The evaluation of corporation operation efficiency (especially innovation efficiency) has been always a hot topic. The currently popular evaluation methods are data envelopment analysis (DEA) and its improved methods. However, these methods have the...
The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while impr...
The dynamic relationship between current and pressure in magnetorheological (MR) valves is essential for the design of adaptive rehabilitation devices aimed at health rehabilitation for disabled individuals, yet it remains under-explored in existing...
In the context of algorithm selection, the careful design of benchmark functions and problem instances plays a pivotal role in evaluating the performance of optimization methods. Traditional benchmark functions have been criticized for their limited...
Black pepper (Piper nigrum L.), is dubbed “the King of Spices”. However, the lack of genic knowledge has limited the understanding of its physiological processes and hindered the development of its molecular breeding. The SBP-box gene fam...
Artificial intelligence (AI) is transforming organizational functions across sectors; however, its application to human resource management (HRM) within healthcare remains underexplored. This study aims to unpack the black-box nature of AI capability...
This article is devoted to the problem of developing a kinetic model of a complex chemical reaction using a parallel optimization method. The design of the kinetic model consists of finding the kinetic parameters of the reaction, which cannot be calc...
Recently, intelligent transport systems have been applied to vehicle cloud environments. Such technology is especially useful for the systematic management of road traffic. Moreover, automobiles are increasingly equipped with a black box for accident...
Heuristic and evolutionary algorithms are proposed to solve challenging real-world optimization problems. In the evolutionary community, many benchmark problems for empirical evaluations of algorithms have been proposed. One of the most important cla...
This paper presents an Interoperable User-Centred Digital Twin (I-UCDT) framework for sustainable energy system management, addressing the growing complexity of energy generation, storage, demand, and grid interaction across industrial and community-...
The selection of the hyper-parameters plays a critical role in the task of prediction based on the recurrent neural networks (RNN). Traditionally, the hyper-parameters of the machine learning models are selected by simulations as well as human experi...
Machine learning research focuses on the improvement of prediction performance. Progress was made with black-box models that flexibly adapt to the given data. However, due to their increased complexity, black-box models are more difficult to interpre...
In order to overcome the drawbacks of expensive function evaluation in the practical reliability-based design optimization (RBDO) problem, researchers have proposed the black box-based RBDO method. The algorithm flow of the commonly employed RBDO met...
This paper presents a novel on-the-fly, black-box, property-checking through learning approach as a means for verifying requirements of recurrent neural networks (RNN) in the context of sequence classification. Our technique steps on a tool for learn...
The paper aims at investigating modelling strategies in HBIM context to identify at what extent the final use of the model might affects, or should affect, the modelling approach itself. Moreover, the discussion wants to shed light on the possibility...
Adversarial example generation is a technique that involves perturbing inputs with imperceptible noise to induce misclassifications in neural networks, serving as a means to assess the robustness of such models. Among the adversarial attack algorithm...
Generating adversarial examples under black-box settings poses significant challenges due to the inaccessibility of internal model information. This complexity is further exacerbated when attempting to achieve a balance between the attack success rat...
Autonomous systems’ “black-box” nature impedes user trust and adoption. To investigate explainable visualizations’ impact on trust and cognitive states, we conducted a within-subjects study with 29 participants performing high...
One of the most widely used models for specifying functional requirements is a use case model. The viewpoint of the use case model that views a system as a black box focuses on descriptions of external interactions between the system and related envi...
Post hoc explanations for black-box machine learning models have been criticized for potentially inaccurate surrogate models and computational burden at prediction time. We propose pre hoc and co hoc explainability frameworks that integrate interpret...
To address the challenges of black-box video adversarial attacks, such as excessive query times and suboptimal attack performance due to the lack of result feedback during the attack process, we propose a reinforcement learning-based sparse adversari...
Deep neural networks (DNNs) are sensitive to adversarial data in a variety of scenarios, including the black-box scenario, where the attacker is only allowed to query the trained model and receive an output. Existing black-box methods for creating ad...
Light is an electromagnetic radiation that has visible and invisible wavelength spectrums. Visible light can only be detected by the eyes through the optic pathways. With the presence of the scalp, cranium, and meninges, the brain is seen as being pr...
Unmanned surface vessels (USVs) are required to perform motion prediction during a task. This is essential for USVs, especially when conducting motion control, and this work has been proven to be complicated. In this paper, an off-line black box mode...
Deep steganography (DS), using neural networks to hide one image in another, has performed well in terms of invisibility, embedding capacity, etc. Current steganalysis methods for DS can only detect or remove secret images hidden in natural images an...
Text data is indispensable for modern machine learning and natural language processing but often contains sensitive information that must be protected before sharing or release. Differential privacy (DP) provides rigorous guarantees for privacy prese...
In recent times, coronary artery disease (CAD) prediction and diagnosis have been the subject of many Medical decision support systems (MDSS) that make use of machine learning (ML) and deep learning (DL) algorithms. The common ground of most of these...
This paper is focused on solving different hard optimization problems that arise in the field of insurance and, more specifically, in reinsurance problems. In this area, the complexity of the models and assumptions considered in the definition of the...
A lack of transparency in machine learning models can limit their application. We show that analysis of variance (ANOVA) methods extract interpretable predictive models from them. This is possible because ANOVA decompositions represent multivariate f...
Buildings are complex assets, characterized by environments and uses that change over time, variable occupancies, and long life cycles. They have high operational costs, mostly due to their energy requirements, and account for 30% to 40% of global gr...
Mainstream transferable adversarial attacks tend to introduce noticeable artifacts into the generated adversarial examples, which will impair the invisibility of adversarial perturbation and make these attacks less practical in real-world scenarios....
Hyperparameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracl...
Cross-site scripting (XSS) vulnerabilities are significant threats to web applications. The number of XSS vulnerabilities reported has increased annually for the past three years, posing a considerable challenge to web application maintainers. Black-...
Adversarial example generation on Synthetic Aperture Radar (SAR) images is an important research area that could have significant impacts on security and environmental monitoring. However, most current adversarial attack methods on SAR images are des...
Artificial intelligence (AI) is progressing rapidly, and in this trend, edge AI has been researched intensively. However, much less work has been performed around the security of edge AI. Machine learning models are a mass of intellectual property, a...
The “black box” model defines the enhancement,
In recent years, unmanned aerial vehicles (UAVs) vision systems based on deep neural networks (DNNs) have made remarkable advancements, demonstrating impressive performance. However, due to the inherent characteristics of DNNs, these systems have bec...
It has been demonstrated that deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) techniques are extremely susceptible to adversarial intrusions, that is, malicious SAR images including deliberately gener...
The internet of things (IoT) refers to all the smart objects that are connected to other objects, devices or servers and that are able to collect and share data, in order to “learn” and improve their functionalities. Smart objects suffer...
The uncertainty of the engineering system increases with its complexity, therefore, the tolerance to the uncertainty becomes important. Even under large variations of design parameters, the system performance should achieve the design goal in the des...
Surrogate modeling techniques are widely employed in solving constrained expensive black-box optimization problems. Therein, Kriging is among the most popular surrogates in which the trend function is considered as a constant mean. However, it also e...
As engineering systems become increasingly complex, performance requirements rise, and tolerance for design parameter variations becomes more crucial due to increased uncertainty. Tolerance to parameter variation can be measured by the volume of the...
One of the most common problems in science is to investigate a function describing a system. When the estimate is made based on a classical mathematical model (white-box), the function is obtained throughout solving a differential equation. Alternati...
Due to the nonlinearity of the deep-seafloor and complexity of the hydrodynamic force of novel structure platforms, realising an accurate motion mechanism modelling of a deep-sea landing vehicle (DSLV) is difficult. The support vector regression (SVR...
Recent studies have demonstrated that machine learning-based generative models can create novel molecules with desirable properties. Among them, Conditional Variational Autoencoder (CVAE) is a powerful approach to generate molecules with desired phys...
Regression is a fundamental task in machine learning, and neural networks have been successfully employed in many applications to identify underlying regression patterns. However, they are often criticised for their lack of interpretability and commo...
The gearbox is a critical component in modern industrial systems, directly determining the operational reliability of machinery. Therefore, effective fault diagnosis is essential to ensure its proper functioning. Modern diagnostic approaches often em...
Dynamic simulation of district heating and cooling networks has an increased importance in the transition towards renewable energy sources and lower temperature district heating grids, as both temporal and spatial behavior need to be considered. Even...
Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerica...
Understanding microbial growth with the use of mathematical models has a long history that dates back to the pioneering work of Jacques Monod in the 1940s. Monod’s famous growth law expressed microbial growth rate as a simple function of the limiting...
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