Expert Systems with Applications: An International Journal

• Large-scale, distributed planning problems arise in semiconductor supply chains.

Semiconductor supply chains that manufacture integrated circuits consist of dozens of semiconductor wafer fabrication, assembly and testing and storage facilities. The sheer size of the geographically distributed facilities and the ...

The integration of inventory and transportation operations is a challenging task in multi-echelon supply chain management. Uncertainties in customer demands can have serious consequences, such as inventory fluctuations and ...

• A joint inventory and transportation optimization framework is established.
• The ...

To acquire high-quality resistivity inversion results, an intelligent resistivity inversion (IRI) framework based on a fuzzy wavelet neural network (FWNN) is proposed in this paper. In the IRI framework, first, the FWNN is applied to ...

• FWNN is an interpretable inversion model for ERI.
• WNN is applied to enhance the ...

With the idea of divide and rule, there exist two different forms of semantic features flowing in the two stage instance segmentation paradigms. They are the global features at the image level and the instance features at the region-...

• We exposed the flaws of the two-stage instance segmentation method.
• A novel two ...

This paper presents a simple approach to the mathematical modelling of the Integer Order (IO) and Fractional Order (FO) Interval Type-2 Fuzzy Proportional Integral Derivative (IT2FPID) controllers. In this approach, the control effort ...

• A simple strategy is proposed for the modelling of IT2 fuzzy PID controller.
• ...

• Proposing a framework of active learning combined with semi-supervised learning.

Lithology identification is the basis of energy exploration and reservoir evaluation, intelligent and accurate identification of underground lithology is a key issue. The establishment of a machine learning lithology identification ...

Stock movement prediction is an important field of study that can help market traders make better trading decisions and earn more profit. The fusion of text from social media platforms such as Twitter and actual stock prices is an ...

• The stock market is a time series problem, leading to temporal dependence.
• A ...

The purpose of few-shot learning is to learn a classifier, even if only a limited number of samples are used, a good generalization effect can be achieved. Recently, many methods based on meta-learning learn a large number of multi-...

• Mutual learning module based on the multi-attention mechanism.
• Combine few-shot ...

Augmentative and Alternative Communication (AAC) boards are essential tools for people with Complex Communication Needs (e.g., a person with down’s syndrome, autism, or cerebral palsy). These boards allow the construction of messages ...

• AAC systems must help users find the most suitable pictograms to complete a phrase.

The interdisciplinary relationship between machine learning and financial markets has long been a theme of great interest among both research communities. Recently, reinforcement learning and deep learning methods gained prominence in ...

• ResNet-LSTM actor as our proposed method for financial trading decision problems.

Accurate mortality prediction allows Intensive Care Units (ICUs) to adequately benchmark clinical practice and identify patients with unexpected outcomes. Traditionally, simple statistical models have been used to assess patient death ...

• Deep Learning can outperform traditional scores such as SAPS-II.
• Results ...

This paper focuses on the methods for the efficient solution of multi-objective non-linear optimization problems with uncertain parameters represented as intuitionistic fuzzy numbers. In most of the existing techniques for such ...

• Investigating the IF non-linear problems with conflicting objectives.
• Extending ...

Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack ...

• Propose a multi-source transfer learning-guided ensemble LSTM method.
• Develop a ...

An important factor that discourages patrons from using bus systems is the long and uncertain waiting times. Therefore, accurate bus travel time prediction is important to improve the serviceability of bus transport systems. Many ...

• First time to achieve city-wide bus travel time prediction with limited data.
• ...

Computational systems to provide studies and diagnoses of non-Hodgkin’s lymphomas have been increasingly developed to assist specialists in their decision-making. On the other hand, the approaches have not yet fully explored the sample ...

• An experimental analysis dedicated to improving techniques for CAD systems on NHL.

• A novel deep learning hybrid model for stock index prediction is proposed.
• ...

Stock index prediction is considered one of the most challenging issues in the financial sector owing to its noise, volatility, and instability. Traditional stock index prediction methods, such as statistical and machine learning ...

Due to the restriction of duality structure, existing discounting methods in Lévy models have not met the requirements from psychologists and economists. To address this weakness, our paper invents a stochastic heterogeneous quasi-...

• Designing a preference tool based on psychological experiments.
• Accommodating ...

The recovery of motion abilities after a physical or neurological trauma is a long and winding road that is usually supported by medical staff. In particular, occupational therapists (OTs) play a fundamental role in assessing the ...

• Foster the patients’ independence during and after rehabilitation.
• Promote the ...

Insurers are increasingly adopting more demand-based strategies to incorporate the indirect effect of premium changes on their policyholders’ willingness to stay. However, since in practice both insurers’ renewal premia and customers’ ...

• A causal inference framework is introduced that allows for discrete or continuous rate changes.

Expensive multi-objective combinatorial optimization problems have constraints in the number of objective function evaluations due to time, financial, or resource restrictions. As most combinatorial problems, they are subject to a high ...

• A greedy strategy that uses knowledge-assisted local search methods is developed.

• With rise in algorithmic trading efficiency the trade size decreases significantly.

Financial markets have come across a phenomenal adoption of advanced and complex technologies in the pursuit of efficient markets. Algorithmic Trading (AT) is one of the prominent moves in this direction and is widely adopted across ...

• We propose several XGBoost extensions to learn class-imbalanced data.
• Fit the ...

There is often a significant class imbalance in credit scoring datasets, mainly in portfolios of secured loans such as mortgage loans. A class imbalance occurs when the number of non-default cases outweighs the number of default cases. ...

• FISEVAL builds upon fuzzy logic, providing tangible project evaluation metrics.

Efficient evaluation of a project is a vital factor for its successful realization during its life-time, as it can offer insight to the factors that could influence its quality. In this vein, a new approach in project evaluation, ...

Due to the rapid growth of the information overload issue, recommender systems have become necessary and are implemented in numerous facets of human life, including the tourism industry. Today, technological advancements have ...

• A three-phase algorithm is proposed for automatic linear defects diagnosis is proposed.

Electroluminescence (EL) is considered an efficient technique for the quality assessment of photovoltaic (PV) modules through observing the cell internal characteristics. Most cell defects exhibit the linear characteristics in the EL ...

• The mixed integer linear programming model (MILP) of DABFSP is designed.
• A ...

The distributed flow shop scheduling problem (DFSP) has become widespread due to the increasing advantages of multi-factories manufacturing in recent years. The distributed assembly blocking flow shop scheduling problem (DABFSP), which ...

Servicing claims, a time consuming and labor-intensive task, plays a pivotal role in how insurance companies serve their policyholders. Claims may not get routed early enough in the process to the correct team, leading to dissatisfied ...

• A channeling system to improve claims management and customer satisfaction.
• ...

Machine learning techniques have one of their main objectives to reduce the generalized prediction error. Support vector models have as a main challenge the choice of an appropriate kernel function, as well as the estimation of its ...

• Random Machines presents a new ensemble method using support vector models.
• ...

Traffic flow forecasting is an essential component of an intelligent transportation system to mitigate congestion. Recurrent neural networks, particularly gated recurrent units and long short-term memory, have been the state-of-the-art ...

• Applicability of transformers in traffic state forecasting is justified.
• A ...