Journal Description
Algorithms
Algorithms
is a peer-reviewed, open access journal which provides an advanced forum for studies related to algorithms and their applications. Algorithms is published monthly online by MDPI. The European Society for Fuzzy Logic and Technology (EUSFLAT) is affiliated with Algorithms and their members receive discounts on the article processing charges.
- Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, and other databases.
- Journal Rank: CiteScore - Q2 (Numerical Analysis)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2023).
- Testimonials: See what our editors and authors say about Algorithms.
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.3 (2022);
5-Year Impact Factor:
2.2 (2022)
Latest Articles
A Data-Driven Approach to Discovering Process Choreography
Algorithms 2024, 17(5), 188; https://doi.org/10.3390/a17050188 (registering DOI) - 29 Apr 2024
Abstract
Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes
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Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments.
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(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
Open AccessArticle
MMD-MSD: A Multimodal Multisensory Dataset in Support of Research and Technology Development for Musculoskeletal Disorders
by
Valentina Markova, Todor Ganchev, Silvia Filkova and Miroslav Markov
Algorithms 2024, 17(5), 187; https://doi.org/10.3390/a17050187 (registering DOI) - 29 Apr 2024
Abstract
Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSDs) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological
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Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSDs) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support for the prevention of back pain or the development of MSDs. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons, made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal and valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures, and task-specific cognitive load detection from physiological signals.
Full article
(This article belongs to the Section Databases and Data Structures)
Open AccessArticle
Synesth: Comprehensive Syntenic Reconciliation with Unsampled Lineages
by
Mattéo Delabre and Nadia El-Mabrouk
Algorithms 2024, 17(5), 186; https://doi.org/10.3390/a17050186 - 29 Apr 2024
Abstract
We present Synesth, the most comprehensive and flexible tool for tree reconciliation that allows for events on syntenies (i.e., on sets of multiple genes), including duplications, transfers, fissions, and transient events going through unsampled species. This model allows for building histories that explicate
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We present Synesth, the most comprehensive and flexible tool for tree reconciliation that allows for events on syntenies (i.e., on sets of multiple genes), including duplications, transfers, fissions, and transient events going through unsampled species. This model allows for building histories that explicate the inconsistencies between a synteny tree and its associated species tree. We examine the combinatorial properties of this extended reconciliation model and study various associated parsimony problems. First, the infinite set of explicatory histories is reduced to a finite but exponential set of Pareto-optimal histories (in terms of counts of each event type), then to a polynomial set of Pareto-optimal event count vectors, and this eventually ends with minimum event cost histories given an event cost function. An inductive characterization of the solution space using different algebras for each granularity leads to efficient dynamic programming algorithms, ultimately ending with an time complexity algorithm for computing the cost of a minimum-cost history (m and n: number of nodes in the input synteny and species trees). This time complexity matches that of the fastest known algorithms for classical gene reconciliation with transfers. We show how Synesth can be applied to infer Pareto-optimal evolutionary scenarios for CRISPR-Cas systems in a set of bacterial genomes.
Full article
(This article belongs to the Section Combinatorial Optimization, Graph, and Network Algorithms)
Open AccessArticle
A Linear Interpolation and Curvature-Controlled Gradient Optimization Strategy Based on Adam
by
Haijing Sun, Wen Zhou, Yichuan Shao, Jiaqi Cui, Lei Xing, Qian Zhao and Le Zhang
Algorithms 2024, 17(5), 185; https://doi.org/10.3390/a17050185 - 29 Apr 2024
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The Adam algorithm is a widely used optimizer for neural network training due to efficient convergence speed. The algorithm is prone to unstable learning rate and performance degradation on some models. To solve these problems, in this paper, an improved algorithm named Linear
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The Adam algorithm is a widely used optimizer for neural network training due to efficient convergence speed. The algorithm is prone to unstable learning rate and performance degradation on some models. To solve these problems, in this paper, an improved algorithm named Linear Curvature Momentum Adam (LCMAdam) is proposed, which introduces curvature-controlled gradient and linear interpolation strategies. The curvature-controlled gradient can make the gradient update smoother, and the linear interpolation technique can adaptively adjust the size of the learning rate according to the characteristics of the curve during the training process so that it can find the exact value faster, which improves the efficiency and robustness of training. The experimental results show that the LCMAdam algorithm achieves 98.49% accuracy on the MNIST dataset, 75.20% on the CIFAR10 dataset, and 76.80% on the Stomach dataset, which is more difficult to recognize medical images. The LCMAdam optimizer achieves significant performance gains on a variety of neural network structures and tasks, proving its effectiveness and utility in the field of deep learning.
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Open AccessArticle
Algorithm Based on Morphological Operators for Shortness Path Planning
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Jorge L. Perez-Ramos, Selene Ramirez-Rosales, Daniel Canton-Enriquez, Luis A. Diaz-Jimenez, Gabriela Xicotencatl-Ramirez, Ana M. Herrera-Navarro and Hugo Jimenez-Hernandez
Algorithms 2024, 17(5), 184; https://doi.org/10.3390/a17050184 - 29 Apr 2024
Abstract
The problem of finding the best path trajectory in a graph is highly complex due to its combinatorial nature, making it difficult to solve. Standard search algorithms focus on selecting the best path trajectory by introducing constraints to estimate a suitable solution, but
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The problem of finding the best path trajectory in a graph is highly complex due to its combinatorial nature, making it difficult to solve. Standard search algorithms focus on selecting the best path trajectory by introducing constraints to estimate a suitable solution, but this approach may overlook potentially better alternatives. Despite the number of restrictions and variables in path planning, no solution minimizes the computational resources used to reach the goal. To address this issue, a framework is proposed to compute the best trajectory in a graph by introducing the mathematical morphology concept. The framework builds a lattice over the graph space using mathematical morphology operators. The searching algorithm creates a metric space by applying the morphological covering operator to the graph and weighing the cost of traveling across the lattice. Ultimately, the cumulative traveling criterion creates the optimal path trajectory by selecting the minima/maxima cost. A test is introduced to validate the framework’s functionality, and a sample application is presented to validate its usefulness. The application uses the structure of the avenues as a graph. It proposes a computable approach to find the most suitable paths from a given start and destination reference. The results confirm that this is a generalized graph search framework based on morphological operators that can be compared to the Dijkstra approach.
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(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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Open AccessArticle
Advancements in Data Analysis for the Work-Sampling Method
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Borut Buchmeister and Natasa Vujica Herzog
Algorithms 2024, 17(5), 183; https://doi.org/10.3390/a17050183 - 29 Apr 2024
Abstract
The work-sampling method makes it possible to gain valuable insights into what is happening in production systems. Work sampling is a process used to estimate the proportion of shift time that workers (or machines) spend on different activities (within productive work or losses).
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The work-sampling method makes it possible to gain valuable insights into what is happening in production systems. Work sampling is a process used to estimate the proportion of shift time that workers (or machines) spend on different activities (within productive work or losses). It is estimated based on enough random observations of activities over a selected period. When workplace operations do not have short cycle times or high repetition rates, the use of such a statistical technique is necessary because the labor sampling data can provide information that can be used to set standards. The work-sampling procedure is well standardized, but additional contributions are possible when evaluating the observations. In this paper, we present our contribution to improving the decision-making process based on work-sampling data. We introduce a correlation comparison of the measured hourly shares of all activities in pairs to check whether there are mutual connections or to uncover hidden connections between activities. The results allow for easier decision-making (conclusions) regarding the influence of the selected activities on the triggering of the others. With the additional calculation method, we can uncover behavioral patterns that would have been overlooked with the basic method. This leads to improved efficiency and productivity of the production system.
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(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
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Open AccessArticle
Sub-Band Backdoor Attack in Remote Sensing Imagery
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Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu and Jiang Li
Algorithms 2024, 17(5), 182; https://doi.org/10.3390/a17050182 - 28 Apr 2024
Abstract
Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent
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Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in the data. Backdoor attacks have been extensively studied in machine learning-based computer vision applications with natural images. However, much less research has been conducted on remote sensing imagery, which typically consists of many more bands in addition to the red, green, and blue bands found in natural images. In this paper, we first extensively studied a popular backdoor attack, BadNets, applied to a remote sensing dataset, where the trigger was planted in all of the bands in the data. Our results showed that SOTA defense mechanisms, including Neural Cleanse, TABOR, Activation Clustering, Fine-Pruning, GangSweep, Strip, DeepInspect, and Pixel Backdoor, had difficulties detecting and mitigating the backdoor attack. We then proposed an explainable AI-guided backdoor attack specifically for remote sensing imagery by placing triggers in the image sub-bands. Our proposed attack model even poses stronger challenges to these SOTA defense mechanisms, and no method was able to defend it. These results send an alarming message about the catastrophic effects the backdoor attacks may have on satellite imagery.
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(This article belongs to the Special Issue Machine Learning Models and Algorithms for Image Processing)
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Open AccessArticle
Application of Evolutionary Computation to the Optimization of Biodiesel Mixtures Using a Nature-Inspired Adaptive Genetic Algorithm
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Vasileios Vasileiadis, Christos Kyriklidis, Vayos Karayannis and Constantinos Tsanaktsidis
Algorithms 2024, 17(5), 181; https://doi.org/10.3390/a17050181 - 28 Apr 2024
Abstract
The present research work introduces a novel mixture optimization methodology for biodiesel fuels using an Evolutionary Computation method inspired by biological evolution. Specifically, the optimal biodiesel composition is deduced from the application of a nature-inspired adaptive genetic algorithm that first examines percentages of
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The present research work introduces a novel mixture optimization methodology for biodiesel fuels using an Evolutionary Computation method inspired by biological evolution. Specifically, the optimal biodiesel composition is deduced from the application of a nature-inspired adaptive genetic algorithm that first examines percentages of the ingredients in the optimal mixtures. The innovative approach’s effectiveness lies in problem simulation with improvements in the evaluation of the specific function and the way to define and tune the genetic algorithm. Environmental imperatives in the era of climate change currently impose the optimized production of alternative environmentally friendly biofuels to replace fossil fuels. Biodiesel in particular, appears to be more attractive in recent years, as it originates from renewable bio-derived resources. The main ingredients of the specific biofuel mixture investigated in this research are diesel and biodiesel (100% from bioresources). The assessment of the new biodiesel examined was performed using a fitness function that estimated both the density and cost of the fuel. Beyond the evaluation criterion of cost, density also influences the suitability of this biofuel for commercial use and market sale. The outcomes from the modeling process can be beneficial in saving cost and time for new biodiesel production by using this novel decision-making tool in comparison with randomized laboratory experimentations.
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(This article belongs to the Special Issue Bio-Inspired Algorithms)
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Maximizing Net Present Value for Resource Constraint Project Scheduling Problems with Payments at Event Occurrences Using Approximate Dynamic Programming
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Tshewang Phuntsho and Tad Gonsalves
Algorithms 2024, 17(5), 180; https://doi.org/10.3390/a17050180 - 28 Apr 2024
Abstract
Resource Constraint Project Scheduling Problems with Discounted Cash Flows (RCPSPDC) focuses on maximizing the net present value by summing the discounted cash flows of project activities. An extension of this problem is the Payment at Event Occurrences (PEO) scheme, where the client makes
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Resource Constraint Project Scheduling Problems with Discounted Cash Flows (RCPSPDC) focuses on maximizing the net present value by summing the discounted cash flows of project activities. An extension of this problem is the Payment at Event Occurrences (PEO) scheme, where the client makes multiple payments to the contractor upon completion of predefined activities, with additional final settlement at project completion. Numerous approximation methods such as metaheuristics have been proposed to solve this NP-hard problem. However, these methods suffer from parameter control and/or the computational cost of correcting infeasible solutions. Alternatively, approximate dynamic programming (ADP) sequentially generates a schedule based on strategies computed via Monte Carlo (MC) simulations. This saves the computations required for solution corrections, but its performance is highly dependent on its strategy. In this study, we propose the hybridization of ADP with three different metaheuristics to take advantage of their combined strengths, resulting in six different models. The Estimation of Distribution Algorithm (EDA) and Ant Colony Optimization (ACO) were used to recommend policies for ADP. A Discrete cCuckoo Search (DCS) further improved the schedules generated by ADP. Our experimental analysis performed on the j30, j60, and j90 datasets of PSPLIB has shown that ADP–DCS is better than ADP alone. Implementing the EDA and ACO as prioritization strategies for Monte Carlo simulations greatly improved the solutions with high statistical significance. In addition, models with the EDA showed better performance than those with ACO and random priority, especially when the number of events increased.
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(This article belongs to the Special Issue Algorithms and Optimization for Project Management and Supply Chain Management)
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Open AccessArticle
CentralBark Image Dataset and Tree Species Classification Using Deep Learning
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Charles Warner, Fanyou Wu, Rado Gazo, Bedrich Benes, Nicole Kong and Songlin Fei
Algorithms 2024, 17(5), 179; https://doi.org/10.3390/a17050179 - 27 Apr 2024
Abstract
The task of tree species classification through deep learning has been challenging for the forestry community, and the lack of standardized datasets has hindered further progress. Our work presents a solution in the form of a large bark image dataset called CentralBark, which
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The task of tree species classification through deep learning has been challenging for the forestry community, and the lack of standardized datasets has hindered further progress. Our work presents a solution in the form of a large bark image dataset called CentralBark, which enhances the deep learning-based tree species classification. Additionally, we have laid out an efficient and repeatable data collection protocol to assist future works in an organized manner. The dataset contains images of 25 central hardwood and Appalachian region tree species, with over 19,000 images of varying diameters, light, and moisture conditions. We tested 25 species: elm, oak, American basswood, American beech, American elm, American sycamore, bitternut hickory, black cherry, black locust, black oak, black walnut, eastern cottonwood, hackberry, honey locust, northern red oak, Ohio buckeye, Osage-orange, pignut hickory, sassafras, shagbark hickory silver maple, slippery elm, sugar maple, sweetgum, white ash, white oak, and yellow poplar. Our experiment involved testing three different models to assess the feasibility of species classification using unaltered and uncropped images during the species-classification training process. We achieved an overall accuracy of 83.21% using the EfficientNet-b3 model, which was the best of the three models (EfficientNet-b3, ResNet-50, and MobileNet-V3-small), and an average accuracy of 80.23%.
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(This article belongs to the Special Issue Recent Advances in Algorithms for Computer Vision Applications)
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Strategic Machine Learning Optimization for Cardiovascular Disease Prediction and High-Risk Patient Identification
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Konstantina-Vasiliki Tompra, George Papageorgiou and Christos Tjortjis
Algorithms 2024, 17(5), 178; https://doi.org/10.3390/a17050178 - 26 Apr 2024
Abstract
Despite medical advancements in recent years, cardiovascular diseases (CVDs) remain a major factor in rising mortality rates, challenging predictions despite extensive expertise. The healthcare sector is poised to benefit significantly from harnessing massive data and the insights we can derive from it, underscoring
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Despite medical advancements in recent years, cardiovascular diseases (CVDs) remain a major factor in rising mortality rates, challenging predictions despite extensive expertise. The healthcare sector is poised to benefit significantly from harnessing massive data and the insights we can derive from it, underscoring the importance of integrating machine learning (ML) to improve CVD prevention strategies. In this study, we addressed the major issue of class imbalance in the Behavioral Risk Factor Surveillance System (BRFSS) 2021 heart disease dataset, including personal lifestyle factors, by exploring several resampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), SMOTE-Tomek, and SMOTE-Edited Nearest Neighbor (SMOTE-ENN). Subsequently, we trained, tested, and evaluated multiple classifiers, including logistic regression (LR), decision trees (DTs), random forest (RF), gradient boosting (GB), XGBoost (XGB), CatBoost, and artificial neural networks (ANNs), comparing their performance with a primary focus on maximizing sensitivity for CVD risk prediction. Based on our findings, the hybrid resampling techniques outperformed the alternative sampling techniques, and our proposed implementation includes SMOTE-ENN coupled with CatBoost optimized through Optuna, achieving a remarkable 88% rate for recall and 82% for the area under the receiver operating characteristic (ROC) curve (AUC) metric.
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(This article belongs to the Collection Feature Papers in Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems)
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Open AccessArticle
Mission Planning of UAVs and UGV for Building Inspection in Rural Area
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Xiao Chen, Yu Wu and Shuting Xu
Algorithms 2024, 17(5), 177; https://doi.org/10.3390/a17050177 - 26 Apr 2024
Abstract
Unmanned aerial vehicles (UAVs) have become increasingly popular in the civil field, and building inspection is one of the most promising applications. In a rural area, the UAVs are assigned to inspect the surface of buildings, and an unmanned ground vehicle (UGV) is
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Unmanned aerial vehicles (UAVs) have become increasingly popular in the civil field, and building inspection is one of the most promising applications. In a rural area, the UAVs are assigned to inspect the surface of buildings, and an unmanned ground vehicle (UGV) is introduced to carry the UAVs to reach the rural area and also serve as a charging station. In this paper, the mission planning problem for UAVs and UGV systems is focused on, and the goal is to realize an efficient inspection of buildings in a specific rural area. Firstly, the mission planning problem (MPP) involving UGVs and UAVs is described, and an optimization model is established with the objective of minimizing the total UAV operation time, fully considering the impact of UAV operation time and its cruising capability. Subsequently, the locations of parking points are determined based on the information about task points. Finally, a hybrid ant colony optimization-genetic algorithm (ACO-GA) is designed to solve the problem. The update mechanism of ACO is incorporated into the selection operation of GA. At the same time, the GA is improved and the defects that make GA easy to fall into local optimal and ACO have insufficient searching ability are solved. Simulation results demonstrate that the ACO-GA algorithm can obtain reasonable solutions for MPP, and the search capability of the algorithm is enhanced, presenting significant advantages over the original GA and ACO.
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(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
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A Survey of the Applications of Text Mining for the Food Domain
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Shufeng Xiong, Wenjie Tian, Haiping Si, Guipei Zhang and Lei Shi
Algorithms 2024, 17(5), 176; https://doi.org/10.3390/a17050176 - 25 Apr 2024
Abstract
In the food domain, text mining techniques are extensively employed to derive valuable insights from large volumes of text data, facilitating applications such as aiding food recalls, offering personalized recipes, and reinforcing food safety regulation. To provide researchers and practitioners with a comprehensive
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In the food domain, text mining techniques are extensively employed to derive valuable insights from large volumes of text data, facilitating applications such as aiding food recalls, offering personalized recipes, and reinforcing food safety regulation. To provide researchers and practitioners with a comprehensive understanding of the latest technology and application scenarios of text mining in the food domain, the pertinent literature is reviewed and analyzed. Initially, the fundamental concepts, principles, and primary tasks of text mining, encompassing text categorization, sentiment analysis, and entity recognition, are elucidated. Subsequently, an analysis of diverse types of data sources within the food domain and the characteristics of text data mining is conducted, spanning social media, reviews, recipe websites, and food safety reports. Furthermore, the applications of text mining in the food domain are scrutinized from the perspective of various scenarios, including leveraging consumer food reviews and feedback to enhance product quality, providing personalized recipe recommendations based on user preferences and dietary requirements, and employing text mining for food safety and fraud monitoring. Lastly, the opportunities and challenges associated with the adoption of text mining techniques in the food domain are summarized and evaluated. In conclusion, text mining holds considerable potential for application in the food domain, thereby propelling the advancement of the food industry and upholding food safety standards.
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(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition)
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Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
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Khadija Javed, Ren Shengbing, Muhammad Asim and Mudasir Ahmad Wani
Algorithms 2024, 17(5), 175; https://doi.org/10.3390/a17050175 - 24 Apr 2024
Abstract
Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target
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Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1- measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1- measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance.
Full article
(This article belongs to the Special Issue Algorithms in Software Engineering)
Open AccessArticle
An Oracle Bone Inscriptions Detection Algorithm Based on Improved YOLOv8
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Qianqian Zhen, Liang Wu and Guoying Liu
Algorithms 2024, 17(5), 174; https://doi.org/10.3390/a17050174 - 24 Apr 2024
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Ancient Chinese characters known as oracle bone inscriptions (OBIs) were inscribed on turtle shells and animal bones, and they boast a rich history dating back over 3600 years. The detection of OBIs is one of the most basic tasks in OBI research. The
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Ancient Chinese characters known as oracle bone inscriptions (OBIs) were inscribed on turtle shells and animal bones, and they boast a rich history dating back over 3600 years. The detection of OBIs is one of the most basic tasks in OBI research. The current research aimed to determine the precise location of OBIs with rubbing images. Given the low clarity, severe noise, and cracks in oracle bone inscriptions, the mainstream networks within the realm of deep learning possess low detection accuracy on the OBI detection dataset. To address this issue, this study analyzed the significant research progress in oracle bone script detection both domestically and internationally. Then, based on the YOLOv8 algorithm, according to the characteristics of OBI rubbing images, the algorithm was improved accordingly. The proposed algorithm added a small target detection head, modified the loss function, and embedded a CBAM. The results show that the improved model achieves an F-measure of 84.3%, surpassing the baseline model by approximately 1.8%.
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Open AccessReview
An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers
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Bo Jiang, Guofu Ding, Jianlin Fu, Jian Zhang and Yong Zhang
Algorithms 2024, 17(5), 173; https://doi.org/10.3390/a17050173 - 23 Apr 2024
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The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems.
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The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development.
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Open AccessArticle
Improved Brain Storm Optimization Algorithm Based on Flock Decision Mutation Strategy
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Yanchi Zhao, Jianhua Cheng and Jing Cai
Algorithms 2024, 17(5), 172; https://doi.org/10.3390/a17050172 - 23 Apr 2024
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To tackle the problem of the brain storm optimization (BSO) algorithm’s suboptimal capability for avoiding local optima, which contributes to its inadequate optimization precision, we developed a flock decision mutation approach that substantially enhances the efficacy of the BSO algorithm. Furthermore, to solve
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To tackle the problem of the brain storm optimization (BSO) algorithm’s suboptimal capability for avoiding local optima, which contributes to its inadequate optimization precision, we developed a flock decision mutation approach that substantially enhances the efficacy of the BSO algorithm. Furthermore, to solve the problem of insufficient BSO algorithm population diversity, we introduced a strategy that utilizes the good point set to enhance the initial population’s quality. Simultaneously, we substituted the K-means clustering approach with spectral clustering to improve the clustering accuracy of the algorithm. This work introduced an enhanced version of the brain storm optimization algorithm founded on a flock decision mutation strategy (FDIBSO). The improved algorithm was compared against contemporary leading algorithms through the CEC2018. The experimental section additionally employs the AUV intelligence evaluation as an application case. It addresses the combined weight model under various dimensional settings to substantiate the efficacy of the FDIBSO algorithm further. The findings indicate that FDIBSO surpasses BSO and other enhanced algorithms for addressing intricate optimization challenges.
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Open AccessArticle
Pediatric Ischemic Stroke: Clinical and Paraclinical Manifestations—Algorithms for Diagnosis and Treatment
by
Niels Wessel, Mariana Sprincean, Ludmila Sidorenko, Ninel Revenco and Svetlana Hadjiu
Algorithms 2024, 17(4), 171; https://doi.org/10.3390/a17040171 - 22 Apr 2024
Abstract
Childhood stroke can lead to lifelong disability. Developing algorithms for timely recognition of clinical and paraclinical signs is crucial to ensure prompt stroke diagnosis and minimize decision-making time. This study aimed to characterize clinical and paraclinical symptoms of childhood and neonatal stroke as
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Childhood stroke can lead to lifelong disability. Developing algorithms for timely recognition of clinical and paraclinical signs is crucial to ensure prompt stroke diagnosis and minimize decision-making time. This study aimed to characterize clinical and paraclinical symptoms of childhood and neonatal stroke as relevant diagnostic criteria encountered in clinical practice, in order to develop algorithms for prompt stroke diagnosis. The analysis included data from 402 pediatric case histories from 2010 to 2016 and 108 prospective stroke cases from 2017 to 2020. Stroke cases were predominantly diagnosed in newborns, with 362 (71%, 95% CI 68.99–73.01) cases occurring within the first 28 days of birth, and 148 (29%, 95% CI 26.99–31.01) cases occurring after 28 days. The findings of the study enable the development of algorithms for timely stroke recognition, facilitating the selection of optimal treatment options for newborns and children of various age groups. Logistic regression serves as the basis for deriving these algorithms, aiming to initiate early treatment and reduce lifelong morbidity and mortality in children. The study outcomes include the formulation of algorithms for timely recognition of newborn stroke, with plans to adopt these algorithms and train a fuzzy classifier-based diagnostic model using machine learning techniques for efficient stroke recognition.
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(This article belongs to the Collection Feature Papers in Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems)
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Open AccessArticle
A Multi-Stage Method for Logo Detection in Scanned Official Documents Based on Image Processing
by
María Guijarro, Juan Bayon, Daniel Martín-Carabias and Joaquín Recas
Algorithms 2024, 17(4), 170; https://doi.org/10.3390/a17040170 - 22 Apr 2024
Abstract
A logotype is a rectangular region defined by a set of characteristics, which come from the pixel information and region shape, that differ from those of the text. In this paper, a new method for automatic logo detection is proposed and tested using
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A logotype is a rectangular region defined by a set of characteristics, which come from the pixel information and region shape, that differ from those of the text. In this paper, a new method for automatic logo detection is proposed and tested using the public Tobacco800 database. Our method outputs a set of regions from an official document with a high probability to contain a logo using a new approach based on the variation of the feature rectangles method available in the literature. Candidate regions were computed using the longest increasing run algorithm over the document blank lines’ indices. Those regions were further refined by using a feature-rectangle-expansion method with forward checking, where the rectangle expansion can occur in parallel in each region. Finally, a C4.5 decision tree was trained and tested against a set of 1291 official documents to evaluate its performance. The strategic combination of the three previous steps offers a precision and recall for logo detention of 98.9% and 89.9%, respectively, being also resistant to noise and low-quality documents. The method is also able to reduce the processing area of the document while maintaining a low percentage of false negatives.
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(This article belongs to the Special Issue Recent Advances in Algorithms for Computer Vision Applications)
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Open AccessArticle
Security and Ownership in User-Defined Data Meshes
by
Michalis Pingos, Panayiotis Christodoulou and Andreas S. Andreou
Algorithms 2024, 17(4), 169; https://doi.org/10.3390/a17040169 - 22 Apr 2024
Abstract
Data meshes are an approach to data architecture and organization that treats data as a product and focuses on decentralizing data ownership and access. It has recently emerged as a field that presents quite a few challenges related to data ownership, governance, security,
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Data meshes are an approach to data architecture and organization that treats data as a product and focuses on decentralizing data ownership and access. It has recently emerged as a field that presents quite a few challenges related to data ownership, governance, security, monitoring, and observability. To address these challenges, this paper introduces an innovative algorithmic framework leveraging data blueprints to enable the dynamic creation of data meshes and data products in response to user requests, ensuring that stakeholders have access to specific portions of the data mesh as needed. Ownership and governance concerns are addressed through a unique mechanism involving Blockchain and Non-Fungible Tokens (NFTs). This facilitates the secure and transparent transfer of data ownership, with the ability to mint time-based NFTs. By combining these advancements with the fundamental tenets of data meshes, this research offers a comprehensive solution to the challenges surrounding data ownership and governance. It empowers stakeholders to navigate the complexities of data management within a decentralized architecture, ensuring a secure, efficient, and user-centric approach to data utilization. The proposed framework is demonstrated using real-world data from a poultry meat production factory.
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(This article belongs to the Special Issue Hybrid Intelligent Algorithms)
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