Liquid bulk cargo (LBC) volume analysis has received considerably great attention recently since LBC is a valuable and high-demand cargo. Thus, it is important to establish an analysis system for LBC volume, as it can help inform strategies for port planning and management. Nevertheless, LBC volume analysis is a challenging task for researchers because trends in LBC volume are highly volatile and non-stationary

Both recognition and 3D tracking of frontal dynamic objects are crucial problems in an autonomous vehicle, while depth estimation as an essential issue becomes a challenging problem using a monocular camera. Since both camera and objects are moving, the issue can be formed as a structure from motion (SFM) problem. In this paper, to elicit features from an image, the YOLOv3 approach is utilized beside

Feature selection aims at selecting important features that can enhance learning performance in data mining, pattern recognition, and machine learning. Filter feature selection methods offer computational efficiency and feature evaluation criteria, while feature interaction information, which may greatly help increase classification accuracy, is often ignored. In this work, we instead propose a novel

Background Analyzing images to accurately estimate the number of different cell types in the brain using automatic methods is a major objective in neuroscience. The automatic and selective detection and segmentation of neurons would be an important step in neuroanatomical studies. New method We present a method to improve the 3D reconstruction of neuronal nuclei that allows their segmentation, excluding

After the invention of Bitcoin as well as other blockchain-based peer-to-peer payment systems, the cryptocurrency market has rapidly gained popularity. Consequently, the volatility of the various cryptocurrency prices attracts substantial attention from both investors and researchers. It is a challenging task to forecast the prices of cryptocurrencies due to the non-stationary prices and the stochastic

Studies on manufacturing cost prediction based on deep learning have begun in recent years, but the cost prediction rationale cannot be explained because the models are still used as a black box. This study aims to propose a manufacturing cost prediction process for 3D computer-aided design (CAD) models using explainable artificial intelligence. The proposed process can visualize the machining features

There are several types of neural networks (NNs) that are widely used for data classification tasks. The supervised learning NN is an advanced network with a training algorithm for setting the weights and biases of the network in its training phase. However, traditional training algorithms such as backpropagation have some drawbacks, such as slow convergence speed and falling into local minima, which

This paper presents a routing optimization model for a wide range of waste collection problems which allows for multiple depots with homogeneous, capacitated vehicles, intermediate stops at multiple processing facilities, and multiple pick-ups per waste collection location. By minimizing collection routing and vehicle investment costs, the model estimates the total transportation costs for a given

Transcription factor binding site (TFBS) is a DNA sequence that binds to transcription factor and regulates the transcription process of the gene. Although deep learning algorithms are superior to traditional methods in predicting transcription factor binding site, they often rely too much on negative sample data, which cannot be verified by experiment. In particular, a training model with such negative

The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide. An important step in combating COVID-19 is the screening of infected patients using chest X-ray (CXR)

Software testing is one of the most important methods of analyzing software quality assurance. This process is very time consuming and expensive and accounts for almost 50% of the software production cost. In addition to the cost problem, the nature of the test, which seeks errors in the program, is such that software engineers are not interested in doing the process, so we are looking to use automated

Volatility forecasting plays an integral role in risk management, investments and security valuation for all assets with uncertain future payoffs. We enrich the literature by presenting computationally intensive variations of the heterogeneous autoregressive (HAR) volatility model: the complete subset linear/quantile regression HAR models, HAR-CSLR and HAR-CSQR. Predictions of 1- to 22-day-ahead volatility

Glucose–insulin models, testing glucose sensors and support systems for health care decisions play an important role in synthesis of glucose control algorithms. In this work we propose an online glucose–insulin identification using the Recurrent High Order Neural Network (RHONN). Then, the model obtained is used to predict n-steps forward of glucose levels, also by RHONN. The used data for identification

News recommendations distinguishes from general content recommendations as it takes in consideration news freshness, sparsity, monotony and time. Recent works approach these features using hybrid Collaborative-Content-based Filtering methods, adapting clustering techniques to handle sparsity and monotony without considering the effects that different clustering methods may have over recommendation

The problem of automatic Fake News detection in digital media of news distribution (DMND - e.g., social networks, online newspaper, etc) has become even more relevant. Among the main detection approaches, the one based on crowd signals from DMND users has stood out by obtaining promising results. In essence, in order to classify a piece of news as fake or not fake, such approach explores the collective

Moth–Flame Optimization (MFO) algorithm is widely used to solve real parameter global optimization problems. However, the original MFO algorithm has some problems. First, the global search ability of the algorithm is insufficient due to the lack of high-quality flames or the lack of diversity of the population. Moreover, due to the reduction of the number of flames mechanism and the simple transverse

Dynamic group optimization has recently appeared as a novel algorithm developed to mimic animal and human socialising behaviours. Although the algorithm strongly lends itself to exploration and exploitation, it has two main drawbacks. The first is that the greedy strategy, used in the dynamic group optimization algorithm, guarantees to evolve a generation of solutions without deteriorating than the

Automatic liver and tumour segmentation in CT images are crucial in numerous clinical applications, such as postoperative assessment, surgical planning, and pathological diagnosis of hepatic diseases. However, there are still a considerable number of difficulties to overcome due to the fuzzy boundary, irregular shapes, and complex tissues of the liver. In this paper, for liver and tumor segmentation

The large extension and complex structure of most industrial and construction areas very often make it unfeasible or inconvenient for human operators to constantly survey all the workers to detect those who do not properly wear their Personal Protection Equipment (PPE) devices. However, such a detection is of utmost importance to reduce the number of worker injuries. Consequently, the adoption of a

The co-occurrence relationship among diseases facilitates the knowledge discovery in the medical field. However, due to limited data, previous researches are mainly based on clinician experience and simple statistics which make it difficult to discover deep associations among diseases. Treating the diagnoses in an electronic medical record (EMR) as interrelated random variables, we use Markov random

This paper investigates the design and implementation of the discrete-time adaptive neural network control with disturbance observer (DO) on a steer-by-wire (SbW) system, to simultaneously realize accurate tracking and anti-interference performance. Specifically, to approximate the lumped system uncertainty including the friction torque and self-aligning torque, the neural network is employed. To improve

Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine

An important aspect of identifying whether items are likely to be forged or infringe upon a copyright is the use of an anti-counterfeiting pattern because it can help in determining and detecting forged anti-counterfeiting labels and patterns. At present, most anti-counterfeiting systems require special materials or large samples of images for training. Once they are geometrically attacked or forged

The distributed reentrant permutation flow shop (DRPFS) is a combination of the reentrant flow shop problem and distributed scheduling. The DRPFS is a NP-hard problem that consists of two subproblems: (1) assigning a set of jobs to a set of available factories and (2) determining the operation sequence of jobs in each factory. This paper is the first study to consider the inclusion of sequence-dependent

Evolution of supply chain management (SCM) in recent years has transformed it beyond the simple logic of benefit and economic point of views. One of such key strategic elements in establishing a sustainable and socially responsive SCM is supplier selection and performance assessment. This study brings forward a sustainable supplier evaluation structure under multiple criteria and interval valued fuzzy

Though the Genetic Algorithm (GA) has received considerable attention recently in solving multi-objective optimization problems, inefficiency regarding performance has been reported in applications related to project scheduling. The degradation in efficiency was magnificent in applications of highly epistatic objective functions, including scheduling problems wherein the parameters being optimized

The Extreme learning machine (ELM) designed by Huang et al. is proved to be a fast and good classifier over a decade, but existing ELM is non-deterministic in nature as well as kernel dependent and needs attention to optimize the selection of kernels. In ELM feature space is obtained with the help of single kernel function. The choice of kernel depends on perceptiveness of classification problem. So

Due to the increase in motor vehicle accidents, there is a growing need for high-performance car crash detection systems. The authors of this research propose a car crash detection system that uses both video data and audio data from dashboard cameras in order to improve car crash detection performance. While most existing car crash detection systems depend on single modal data (i.e., video data or

In this paper, we develop topological data analysis methods for classification tasks on univariate time series. As an application, we perform binary and ternary classification tasks on two public datasets that consist of physiological signals collected under stress and non-stress conditions. We accomplish our goal by using persistent homology to engineer stable topological features after we use a time

Filling arbitrary missing regions with visually plausible contents is a challenging task. Existing methods have accomplished promising performance for inpainting regular or small defects. However, filling large continuous holes is still difficult due to out of step with the performance requirement for inferring large region contents. Considering that the model needs to predict more missing contents

The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time

A new biologically inspired derivative-free global optimization algorithm called Firebug Swarm Optimization (FSO) inspired by reproductive swarming behaviour of Firebugs (Pyrrhocoris apterus) is proposed. The search for fit reproductive partners by individual bugs in a swarm of Firebugs can be viewed naturally as a search for optimal solutions in a search space. This work proposes a mathematical model

Recently, deep learning has dominated the recommender system, as it is able to effectively capture nonlinear and nontrivial user–item relationships, and perform complex nonlinear transformations. However, there are still some issues with respects to the existing methods. Firstly, they always treat user–item interactions independently, and may fail to cover more complex and hidden information that is

Numerical simulations of non-Newtonian fluids are indispensable for optimization and monitoring of several industrial processes such as crude oil transportation, nuclear cooling, geothermal and fossil fuel production. The governing equations derived for non-Newtonian fluid models result in nonlinear differential equations. Thus, increasing the complexity even for simple geometries. The cumbersome numerical

In syntactic and structural pattern recognition, data represented as strings appear in several supervised classification applications. In some situations, data collections show imbalanced class distributions, which typically results in the classifier biasing its performance to the class representing the majority of objects. To solve this problem, some oversampling methods have been proposed for data

When reading an article, especially a professional article, we often encounter words or phrases that we don't recognize. They may be specific domain terms or emerging entities. When we can't guess their meaning from the article, we generally refer to the terminology dictionary or search for related content on the Internet to understand them. Some researchers have used natural language generation (NLG)

In order to be competitive in today’s rapidly changing business world, enterprises have transformed a centralized to a decentralized structure in many areas of decision. It brings a critical problem that is how to schedule the production resources efficiently among these decentralized production centers. This paper studies a multi-objective distributed hybrid flow shop scheduling problem (MDHFSP) with

As an extension of the fuzzy set, the hesitant fuzzy set (HFS) is an effective tool for handling uncertainty and vagueness in decision making problems. Considering that the correlation coefficient (CC) has a strong ability to process and analyze data, we are developing a novel CC to measure the strength of the relationship between HFSs in this article. The CC presented between the HFSs has more desirable

The most significant advantage of the group recommender system over personalization is the low computational cost because the former analyzes the preferences of many users at once by integrating their preferences. The clustering step is the most time-consuming part of the entire process in a group recommender system. Existing studies either measured the similarities among all users or utilized a clustering

The adverse effects of dust storms such as air quality degradation, reduced visibility and property damage are particularly prominent in estuarian regions. To reduce significant property and health risks posed by such river dust storms, an early-warning system is a useful tool that is urgently needed. In this case study of an area near an estuary, an artificial neural network with various sampling

Real-time vehicle type classification has become more popular and significant in recent years owing to its potential applications. In this paper, a real-time Convolutional Neural Network is proposed for vehicle type classification. This novel CNN structure is a combination of classic CNN layers along with squeeze-and-excitation (SE) modules and Haar wavelet as the pooling layer. This structure improves

The paper introduces a two-stage network Nerlovian profit inefficiency estimator, modelling bank’ “black box” production processes. The unique features of the model include the modelling of a risk factor measured by period non-performing loans (NPLs). Specifically, NPLs enter in the second stage of banks production processes, alongside a human capital factor. The model is applied to Japanese local

Uncertainty is always encountered by decision makers (DMs) when addressing a complex decision-making problem. The existing axiomatic foundation of the analytic hierarchy process (AHP) cannot reflect the uncertainty experienced by DMs. In this paper, it is first to find that the uncertainty experienced by DMs can be characterized by the non-reciprocal property of pairwise comparisons. The novel concept

Disassembly lines are utilized frequently to disassemble the end-of-life products completely or partially to retain the valuable components for remanufacturing or recycling. This research introduces and solves the profit-oriented U-shaped partial disassembly line balancing problem (PUPDLBP) for the first time. A 0–1 integer linear programming model is formulated to tackle the PUPDLBP with AND/OR precedence

The capital market plays a vital role in marketing operations for the rapid development of the aerospace industry. However, due to the uncertainty and complexity of the stock market and many cyclical factors, the stock prices of listed aerospace companies fluctuate significantly. This makes the share price prediction challengeable. To improve the prediction of share price for aerospace industry sector

Privacy preserving data mining is a hot research field of data mining. The aim of privacy preserving data mining is to prevent the leakage of the sensitive information of individuals while performing data mining techniques. Classification task is one of the most studied fields in data mining hence in privacy preserving data mining as well. On the other hand, differential privacy is a powerful privacy

In scene understanding for autonomous vehicles (AVs), models trained on the available datasets fail to generalize well to the complex, real-world scenarios with higher dynamics. In this work, we attempt to handle the distribution mismatch by employing the generative adversarial network (GAN) and weather modeling to strengthen the intra-domain data. We also alleviate the fragility of our trained models

This paper presents a novel inertia moment estimation algorithm to enable the Active Force Control Scheme for tracking a wheeled mobile robot (WMR) effectively in a specific trajectory within constrained environments such as on roads or in factories. This algorithm, also known as laser simulator logic, has the capability to estimate the inertia moment of the AFC-controller when the robot is moving

Code clone detection helps to reduce the costs associated with software maintenance and bug prevention. Machine learning methods have previously suggested many ways by which to detect code clones. The majority of clone detectors are traditional in their approach, they can detect syntactic clones but are poor at detecting semantic clones. Researchers use machine learning to detect semantic clones and

Implementing ERP (Enterprise Resource Planning) system successfully comes with a set of costly and complex challenges. The purpose of this paper is to offer an effective cloud-based ERP software evaluation framework to gain a better understanding of these complexities and address such challenges by identifying the selection criteria. The PFS (Pythagorean Fuzzy Set) is the new extension of Intuitionistic

The COVID-19 outbreak has catastrophically affected both public health system and world economy. Swift diagnosis of the positive cases will help in providing proper medical attention to the infected individuals and will also aid in effective tracing of their contacts to break the chain of transmission. Blending Artificial Intelligence (AI) with chest X-ray images and incorporating these models in a

Social media platforms are considered one of the most effective intermediaries for companies to interact with consumers. Social media-based decision support systems for the marketing domain are highly developed, but product development and innovation-oriented studies remain limited. This study offers a novel approach which utilises opinion retrieval theme along with sentiment analysis to support the

The concept of software-defined networking is introduced to the networking domain to enhance networking capabilities to cope with network resources’ fast-growing requirements. Initially, security in Software Defined Network (SDN) is not considered as a priority concern, but later many research surveys taken place for determining the security loopholes in SDN architecture. To date, no algorithm exists

In the last decades, a wide portfolio of Feature Weighting (FW) methods have been proposed in the literature. Their main potential is the capability to transform the features in order to contribute to the Machine Learning (ML) algorithm metric proportionally to their estimated relevance for inferring the output pattern. Nevertheless, the extensive number of FW related works makes difficult to do a

In this paper, we study the agency problem in an organisation within a Markovian framework. More specifically, the paper presents the case of a principal imposing an incentive-control structure upon an agent to force him to follow the principal’s interests for which he was hired, against the tendency of the agent to follow his own interests. Findings point toward the principal’s difficulty in controlling

One of the most studied application areas of intelligent systems is the classification area, and this is because classification covers a wide range of real-world problems. Some examples are fault-diagnosis, image segmentation, medical diagnosis, among others. In most cases, the intelligent systems designed for the solution of this kind of problems are based on supervised learning, which is based on

Feature selection aims at finding the minimum number of features that result in high classification accuracy. Accordingly, the feature selection is considered as a multi-objective problem. However, most existing approaches treat feature selection as single-objective problem, and they are often divided into two main categories: filter and wrapper methods. Filters are known as fast methods but less accurate

We study exam timetabling problem (ETP) and supervisor assignment problem (SAP) of a vocational school offering associate level degrees in a university. The school has seven departments and plans almost 170 exams in each semester. We propose mixed integer programming (MIP) models for the ETP and the SAP. The optimal solution for ETP of the school is not attainable in two days with a commercial solver