To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with opti...
Hyperspectral image classification (HSIC) is a key task in the field of remote sensing, but the complex nature of hyperspectral data poses a serious challenge to traditional methods. Although deep learning significantly improves classification perfor...
Many recent studies have shown that various multi-objective evolutionary algorithms have been widely applied in the field of search-based software engineering (SBSE) for optimal solutions. Most of them either focused on solving newly re-formulated pr...
Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gra...
In this paper, a two-point step-size gradient technique is proposed by which the approximate solutions of a non-linear system are found. The two-point step-size includes two types of parameters deterministic and random. A new adaptive backtracking li...
Widespread usage of unmanned aerial vehicles (UAVs) in new and emerging applications needs dynamic and adaptive networking. The development of routing protocols for UAV ad hoc networks faces numerous issues because of the unique characteristics of UA...
In the context of neural network optimization, this study explores the performance and computational efficiency of learning rate adjustment strategies applied with Adam and SGD optimizers. Methods evaluated include exponential annealing, step decay,...
Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially i...
Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficie...
In the pursuit of scalable optimization strategies for neural networks, this study addresses the computational challenges posed by SHAP-informed learning methods introduced in prior work. Specifically, we extend the SHAP-based optimization family by...
Speckle noise in ultrasound images (UIs) significantly reduces the accuracy of disease diagnosis. The aim of this study was to quantitatively evaluate its feasibility in salivary gland ultrasound imaging by modeling the adaptive non-local means (NLM)...
Objective: The main objective of this study is to evaluate the impact of sustainable human resources management (SHRM) and internal communication (IC) on turnover intention (TI) and employee performance (PER) while assessing the mediating role of org...
Neural Architecture Search (NAS) has made significant advancements in autonomously constructing high-performance network architectures, capturing extensive attention. However, a key challenge of existing NAS approaches is the intensive performance ev...
The Medical Device Regulation (MDR) in Europe aims to improve patient safety by increasing requirements, particularly for the clinical evaluation of medical devices. Before the clinical evaluation is initiated, a first literature review of existing c...
This paper presents a novel composite heuristic algorithm for global optimization by organically integrating the merits of a water cycle algorithm (WCA) and gravitational search algorithm (GSA). To effectively reinforce the exploration and exploitati...
This study presents a graph knowledge-enhanced iterated greedy algorithm that incorporates dual directional decoding strategies, disjunctive graphs, neighborhood structures, and a rapid evaluation method to demonstrate its superior performance for th...
This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously sea...
This paper proposes a trajectory optimization approach for multi-sensor multi-target search and tracking using bearing-only sensors. Based on the framework of the joint integrated probabilistic data association (JIPDA) filter, the intensity of potent...
Swarm intelligence has promising applications for firm search and decision-choice problems and is particularly well suited for examining how other firms influence the focal firm’s search. To evaluate search performance, researchers examining fi...
The Squirrel Search Algorithm (SSA) is widely used due to its simple structure and efficient search ability. However, SSA exhibits relatively slow convergence speed and imbalanced exploration and exploitation. To address these limitations, this paper...
In RFID-assisted applications, the customers often request to provide the tag searching service to determine which specific tags are present in the system. In practice, tags are usually divided into different groups to represent different categories...
For a target search of autonomous underwater vehicles (AUVs) in a completely unknown three-dimensional (3D) underwater environment, a multi-AUV collaborative target search algorithm based on adaptive prediction is proposed in this paper. The environm...
Visual search becomes challenging when the time to find the target is limited. Here we focus on how performance in visual search can be improved via a subtle saliency-aware modulation of the scene. Specifically, we investigate whether blurring salien...
In control research and design it is frequently necessary to explore, evaluate, tune and compare many control strategies. These activities are assisted by software tools of increasing complexity; however, even with the existing high performance tools...
Metagenomic analysis, a technique used to comprehensively analyze microorganisms present in the environment, requires performing high-precision homology searches on large amounts of sequencing data, the size of which has increased dramatically with t...
Neural Architecture Search (NAS) has emerged as a pivotal technique in optimizing the design of Generative Adversarial Networks (GANs), automating the search for effective architectures while addressing the challenges inherent in manual design. This...
The job shop scheduling problem (JSSP) is a fundamental operational research topic with numerous applications in the real world. Since the JSSP is an NP-hard (nondeterministic polynomial time) problem, approximation approaches are frequently used to...
This paper discusses the challenges of the hyperparameter tuning in deep learning models and proposes a green approach to the neural architecture search process that minimizes its environmental impact. The traditional approach of neural architecture...
Gas turbine failure diagnosis is performed in this work based on seven types of tag data consisting of a total of 7976 data. The data consist of about 7000 normal data and less than 500 abnormal data. While normal data are easy to extract, failure da...
The Sparrow Search Algorithm (SSA), a metaheuristic renowned for rapid convergence, good stability, and high search accuracy in continuous optimization, faces inherent limitations when applied to discrete multi-variable combinatorial optimization pro...
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the most commonly used technique in explorative proteomic research. A variety of open-source tools for peptide-spectrum matching have become available. Most analyses of explorative...
Feature selection (FS) methods play essential roles in different machine learning applications. Several FS methods have been developed; however, those FS methods that depend on metaheuristic (MH) algorithms showed impressive performance in various do...
The neural architecture search technique is used to automate the engineering of neural network models. Several studies have applied this approach, mainly in the fields of image processing and natural language processing. Its application generally req...
In rational drug design, the concept of molecular similarity searching is frequently used to identify molecules with similar functionalities by looking up structurally related molecules in chemical databases. Different methods have been developed to...
Recently end-to-end (E2E) automatic speech recognition (ASR) models have achieved promising performance. However, existing models tend to adopt increasing model sizes and suffer from expensive resource consumption for real-world applications. To comp...
The search for environmental data typically involves lexical approaches, where query terms are matched with metadata records based on measures of term frequency. In contrast, dense retrieval approaches employ language models to comprehend the context...
To deal with the complexities of decision-making for unmanned aerial vehicles (UAVs) in denial environments, this paper applies deep reinforcement learning algorithms to search and rescue (SAR) tasks. It proposes a two-stage target search and trackin...
Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large ste...
Developers usually search for reusable code snippets to improve software development efficiency. Existing code search methods, including methods based on full-text or deep learning, have two disadvantages: (1) ignoring structural information of code...
In this study, a set of different search strategies for locating submarine groundwater discharge (SGD) are investigated. This set includes pre-defined path planning (PPP), adapted random walk (RW), particle swarm optimisation (PSO), inertia Levy-flig...
Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications ba...
Satellite remote sensing can be used effectively with a wide coverage and repeatability in large-scale Arctic sea-ice analysis. To produce reliable sea-ice information, satellite remote-sensing methods should be established and validated using accura...
Along with the use of cloud-based services, infrastructure, and storage, the use of application logs in business critical applications is a standard practice. Application logs must be stored in an accessible manner in order to be used whenever needed...
Performance evaluation is one of the most critical components in assuring the comprehensive development of e-learning in medical education (e-LMED). Although several studies evaluate performance in e-LMED, no study presently maps the rising scientifi...
To maximize the performance of IoT devices in edge computing, an adaptive polling technique that efficiently and accurately searches for the workload-optimized polling interval is required. In this paper, we propose NetAP-ML, which utilizes a machine...
The location of logistics distribution centers is a crucial issue in modern logistics distribution systems. In order to obtain a more reasonable solution, an effective optimization algorithm is essential. This paper proposes a new hybrid method, name...
Deep learning-based algorithms for single object tracking (SOT) have shown impressive performance but remain susceptible to adversarial patch attacks. However, existing adversarial patch generation methods primarily focus on generating patches within...
In response to the demand for the evaluation of the actual navigation performance (ANP) of rotorcraft logistics uncrewed aerial vehicle (UAV) navigation systems in urban scenarios, this paper proposes a method for evaluating the ANP of rotorcraft log...
Neural network pruning has been widely studied for model compression and acceleration, to facilitate model deployment in resource-limited scenarios. Conventional methods either require domain knowledge to manually design the pruned model architecture...
Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of...
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