Expert Systems with Applications: An International Journal

• Geometry of temporal-derivatives contains discriminatory features.
• Late fusion ...

Visual anomaly recognition (VAR) is the core part of many intelligent systems. However, vagueness in definitions and lack of a priori knowledge about the distribution of anomalies make VAR a challenging problem. Supervised solutions ...

• Classical risk measures perform poorly in bubble condition.
• A new portfolio ...

In this paper, a new mathematical formulation for a portfolio selection problem is developed. This model is based on the difference between fundamental value and market value of assets. The proposed model is especially applicable in ...

• We theoretically describe the expert systems.
• We investigate the fuzzy, medical ...

The aim of this review is to provide a broad overview of the state-of-the-art works mainly published in the last ten years on expert systems applied in different medical domains.

Being able to support and sometimes ...

• A performance-based algorithm (HGS) is proposed for global search and optimization in real world.

A recent set of overused population-based methods have been published in recent years. Despite their popularity, most of them have uncertain, immature performance, partially done verifications, similar overused metaphors, similar ...

• Novel application of deep learning techniques to fluid dynamics and complex flows.

Deep learning models are not yet fully applied to fluid dynamics predictions, while they are the state-of-the-art solution in many other areas i.e. video and language processing, finance, robotics . Prediction problems on high-...

• A new function for computing local self-organizing clustering center with SOM clustering algorithm.

Supervised active contour models can use information extracted from supervised samples to guide contour evolution. However, their applicability is limited by the accuracy of the probabilistic models they use, especially when processing ...

• We propose a deep learning approach to perform multi-disease prediction.
• The ...

Prediction of future clinical events (e.g., disease diagnoses) is an important machine learning task in healthcare informatics research. In this work, we propose a deep learning approach to perform multi-disease prediction for ...

• A novel graph embedding using dominating-set theory and deep learning is proposed.

A collaborative network is a social network that is comprised of experts who cooperate with each other to fulfill a special goal. Analyzing this network yields meaningful information about the expertise of these experts and their ...

• Proposed a deep learning clustering method for solar irradiance feature learning.

Solar radiation forecasting is a key technology to improve the control and scheduling performance of photovoltaic power plants. In this paper, a deep learning based hybrid method for 1-hour ahead Global Horizontal Irradiance (GHI) ...

• Recovery of fossil/ geothermal energy requires subsurface mechanical characterization.

Accurate subsurface geomechanical characterization is critical for fossil and geothermal energy recovery and extraction of earth resources. Compressional and shear travel time logs (DTC and DTS) acquired using sonic logging tools ...

• Predict cumulative abnormal return of stocks following earnings release using XGBoost.

Post-Earnings-Announcement Drift (PEAD) is a stock market phenomenon when a stock’s cumulative abnormal return has a tendency to drift in the direction of an earnings surprise in the near term following an earnings announcement. ...

• High-performance of deep belief networks for environmental sound classification.

In this paper, we propose a Deep Belief Network (DBN) based approach for the classification of audio signals to improve work activity identification and remote surveillance of construction projects. The aim of the work is to obtain an ...

• We propose a new probabilistic verification approach for agent-based systems.
• ...

In recent years, the use of Multi-Agent Systems (MASs) to solve complex problems has grown rapidly. Social communicative commitments have been widely employed in such systems as a means of communication allowing heterogeneous agents to ...

• Sine cosine algorithm is improved to solve large-scale global optimization problem.

The sine cosine algorithm (SCA) is a recently proposed swarm intelligence optimization based on sine and cosine mathematical functions. It has a novel principle to process global optimization, but when solving large-scale global ...

• An interpretable distributed data analysis with sharing intermediate representations.

This paper proposes an interpretable non-model sharing collaborative data analysis method as a federated learning system, which is an emerging technology for analyzing distributed data. Analyzing distributed data is essential in many ...

• Denoising the distant supervision assumption using semantic similarity is proposed.

Ontology lexicalization aims to provide information about how the elements of an ontology are verbalized in a given language. Most ontology lexicalization techniques require labeled training data, which are usually generated ...

• Virtual Experts are used to develop crowd wisdom to outperform market indexes.
• ...

Effective portfolio management requires vast quantities of information and accurate forecasts to make decisions that generate a profitable strategy. In this study, we propose a framework that extracts useful information from Virtual ...

• The need of deep neural networks for stock price and trend prediction is discussed.

The stock market has been an attractive field for a large number of organizers and investors to derive useful predictions. Fundamental knowledge of stock market can be utilised with technical indicators to investigate different ...

• Custom Convolutional Neural Networks improve WiFi localisation performance.
• ...

Different technologies have been proposed to provide indoor localisation: magnetic field, Bluetooth, WiFi, etc. Among them, WiFi is the one with the highest availability and highest accuracy. This fact allows for an ubiquitous accurate ...

• Quantitatively compare the performance of three types of VEPs in person identification.

Electroencephalogram (EEG)-based biometrics have attracted increasing attention in recent years. A few studies have used visual evoked potentials (VEPs) in EEG biometrics due to their high signal-to-noise ratio (SNR) and good ...

• A dynamic weight particle swarm optimization-based sine map method is presented.

Aiming at the problem of low accuracy of reliability prediction, a back propagation neural network (BPNN) model is developed. In the process of reliability prediction, a dynamic weight particle swarm optimization-based sine map (SDWPSO)...

• Novel grey forecasting model called the neural ordinary differential grey model (NODGM) is proposed.

Due to the efficiency of grey models in predicting the time series of small samples, grey system theory has been well studied since it was first proposed and has now become an important method for small sample prediction. Inspired by ...

• Scalability is improved using an overlapped clustering.
• Clusters of diverse and ...

Recommender systems aim to provide users with recommendations of quality. New evaluation metrics such as diversity, have taken an increasing interest in a wide spectrum of applications, including the ecommerce, due to their ability to ...

• Bidirectional embedding has impactful contributions to meaningful answer discovery.

A patent gives the owner of an invention the exclusive rights to make, use and sell their invention. Before a new patent application is filed, patent lawyers are required to engage in Prior Art Search to determine the likelihood that ...

• We introduce an analytical framework for an assessment of Value-at-Risk backtesting.

The quality of Value at Risk (VaR) forecasts is typically determined by the empirical assessment of the frequency of VaR misspecifications. Additionally, the risk of clustered VaR misspecification over time, especially in volatile ...

• We introduced the keystroke barcodes for the first time in the literature.
• The ...

Investigation of new intelligent solutions for user identification and authentication is and will be essential for enhancing the security of the alphanumeric passwords entered on touchscreen and traditional keyboards. Extraction of the ...

This paper develops smoothing data association based on integrated probabilistic data association (FLIPDA-S) tracker to identify a multicopter UAV (MUAV) and estimate its trajectory in an uncertain and cluttered environment. Recently, ...

Display Omitted

• Three novel text data augmentation techniques.
• Accuracy improvement across ...

Text has traditionally been used to train automated classifiers for a multitude of purposes, such as: classification, topic modelling and sentiment analysis. State-of-the-art LSTM classifier require a large number of training examples ...

• We propose a systematic classification scheme of drone-based models for logistics.

The operational design and planning of drone-based logistics models is a rapidly growing area of scientific research. In this paper, we present a structured, comprehensive, and scalable framework for classifying drone-based delivery ...