Expert Systems with Applications: An International Journal

The Recent development of Wireless multimedia sensor networks (WMSN), and the Internet of Things (IoT) have been improved for resolving day-to-day concerns in the agricultural field.. Furthermore, agriculture fields near the forest areas face a ...

The product design process in manufacturing involves iterative design modeling and analysis to achieve the target engineering performance, but such an iterative process is time consuming and computationally expensive. Recently, deep learning-...

• 27 Gastrointestinal (GI) diseases classified from GI tract areas.
• Innovative preprocessing enhances GastroVision dataset image quality.
• Novel lightweight PD-CNN and PCC for feature extraction and refining.
• Novel Ensemble ELM ...

The rising prevalence of gastrointestinal (GI) tract disorders worldwide highlights the urgent need for precise diagnosis, as these diseases greatly affect human life and contribute to high mortality rates. Fast identification, accurate ...

Glaucoma, a leading cause of irreversible blindness worldwide, poses significant diagnostic challenges due to its reliance on subjective evaluation. Recent advances in computer vision and deep learning have demonstrated the potential for ...

• Surveyed deep learning techniques (2017–2023) for glaucoma diagnosis.
• Categorized glaucoma diagnosis feature extraction methods.
• Studied datasets, architectures, and metrics for glaucoma diagnosis.
• Outlined challenges and ...

Against the backdrop of the new-generation intelligent manufacturing and Industrial Internet of Things, manufacturing processes are evolving towards integration, large-scale operations, and complexity, and the requirements for process safety and ...

• A cloud–edge collaborative quality-related fault detection framework is proposed.
• Two quality supervised training mechanisms based on MGU methods are respectively presented.
• A federated bi-directional knowledge distillation-based ...

Finance sector loss can be minimized by reducing the number of defaulters who often miss payments during debt collection. Most research focused on the credit risk analysis before approving the loan to the customer, and few papers related to ...

The recommender system recommends items to the users based on their preferences of implicit feedback. However, implicit feedback often contains noise that deviates from the user’s true preferences, thereby influencing the accuracy of the ...

• A recommender system that adapts to noise in implicit feedback.
• Balancing accuracy and diversity through data collaborative training.
• Experiments on three datasets verify the effectiveness of the model.

The rapid growth of the Industry 4.0 paradigm is increasing the pressure to develop effective automated monitoring systems. Artificial Intelligence (AI) is a convenient tool to improve the efficiency of industrial processes while reducing errors ...

• Using Artificial Intelligence to increase the efficiency of industrial process.
• A novel automated monitoring system to enhance control processes.
• Addressing the production control issue of test tubes for analysis laboratories.
• ...

As a decision model to depict the human cognitive process, three-way decision (3WD) provides a reasonable semantic interpretation for solving practical multi-attribute group decision-making (MAGDM) problems. At the same time, there often exist ...

• Compute the weights of decision-makers by using the consensus degree.
• Calculates the collective evaluation result from an optimization perspective.
• Design an SMA3WGD model with the aid of 3WD theory and sequential strategy.
• ...

This paper studies a problem of determining the state of a system through costly tests of its components, where components can be tested simultaneously in batches to exploit economies of scale. This problem is a generalization of the classical ...

• We study sequential testing in batches with resource constraints.
• We consider a fixed cost per batch, variable testing costs, and independent prior probabilities for each component.
• We propose a mixed-integer programming ...

The objective of this study is to craft a novel S-Box tailored to stringent security standards, achieved through iterative application of crossover and mutation operators inherent to real-coded genetic algorithms, ensuring robust image ...

• Proposed S-Box is robust against differential and cryptanalysis techniques.
• The crossover and mutation operators real coded GA is used for the design S-Box.
• S-Box has no fixed-points, reverse fixed-points and a short iteration ...

Bolt assembly is widely used in modern manufacturing. And with the development of industrial automation, there is a growing demand for automatic assembly. To form a fully automatic and highly intelligent system for bolt assembly, a novel strategy ...

Owing to outstanding representation abilities, deep neural networks (DNNs) have recently been extensively studied and attracted increasing attention in virtual sensing of key industrial variables. Although various learning algorithms have been ...

• A novel model BSsMSLNN is proposed, exploiting full potentials of shallow networks.
• A unified training framework based on VI and SGD is developed for the BSsMSLNN.
• Thorough performance evaluations show the superiorities of the ...

The vast amount of now available genomic, biomolecular, and clinical patient data may revolutionize personalized medicine. Yet, this availability poses significant challenges to artificial intelligence as the major obstacle is that retrospective ...

• Rethinking historical patient data for drug ranking in Personalized Medicine with Machine Learning.
• Two new metrics to evaluate drug ranking models over historical patients: ASR and ASRS.
• The classification of drug ranking models ...

Load prediction is crucial to unit commitment and scheduling in electricity systems; however, load prediction models consume considerable electricity. Most conventional prediction methods are based on deep learning (DL) models, such as long short-...

The service quality of multimodal public transport system plays a crucial role in maintaining the overall efficiency and sustainability of urban mobility and the seamless integration of transportation. This study evaluates the determinants of ...

• RFM generates multiple feature subsets by random feature mapping for stabilizing.
• RFM enhances classification effect by fusing results of multiple feature subsets.
• RFM assigns optimal weights to different weak classifiers by ...

The feature mapping method can improve data separability, enhance data representation ability, and reduce data processing complexity. However, on the one hand, the existing feature mapping methods have difficulty processing datasets of different ...

In this paper, we address a sampling procedure for approximating the Deegan-Packel index, which is particularly advantageous when tackling with large-scale problems. It is based on the estimation of an expectation in a subdomain. Its performance ...

• The Deegan-Packel power index is difficult to compute in large-scale problems.
• We propose a sampling method to estimate it and that solves this drawback.
• We analyze the statistical properties of the resulting estimator.
• We rank ...

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Feature Selection (FS) is considered a crucial procedure for eliminating unnecessary features from datasets. FS is considered a challenging problem that is difficult to solve efficiently due to its combinatorial nature. As the problem size ...

The luminance dynamic range in natural scenes is exceedingly wide. However, capturing the wide dynamic range information of natural scenes through a single exposure is challenging for commercial light field cameras. One solution to expand the ...

Aspect Sentiment Triplet Extraction (ASTE) aims to identify triplets consisting of aspect terms, opinion terms, and sentiment polarities in reviews. Although previous methods of training specific models for a single domain have achieved ...

• A novel syntactic representation method is proposed to transfer rule knowledge.
• Task-related rule knowledge is captured by two designed perceptrons.
• Experimental results demonstrate the effectiveness of the proposed method.
• The ...

Image encryption-then-compression (ETC), combining encryption and compression techniques, is a powerful strategy for image privacy protection. One of the most significant concerns in ETC is to realize a trade-off between high compression and high-...

• A new ring-network optical dynamic system is constructed.
• A novel optical reservoir computing system is presented.
• A novel secret key generation method is introduced.
• A novel encryption-then-compression scheme is proposed.

For biomimetic robots to be used in the real world, it is very important to have animal-like turning motion. Excellent turning ability can help quadruped robots navigate more complex obstacle terrain. To solve this problem, we conducted the ...

• The turning ability help quadruped robot pass through various complex obstacles.
• The key movement joints extraction method can identify important biomimetic joints.
• N-step turning strategy and biomimetic turning trajectory are ...

Graph Convolution Network-based (GCN-based) approaches show promising performance on 3D skeleton-based human motion prediction due to its natural graph representation and outstanding ability for spatial–temporal dependencies modeling for human ...

• A novel method to solve over-smoothing issue in motion prediction.
• A Progressive Dynamic Graph is developed.
• A Target-guide progressive learning framework is presented.

The advent of machine learning (ML) has significantly advanced the recognition and translation of sign languages, bridging communication gaps for hearing-impaired communities. At the heart of these technologies is data labeling, crucial for ...

• HamNoSys notation system is not sufficient for ML labeling purposes
• HamNoSys-based labels are often subjective and inconsistent
• There is a need for a notation system useful in computer-aided systems

The identification of informative and distinguishing features from high-dimensional data has gained significant attention in the field of machine learning. Recently, there has been growing interest in employing matrix factorization-based ...

The blockchain platforms (BPs) have emerged as important tools for improving enterprise data security. However, the selection of a suitable BP for is a challenging issue for enterprises. In this paper, a novel heterogenous multi-criteria decision-...

Multi-Label Classification (MLC) extends standard classification in the sense that an instance might belong to multiple labels simultaneously. Many lazy approaches to MLC have been proposed so far. The majority of them, to classify an instance, ...

• We propose lazy Multi-Label Classification methods based on imprecise probabilities.
• They are more suitable than other methods for class-imbalance in multi-label.
• Our proposed algorithms outperform other methods of the state-of-the-...

Drought stands as a highly perilous natural catastrophe that impacts numerous facets of human existence. Drought data is nonstationary and noisy, posing challenges for accurate forecasting. This study proposes a novel hybrid framework integrating ...