Expert Systems with Applications: An International Journal

Cardiac arrhythmias indicate cardiovascular disease which is the leading cause of mortality worldwide, and can be detected by an electrocardiogram (ECG). Automated deep learning methods have been developed to overcome the disadvantages ...

• A novel memory-enhanced AHA-based automated CNN arrhythmia classifier is proposed.

Mental health has become a major concern in recent years. Social media have been increasingly used as platforms to gain insight into a person’s mental health condition by analysing the posts and comments, which are textual in nature. ...

• The proposed method detects depression from Bangla social media texts.
• The ...

Automated and accurate real estate valuation benefits buyers and sellers in real estate markets. So far, the literature on expert systems for real estate valuation has primarily focused on structured features like the age of the ...

• Expert system for automated real estate valuation using textual descriptions.
• ...

Brain tumor classification is an expensive complicated challenge in the sector of clinical image analysis. Machine learning algorithms enabled radiologists to accurately diagnose tumors without requiring major surgery. However, several ...

• A novel deep neuroevolution algorithm for detecting brain tumor classification using MRI images.

• Design a novel multi-objective deep learning system.
• Propose an improved multi-...

Advances in volatility index prediction based on computational intelligence have brought wide-ranging benefits to financial risk management. However, current studies in the field remain limited and need further improvement due to these ...

• Design and developed internet of vehicles based on fog.
• Propose hybrid DRNN and ...

The development and use of intelligent transportation systems as an emerging trend in the application of computational intelligence within the concept of internet of vehicles (IoV) is attracting attention in the academia and ...

• First study to observe three multivariate decomposition tools for ADHD detection.

Early detection and timely therapeutic intervention are of prime importance to prevent the severity of attention deficit hyperactivity disorder (ADHD) in children. Conventional diagnostic methods are time-taking as they are based on ...

Reversible data hiding (RDH) based on pixel value ordering (PVO) is an effective information security hiding technique that allows complete recovery of the cover image after data extraction. Current methods mainly adopt PVO-based ...

• A reversible information hiding method for PPVO based on Multi-Dimensional constraints is proposed.

In recent decades, RRT* algorithm has attracted much attention because of its asymptotic optimization. However, the RRT* algorithm still suffers from slow convergence rate and large randomness of search range. To overcome the ...

• The extension of the RRT* can reduce the convergence time.
• The implementation ...

Dempster–Shafer evidence theory can combine multiple sources of uncertain information to form a consistent basic probability assignment (BPA). How to transform BPA into decision probability is still an open issue. To solve this problem,...

• Importance is introduced for the first time to weigh the aggressive of decision.

Object-centric processes (also known as Artifact-centric processes) are implementations of a paradigm where an instance of one process is not executed in isolation but interacts with other instances of the same or other processes. ...

• Object-centric processes as a new emerging paradigm in industry.
• Traditional ...

• The chaos method is introduced to quantitative evaluate the randomness of dataset.

With the development of wind power which is the great substitute for traditional energy, it is worth conducting an in-depth exploration of the hourly wind speed time series which is chaotic due to the complex weather. In this paper, ...

• Dynamic scheduling is a challenge for implementing smart manufacturing in the steel industry.

Steelmaking-continuous casting (SCC) is one of the most critical building blocks in the modern steel industry. Many random events occur in the real-world SCC production system. In this paper, we propose a two-stage online scheduling ...

• Deep learning and boosted tree approaches for cryptocurrency price modeling.
• ...

The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with ...

The problem of effective stock trend prediction has aroused much attention these years for its profitability. The development of algorithmic trading drives explosive growth in fast and effective techniques for trend predictions. ...

• Propose the notion of trend generating vectors to represent hidden market states.

COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named ...

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• An efficient vision-based framework to detect COVID-19 through cough sounds.
• ...

• We propose a ML decision support system for aerodrome weather forecasting.
• An ...

Weather forecasting is a critical factor for aerodrome and enroute flight operations. Airport decision-makers rely on assessments made by forecasters to ensure operations safety and optimize flight schedule despite potential adverse ...

The successful instructional design of self and peer assessment in higher education poses several challenges that instructors need to be aware of. One of these is the influence of students’ personalities on their intention to adopt ...

• Effect of personality traits on the acceptance of peer assessment (PA) in students.

• We proposed new singular value decomposition in the reduced biquaternion domain.

Traditional sparse representation models treat color image either represent color channels independently using the monochromatic model or concatenate color channels using the concatenation model. However, these two strategies cannot ...

• An adaptive decomposition approach is proposed to guide the evolution process.
• ...

In decomposition-based multi-objective evolutionary algorithms (MOEAs), a set of uniformly distributed reference vectors (RVs) is usually adopted to decompose a multi-objective optimization problem (MOP) into several single-objective ...

• Building IDS in non-stationary environment.
• Proposed a module to track the ...

Building an Intrusion Detection System (IDS) in non-stationary environment is challenging because, in such an environment, intrusion-related data grow every day. A machine learning model trained in a stationary environment where ...

• A multi-stage design space reduction technique based on SOM and rough sets.
• ...

Hull form design optimization is a highly computationally intensive and complex engineering problem. It has the following characteristics. (1) There are many design parameters that express hull form and the optimization space is large, ...

Both traffic flow and speed forecasting are of great importance to intelligent transportation systems, which have been studied intensely in the past decades. In recent years, forecasting approaches based on deep learning have attracted ...

• Using MTL method to predict traffic flow and speed simultaneously.
• An MRN is ...

Sentiment classification aims to predict the sentiment label for a given text. Recently, several research efforts have been devoted to incorporate matching clues between text words and class labels into the learning process of text ...

• Propose an inverse label entropy based strategy for generating effective label descriptions.

• Stabilization and path planning of humanoid robot is performed.
• OTA, Surface ...

In this study, path planning and stabilization of humanoids are carried out in an uneven path and dynamic environment. The importance of the work focuses on avoiding local minima and trapping in dead-ends during navigation. The sole ...

• Applying reinforcement learning methods to operations management.
• A simulation ...

This study investigates whether the presence of both quality- and value-based online reviews help firms make decisions. To adapt to a complex real-world environment, we construct two simulated environments with high and low initial ...

The community structure is a collection of individuals with common characteristics that commonly exists in complex networks. The detection of community structures aids in mining information in the network and exploring the evolution ...

• An overlapping community detection algorithm based on adaptive DPC is proposed.

• Unlimited angular motion cues via a spherical motion platform reduce motion sickness.

Simulator sickness is a crucial concern undermining several benefits of simulator training, such as a realistic environment, low costs, and safe practice of emergencies. This study investigated the effects of unbounded angular motions ...

Deep neural networks and model-based methods are both popular for their wide and great success in many inference problems. In this paper, resorting to deep learning, we study the efficient algorithms for two popular nonconvex ...

• We develop efficient algorithms for solving nonconvex regularization methods.
• ...