COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can

Concernings related to image security have increased in the last years. One of the main reasons relies on the replacement of conventional photography to digital images, once the development of new technologies for image processing, as much as it has helped in the evolution of many new techniques in forensic studies, it also provided tools for image tampering. In this context, many companies and researchers

By exploiting two simplified nonlinear activation functions, two zeroing neural network (ZNN) models are designed and studied to efficiently tackle the time-varying matrix pseudoinversion problem. Compared with ZNN activated by previously presented activation functions, these two simplified finite-time ZNN (SFTZNN) models (called SFTZNN1 and SFTZNN2) not only achieve faster finite-time convergence

Solving nonlinear equations systems (NESs) is one of the most important tasks in numerical computation. It is common that most NESs contain more than one root. Generally, these roots are equally important. Therefore, locating as many of these roots as possible is extremely useful; however, it is a difficult task. Recently, the use of evolutionary algorithms (EAs) for NESs has been given more consideration

Robotic assembly line becomes popular in recent years for its ability to outperform labor assembly lines in terms of efficiency and flexibility. This paper focuses on the mixed-model U-shaped robotic assembly line balancing and sequencing problem (MURALBSP). Different from most reported works, this paper introduces the energy consideration into the problem. Two conflict objectives, i.e., energy consumption

Attribute reduction, as a technique for selecting qualified attributes which can satisfy the intended constraint related to considered measure, has been widely explored. Notably, one and only one reduct is derived through using one searching strategy in most cases. Nevertheless, only one reduct may be not enough for us to evaluate its effectiveness. To fill such gap, an approach of crosswise computing

Reliability and safety analyses are the most important activities for reducing risk of failure events and upgrading availability of manufacturing industries. The traditional statistical models have been currently used; however, the complexity growth and diversity of systems as well as uncertainty of their functions result in extreme difficulties in analyzing the reliability by such models. To overcome

This paper develops a novel population-based evolutionary method called cooperation search algorithm (CSA) to address the complex global optimization problem. Inspired by the team cooperation behaviors in modern enterprise, the CSA method randomly generates a set of candidate solutions in the problem space, and then three operators are repeatedly executed until the stopping criterion is met: the team

Robotic autonomous grasping of food-related objects requires a nondestructive and safe grasp system for picking up various objects. A novel underactuated flexible hand consisting of a variable palm and four soft fingers is designed and manufactured to enhance the grasp space and deformability during interaction with unknown objects. A position deviation formulation is fitted to estimate the free bend

In this research, we investigate a transshipment scheduling problem in which a set of loading and unloading jobs must be performed at a single station of a transshipment terminal. We assume that there is a set of product types and an inventory storage center with a constant capacity for each. Each loading job decreases the inventory level of a particular product type and has a predetermined due date

Classical Rough Set Theory (RST) is a prominent tool to deal with uncertainty of categorical data. However, it does not consider the preference order between the values of the attributes. Dominance Based Rough Set Approach (DRSA) provides dominance relation in this regard. Computation of the upper and lower approximation is a critical step in DRSA. However, computing these approximations is a computationally

The Container Relocation Problem for multiple bays consists of finding the minimum number of moves to load a set of stacked containers on a ship according to a given loading sequence, and in minimizing the crane’s working time for an entire yard of multiple bays. This is a crucial problem for every commercial port in the world given the maximum time requirements and the costs associated with the containers’

Fault diagnosis of rotary machinery is essential to fixing defective rotary machines and preventing rotary systems from breaking. Recently, fault diagnosis techniques have moved from traditional methods to artificial intelligence techniques. Many research groups have reported on the development of various artificial intelligence-based classifiers to improve diagnostic performance. In classifier design

This paper presents an Artificial Bee Colony (ABC) optimization based algorithm for co-clustering of high-dimensional data. The ABC algorithm is used for optimization problems including data clustering. We incorporate aspects of co-clustering by embedding it into the objective function used for clustering by the ABC algorithm. Instead of a linear metric, such as the Euclidean distance, we propose the

Traffic Signal Control (TSC) is a crucial component in the modern intelligent transportation systems. Typically, the TSC can be formulated as a bilevel optimization problem, which is comprised of the signal timing and the traffic assignment modules. The problem is challenging that the existing approaches usually endure a huge computational cost. As a result, many TSC approaches focus on relatively

In this research, we develop a method integrating a growing self-organizing map and differential learning system for online reinforcement learning which adaptively builds the state structure. In the conventional method, models and information on the environment are required beforehand, whereas the proposed method automatically estimates the state transitions from differentials of input signals and

Gas sensors are present in almost every environmental monitoring application. They can be realized using different materials and production techniques while exploiting different physical/chemical processes to detect one or more target gas. Historically, their research and development are mainly driven by the materials science community. However, the seamless combination of artificial intelligence techniques

The majority of optimization algorithms require proper parameter tuning to achieve the best performance. However, it is well-known that parameters are problem-dependant as different problems or even different instances have different optimal parameter settings. Parameter tuning through the testing of parameter combinations is a computationally expensive procedure that is infeasible on large-scale real-world

Minimum spanning tree (MST) based unsupervised learning techniques are popular due to their ability to identify intrinsic clusters of heterogeneous structures. One of the important factors which affects their effectiveness is how to construct a sparse similarity graph which can effectively capture the local neighborhood information in sub quadratic time. In this paper, we propose a technique which

Expansion is a novel mutation operator for Genetic Programming (GP). It uses Monte Carlo simulation to repeatedly expand and evaluate programs using unit instructions, which extends the search beyond the immediate - often misleading - horizon of offspring programs. To evaluate expansion, a standard Koza-style tree-based representation is used and a comparison is carried out between expansion and sub-tree

With the development of deep learning and neural network techniques, human action recognition has made great progress in recent years. However, it remains challenging to analyse temporal information and identify human actions with few training samples. In this paper, an effective motion image called a dense joint motion image (DJMI) was proposed to transform an action to an image. Our method was compared

Cell formation, cell scheduling, and group layout are three important problems in designing and configuring a Cellular Manufacturing System (CMS). This paper addresses the integration of these problems in virtual and classical CMSs considering alternative processing routes. The objective is to minimize the total handling costs and cycle time. Due to the computational complexity of the problem, hybrid

The control of constrained nonlinear liquid level systems is a problem of fundamental importance in pharmaceutical, chemical, food-processing, oil refining and natural liquid gas separation industries. This paper proposes a novel control strategy for the control of such constrained high-dimensional interacting liquid level systems. The nonlinear liquid level regulation problem is formulated as a path

Photovoltaic systems (PV) are having an increased importance in modern smart grids systems. Usually, in order to maximize the energy output of the PV arrays a maximum power point tracking (MPPT) algorithm is used. However, once deployed, weather conditions such as clouds can cause shades in the PV arrays affecting the dynamics of each panel differently. These conditions directly affect the available

Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples

Unmanned Aerial Vehicles (UAV) can be used to great effect for the purposes of surveillance or search and rescue operations. UAV enable search and rescue teams to cover large areas more efficiently and in less time. However, using UAV for this purpose involves the creation of large amounts of data (typically video) which must be analyzed before any potential findings can be uncovered and actions taken

This research examines the problem of designing a two-echelon freight distribution system in a dense urban area that considers third-party logistics (TPL) and loading-unloading zones (LUZs). The proposed system takes advantage of outsourcing the last mile deliveries to a TPL provider and utilizing LUZs as temporary intermediate facilities instead of using permanent intermediate facilities to consolidate

In this paper, a novel and efficient pairing support vector regression learning method using ε−insensitive Huber loss function (PHSVR) is proposed where the ε−insensitive zone having flexible shape is determined by tightly fitting the training samples. Our approach leads to solving a pair of unconstrained minimization problems in primal and the solutions are obtained by two algorithms: a functional

This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search

Productivity has always been a major concern in the industry. It can be improved by increasing material removal rate. Regenerative chatter during machining is the major obstacle to attain this. In the present work, a methodology has been proposed to select a proper combination of input cutting parameters for stable turning with improved metal removal rate (MRR). Chatter signals generated during the

Recently there has been an increase in the studies on time-series data mining specifically time-series clustering due to the vast existence of time-series in various domains. The large volume of data in the form of time-series makes it necessary to employ techniques such as clustering to understand the data and to extract information and hidden patterns. The most important aspect of time-series clustering

Verifying if two audio segments belong to the same speaker has been recently put forward as a flexible way to carry out speaker identification, since it does not require to be re-trained when new speakers appear on the auditory scene. Although many of the current techniques have achieved high performances, they require a considerably high amount of memory, and a specific minimum length for their input

We present a novel approach to obtaining dynamic nonlinear models using genetic programming (GP) for the model predictive control (MPC) of the indoor temperatures of buildings. Currently, the large-scale adoption of MPC in buildings is economically unviable due to the time and cost involved in the design and tuning of predictive models by expert control engineers. We show that GP is able to automate

Single-label text classification has been extensively studied in the last decades, and usually, more attention has been given to offline learning scenarios, where all of the training data is available in advance. However, real-world text classification problems often involve multilabel instances and have dynamic textual patterns that can change frequently. In this context, the methods must predict

Several approaches have been employed for gas turbine Fault Detection and Identification (FDI), as a reliable FDI system with minimum false alarm rate can effectively reduce maintenance cost and downtime. This paper introduces the application of Interval Type-2 Fuzzy Logic Systems (IT2FLSs) to gas turbine fault diagnosis for the first time. The proposed FDI system is composed of a bank of IT2FLSs,

Robots for rescue operations after a disaster are an interesting and challenging research problem that has the potential to save lives and reduce economic losses after a disaster. We developed TOPTWR, an extension of the popular TOPTW model, to model the issues in task allocation for teams of rescue robots. Our hybrid algorithm is based on a team of heterogeneous humanoid robots trying to optimize

As a branch of machine learning, deep learning has been used for tackling with the music auto-tagging problem. Deep learning methods, especially those with convolutional neural network (CNN) architecture, have exhibited good performance on this multi-label classification task. However, the feature extracting part and preprocessing part of this architecture need to be improved. In this paper, we propose

SARS-CoV-2 (COVID-19) virus is a havoc pandemic that infects millions of people over the world and thousands of infected cases dead. So, it is vital to propose new intelligent data analysis tools and enhance the existed ones to aid scientists in analyzing the COVID-19 virus. Fragmented Local Aligner Technique (FLAT) is a data analysis tool that is used for detecting the longest common consecutive subsequence

Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation

Bug assignment is a burden for projects receiving many bug reports. To automate the process of assigning bug reports to the appropriate developers, several studies have relied on combining natural language processing and information retrieval techniques to extract two categories of features. One of these categories targets developers who have fixed similar bugs before, and the other determines developers

The extensive computational burden limits the usage of convolutional neural networks (CNNs) in edge devices for image semantic segmentation, which plays a significant role in many real-world applications, such as augmented reality, robotics, and self-driving. To address this problem, this paper presents an attention-guided lightweight network, namely AGLNet, which employs an encoder–decoder architecture

In this study, a novel L-SHADE variant with collaborative scheme and comprehensive learning mechanism, named LSHADE-CLM, was proposed to improve the exploration and exploitation capabilities of the L-SHADE algorithm. In LSHADE-CLM, a novel cooperative mutation mechanism including “DE∕current−to−pbetter∕r” and “DE∕current−to−pbest−w∕1” is proposed in the mutation operation. In the “DE∕current−to−pbetter∕r”

A three-dimensional objective space (3DOS) optimization strategy using an enhanced multi-objective artificial bee colony (ABC) algorithm for the design optimization of layered radar absorbing material (LRAM) is presented in this study. The multi-objective exploitation ability of ABC is improved with regard to the convergence and diversity by integrating a pioneer Pareto (PP) solution to the onlooker

In the retail market, gathering marketing data is essential at different stages of advertisement and promotion; currently, this is achieved via online crowdsourcing. The jobs involving such tasks must be reasonably priced to attract part-time employees depending on the retail budget. Herein, a new approach is presented to enhance the task repricing performance of online crowdsourcing platforms using

This paper proposes a new approach to the design of natural gas network expansion under load evolution uncertainty in a time horizon. A predefined network topology is assumed with pipe diameters as the design variables. A dedicated optimization tool is developed so as to find not only an estimate of the optimal network, but also a set of local optimal ones, for a given most probable scenario. A Monte-Carlo

Artificial immune systems are bio-inspired machine learning algorithms based on the mammalian immune paradigms. One of the possible uses of these methods is Software Fault Prediction, which consists of classifying the modules of an application as being fault-prone or not, thus allowing a developer to better target the modules during the test phase leading to a high-quality software with lower cost

Molecular docking is often used in computational chemistry to accelerate drug discovery at early stages. Many molecular simulations are performed to select the right pharmacological candidate. However, traditional docking methods are based on optimization heuristics such as Monte Carlo or genetic that try several hundreds of these candidates giving rise to expensive computations. Thus, an alternative

Speech signals reaching our ears are in general contaminated by the background noise distortion which is detrimental to both speech quality and intelligibility. In this paper, we propose a nonlinear multi-scale decomposition-based deep speech enhancement method to improve the quality and intelligibility of the contaminated speech. In the proposed method, we have applied Hurst exponent-based Empirical

Previous scientific studies as well as consulting firms have developed numerous Online Reputation Indices (ORIs), i.e. custom-tailored metrics intended to measure the emotions that people express towards a brand, product, or service in social media. These ORIs can provide useful information to assess the impact of marketing campaigns, social approval, and viral behavior of news and memes, among others

COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation

The hospital readmission prediction becomes a significant task for healthcare systems and patients. Many predictive models have been developed and make progress in this task. However, most of them roughly combine the patient’s long-term(e.g., the history of present illness) and short-term(e.g., the performed laboratory test results when the patients are discharged) information without considering the

COVID-19 pandemic is severely impacting the lives of billions across the globe. Even after taking massive protective measures like nation-wide lockdowns, discontinuation of international flight services, rigorous testing etc., the infection spreading is still growing steadily, causing thousands of deaths and serious socio-economic crisis. Thus, the identification of the major factors of this infection