildfire management efforts.

The growing availability of conversational data across multiple platforms has intensified interest in dynamic emotion recognition. Speech plays a pivotal role in shaping the emotional climate (EC) of peer conversations. We propose DeepBispec, the first framework to integrate deep bispectral image analysis with affect dynamics (AD) for speech-based EC recognition. Bispectrum representations capture nonlinear and non-Gaussian speech characteristics, while AD descriptors model temporal emotion fluctuations. Evaluated on K-EmoCon, IEMOCAP and SEWA datasets, DeepBispec consistently improved EC classification performance. For example, on K-EmoCon, arousal accuracy increased from 79.0% (bispectrum only) to 81.4% (with AD), while valence accuracy improved from 76.8% to 77.5%; similar trends were observed for IEMOCAP and SEWA. DeepBispec outperformed strong CNN, LSTM, and Transformer baselines, demonstrating robust cross-lingual performance across seven languages. These findings highlight its potential for real-world applications such as mental health monitoring, affect-aware learning platforms and empathetic dialogue systems.

Time-series forecasting is essential for predicting events in the future and for tracking objects. The conventional recurrent neural network model needs to pad the target with zeros when handling long inputs, resulting in a loss in accuracy. Recently, it was proposed to divide a time series input into patches and merge the learned weights. However, such a model is difficult to interpret. In this article, we consider a mixture of continuous and discrete Markov states to model long-range time dependencies. For example, in a vehicle, each gear level can be a discrete state and the throttle input is continuously controlled to maximise the efficiency of the engine. Data collected from the sensor is prone to noise due to component faults or external disturbances. Hence, we apply a stability constraint to select samples for training. We validate our algorithm on three datasets: (1) Apple Watch, (2) Car engine and (3) Election tweets. On all datasets, we achieve an improvement in the range of 5%–20% in the F-measure. Furthermore, the features learned are easy to explain in terms of real-world scenarios.

Retraction: Y. Fang, “ The Model of Tibetan Thangka Sales under Blockchain Technology”, Expert Systems 41, no. 5 (2024): e12989. https://doi.org/10.1111/exsy.12989.

The above article, published online on 09 March 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. In addition, further investigation by the editors found that the article's topic was not within the scope of the journal. In view of the clear evidence of compromised peer review, the parties agreed that the paper must be retracted. The author did not respond to our notice regarding the retraction.

Retraction: H. Li, “ Analysis and Design of Financial Data Mining System Based on Fuzzy Clustering”, Expert Systems 41, no. 5 (2024): e13031. https://doi.org/10.1111/exsy.13031.

The above article, published online on 20 March 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The author did not respond to our notice regarding the retraction.

Retraction: H. Wang, “ Linking AI Supply Chain Strength to Sustainable Development and Innovation: A Country-Level Analysis”, Expert Systems 41, no. 5 (2024): e12973. https://doi.org/10.1111/exsy.12973.

The above article, published online on 03 March 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The author did not respond to our notice regarding the retraction.

Retraction: M. Chen and W. Du, “ Dynamic Relationship Network and International Management of Enterprise Supply Chain by Particle Swarm Optimization Algorithm under Deep Learning”, Expert Systems 41, no. 5 (2024): e13081. https://doi.org/10.1111/exsy.13081.

The above article, published online on 18 June 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors disagree with the retraction.