Expert Systems with Applications: An International Journal

Nowadays, cloud organizations face challenges in managing huge count of data with various resources due to the prompt expansion of cloud computing (CC) environments with customers ranging from individual users to large commercial organisations. ...

As an increasing number of investors share their opinions on social networks, a critical challenge is to provide advice and assistance in making well-informed investment decisions by considering enormous online sentiments. A typical way is to ...

• A novel machine learning-based method is proposed to aggregate investor sentiments.
• Sentiments from various investors matter for the stock recommendation.
• Applying machine learning to follow various investors boosts performance.

• Proposing a novel MDCNN-HAHG model to recognize different hand gestures accurately.
• The images of hand gestures obtained from DHG and FHPA datasets are used as input.
• The data distortions present in datasets are eliminated by ...

Sign language is a non-verbal communication between deaf and dumb communities that helps them to communicate or interact with other individuals. Although many sign language recognition models restrict the gap between the deaf-dumb community and ...

Gender identification at the word level in a multi-script environment is challenging due to variations posed by free-style handwriting of individuals and geographical differences in writing styles. This paper presents a new approach, Multi-...

In this manuscript, a High Throughput and Low Latency DA-FIR filter design is integrated with Approximate Karatsuba Multiplier (AKM) and Variable Latency Carry Skip Adder (VLCSA) is proposed for the noise removal application in SDR. In this ...

Currently, heart disease is the leading cause of death in world. Since cardiac sickness requires knowledge and detailed information, it is challenging to anticipate. Healthcare systems have been using Internet of Things (IoT) technologies for ...

This paper presents a novel deep learning model called Self-Attention Layer within a Convolutional Neural Network (SACNN), specifically designed for detecting acoustic data in extensive datasets containing emergency vehicle sirens and road noise. ...

Cow recognition forms the foundation of smart livestock management. Current closed-set cow recognition models can exclusively identify cows within their training set and are ill-equipped to recognize other subjects in the open world. Additionally,...

The rapid development of natural language processing (NLP) technologies has enabled the emergence of legal intelligence assistance systems, with legal charge prediction (LCP) being a critical technology. The automatic LCP aims to determine the ...

• A legal schematic knowledge-aware model for confusing charges.
• Dual-graph interaction to integrate information from structural and semantic graphs.
• A hierarchical transformer to obtain legal knowledge representations.
• A deep ...

In the decade, researchers have proposed many remarkable algorithms in structural design, training modes, etc., in the field of Generative AI. However, with the explosive growth of demand for data and annotation in generative models and the ...

The joint classification of hyperspectral image (HSI) and Light Detection and Ranging (LiDAR) data can provide complementary information for each other, which has become a prominent topic in the field of remote sensing. Nevertheless, the common ...

• A novel graph-attention based multimodal fusion network is designed.
• A triple feature extraction backbone is designed for the joint HSI–LiDAR dataset.
• An undirected weighted graph is constructed, then fused by attention strategy.

Smart vehicles have a rising demand for computation resources, and recently vehicular edge computing has been recognized as an effective solution. Edge servers deployed in roadside units are capable of accomplishing tasks beyond the capacity ...

• We propose a Rainbow-based task offloading algorithm for vehicular networks.
• The proposed algorithm aims to jointly minimize average delay and energy consumption.
• The performance of the proposed approach is evaluated using real-...

Due to their high performance in modeling and forecasting a large amount of real-world complex phenomena, deep convolutional neural networks have received a great deal of attention over the past ten years. It has been extensively utilized in ...

• We treat a variational PDNet for image denoising problem.
• We propose a propose flexible learning reaction–diffusion system.
• The introduced network and the proposed procedure achieves superior performance comparing with other ...

It remains an important and challenging problem for the classification of long-tailed data. Most existing methods focus on sampling strategies based on tail classes. However, adequately representing tail classes becomes challenging when they ...

Social media data is an opportunistic resource for post-marketing surveillance of COVID-19 (Coronavirus disease of 2019) vaccines. However, the usage of the term Adverse Drug Reaction (ADR) in different contexts, which do not refer to adverse ...

• A framework titled “Sequence Labelling with 2 Level Segregation” (SL2LS) has been proposed to extract ADRs of COVID-19 vaccines from tweets.
• The proposed SL2LS framework has been compared to various state-of- the-art sequence labelling ...

Dynamic constrained optimization problems (DCOPs) are common and important optimization problems in real-world, which have great difficulty to solve. Dynamic constrained evolutionary algorithms (DCEAs) are widely used methods for solving DCOPs. ...

• A change response strategy based on DQN is proposed for complex DCOPs.
• An offspring generation strategy based on penalty and DQN is proposed for complex DCOPs.
• A new complex dynamic constrained test suite C-GMPB is proposed.

In the case of image processing or understanding, one of the common important tasks is to fit closed curves (e.g., circles, ellipses, etc.) to the underlying image. In higher-dimensional situations, the problem of modeling clusters as closed ...

• We propose a general framework for clustering using a closed curve.
• We present a novel density representation of the closed curve.
• Our model is able to describe complicated shapes.

This study presents a clinical treatment decision support system (CTDSS) through which clinicians can simplify the shared-decision making (SDM) process for treating knee osteoarthritis (OA). This system enables patients to participate in ...

Craters are the primary topographic features on celestial bodies and play a vital role in deep space exploration missions. Recently, artificial neural networks (ANNs) have excelled in crater detection. However, when compared to ANNs, spike neural ...

• We propose cluster neurons and vary-time windows neurons to reduce the loss between conversions.
• We propose a bias calibration algorithm that adjusts parameters and facilitates error correction.
• Our model exhibits high precision ...

Largely inspired by the practice that both the brand name supplier and the platform motivate to encroach on the online retailing market, this paper investigates the optimal encroaching decisions in an online retailing marketplace that is ...

• Develop a game theoretical model in an E-commerce platform supply chain with multiple online retailers.
• Explore an endogenous commission set by the platform across different encroaching scenarios.
• Investigate the optimal retailing ...

In this paper, we propose a technique for hierarchical yoga pose classification (YPC) in a multi-stage multi-tasking framework. We propose a three-stage transfer learning based end-to-end training methodology. Novelty lies in (a) proposed ...

Display Omitted

• Novel spatial context aware multi-tasking multi-stage end-to-end training method.
• Improved spatial context awareness, evident through improved CAM explanations.
• Improved inter class margins and intra class compactness.
• Achieved ...

The security of chaos-based Image Encryption (IE) algorithms inherently depends on the permutation and diffusion strategy of the algorithm and the dynamic performance of chaotic maps. However, the existing studies suffer from low disordering ...

Feature selection facilitates uncertainty disposal and information mining, and it has received widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind of fuzzy rough sets, have been applied to feature selection and ...

• Upper approximation is first defined in Div-FRSs to induce measurement.
• Class-specific information of D s enriches decision analysis.
• Spearman correlation coefficient reveals the relevance between classes.
• Algorithm SPESI ...

Wafer bin map (WBM) automatic classification is one of the critical challenges for semiconductor intelligent manufacturing. Many deep learning-based classification models have performed well in WBM classification, but all require a large amount ...

• Masked auto-encoder (MAE) is proposed for complex WBM self-supervised learning.
• The MAE’s encoder combined with 3DCNN improves the classification accuracy.
• A multi-label fine-tuning is proposed for complex WBM classification.
• ...

The transformer architecture with its attention mechanism is the state-of-the-art deep learning method for sequence learning tasks and has achieved superior results in many areas such as NLP. Utilizing the transformer architecture for the ...

Effective intelligent driving test and evaluation methods can improve the development and deployment process of autonomous vehicles (AVs). However, due to the extreme complexity and high dimensionality of driving behavior, how to objectively and ...

• Scenario complexity model is built as a scaling metric to reduce scoring variance.
• Altruism performance evaluation is first proposed to portray AVs’ intelligence.
• MEREC and MARCOS combining complexity model are proposed to quantify ...

Facial beauty prediction (FBP) aims to develop a system to assess facial attractiveness automatically. Through prior research and our own observations, it has become evident that attribute information, such as gender and race, is a key factor ...

• Deep learning techniques for facial beauty prediction.
• Learning feature alignment across multiple attribute domain data.
• Learning attribute-guided feature representation for better feature alignment.
• Attribute-guided ...

Differential Evolution (DE) stands out as an exceptional intelligent evolutionary algorithm, acclaimed for its simplicity in implementation and the ability to optimize without necessitating differentiable conditions. However, a significant ...

• An adaptive DE with Diversity Maintenance and Restart Mechanism is proposed.
• The external archive is selectively optimized based on the successful rate of the population.
• A novel restart mechanism is proposed to avoid premature ...

Fault detection is a critical step in structural modeling and characterizing reservoirs. 3D seismic fault labeling is almost impossible to obtain, while networks trained on synthetic fault data have limited generalization to real data. We use ...

• Seismic fault is crucial in aiding reservoir discovery and geological analysis.
• Manual 3D labels are hard to obtain; synthetic data generalization has limitations.
• We propose Sparse Distance Matching & Adaptive Spatial Aggregation.