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Volume 18
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Algorithms, Volume 18, Issue 1 (January 2025) – 53 articles

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18 pages, 4962 KiB  
Article
Analysis and Simulation of Polishing Robot Operation Trajectory Planning
by Xinhong Zeng and Yongxiang Wang
Algorithms 2025, 18(1), 53; https://doi.org/10.3390/a18010053 - 18 Jan 2025
Viewed by 410
Abstract
Trajectory planning is essential for robotic polishing tasks, as the effectiveness of this planning directly influences the quality of the work and the energy efficiency of the operation. This study introduces an innovative trajectory planning method for robotic polishing tasks, focusing on the [...] Read more.
Trajectory planning is essential for robotic polishing tasks, as the effectiveness of this planning directly influences the quality of the work and the energy efficiency of the operation. This study introduces an innovative trajectory planning method for robotic polishing tasks, focusing on the development and application of quintic B-spline interpolation. Recognizing the critical impact of trajectory planning on the quality and energy efficiency of robotic operations, we analyze the structure and parameters of the ABB-IRB120 robot within a laboratory setting. Using the Denavit–Hartenberg parameter method, a kinematic model is established, and the robot’s motion equations are derived through matrix transformation. We then propose a novel approach by implementing both fifth-degree polynomial and quintic B-spline interpolation algorithms for planning the robot’s spatial spiral arc trajectory, which is a key contribution of this work. The effectiveness of these methodologies is validated through simulation in MATLAB’s robotics toolbox. Our findings demonstrate that the quintic B-spline interpolation not only significantly improves task precision but also optimizes energy consumption, making it a superior method for trajectory planning in robotic grinding applications. By integrating advanced interpolation techniques, this study provides substantial technological and environmental benefits, offering a groundbreaking reference for enhancing the precision and efficiency of robotic control systems. Full article
(This article belongs to the Section Combinatorial Optimization, Graph, and Network Algorithms)
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20 pages, 3380 KiB  
Article
Application Layer Protocol Identification Method Based on ResNet
by Zhijian Fang, Xiang Gao, Huaxiong Zhang, Jingpeng Tang and Qiang Gao
Algorithms 2025, 18(1), 52; https://doi.org/10.3390/a18010052 - 18 Jan 2025
Viewed by 237
Abstract
Most network attacks occur at the application layer, where many application layer protocols exist. These protocols have different structures and functionalities, posing feature extraction challenges and resulting in low identification accuracy. This significantly affects application layer protocol recognition, analysis, and detection. We propose [...] Read more.
Most network attacks occur at the application layer, where many application layer protocols exist. These protocols have different structures and functionalities, posing feature extraction challenges and resulting in low identification accuracy. This significantly affects application layer protocol recognition, analysis, and detection. We propose a data protocol identification method based on a Residual Network (ResNet) to address this issue. The method involves the following steps: (1) utilizing a delimiter determination algorithm based on information entropy proposed in this paper to determine an optimal set of delimiters; (2) segmenting the original data using the optimal set of delimiters and constructing a feature data block frequency table based on the frequency of segmented data blocks; (3) employing a composite-feature-based RGB image generation algorithm proposed in this paper to generate feature images by combining feature data blocks and original data; and (4) training the ResNet model with the generated feature images to automatically learn protocol features and achieve classification recognition of application layer protocols. Experimental results demonstrate that this method achieves over 98% accuracy, precision, recall, and F1 score across these four metrics. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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26 pages, 2476 KiB  
Article
Control of a Mobile Line-Following Robot Using Neural Networks
by Hugo M. Leal, Ramiro S. Barbosa and Isabel S. Jesus
Algorithms 2025, 18(1), 51; https://doi.org/10.3390/a18010051 - 17 Jan 2025
Viewed by 227
Abstract
This work aims to develop and compare the performance of a line-following robot using both neural networks and classical controllers such as Proportional–Integral–Derivative (PID). Initially, the robot’s infrared sensors were employed to follow a line using a PID controller. The data from this [...] Read more.
This work aims to develop and compare the performance of a line-following robot using both neural networks and classical controllers such as Proportional–Integral–Derivative (PID). Initially, the robot’s infrared sensors were employed to follow a line using a PID controller. The data from this method were then used to train a Long Short-Term Memory (LSTM) network, which successfully replicated the behavior of the PID controller. In a subsequent experiment, the robot’s camera was used for line-following with neural networks. Images of the track were captured, categorized, and used to train a convolutional neural network (CNN), which then controlled the robot in real time. The results showed that neural networks are effective but require more processing and calibration. On the other hand, PID controllers proved to be simpler and more efficient for the tested tracks. Although neural networks are very promising for advanced applications, they are also capable of handling simpler tasks effectively. Full article
(This article belongs to the Special Issue Algorithms for PID Controller 2024)
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24 pages, 1249 KiB  
Article
Solution Algorithms for the Capacitated Location Tree Problem with Interconnections
by Nidia Mendoza-Andrade, Efrain Ruiz-y-Ruiz and Suemi Rodriguez-Romo
Algorithms 2025, 18(1), 50; https://doi.org/10.3390/a18010050 - 17 Jan 2025
Viewed by 304
Abstract
This paper addresses the Capacitated Location Tree Problem with Interconnections, a new combinatorial optimization problem with applications in network design. In this problem, the required facilities picked from a set of potential facilities must be opened to serve customers using a tree-shaped network. [...] Read more.
This paper addresses the Capacitated Location Tree Problem with Interconnections, a new combinatorial optimization problem with applications in network design. In this problem, the required facilities picked from a set of potential facilities must be opened to serve customers using a tree-shaped network. Costs and capacities are associated with the opening of facilities and the establishment of network links. Customers have a given demand that must be satisfied while respecting the facilities and link capacities. The problem aims to minimize the total cost of designing a distribution network while considering facility opening costs, demand satisfaction, capacity constraints, and the creation of interconnections to enhance network resilience. A valid mixed-integer programming was proposed and an exact solution method based on the formulation was used to solve small- and medium-sized instances. To solve larger instances two metaheuristic approaches were used. A specific decoder procedure for the metaheuristic solution approaches was also proposed and used to help find solutions, especially for large instances. Computational experiments and results using the three solution approaches are also presented. Finally, a case study on the design of electrical transportation systems was presented and solved. Full article
(This article belongs to the Section Combinatorial Optimization, Graph, and Network Algorithms)
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21 pages, 2604 KiB  
Article
Algorithm and Methods for Analyzing Power Consumption Behavior of Industrial Enterprises Considering Process Characteristics
by Pavel Ilyushin, Boris Papkov, Aleksandr Kulikov and Konstantin Suslov
Algorithms 2025, 18(1), 49; https://doi.org/10.3390/a18010049 - 16 Jan 2025
Viewed by 311
Abstract
Power consumption management is crucial to maintaining the reliable operation of power grids, especially in the context of the decarbonization of the electric power industry. Managing power consumption of industrial enterprises by personnel proved ineffective, which required the development and implementation of automatic [...] Read more.
Power consumption management is crucial to maintaining the reliable operation of power grids, especially in the context of the decarbonization of the electric power industry. Managing power consumption of industrial enterprises by personnel proved ineffective, which required the development and implementation of automatic energy consumption management systems. Optimization of power consumption behavior requires comprehensive and reliable information on the parameters of the technological processes of an industrial enterprise. The paper explores the specific features of non-stationary conditions of output production and assesses the potential for power consumption management under these conditions. The analysis of power consumption modes was carried out based on the consideration of random factors determined by both internal and external circumstances, subject to the fulfillment of the production plan. This made it possible to increase the efficiency of power consumption in mechanical engineering production by taking into account the uncertainty of seasonal and technological fluctuations by 15–20%, subject to the fulfillment of the production plan. This study presents a justification for utilizing the theory of level-crossings of random processes to enhance the reliability of input information. The need to analyze the specific features of technological processes based on the probabilistic structure and random functions is proven. This is justified because it becomes possible to fulfill the production plan with technological fluctuations in productivity and, accordingly, power consumption, which exceeds the nominal values by more than 5%. In addition, the emission characteristics are clear, easy to measure, and allow the transition from analog to digital information presentation. The algorithm and methods developed to analyze the power consumption patterns of industrial enterprises can be used to develop automatic power consumption management systems. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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19 pages, 4528 KiB  
Article
Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network
by Ke Zhu, Donghui Luo, Zhengzheng Fu, Zhihang Xue and Xianghang Bu
Algorithms 2025, 18(1), 48; https://doi.org/10.3390/a18010048 - 15 Jan 2025
Viewed by 309
Abstract
The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, [...] Read more.
The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 2nd Edition)
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23 pages, 998 KiB  
Article
AI-Enhanced Design and Application of High School Geography Field Studies in China: A Case Study of the Yellow (Bohai) Sea Migratory Bird Habitat Curriculum
by Binglin Liu, Weijia Zeng, Weijiang Liu, Yi Peng and Nini Yao
Algorithms 2025, 18(1), 47; https://doi.org/10.3390/a18010047 - 15 Jan 2025
Viewed by 313
Abstract
China’s Yellow (Bohai) Sea bird habitat is an important ecological region. Its unique ecology and challenges provide rich resources for research and study. Our course design concept is supported by AI technology, and improves students’ abilities through innovative functions such as dynamic data [...] Read more.
China’s Yellow (Bohai) Sea bird habitat is an important ecological region. Its unique ecology and challenges provide rich resources for research and study. Our course design concept is supported by AI technology, and improves students’ abilities through innovative functions such as dynamic data support, personalized learning paths, immersive research and study experience, and diversified evaluation mechanisms. The course content revolves around the “human–land coordination concept”, including pre-trip thinking, research and study during the trip, and post-trip exhibition learning, covering regional cognition, remote sensing image analysis, field investigation, and protection plan display activities. ERNIE Bot participates in optimizing the learning path throughout the process. The course evaluation system starts from the three dimensions of “land to people”, “people to land”, and the “coordination of the human–land relationship”, adopts processes and final evaluation, and uses ERNIE Bot to achieve real-time monitoring, data analysis, personalized reports, and dynamic feedback, improving the objectivity and efficiency of evaluation, and helping students and teachers optimize learning and teaching. However, AI has limitations in geographical research and study, such as insufficient technical adaptability, the influence of students’ abilities and habits, and the adaptation of teachers’ role changes. To this end, optimization strategies such as improving data quality and technical platforms, strengthening student technical training, enhancing teachers’ AI application capabilities, and enriching AI functions and teaching scenarios are proposed to enhance the application effect of AI in geographical research and promote innovation in educational models and student capacity building. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms and Generative AI in Education)
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16 pages, 724 KiB  
Article
On Assessing the Performance of LLMs for Target-Level Sentiment Analysis in Financial News Headlines
by Iftikhar Muhammad and Marco Rospocher
Algorithms 2025, 18(1), 46; https://doi.org/10.3390/a18010046 - 13 Jan 2025
Viewed by 416
Abstract
The importance of sentiment analysis in the rapidly evolving financial markets is widely recognized for its ability to interpret market trends and inform investment decisions. This study delves into the target-level financial sentiment analysis (TLFSA) of news headlines related to stock. The study [...] Read more.
The importance of sentiment analysis in the rapidly evolving financial markets is widely recognized for its ability to interpret market trends and inform investment decisions. This study delves into the target-level financial sentiment analysis (TLFSA) of news headlines related to stock. The study compares the performance in the TLFSA task of various sentiment analysis techniques, including rule-based models (VADER), fine-tuned transformer-based models (DistilFinRoBERTa and Deberta-v3-base-absa-v1.1) as well as zero-shot large language models (ChatGPT and Gemini). The dataset utilized for this analysis, a novel contribution of this research, comprises 1476 manually annotated Bloomberg headlines and is made publicly available (due to copyright restrictions, only the URLs of Bloomberg headlines with the manual annotations are provided; however, these URLs can be used with a Bloomberg terminal to reconstruct the complete dataset) to encourage future research on this subject. The results indicate that the fine-tuned Deberta-v3-base-absa-v1.1 model performs better across all evaluation metrics than other evaluated models in TLFSA. However, LLMs such as ChatGPT-4, ChatGPT-4o, and Gemini 1.5 Pro provide similar performance levels without the need for task-specific fine-tuning or additional training. The study contributes to assessing the performance of LLMs for financial sentiment analysis, providing useful insights into their possible application in the financial domain. Full article
(This article belongs to the Special Issue Recent Advances in the Synergy Between Federated Learning and Foundation Models)
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16 pages, 2336 KiB  
Article
Vertex-Weighted Consensus-Based Formation Control with Area Constraints and Collision Avoidance
by Ulises Hernandez-Venegas, Jesus Hernandez-Barragan, Irene Gomez Jimenez, Gabriel Martinez-Soltero and Alma Y. Alanis
Algorithms 2025, 18(1), 45; https://doi.org/10.3390/a18010045 - 13 Jan 2025
Viewed by 325
Abstract
In this paper, a consensus-based formation control strategy is presented, subject to area constraints and collision avoidance. To achieve a desired formation pattern, a control law is proposed that incorporates a vertex-tension function along with signed area constraints. The vertex-tension function provides the [...] Read more.
In this paper, a consensus-based formation control strategy is presented, subject to area constraints and collision avoidance. To achieve a desired formation pattern, a control law is proposed that incorporates a vertex-tension function along with signed area constraints. The vertex-tension function provides the capabilities of collision avoidance among agents. Moreover, signed area constraints avoid local minimum stagnation and mitigate ambiguities within the formation shape. Additionally, the proposed approach can be implemented considering a group of differential-drive mobile robots in both centralized and decentralized settings. Simulation and real-world experiments are performed to validate the effectiveness of the proposed approach, where the experimental setup includes a multi-robot system composed of Turtlebot3® Waffle Pi robots. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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25 pages, 641 KiB  
Article
A Lexicon-Based Framework for Mining and Analysis of Arabic Comparative Sentences
by Alaa Hamed, Arabi Keshk and Anas Youssef
Algorithms 2025, 18(1), 44; https://doi.org/10.3390/a18010044 - 13 Jan 2025
Viewed by 301
Abstract
People tend to share their opinions on social media daily. This text needs to be accurately mined for different purposes like enhancements in services and/or products. Mining and analyzing Arabic text have been a big challenge due to many complications inherited in Arabic [...] Read more.
People tend to share their opinions on social media daily. This text needs to be accurately mined for different purposes like enhancements in services and/or products. Mining and analyzing Arabic text have been a big challenge due to many complications inherited in Arabic language. Although, many research studies have already investigated the Arabic text sentiment analysis problem, this paper investigates the specific research topic that addresses Arabic comparative opinion mining. This research topic is not widely investigated in many research studies. This paper proposes a lexicon-based framework which includes a set of proposed algorithms for the mining and analysis of Arabic comparative sentences. The proposed framework comprises a set of contributions including an Arabic comparative sentence keywords lexicon and a proposed algorithm for the identification of Arabic comparative sentences, followed by a second proposed algorithm for the classification of identified comparative sentences into different types. The framework also comprises a third proposed algorithm that was developed to extract relations between entities in each of the identified comparative sentence types. Finally, two proposed algorithms were developed for the extraction of the preferred entity in each sentence type. The framework was evaluated using three different Arabic language datasets. The evaluation metrics used to obtain the evaluation results include precision, recall, F-score, and accuracy. The average values of the evaluation metrics for the proposed sentences identification algorithm reached 97%. The average evaluation values of the evaluation metrics for the proposed sentence type identification algorithm reached 96%. Finally, the average results showed 97% relation word extraction precision for the proposed relation extraction algorithm. Full article
(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)
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6 pages, 164 KiB  
Editorial
AI Algorithms for Positive Change in Digital Futures
by Manolya Kavakli-Thorne and Zhuangzhuang Dai
Algorithms 2025, 18(1), 43; https://doi.org/10.3390/a18010043 - 13 Jan 2025
Viewed by 348
Abstract
Artificial Intelligence (AI) is transforming industries and revolutionizing how we interact with technology at an unprecedented pace, playing a crucial role in shaping our digital future [...] Full article
(This article belongs to the Special Issue AI Algorithms for Positive Change in Digital Futures)
15 pages, 2843 KiB  
Article
MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
by Botao Liu, Changqi Shi and Ming Zhao
Algorithms 2025, 18(1), 42; https://doi.org/10.3390/a18010042 - 12 Jan 2025
Viewed by 529
Abstract
The colonoscopy procedure heavily relies on the operator’s expertise, underscoring the importance of automated polyp segmentation techniques in enhancing the efficiency and accuracy of colorectal cancer diagnosis. Nevertheless, achieving precise segmentation remains a significant challenge due to the high visual similarity between polyps [...] Read more.
The colonoscopy procedure heavily relies on the operator’s expertise, underscoring the importance of automated polyp segmentation techniques in enhancing the efficiency and accuracy of colorectal cancer diagnosis. Nevertheless, achieving precise segmentation remains a significant challenge due to the high visual similarity between polyps and their backgrounds, blurred boundaries, and complex localization. To address these challenges, a Multi-scale Selective Edge-Aware Network has been proposed to facilitate polyp segmentation. The model consists of three key components: (1) an Edge Feature Extractor (EFE) that captures polyp edge features with precision during the initial encoding phase, (2) the Cross-layer Context Fusion (CCF) block designed to extract and integrate multi-scale contextual information from diverse receptive fields, and (3) the Selective Edge Aware (SEA) module that enhances sensitivity to high-frequency edge details during the decoding phase, thereby improving edge preservation and segmentation accuracy. The effectiveness of our model has been rigorously validated on the Kvasir-SEG, Kvasir-Sessile, and BKAI datasets, achieving mean Dice scores of 91.92%, 82.10%, and 92.24%, respectively, on the test sets. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine (2nd Edition))
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17 pages, 1222 KiB  
Article
A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
by Zhaohui Wang, Meng Zhang, Shanqing Liang, Shuang Yu, Chengchun Zhang and Sheng Du
Algorithms 2025, 18(1), 41; https://doi.org/10.3390/a18010041 - 12 Jan 2025
Viewed by 349
Abstract
With the development of smart cities and intelligent transportation systems, path planning in multi-scenario urban mobility has become increasingly complex. Traditional path-planning approaches typically focus on a single optimization objective, limiting their applicability in complex urban traffic systems. This paper proposes a multi-objective [...] Read more.
With the development of smart cities and intelligent transportation systems, path planning in multi-scenario urban mobility has become increasingly complex. Traditional path-planning approaches typically focus on a single optimization objective, limiting their applicability in complex urban traffic systems. This paper proposes a multi-objective vehicle path-planning approach tailored for diverse scenarios, addressing multi-objective optimization challenges within complex road networks. The proposed method simultaneously considers multiple objectives, including total distance, congestion distance, travel time, energy consumption, and safety, and incorporates a dynamic weight-adjustment mechanism. This allows the algorithm to provide optimal route choices across four application scenarios: urban commuting; energy-efficient driving; holiday travel; and nighttime travel. Experimental results indicate that the proposed multi-objective planning algorithm outperforms traditional single-objective algorithms by effectively meeting user demands in various scenarios, offering an efficient solution to multi-objective optimization challenges in diverse environments. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 2nd Edition)
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30 pages, 4061 KiB  
Article
Finite Differences on Sparse Grids for Continuous-Time Heterogeneous Agent Models
by Jochen Garcke and Steffen Ruttscheidt
Algorithms 2025, 18(1), 40; https://doi.org/10.3390/a18010040 - 12 Jan 2025
Viewed by 296
Abstract
We present a finite difference method working on sparse grids to solve higher dimensional heterogeneous agent models. If one wants to solve the arising Hamilton–Jacobi–Bellman equation on a standard full grid, one faces the problem that the number of grid points grows exponentially [...] Read more.
We present a finite difference method working on sparse grids to solve higher dimensional heterogeneous agent models. If one wants to solve the arising Hamilton–Jacobi–Bellman equation on a standard full grid, one faces the problem that the number of grid points grows exponentially with the number of dimensions. Discretizations on sparse grids only involve O(N(logN)d−1) degrees of freedom in comparison to the O(Nd) degrees of freedom of conventional methods, where N denotes the number of grid points in one coordinate direction and d is the dimension of the problem. While one can show convergence for the used finite difference method on full grids by using the theory introduced by Barles and Souganidis, we explain why one cannot simply use their results for sparse grids. Our numerical studies show that our method converges to the full grid solution for a two-dimensional model. We analyze the convergence behavior for higher dimensional models and experiment with different sparse grid adaptivity types. Full article
(This article belongs to the Special Issue AI and Computational Methods in Engineering and Science)
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19 pages, 569 KiB  
Article
SynthSecureNet: An Improved Deep Learning Architecture with Application to Intelligent Violence Detection
by Ntandoyenkosi Zungu, Peter Olukanmi and Pitshou Bokoro
Algorithms 2025, 18(1), 39; https://doi.org/10.3390/a18010039 - 10 Jan 2025
Viewed by 348
Abstract
We present a new deep learning architecture, named SynthSecureNet, which hybridizes two popular architectures: MobileNetV2 and ResNetV2. The latter have been shown to be promising in violence detection. The aim of our architecture is to harness the combined strengths of the two known [...] Read more.
We present a new deep learning architecture, named SynthSecureNet, which hybridizes two popular architectures: MobileNetV2 and ResNetV2. The latter have been shown to be promising in violence detection. The aim of our architecture is to harness the combined strengths of the two known methods for improved accuracy. First, we leverage the pre-trained weights of MobileNetV2 and ResNet50V2 to initialize the network. Next, we fine-tune the network by training it on a dataset of labeled surveillance videos, with a focus on optimizing the fusion process between the two architectures. Experimental results demonstrate a significant improvement in accuracy compared with individual models. MobileNetV2 achieves an accuracy of 90%, while ResNet50V2 achieves a 94% accuracy in violence detection tasks. SynthSecureNet achieves an accuracy of 99.22%, surpassing the performance of individual models. The integration of MobileNetV2 and ResNet50V2 in SynthSecureNet offers a comprehensive solution that addresses the limitations of the existing architectures, paving the way for more effective surveillance and crime prevention strategies. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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38 pages, 1822 KiB  
Article
A Survey on Variable Neighborhood Search for Sustainable Logistics
by Jesica de Armas and José A. Moreno-Pérez
Algorithms 2025, 18(1), 38; https://doi.org/10.3390/a18010038 - 10 Jan 2025
Viewed by 368
Abstract
Sustainable logistics aims to balance economic efficiency, environmental responsibility, and social well-being in supply chain operations. This study explores the use of Variable Neighborhood Search (VNS), a metaheuristic optimization method, in addressing sustainable logistics challenges and provides insights into the potential it has [...] Read more.
Sustainable logistics aims to balance economic efficiency, environmental responsibility, and social well-being in supply chain operations. This study explores the use of Variable Neighborhood Search (VNS), a metaheuristic optimization method, in addressing sustainable logistics challenges and provides insights into the potential it has to support them by delivering efficient solutions that align with global sustainability goals. The review identifies key trends, including a significant increase in research since 2019, with a strong focus on routing, scheduling, and location problems. Hybrid approaches, combining VNS with other methods, and multiobjective optimization to address trade-offs between sustainability goals are prominent. The most frequently applied VNS versions align closely with those commonly used in the broader literature, reflecting similar adoption proportions. In recent years, a noticeable increase in studies incorporating adaptation mechanisms into VNS frameworks has emerged. This trend is largely driven by the growing influence of Artificial Intelligence approaches across numerous fields of science and engineering, highlighting the need for more dynamic and intelligent optimization techniques. However, important research gaps remain. These include limited consideration of uncertainty and dynamic logistics systems, underrepresentation of social sustainability, and a lack of standardized benchmarks for comparing results. Future work should address these challenges and explore emerging applications. Full article
(This article belongs to the Special Issue Heuristic Optimization Algorithms for Logistics)
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16 pages, 239 KiB  
Article
SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models
by Gazi Husain, Daniel Nasef, Rejath Jose, Jonathan Mayer, Molly Bekbolatova, Timothy Devine and Milan Toma
Algorithms 2025, 18(1), 37; https://doi.org/10.3390/a18010037 - 10 Jan 2025
Viewed by 415
Abstract
Class imbalance is a prevalent challenge in machine learning that arises from skewed data distributions in one class over another, causing models to prioritize the majority class and underperform on the minority classes. This bias can significantly undermine accurate predictions in real-world scenarios, [...] Read more.
Class imbalance is a prevalent challenge in machine learning that arises from skewed data distributions in one class over another, causing models to prioritize the majority class and underperform on the minority classes. This bias can significantly undermine accurate predictions in real-world scenarios, highlighting the importance of the robust handling of imbalanced data for dependable results. This study examines one such scenario of real-time monitoring systems for fall risk assessment in bedridden patients where class imbalance may compromise the effectiveness of machine learning. It compares the effectiveness of two resampling techniques, the Synthetic Minority Oversampling Technique (SMOTE) and SMOTE combined with Edited Nearest Neighbors (SMOTEENN), in mitigating class imbalance and improving predictive performance. Using a controlled sampling strategy across various instance levels, the performance of both methods in conjunction with decision tree regression, gradient boosting regression, and Bayesian regression models was evaluated. The results indicate that SMOTEENN consistently outperforms SMOTE in terms of accuracy and mean squared error across all sample sizes and models. SMOTEENN also demonstrates healthier learning curves, suggesting improved generalization capabilities, particularly for a sampling strategy with a given number of instances. Furthermore, cross-validation analysis reveals that SMOTEENN achieves higher mean accuracy and lower standard deviation compared to SMOTE, indicating more stable and reliable performance. These findings suggest that SMOTEENN is a more effective technique for handling class imbalance, potentially contributing to the development of more accurate and generalizable predictive models in various applications. Full article
(This article belongs to the Special Issue Algorithms in Data Classification (2nd Edition))
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21 pages, 1481 KiB  
Article
Design of a New Energy Microgrid Optimization Scheduling Algorithm Based on Improved Grey Relational Theory
by Dong Mo, Qiuwen Li, Yan Sun, Yixin Zhuo and Fangming Deng
Algorithms 2025, 18(1), 36; https://doi.org/10.3390/a18010036 - 9 Jan 2025
Viewed by 344
Abstract
In order to solve the problem of the large-scale integration of new energy into power grid output fluctuations, this paper proposes a new energy microgrid optimization scheduling algorithm based on a two-stage robust optimization and improved grey correlation theory. This article simulates the [...] Read more.
In order to solve the problem of the large-scale integration of new energy into power grid output fluctuations, this paper proposes a new energy microgrid optimization scheduling algorithm based on a two-stage robust optimization and improved grey correlation theory. This article simulates the fluctuation of the outputs of wind turbines and distributed photovoltaic power plants by changing their robustness indicators, generates economic operating cost data for microgrids in multiple scenarios, and uses an improved grey correlation theory algorithm to analyze the correlation between new energy and various scheduling costs. Subsequently, a weighted analysis is performed on each correlation degree to obtain the correlation degree between new energy and total scheduling operating costs. The experimental results show that the improved grey correlation theory optimization scheduling algorithm for new energy microgrids proposed obtains weighted correlation degrees of 0.730 and 0.798 for photovoltaic power stations and wind turbines, respectively, which are 3.1% and 4.6% higher than traditional grey correlation theory. In addition, the equipment maintenance costs of this method are 0.413 and 0.527, respectively, which are 25.1% and 5.4% lower compared to the traditional method, respectively, indicating that the method effectively improves the accuracy of quantitative analysis. Full article
(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources)
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18 pages, 13100 KiB  
Article
Enhancing Hydraulic Efficiency of Pelton Turbines Through Computational Fluid Dynamics and Metaheuristic Optimization
by Guillermo Barragan, Sebastian Atarihuana, Edgar Cando and Victor Hidalgo
Algorithms 2025, 18(1), 35; https://doi.org/10.3390/a18010035 - 9 Jan 2025
Viewed by 421
Abstract
In this work, the NSGA-II multi objective genetic algorithm, numerical methods, and parametric design techniques found in the Autodesk Inventor professional 2023 CAD software were combined to perform the geometrical optimization of the Pelton bucket geometry. The validation of the proposed method was [...] Read more.
In this work, the NSGA-II multi objective genetic algorithm, numerical methods, and parametric design techniques found in the Autodesk Inventor professional 2023 CAD software were combined to perform the geometrical optimization of the Pelton bucket geometry. The validation of the proposed method was carried out with numerical simulations using the OpenFOAM CFD program and taking into account the case study turbine’s operating conditions, as well as the k-SST turbulence model. The CFD simulation results and operational data from the case study turbine from the “Illuchi N°2” hydrocenter have been compared in order to validate the proposed methodology. The implementation of the NSGA-II in the design process resulted in optimized bucket geometrical parameters: bucket length, width, inlet angle, and outlet angle. These parameters not only resulted in a 2.56% increase in hydraulic efficiency, but also led to a 0.1 [kPa] reduction in the maximum pressure at the bottom of the bucket. Further research will involve testing these parameters using 3D printing methods to validate their effectiveness. Full article
(This article belongs to the Special Issue Engineering Applications of Optimization Algorithms: Heuristics, Metaheuristics, and Hyperheuristics)
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27 pages, 553 KiB  
Systematic Review
Integrating Artificial Intelligence, Internet of Things, and Sensor-Based Technologies: A Systematic Review of Methodologies in Autism Spectrum Disorder Detection
by Georgios Bouchouras and Konstantinos Kotis
Algorithms 2025, 18(1), 34; https://doi.org/10.3390/a18010034 - 9 Jan 2025
Viewed by 738
Abstract
This paper presents a systematic review of the emerging applications of artificial intelligence (AI), Internet of Things (IoT), and sensor-based technologies in the diagnosis of autism spectrum disorder (ASD). The integration of these technologies has led to promising advances in identifying unique behavioral, [...] Read more.
This paper presents a systematic review of the emerging applications of artificial intelligence (AI), Internet of Things (IoT), and sensor-based technologies in the diagnosis of autism spectrum disorder (ASD). The integration of these technologies has led to promising advances in identifying unique behavioral, physiological, and neuroanatomical markers associated with ASD. Through an examination of recent studies, we explore how technologies such as wearable sensors, eye-tracking systems, virtual reality environments, neuroimaging, and microbiome analysis contribute to a holistic approach to ASD diagnostics. The analysis reveals how these technologies facilitate non-invasive, real-time assessments across diverse settings, enhancing both diagnostic accuracy and accessibility. The findings underscore the transformative potential of AI, IoT, and sensor-based driven tools in providing personalized and continuous ASD detection, advocating for data-driven approaches that extend beyond traditional methodologies. Ultimately, this review emphasizes the role of technology in improving ASD diagnostic processes, paving the way for targeted and individualized assessments. Full article
(This article belongs to the Special Issue Advancing Smart Health and Biomedical Research with Artificial Intelligence Algorithms)
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12 pages, 3367 KiB  
Article
Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
by Mateus Martinez de Lucena, Josafat Leal Ribeiro, Matheus Wagner and Antônio Augusto Fröhlich
Algorithms 2025, 18(1), 33; https://doi.org/10.3390/a18010033 - 9 Jan 2025
Viewed by 285
Abstract
Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these data require can only be achieved [...] Read more.
Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these data require can only be achieved through compression. Careful consideration must be given to minimize the reconstruction error of compressed data caused by lossy compression. This paper investigates the combination of principal component analysis (PCA), discrete wavelet transform (DWT), thresholding, quantization, and entropy encoding to compress such datasets. The proposed method is a lossy compression algorithm tuned by evaluating the reconstruction error in frequency ranges of interest, namely 0–20 Hz and 15–65 Hz. The PCA compression and decompression acts as a noise filter while the DWT drives the compression. The proposed method can be tuned through threshold and quantization percentages and the number of principal components to achieve compression rates of up to 31:1 with reconstruction residues energy of less than 4% in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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18 pages, 4015 KiB  
Article
Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
by Tingzhe Pan, Jue Hou, Xin Jin, Chao Li, Xinlei Cai and Xiaodong Zhou
Algorithms 2025, 18(1), 32; https://doi.org/10.3390/a18010032 - 8 Jan 2025
Viewed by 358
Abstract
Load forecasting plays a fundamental role in the new type of power system. To address the data heterogeneity and security issues encountered in load forecasting for smart grids, this paper proposes a load-forecasting framework suitable for residential energy users, which allows users to [...] Read more.
Load forecasting plays a fundamental role in the new type of power system. To address the data heterogeneity and security issues encountered in load forecasting for smart grids, this paper proposes a load-forecasting framework suitable for residential energy users, which allows users to train personalized forecasting models without sharing load data. First, the similarity of user load patterns is calculated under privacy protection. Second, a complex network is constructed, and a federated user clustering method is developed based on the Louvain algorithm, which divides users into multiple clusters based on load pattern similarity. Finally, a personalized and adaptive differentially private federated learning Long Short-Term Memory (LSTM) model for load forecasting is developed. A case study analysis shows that the proposed method can effectively protect user privacy and improve model prediction accuracy when dealing with heterogeneous data. The framework can train load-forecasting models with a fast convergence rate and better prediction performance than current mainstream federated learning algorithms. Full article
(This article belongs to the Special Issue Intelligent Algorithms for High-Penetration New Energy)
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19 pages, 421 KiB  
Article
Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing
by Wang Feng, Sihai Tang, Shengze Wang, Ying He, Donger Chen, Qing Yang and Song Fu
Algorithms 2025, 18(1), 31; https://doi.org/10.3390/a18010031 - 8 Jan 2025
Viewed by 377
Abstract
Vehicular edge computing relies on the computational capabilities of interconnected edge devices to manage incoming requests from vehicles. This offloading process enhances the speed and efficiency of data handling, ultimately boosting the safety, performance, and reliability of connected vehicles. While previous studies have [...] Read more.
Vehicular edge computing relies on the computational capabilities of interconnected edge devices to manage incoming requests from vehicles. This offloading process enhances the speed and efficiency of data handling, ultimately boosting the safety, performance, and reliability of connected vehicles. While previous studies have concentrated on processor characteristics, they often overlook the significance of the connecting components. Limited memory and storage resources on edge devices pose challenges, particularly in the context of deep learning, where these limitations can significantly affect performance. The impact of memory contention has not been thoroughly explored, especially regarding perception-based tasks. In our analysis, we identified three distinct behaviors of memory contention, each interacting differently with other resources. Additionally, our investigation of Deep Neural Network (DNN) layers revealed that certain convolutional layers experienced computation time increases exceeding 2849%, while activation layers showed a rise of 1173.34%. Through our characterization efforts, we can model workload behavior on edge devices according to their configuration and the demands of the tasks. This allows us to quantify the effects of memory contention. To our knowledge, this study is the first to characterize the influence of memory on vehicular edge computational workloads, with a strong emphasis on memory dynamics and DNN layers. Full article
(This article belongs to the Special Issue Machine Learning for Edge Computing)
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21 pages, 257 KiB  
Review
A Review of Multi-Source Data Fusion and Analysis Algorithms in Smart City Construction: Facilitating Real Estate Management and Urban Optimization
by Binglin Liu, Qian Li, Zhihua Zheng, Yanjia Huang, Shuguang Deng, Qiongxiu Huang and Weijiang Liu
Algorithms 2025, 18(1), 30; https://doi.org/10.3390/a18010030 - 8 Jan 2025
Viewed by 376
Abstract
In the context of the booming construction of smart cities, multi-source data fusion and analysis algorithms play a key role in optimizing real estate management and improving urban efficiency. In this review, we comprehensively and systematically review the relevant algorithms, covering the types, [...] Read more.
In the context of the booming construction of smart cities, multi-source data fusion and analysis algorithms play a key role in optimizing real estate management and improving urban efficiency. In this review, we comprehensively and systematically review the relevant algorithms, covering the types, characteristics, fusion techniques, analysis algorithms, and their synergies of multi-source data. We found that multi-source data, including sensors, social media, citizen feedback, and GIS data, face challenges such as data quality and privacy security when being fused. Data fusion algorithms are diverse and have their own advantages and disadvantages. Data analysis algorithms help urban management in areas such as spatial analysis and deep learning. Algorithm collaboration can improve decision-making accuracy and efficiency and promote the rational allocation of urban resources. In the future, algorithm development will focus on data quality, real-time, deep mining, interdisciplinary research, privacy protection, and collaborative application expansion, providing strong support for the sustainable development of smart cities. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
16 pages, 648 KiB  
Article
Parallelizing the Computation of Grid Resistance to Measure the Strength of Skyline Tuples
by Davide Martinenghi
Algorithms 2025, 18(1), 29; https://doi.org/10.3390/a18010029 - 7 Jan 2025
Viewed by 318
Abstract
Several indicators have been recently proposed for the measurement of various characteristics of the tuples of a dataset—particularly the so-called skyline tuples, i.e., those that are not dominated by other tuples. Numeric indicators are very important as they may, e.g., provide an additional [...] Read more.
Several indicators have been recently proposed for the measurement of various characteristics of the tuples of a dataset—particularly the so-called skyline tuples, i.e., those that are not dominated by other tuples. Numeric indicators are very important as they may, e.g., provide an additional criterion to be used to rank skyline tuples and focus on a subset thereof. We focus on an indicator of robustness that may be measured for any skyline tuple t: the grid resistance, i.e., how large-value perturbations can be tolerated for t to remain non-dominated (and thus in the skyline). The computation of this indicator typically involves one or more rounds of computation of the skyline itself or, at least, of dominance relationships. Building on recent advances in partitioning strategies allowing the parallel computation of skylines, we discuss how these strategies can be adapted to the computation of the indicator. Full article
(This article belongs to the Special Issue Surveys in Algorithm Analysis and Complexity Theory, Part II)
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26 pages, 6386 KiB  
Article
Spatial Intelligence in E-Commerce: Integrating Mobile Agents with GISs for a Dynamic Recommendation System
by Mohamed Shili, Salah Hammedi and Mahmoud Elkhodr
Algorithms 2025, 18(1), 28; https://doi.org/10.3390/a18010028 - 7 Jan 2025
Viewed by 385
Abstract
The evolving capabilities of Geographic Information Systems (GISs) are transforming various industries, including e-commerce, by providing enhanced spatial analysis and precision in customer targeting, and improving the ability of recommender systems. This paper proposes a novel framework that integrates mobile agents with GISs [...] Read more.
The evolving capabilities of Geographic Information Systems (GISs) are transforming various industries, including e-commerce, by providing enhanced spatial analysis and precision in customer targeting, and improving the ability of recommender systems. This paper proposes a novel framework that integrates mobile agents with GISs to deliver real-time, personalized recommendations in e-commerce. By utilizing the OpenStreetMap API for geographic mapping and the Java Agent Development Environment (JADE) platform for mobile agents, the system leverages both geospatial data and customer preferences to offer highly relevant product suggestions based on location and behaviour. Mobile agents enable real-time data collection, processing, and interaction with customers, facilitating dynamic adaptations to their needs. The combination of GISs and mobile agents enhances the system’s ability to analyze spatial data, providing tailored recommendations that align with user preferences and geographic context. This integrated approach not only improves the online shopping experience but also introduces new opportunities for location-specific marketing strategies, boosting the effectiveness of targeted advertising. The validation of this system highlights its potential to significantly enhance customer engagement and satisfaction through context-aware recommendations. The integration of GISs and mobile agents lays a strong foundation for future advancements in personalized e-commerce solutions, offering a scalable model for businesses looking to optimize marketing efforts and customer experiences. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))
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17 pages, 6914 KiB  
Article
YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane Operations
by Dong Ding, Zhengrong Deng and Rui Yang
Algorithms 2025, 18(1), 27; https://doi.org/10.3390/a18010027 - 6 Jan 2025
Viewed by 389
Abstract
Ensuring operational safety within high-risk environments, such as construction sites, is paramount, especially for tower crane operations where distractions can lead to severe accidents. Despite existing behavioral monitoring approaches, the task of identifying small yet hazardous objects like mobile phones and cigarettes in [...] Read more.
Ensuring operational safety within high-risk environments, such as construction sites, is paramount, especially for tower crane operations where distractions can lead to severe accidents. Despite existing behavioral monitoring approaches, the task of identifying small yet hazardous objects like mobile phones and cigarettes in real time remains a significant challenge in ensuring operator compliance and site safety. Traditional object detection models often fall short in crane operator cabins due to complex lighting conditions, cluttered backgrounds, and the small physical scale of target objects. To address these challenges, we introduce YOLO-TC, a refined object detection model tailored specifically for tower crane monitoring applications. Built upon the robust YOLOv7 architecture, our model integrates a novel channel–spatial attention mechanism, ECA-CBAM, into the backbone network, enhancing feature extraction without an increase in parameter count. Additionally, we propose the HA-PANet architecture to achieve progressive feature fusion, addressing scale disparities and prioritizing small object detection while reducing noise from unrelated objects. To improve bounding box regression, the MPDIoU Loss function is employed, resulting in superior accuracy for small, critical objects in dense environments. The experimental results on both the PASCAL VOC benchmark and a custom dataset demonstrate that YOLO-TC outperforms baseline models, showcasing its robustness in identifying high-risk objects under challenging conditions. This model holds significant promise for enhancing automated safety monitoring, potentially reducing occupational hazards by providing a proactive, resilient solution for real-time risk detection in tower crane operations. Full article
(This article belongs to the Special Issue Advances in Computer Vision: Emerging Trends and Applications)
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14 pages, 252 KiB  
Article
Impossibility Results for Byzantine-Tolerant State Observation, Synchronization, and Graph Computation Problems
by Ajay D. Kshemkalyani and Anshuman Misra
Algorithms 2025, 18(1), 26; https://doi.org/10.3390/a18010026 - 5 Jan 2025
Viewed by 335
Abstract
This paper considers the solvability of several fundamental problems in asynchronous message-passing distributed systems in the presence of Byzantine processes using distributed algorithms. These problems are the following: mutual exclusion, global snapshot recording, termination detection, deadlock detection, predicate detection, causal ordering, spanning tree [...] Read more.
This paper considers the solvability of several fundamental problems in asynchronous message-passing distributed systems in the presence of Byzantine processes using distributed algorithms. These problems are the following: mutual exclusion, global snapshot recording, termination detection, deadlock detection, predicate detection, causal ordering, spanning tree construction, minimum spanning tree construction, all–all shortest paths computation, and maximal independent set computation. In a distributed algorithm, each process has access only to its local variables and incident edge parameters. We show the impossibility of solving these fundamental problems by proving that they require a solution to the causality determination problem which has been shown to be unsolvable in asynchronous message-passing distributed systems. Full article
(This article belongs to the Special Issue Graph Theory and Algorithmic Applications: Theoretical Developments)
34 pages, 2158 KiB  
Article
Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
by Eduardo Esquivel-Cruz, Francisco Beltran-Carbajal, Ivan Rivas-Cambero, José Humberto Arroyo-Núñez, Ruben Tapia-Olvera and Daniel Guillen
Algorithms 2025, 18(1), 25; https://doi.org/10.3390/a18010025 - 5 Jan 2025
Viewed by 305
Abstract
Signal analysis is a fundamental field in engineering and data science, focused on the study of signal representation, transformation, and manipulation. The accurate estimation of harmonic vibration components and their associated parameters in vibrating mechanical systems presents significant challenges in the presence of [...] Read more.
Signal analysis is a fundamental field in engineering and data science, focused on the study of signal representation, transformation, and manipulation. The accurate estimation of harmonic vibration components and their associated parameters in vibrating mechanical systems presents significant challenges in the presence of very similar frequencies and mode mixing. In this context, a hybrid strategy to estimate harmonic vibration modes in weakly damped, multi-degree-of-freedom vibrating mechanical systems by combining Empirical Mode Decomposition and Variational Mode Decomposition is described. In this way, this hybrid approach leverages the detection of mode mixing based on the analysis of intrinsic mode functions through Empirical Mode Decomposition to determine the number of components to be estimated and thus provide greater information for Variational Mode Decomposition. The computational time and dependency on a predefined number of modes are significantly reduced by providing crucial information about the approximate number of vibratory components, enabling a more precise estimation with Variational Mode Decomposition. This hybrid strategy is employed to compute unknown natural frequencies of vibrating systems using output measurement signals. The algorithm for this hybrid strategy is presented, along with a comparison to conventional techniques such as Empirical Mode Decomposition, Variational Mode Decomposition, and the Fast Fourier Transform. Through several case studies involving multi-degree-of-freedom vibrating systems, the superior and satisfactory performance of the hybrid method is demonstrated. Additionally, the advantages of the hybrid approach in terms of computational efficiency and accuracy in signal decomposition are highlighted. Full article
(This article belongs to the Special Issue AI and Computational Methods in Engineering and Science)
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21 pages, 2229 KiB  
Article
Multi-Server Two-Way Communication Retrial Queue Subject to Disaster and Synchronous Working Vacation
by Tzu-Hsin Liu, He-Yao Hsu and Fu-Min Chang
Algorithms 2025, 18(1), 24; https://doi.org/10.3390/a18010024 - 5 Jan 2025
Viewed by 326
Abstract
This research analyzes a multi-server retrial queue with two types of calls: working vacation and working breakdown. The incoming call may enter the retrial queue and attempt to seize a server after a random delay if all the servers are unavailable upon arrival. [...] Read more.
This research analyzes a multi-server retrial queue with two types of calls: working vacation and working breakdown. The incoming call may enter the retrial queue and attempt to seize a server after a random delay if all the servers are unavailable upon arrival. In its idle time, the server makes outgoing calls. All the servers take a synchronous working vacation when the system empties after regular service. The system may fail at any time due to disasters, forcing all the calls within the service area to leave the system and causing all the main servers to fail. When the main servers fail, the repair process begins immediately. The standby servers serve arriving customers at a lower level of service during the working breakdown or working vacation. For this model, we derive an explicit expression for the stationary distribution with the help of the quasi-birth-and-death process and the matrix geometric method. Further, the formulas of various system performance indices are developed. An application example is given and several numerical experiments are performed to verify the analytical results. We also perform the comparative analysis of retrial queues with/without two-way communication and two-way communication retrial queues with/without disasters. The results reveal that the proper consideration of outgoing calls to the server can reduce the average time spent in the buffer. Furthermore, a more reliable server reduces the server idle rate. Full article
(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)
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