Expert Systems with Applications: An International Journal

We consider a multilabel all-relevant feature selection task which is more general than the classical minimal-optimal subset task. Whereas the goal of the minimal-optimal methods is to find the smallest subset of features allowing ...

• We describe the all-relevant feature selection in multi-label data analysis.
• We ...

Spatio-temporal prediction on multivariate time series has received tremendous attention for extensive applications in the real world, where the dynamic unknown spatio-temporal dependencies among variables make the task challenging. ...

• Modeling dynamic dependencies among variables with proposed graph matrix estimation.

• The optimised fMACDH indicator and fMACDH-fRSI indicator are proposed in the paper.

Some common technical indicators, such as moving average convergence divergence (MACD), relative strength index (RSI), and MACD histogram (MACDH) are used in technical analyses and stock trading. However, some of them are lagging ...

• Effective decision making for residential design is essential for home comfort.

A user-oriented residential environment is considered essential to providing occupants with a comfortable living experience while meeting their demands. However, design assessment in the early stage of residential design is a complex ...

• Proposing a new last aggregation fuzzy compromise solution for evaluating 3PRLPs.

In today’s world, reverse logistics (RLs) activities have become increasingly crucial for companies looking for improved customer service, cost reduction, and sustainability perspectives. Limited resources, and insufficient technology ...

Multimodal multiobjective optimization problems (MMOPs) possess multiple equivalent Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). Numerous multimodal multiobjective evolutionary algorithms (MMEAs) have been ...

• Propose a clustering differential evolution with neighborhood-based dual mutation.

• The modeling of fog/cloud offloading using replicator dynamics is scalable.
• It ...

Due to the growing demand for delay-sensitive applications, partial offloading is very important in fog/cloud computing. In partial offloading, different parts of each task are run in parallel on the end device, fog device, and remote ...

• This work included sentiment analysis into recommender systems.
• Reviews and ...

Identifying user preferences is a complex operation, which makes its automa- tion challenging, and existing recommendation systems that rely on one of the parameters ratings or reviews are incapable of performing effectively. In this ...

Cross-modal retrieval takes a query of one modality to retrieve relevant results from another modality, and its key issue lies in how to learn the cross-modal similarity. Note that the complete semantic information of a specific ...

• Proposes a multi-modal and multi-grained hierarchical semantic enhancement network.

• A Deep Neural Collaborative Filtering method is proposed using WMF and FC-MLP.
• ...

With the advent of the modern era, deep neural networks have dominated recommender systems, as they can effectively capture complex interactions. Nevertheless, there is still a research gap in fertilizer recommendations based on soil ...

• A part-based convolutional network is proposed for yak identification.
• An ...

Yaks (Bos grunniens) are the most important domestic animals for people living at high altitudes. In order to implement precise livestock management for yaks, it is of significant importance to automatically identify, keep track of, ...

• The SSN sparse learning model is proposed for BFCN construction.
• The FSAE model ...

Obsessive-compulsive disorder (OCD) brings many problems to patients. Redundant information in the OCD data can be removed to preserve valuable biological functions through sparse learning methods. Therefore, constructing a brain ...

Super-high arch dams are affected by similar environmental factors, and there is some spatial and temporal correlation among the deformation measurement points, while the deformation law varies in different parts of the arch dam. Using ...

Implementing internet of things technologies in health monitoring systems attracts a lot of attention. Running the model at edge can continuously and in real-time monitor the user’s physiological information, which can be adopted in ...

• A framework is proposed for extremely resource-constrained edge devices.
• Neural ...

Privacy concerns emerge when online users of popular user-generated content (UGC) platforms are identified through a combination of their structured data (e.g., location and name) and textual content (e.g., word choices and writing ...

• Online platforms’ users can be identified given their posted demographics.
• We ...

• Construct a general incomplete data processing model for the same structure incomplete data set.

Aiming at the problem of incomplete data such as outliers and missing values in machine learning, an incomplete data processing method based on the measurement of missing rate and abnormal degree was proposed in this paper. In this ...

• Segmenting brain abnormality is a complex task.
• Detecting and segmenting brain ...

The Detection of brain abnormality is a complex task. The images captured from the MRI scan machines have numerous information, and it is difficult to segment the appropriate information from the images. Earlier studies have shown ...

• A causal discovery algorithm based on rank correlation.
• Apply Kendall ...

Early diagnosis can significantly improve the survival rate of lung cancer patients. This study attempts to construct a causal structure network between the computational and semantic features of lung nodules through causal discovery ...

• A new GDM method is developed based on incomplete PFPRs.
• The sufficient ...

Completing the Pythagorean fuzzy preference relations (PFPRs) based on additive consistency may exceed the defined domain. Therefore, we develop a group decision-making (GDM) method with incomplete PFPRs. Firstly, sufficient conditions ...

• Counterfactual example-based explanation.
• Explainable bankruptcy prediction ...

In recent years, there have been many studies on the application and implementation of machine learning techniques in the financial domain. Implementation of such state-of-the-art models inevitably requires interpretability for users ...

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• Compression is vital for the efficiency of channel capacity in telemedicine network.

Obtaining a 3D medical visualization is a tedious process requiring several processing steps (such as segmentation) and assigning various rendering parameters (such as color and opacity). Current systems use video/image exporting or ...

• Extraction of image features for some classifiers.
• Selection of the five most successful classifiers ...

The COVID-19 pandemic has been affecting the world since December 2019, and nowadays, the number of infected is increasing rapidly. Chest X-ray images are clinical adjuncts that can be used in the diagnosis of COVID-19 disease. Because ...

Social media platforms have evolved into an online representation of our social interactions. We may use the resources they provide to analyze phenomena that occur within them, such as the development and viralization of offensive and ...

• HaterBERT, a BETO-based Spanish hate speech classifier that outperforms previous.

Classification tasks are of great importance in machine learning. However, class imbalance is a universal problem that needs to be solved in classification and can greatly affect the performance of machine learning classifiers. ...

As Factorization Machine (FM) models linearly describe feature interactions, they cannot accurately capture complex non-linear features of data. Furthermore, random initialization in these FM models seriously affects system convergence ...

• A novel neural embedding factorization machine (NEFM) was proposed.
• NEFM ...

• We study the strategic experts’ weight manipulation in 2-rank consensus reaching in GDM.

In group decision-making (GDM) problems, decision makers may prefer to divide the alternatives into two preference-ordered categories, which is called a 2-rank GDM problem. In the process of 2-rank GDM, consensus is of great ...

In industrial composites applications, drilling is one of the most common operations and complex processes during the final assembly, which can generate undesirable damage to the manufactured part. Data collection from a given ...

• A multi-objective online multi-label feature selection method is proposed.
• A ...

Multi-label classification methods aim at assigning more than one label to each instance. In many real-world classification problems such as image multi-label classification tasks such as cancer detection, and text classification, we ...

• A novel consensus method has been developed for multi-agent decision systems.
• ...

Aggregating multiple opinions or assessments in a decision has always been a challenging field topic for researchers. Over the last decade, different approaches, mainly based on weighting data sources or decision-makers (DMs), have ...