Algorithms

32 pages, 4797 KiB

Open AccessReview

Generative Adversarial Network for Overcoming Occlusion in Images: A Survey

by Kaziwa Saleh, Sándor Szénási and Zoltán Vámossy

Algorithms 2023, 16(3), 175; https://doi.org/10.3390/a16030175 - 22 Mar 2023

Cited by 2 | Viewed by 2923

Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex environment, we encounter more occluded [...] Read more.

Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex environment, we encounter more occluded objects than fully visible ones. Therefore, instilling the capability of amodal perception into those vision systems is crucial. However, overcoming occlusion is difficult and comes with its own challenges. The generative adversarial network (GAN), on the other hand, is renowned for its generative power in producing data from a random noise distribution that approaches the samples that come from real data distributions. In this survey, we outline the existing works wherein GAN is utilized in addressing the challenges of overcoming occlusion, namely amodal segmentation, amodal content completion, order recovery, and acquiring training data. We provide a summary of the type of GAN, loss function, the dataset, and the results of each work. We present an overview of the implemented GAN architectures in various applications of amodal completion. We also discuss the common objective functions that are applied in training GAN for occlusion-handling tasks. Lastly, we discuss several open issues and potential future directions. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms)

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19 pages, 515 KiB

Open AccessArticle

How Optimal Transport Can Tackle Gender Biases in Multi-Class Neural Network Classifiers for Job Recommendations

by Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher, Jean-Michel Loubes and Laurent Risser

Algorithms 2023, 16(3), 174; https://doi.org/10.3390/a16030174 - 22 Mar 2023

Cited by 3 | Viewed by 1864

Abstract

Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can, however, be used in applications that are ranked as High Risk by the European Commission in the AI act—for instance, online job [...] Read more.

Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can, however, be used in applications that are ranked as High Risk by the European Commission in the AI act—for instance, online job candidate recommendations. When used in the European Union, commercial AI systems in such applications will be required to have proper statistical properties with regard to the potential discrimination they could engender. This motivated our contribution. We present a novel optimal transport strategy to mitigate undesirable algorithmic biases in multi-class neural network classification. Our strategy is model agnostic and can be used on any multi-class classification neural network model. To anticipate the certification of recommendation systems using textual data, we used it on the Bios dataset, for which the learning task consists of predicting the occupation of female and male individuals, based on their LinkedIn biography. The results showed that our approach can reduce undesired algorithmic biases in this context to lower levels than a standard strategy. Full article

(This article belongs to the Special Issue Interpretability, Accountability and Robustness in Machine Learning)

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11 pages, 278 KiB

Open AccessCommunication

Equivalence between LC-CRF and HMM, and Discriminative Computing of HMM-Based MPM and MAP

by Elie Azeraf, Emmanuel Monfrini and Wojciech Pieczynski

Algorithms 2023, 16(3), 173; https://doi.org/10.3390/a16030173 - 21 Mar 2023

Cited by 1 | Viewed by 1313

Abstract

Practitioners have used hidden Markov models (HMMs) in different problems for about sixty years. Moreover, conditional random fields (CRFs) are an alternative to HMMs and appear in the literature as different and somewhat concurrent models. We propose two contributions: First, we show that [...] Read more.

Practitioners have used hidden Markov models (HMMs) in different problems for about sixty years. Moreover, conditional random fields (CRFs) are an alternative to HMMs and appear in the literature as different and somewhat concurrent models. We propose two contributions: First, we show that the basic linear-chain CRFs (LC-CRFs), considered as different from HMMs, are in fact equivalent to HMMs in the sense that for each LC-CRF there exists an HMM—that we specify—whose posterior distribution is identical to the given LC-CRF. Second, we show that it is possible to reformulate the generative Bayesian classifiers maximum posterior mode (MPM) and maximum a posteriori (MAP), used in HMMs, as discriminative ones. The last point is of importance in many fields, especially in natural language processing (NLP), as it shows that in some situations dropping HMMs in favor of CRFs is not necessary. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications IV)

14 pages, 730 KiB

Open AccessArticle

A Scheduling Solution for Robotic Arm-Based Batching Systems with Multiple Conveyor Belts

by Kasper Gaj Nielsen, Inkyung Sung, Mohamed El Yafrani, Deniz Kenan Kılıç and Peter Nielsen

Algorithms 2023, 16(3), 172; https://doi.org/10.3390/a16030172 - 21 Mar 2023

Cited by 1 | Viewed by 1589

Abstract

In this study, we tackle a key scheduling problem in a robotic arm-based food processing system, where multiple conveyors—an infeed conveyor that feeds food items to robotic arms and two tray lane conveyors, on which trays to batch food items are placed—are implemented. [...] Read more.

In this study, we tackle a key scheduling problem in a robotic arm-based food processing system, where multiple conveyors—an infeed conveyor that feeds food items to robotic arms and two tray lane conveyors, on which trays to batch food items are placed—are implemented. The target scheduling problem is to determine what item on an infeed conveyor belt is picked up by which robotic arm at what position, and on which tray the picked up item will be placed. This problem involves critical constraints, such as sequence-dependent processing time and dynamic item and tray positions. Moreover, due to the speed of the infeed conveyor and latency in the information about entering items into the system, this scheduling problem must be solved in near real time. To address these challenges, we propose a scheduling solution that first decomposes the original scheduling problem into sub-problems, where a sub-problem formulated as a goal program schedules robotic arms only for a single tray. The performance of the proposed solution approach is then tested under a simulation environment, and from the experiments, the proposed approach produces acceptable performance. Full article

(This article belongs to the Special Issue Scheduling: Algorithms and Applications)

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17 pages, 1701 KiB

Open AccessArticle

MixFormer: A Self-Attentive Convolutional Network for 3D Mesh Object Recognition

by Lingfeng Huang, Jieyu Zhao and Yu Chen

Algorithms 2023, 16(3), 171; https://doi.org/10.3390/a16030171 - 21 Mar 2023

Viewed by 1600

Abstract

3D mesh as a complex data structure can provide effective shape representation for 3D objects, but due to the irregularity and disorder of the mesh data, it is difficult for convolutional neural networks to be directly applied to 3D mesh data processing. At [...] Read more.

3D mesh as a complex data structure can provide effective shape representation for 3D objects, but due to the irregularity and disorder of the mesh data, it is difficult for convolutional neural networks to be directly applied to 3D mesh data processing. At the same time, the extensive use of convolutional kernels and pooling layers focusing on local features can cause the loss of spatial information and dependencies of low-level features. In this paper, we propose a self-attentive convolutional network MixFormer applied to 3D mesh models. By defining 3D convolutional kernels and vector self-attention mechanisms applicable to 3D mesh models, our neural network is able to learn 3D mesh model features. Combining the features of convolutional networks and transformer networks, the network can focus on both local detail features and long-range dependencies between features, thus achieving good learning results without stacking multiple layers and saving arithmetic overhead compared to pure transformer architectures. We conduct classification and semantic segmentation experiments on SHREC15, SCAPE, FAUST, MIT, and Adobe Fuse datasets. Experimental results show that the network can achieve 96.7% classification and better segmentation results by using fewer parameters and network layers. Full article

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28 pages, 655 KiB

Open AccessArticle

Multiset-Trie Data Structure

by Mikita Akulich, Iztok Savnik, Matjaž Krnc and Riste Škrekovski

Algorithms 2023, 16(3), 170; https://doi.org/10.3390/a16030170 - 20 Mar 2023

Cited by 1 | Viewed by 1663

Abstract

This paper proposes a new data structure, multiset-trie, that is designed for storing and efficiently processing a set of multisets. Moreover, multiset-trie can operate on a set of sets without efficiency loss. The multiset-trie structure is a search tree with properties similar to [...] Read more.

This paper proposes a new data structure, multiset-trie, that is designed for storing and efficiently processing a set of multisets. Moreover, multiset-trie can operate on a set of sets without efficiency loss. The multiset-trie structure is a search tree with properties similar to those of a trie. It implements all standard search tree operations together with the multiset containment operations for searching sub-multisets and super-multisets. Suppose that we have a set of multisets S and a multiset X. The multiset containment operations retrieve multisets from S that are either sub-multisets or super-multisets of X. We present the mathematical analysis of a multiset-trie that gives the time complexity of the algorithms and the space complexity of the data structure. Further, the empirical analysis of the data structure is implemented in a series of experiments. The experiments illuminate the time complexity space of the multiset containment operations. Full article

(This article belongs to the Section Databases and Data Structures)

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23 pages, 563 KiB

Open AccessArticle

Pushing the Limits of Clingo’s Incremental Grounding and Solving Capabilities in Practical Applications

by Marcello Balduccini, Michael Barborak and David Ferrucci

Algorithms 2023, 16(3), 169; https://doi.org/10.3390/a16030169 - 20 Mar 2023

Cited by 1 | Viewed by 1646

Abstract

Incremental techniques aim at making it possible to improve the performance of the grounding and solving processes by reusing the results of previous executions. Clingo supports both incremental grounding and incremental solving computations. In order to leverage incremental computations in clingo, the incremental [...] Read more.

Incremental techniques aim at making it possible to improve the performance of the grounding and solving processes by reusing the results of previous executions. Clingo supports both incremental grounding and incremental solving computations. In order to leverage incremental computations in clingo, the incremental fragments of ASP programs must satisfy certain safety-related conditions. In a number of problem domains and reasoning tasks, these conditions can be satisfied in a fairly straightforward way. However, we have observed that in certain practical applications, satisfying the conditions becomes more challenging, to the point that it is sometimes unclear how or even if it is possible to leverage incremental computations. In this paper, we report our findings, and ultimate success, with the use of incremental grounding and solving techniques in one of these challenging cases. We describe the domain, which is linked to a large practical application, discuss the challenges we faced in attempting to leverage incremental computations, and then describe the techniques that we developed, in particular at the level of methods for encoding the domain knowledge and of algorithms supporting the intended interleaving of grounding and solving. We believe that our findings may provide valuable information to practitioners facing similar challenges and ultimately increase the adoption of clingo’s incremental capabilities for complex practical applications. Full article

(This article belongs to the Special Issue Hybrid Answer Set Programming Systems and Applications)

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14 pages, 542 KiB

Open AccessArticle

Network Modeling of Murine Lymphatic System

by Dmitry Grebennikov, Rostislav Savinkov, Ekaterina Zelenova, Gennady Lobov and Gennady Bocharov

Algorithms 2023, 16(3), 168; https://doi.org/10.3390/a16030168 - 20 Mar 2023

Viewed by 1332

Abstract

Animal models of diseases, particularly mice, are considered to be the cornerstone for translational research in immunology. The aim of the present study is to model the geometry and analyze the network structure of the murine lymphatic system (LS). The algorithm for building [...] Read more.

Animal models of diseases, particularly mice, are considered to be the cornerstone for translational research in immunology. The aim of the present study is to model the geometry and analyze the network structure of the murine lymphatic system (LS). The algorithm for building the graph model of the LS makes use of anatomical data. To identify the edge directions of the graph model, a mass balance approach to lymph dynamics based on the Hagen–Poiseuille equation is applied. It is the first study in which a geometric model of the murine LS has been developed and characterized in terms of its structural organization and the lymph transfer function. Our study meets the demand for quantitative mechanistic approaches in the growing field of immunoengineering to utilize or exploit the lymphatic system for immunotherapy. Full article

(This article belongs to the Collection Feature Papers in Combinatorial Optimization, Graph, and Network Algorithms)

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35 pages, 558 KiB

Open AccessReview

Literature Review on Hybrid Evolutionary Approaches for Feature Selection

by Jayashree Piri, Puspanjali Mohapatra, Raghunath Dey, Biswaranjan Acharya, Vassilis C. Gerogiannis and Andreas Kanavos

Algorithms 2023, 16(3), 167; https://doi.org/10.3390/a16030167 - 20 Mar 2023

Cited by 6 | Viewed by 2400

Abstract

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over [...] Read more.

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works. The purpose of this paper is to critically assess the existing hybrid FS approaches and to give a thorough literature review on the hybridization of different metaheuristic/evolutionary strategies that have been employed for supporting FS. This article reviews pertinent documents on hybrid frameworks that were published in the period from 2009 to 2022 and offers a thorough analysis of the used techniques, classifiers, datasets, applications, assessment metrics, and schemes of hybridization. Additionally, new open research issues and challenges are identified to pinpoint the areas that have to be further explored for additional study. Full article

(This article belongs to the Collection Feature Paper in Metaheuristic Algorithms and Applications)

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14 pages, 1399 KiB

Open AccessArticle

Framework for Evaluating Potential Causes of Health Risk Factors Using Average Treatment Effect and Uplift Modelling

by Daniela Galatro, Rosario Trigo-Ferre, Allana Nakashook-Zettler, Vincenzo Costanzo-Alvarez, Melanie Jeffrey, Maria Jacome, Jason Bazylak and Cristina H. Amon

Algorithms 2023, 16(3), 166; https://doi.org/10.3390/a16030166 - 19 Mar 2023

Viewed by 1790

Abstract

Acute myeloid leukemia (AML) is a type of blood cancer that affects both adults and children. Benzene exposure has been reported to increase the risk of developing AML in children. The assessment of the potential relationship between environmental benzene exposure and childhood has [...] Read more.

Acute myeloid leukemia (AML) is a type of blood cancer that affects both adults and children. Benzene exposure has been reported to increase the risk of developing AML in children. The assessment of the potential relationship between environmental benzene exposure and childhood has been documented in the literature using odds ratios and/or risk ratios, with data fitted to unconditional logistic regression. A common feature of the studies involving relationships between environmental risk factors and health outcomes is the lack of proper analysis to evidence causation. Although statistical causal analysis is commonly used to determine causation by evaluating a distribution’s parameters, it is challenging to infer causation in complex systems from single correlation coefficients. Machine learning (ML) approaches, based on causal pattern recognition, can provide an accurate alternative to model counterfactual scenarios. In this work, we propose a framework using average treatment effect (ATE) and Uplift modeling to evidence causation when relating exposure to benzene indoors and outdoors to childhood AML, effectively predicting causation when exposed indoors to this contaminant. An analysis of the assumptions, cross-validation, sample size, and interaction between predictors are also provided, guiding future works looking at the universalization of this approach in predicting health outcomes. Full article

(This article belongs to the Special Issue Deep Learning Architecture and Applications)

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44 pages, 5170 KiB

Open AccessReview

Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods

by Viacheslav Moskalenko, Vyacheslav Kharchenko, Alona Moskalenko and Borys Kuzikov

Algorithms 2023, 16(3), 165; https://doi.org/10.3390/a16030165 - 18 Mar 2023

Cited by 4 | Viewed by 4064

Abstract

Artificial intelligence systems are increasingly being used in industrial applications, security and military contexts, disaster response complexes, policing and justice practices, finance, and healthcare systems. However, disruptions to these systems can have negative impacts on health, mortality, human rights, and asset values. The [...] Read more.

Artificial intelligence systems are increasingly being used in industrial applications, security and military contexts, disaster response complexes, policing and justice practices, finance, and healthcare systems. However, disruptions to these systems can have negative impacts on health, mortality, human rights, and asset values. The protection of such systems from various types of destructive influences is thus a relevant area of research. The vast majority of previously published works are aimed at reducing vulnerability to certain types of disturbances or implementing certain resilience properties. At the same time, the authors either do not consider the concept of resilience as such, or their understanding varies greatly. The aim of this study is to present a systematic approach to analyzing the resilience of artificial intelligence systems, along with an analysis of relevant scientific publications. Our methodology involves the formation of a set of resilience factors, organizing and defining taxonomic and ontological relationships for resilience factors of artificial intelligence systems, and analyzing relevant resilience solutions and challenges. This study analyzes the sources of threats and methods to ensure each resilience properties for artificial intelligence systems. As a result, the potential to create a resilient artificial intelligence system by configuring the architecture and learning scenarios is confirmed. The results can serve as a roadmap for establishing technical requirements for forthcoming artificial intelligence systems, as well as a framework for assessing the resilience of already developed artificial intelligence systems. Full article

(This article belongs to the Topic Advances in Artificial Neural Networks)

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17 pages, 5821 KiB

Open AccessArticle

Comparison of Single-Lane Roundabout Entry Degree of Saturation Estimations from Analytical and Regression Models

by Ana Čudina Ivančev, Maja Ahac, Saša Ahac and Vesna Dragčević

Algorithms 2023, 16(3), 164; https://doi.org/10.3390/a16030164 - 18 Mar 2023

Cited by 2 | Viewed by 1801

Abstract

Roundabout design is an iterative process consisting of a preliminary geometry design, geometry performance checks, and the estimation of intersection functionality (based on the results of analytical or regression models). Since both roundabout geometry design procedures and traffic characteristics vary around the world, [...] Read more.

Roundabout design is an iterative process consisting of a preliminary geometry design, geometry performance checks, and the estimation of intersection functionality (based on the results of analytical or regression models). Since both roundabout geometry design procedures and traffic characteristics vary around the world, the discussion on which functionality estimation model is more appropriate is ongoing. This research aims to reduce the uncertainty in decision-making during this final roundabout design stage. Its two objectives were to analyze and compare the results of roundabout performance estimations derived from one analytical and one regression model, and to quantify the model results’ susceptibility to changes in roundabout geometric parameters. For this, 60 four-legged single-lane roundabout schemes were created, varying in size and leg alignment. Their geometric parameters resulted from the assumption of their location in a suburban environment and chosen design vehicle swept path analysis. To compare the models’ results, the degree of saturation of roundabout entries was calculated based on presumed traffic flows. The results showed that the regression model estimates higher functionality and that this difference (both between the two models and regression models applied on different schemes) is more pronounced as the outer radius and angle between the legs increase. Full article

(This article belongs to the Special Issue Scheduling: Algorithms and Applications)

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21 pages, 740 KiB

Open AccessArticle

Convergence and Stability of a New Parametric Class of Iterative Processes for Nonlinear Systems

by Alicia Cordero, Javier G. Maimó, Antmel Rodríguez-Cabral and Juan R. Torregrosa

Algorithms 2023, 16(3), 163; https://doi.org/10.3390/a16030163 - 16 Mar 2023

Cited by 1 | Viewed by 1168

Abstract

In this manuscript, we carry out a study on the generalization of a known family of multipoint scalar iterative processes for approximating the solutions of nonlinear systems. The convergence analysis of the proposed class under various smooth conditions is provided. We also study [...] Read more.

In this manuscript, we carry out a study on the generalization of a known family of multipoint scalar iterative processes for approximating the solutions of nonlinear systems. The convergence analysis of the proposed class under various smooth conditions is provided. We also study the stability of this family, analyzing the fixed and critical points of the rational operator resulting from applying the family on low-degree polynomials, as well as the basins of attraction and the orbits (periodic or not) that these points produce. This dynamical study also allows us to observe which members of the family are more stable and which have chaotic behavior. Graphical analyses of dynamical planes, parameter line and bifurcation planes are also studied. Numerical tests are performed on different nonlinear systems for checking the theoretical results and to compare the proposed schemes with other known ones. Full article

(This article belongs to the Special Issue 2022 and 2023 Selected Papers from Algorithms Editorial Board Members)

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19 pages, 561 KiB

Open AccessArticle

Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects

by Juarez Machado da Silva, Gabriel de Oliveira Ramos and Jorge Luis Victória Barbosa

Algorithms 2023, 16(3), 162; https://doi.org/10.3390/a16030162 - 16 Mar 2023

Cited by 1 | Viewed by 1755

Abstract

The Shortest Path (SP) problem resembles a variety of real-world situations where one needs to find paths between origins and destinations. A generalization of the SP is the Dynamic Shortest Path (DSP) problem, which also models changes in the graph at any time. [...] Read more.

The Shortest Path (SP) problem resembles a variety of real-world situations where one needs to find paths between origins and destinations. A generalization of the SP is the Dynamic Shortest Path (DSP) problem, which also models changes in the graph at any time. When a graph changes, DSP algorithms partially recompute the paths while taking advantage of the previous computations. Although the DSP problem represents many real situations, it leaves out some fundamental aspects of decision-making. One of these aspects is the existence of multiple, potentially conflicting objectives that must be optimized simultaneously. Recently, we performed a first incursion on the so-called Multi-Objective Dynamic Shortest Path (MODSP), presenting the first algorithm able to take the MODM perspective into account when solving a DSP problem. In this paper, we go beyond and formally define the MODSP problem, thus establishing and clarifying it with respect to its simpler counterparts. In particular, we start with a brief overview of the related literature and then present a complete formalization of the MODSP problem class, highlighting its distinguishing features as compared to similar problems and representing their relationship through a novel taxonomy. This work also motivates the relevance of the MODSP problem by enumerating real-world scenarios that involve all its ingredients, such as multiple objectives and dynamically updated graph topologies. Finally, we discuss the challenges and open questions for this new class of shortest path problems, aiming at future work directions. We hope this work sheds light on the theme and contributes to leveraging relevant research on the topic. Full article

(This article belongs to the Collection Feature Papers in Combinatorial Optimization, Graph, and Network Algorithms)

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18 pages, 10352 KiB

Open AccessArticle

Human Body Shapes Anomaly Detection and Classification Using Persistent Homology

by Steve de Rose, Philippe Meyer and Frédéric Bertrand

Algorithms 2023, 16(3), 161; https://doi.org/10.3390/a16030161 - 15 Mar 2023

Viewed by 2393

Abstract

Accurate sizing systems of a population permit the minimization of the production costs of the textile apparel industry and allow firms to satisfy their customers. Hence, information about human body shapes needs to be extracted in order to examine, compare and classify human [...] Read more.

Accurate sizing systems of a population permit the minimization of the production costs of the textile apparel industry and allow firms to satisfy their customers. Hence, information about human body shapes needs to be extracted in order to examine, compare and classify human morphologies. In this paper, we use topological data analysis to study human body shapes. Persistence theory applied to anthropometric point clouds together with clustering algorithms show that relevant information about shapes is extracted by persistent homology. In particular, the homologies of human body points have interesting interpretations in terms of human anatomy. In the first place, anomalies of scans are detected using complete-linkage hierarchical clusterings. Then, a discrimination index shows which type of clustering separates gender accurately and if it is worth restricting to body trunks or not. Finally, Ward-linkage hierarchical clusterings with Davies–Bouldin, Dunn and Silhouette indices are used to define eight male morphotypes and seven female morphotypes, which are different in terms of weight classes and ratios between bust, waist and hip circumferences. The techniques used in this work permit us to classify human bodies and detect scan anomalies directly on the full human body point clouds rather than the usual methods involving the extraction of body measurements from individuals or their scans. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition)

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22 pages, 2092 KiB

Open AccessArticle

Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification

by Min-Jen Tsai, Ya-Chu Lee and Te-Ming Chen

Algorithms 2023, 16(3), 160; https://doi.org/10.3390/a16030160 - 14 Mar 2023

Viewed by 3665

Abstract

QR codes (short for Quick Response codes) were originally developed for use in the automotive industry to track factory inventories and logistics, but their popularity has expanded significantly in the past few years due to the widespread applications of smartphones and mobile phone [...] Read more.

QR codes (short for Quick Response codes) were originally developed for use in the automotive industry to track factory inventories and logistics, but their popularity has expanded significantly in the past few years due to the widespread applications of smartphones and mobile phone cameras. QR codes can be used for a variety of purposes, including tracking inventory, advertising, electronic ticketing, and mobile payments. Although they are convenient and widely used to store and share information, their accessibility also means they might be forged easily. Digital forensics can be used to recognize direct links of printed documents, including QR codes, which is important for the investigation of forged documents and the prosecution of forgers. The process involves using optical mechanisms to identify the relationship between source printers and the duplicates. Techniques regarding computer vision and machine learning, such as convolutional neural networks (CNNs), can be implemented to study and summarize statistical features in order to improve identification accuracy. This study implemented AlexNet, DenseNet201, GoogleNet, MobileNetv2, ResNet, VGG16, and other Pretrained CNN models for evaluating their abilities to predict the source printer of QR codes with a high level of accuracy. Among them, the customized CNN model demonstrated better results in identifying printed sources of grayscale and color QR codes with less computational power and training time. Full article

(This article belongs to the Special Issue Deep Neural Networks and Optimization Algorithms)

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14 pages, 2892 KiB

Open AccessArticle

ASP-Based Declarative Reasoning in Data-Intensive Enterprise and IoT Applications

by Francesco Calimeri, Nicola Leone, Giovanni Melissari, Francesco Pacenza, Simona Perri, Kristian Reale, Francesco Ricca and Jessica Zangari

Algorithms 2023, 16(3), 159; https://doi.org/10.3390/a16030159 - 14 Mar 2023

Cited by 1 | Viewed by 1569

Abstract

In the last few years, we have witnessed the spread of computing devices getting smaller and smaller (e.g., Smartphones, Smart Devices, Raspberry, etc.), and the production and availability of data getting bigger and bigger. This work presents DLV-EE, a framework based on Answer [...] Read more.

In the last few years, we have witnessed the spread of computing devices getting smaller and smaller (e.g., Smartphones, Smart Devices, Raspberry, etc.), and the production and availability of data getting bigger and bigger. This work presents DLV-EE, a framework based on Answer Set Programming (ASP) for performing declarative reasoning tasks over data-intensive, distributed applications. It relies on the DLV2 system and it features interoperability means for dealing with Big-Data over modern industry-level databases (relational and NoSQL). Furthermore, the work introduces DLV-IoT, an ASP system compatible with “mobile” technologies for enabling advanced reasoning capabilities on smart/IoT devices; eventually, DLV-EE and DLV-IoT via some real-world applications are illustrated as well. Full article

(This article belongs to the Special Issue Hybrid Answer Set Programming Systems and Applications)

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17 pages, 820 KiB

Open AccessArticle

Learning Distributed Representations and Deep Embedded Clustering of Texts

by Shuang Wang, Amin Beheshti, Yufei Wang, Jianchao Lu, Quan Z. Sheng, Stephen Elbourn and Hamid Alinejad-Rokny

Algorithms 2023, 16(3), 158; https://doi.org/10.3390/a16030158 - 13 Mar 2023

Cited by 1 | Viewed by 1639

Abstract

Instructors face significant time and effort constraints when grading students’ assessments on a large scale. Clustering similar assessments is a unique and effective technique that has the potential to significantly reduce the workload of instructors in online and large-scale learning environments. By grouping [...] Read more.

Instructors face significant time and effort constraints when grading students’ assessments on a large scale. Clustering similar assessments is a unique and effective technique that has the potential to significantly reduce the workload of instructors in online and large-scale learning environments. By grouping together similar assessments, marking one assessment in a cluster can be scaled to other similar assessments, allowing for a more efficient and streamlined grading process. To address this issue, this paper focuses on text assessments and proposes a method for reducing the workload of instructors by clustering similar assessments. The proposed method involves the use of distributed representation to transform texts into vectors, and contrastive learning to improve the representation that distinguishes the differences among similar texts. The paper presents a general framework for clustering similar texts that includes label representation, K-means, and self-organization map algorithms, with the objective of improving clustering performance using Accuracy (ACC) and Normalized Mutual Information (NMI) metrics. The proposed framework is evaluated experimentally using two real datasets. The results show that self-organization maps and K-means algorithms with Pre-trained language models outperform label representation algorithms for different datasets. Full article

(This article belongs to the Special Issue Nature-Inspired Algorithms in Machine Learning)

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15 pages, 396 KiB

Open AccessArticle

Algorithm for Generating S-Boxes with Prescribed Differential Properties

by Stanislav Marochok and Pavol Zajac

Algorithms 2023, 16(3), 157; https://doi.org/10.3390/a16030157 - 13 Mar 2023

Cited by 4 | Viewed by 2150

Abstract

Cryptographic S-boxes are vectorial Boolean functions that must fulfill strict criteria to provide security for cryptographic algorithms. There are several existing methods for generating strong cryptographic S-boxes, including stochastic search algorithms. These search algorithms typically generate random candidate Boolean functions (or permutations) that [...] Read more.

Cryptographic S-boxes are vectorial Boolean functions that must fulfill strict criteria to provide security for cryptographic algorithms. There are several existing methods for generating strong cryptographic S-boxes, including stochastic search algorithms. These search algorithms typically generate random candidate Boolean functions (or permutations) that are improved during the search by examining the search space in a specific way. Here, we introduce a new type of stochastic algorithm for generating cryptographic S-boxes. We do not generate and then improve the Boolean function; instead, we build the vector of values incrementally. New values are obtained by randomized search driven by restrictions on the differential spectrum of the generated S-box. In this article, we formulate two new algorithms based on this new approach and study the better one in greater detail. We prove the correctness of the proposed algorithm and evaluate its complexity. The final part contains an experimental evaluation of the method. We show that the algorithm generates S-boxes with better properties than a random search. We believe that our approach can be extended in the future by adopting more advanced stochastic search methods. Full article

(This article belongs to the Collection Feature Papers in Algorithms)

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14 pages, 1095 KiB

Open AccessArticle

A New Third-Order Family of Multiple Root-Findings Based on Exponential Fitted Curve

by Vinay Kanwar, Alicia Cordero, Juan R. Torregrosa, Mithil Rajput and Ramandeep Behl

Algorithms 2023, 16(3), 156; https://doi.org/10.3390/a16030156 - 12 Mar 2023

Cited by 2 | Viewed by 1442

Abstract

In this paper, we present a new third-order family of iterative methods in order to compute the multiple roots of nonlinear equations when the multiplicity

(m \geq 1)

is known in advance. There is a plethora of third-order point-to-point methods, available [...] Read more.

In this paper, we present a new third-order family of iterative methods in order to compute the multiple roots of nonlinear equations when the multiplicity

(m \geq 1)

is known in advance. There is a plethora of third-order point-to-point methods, available in the literature; but our methods are based on geometric derivation and converge to the required zero even though derivative becomes zero or close to zero in vicinity of the required zero. We use the exponential fitted curve and tangency conditions for the development of our schemes. Well-known Chebyshev, Halley, super-Halley and Chebyshev–Halley are the special members of our schemes for

m = 1

. Complex dynamics techniques allows us to see the relation between the element of the family of iterative schemes and the wideness of the basins of attraction of the simple and multiple roots, on quadratic polynomials. Several applied problems are considered in order to demonstrate the performance of our methods and for comparison with the existing ones. Based on the numerical outcomes, we deduce that our methods illustrate better performance over the earlier methods even though in the case of multiple roots of high multiplicity. Full article

(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour)

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17 pages, 1669 KiB

Open AccessArticle

A Cognitive Model for Technology Adoption

by Fariborz Sobhanmanesh, Amin Beheshti, Nicholas Nouri, Natalia Monje Chapparo, Sandya Raj and Richard A. George

Algorithms 2023, 16(3), 155; https://doi.org/10.3390/a16030155 - 10 Mar 2023

Cited by 2 | Viewed by 2650

Abstract

The widespread adoption of advanced technologies, such as Artificial Intelligence (AI), Machine Learning, and Robotics, is rapidly increasing across the globe. This accelerated pace of change is drastically transforming various aspects of our lives and work, resulting in what is now known as [...] Read more.

The widespread adoption of advanced technologies, such as Artificial Intelligence (AI), Machine Learning, and Robotics, is rapidly increasing across the globe. This accelerated pace of change is drastically transforming various aspects of our lives and work, resulting in what is now known as Industry 4.0. As businesses integrate these technologies into their daily operations, it significantly impacts their work tasks and required skill sets. However, the approach to technological transformation varies depending on location, industry, and organization. However, there are no published methods that can adequately forecast the adoption of technology and its impact on society. It is essential to prepare for the future impact of Industry 4.0, and this requires policymakers and business leaders to be equipped with scientifically validated models and metrics. Data-driven scenario planning and decision-making can lead to better outcomes in every area of the business, from learning and development to technology investment. However, the current literature falls short in identifying effective and globally applicable strategies to predict the adoption rate of emerging technologies. Therefore, this paper proposes a novel parametric mathematical model for predicting the adoption rate of emerging technologies through a unique data-driven pipeline. This approach utilizes global indicators for countries to predict the technology adoption curves for each country and industry. The model is thoroughly validated, and the paper outlines highly promising evaluation results. The practical implications of this proposed approach are significant because it provides policymakers and business leaders with valuable insights for decision-making and scenario planning. Full article

(This article belongs to the Special Issue AI-Based Algorithms in IoT-Edge Computing)

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21 pages, 6194 KiB

Open AccessArticle

Fusion of CCTV Video and Spatial Information for Automated Crowd Congestion Monitoring in Public Urban Spaces

by Vivian W. H. Wong and Kincho H. Law

Algorithms 2023, 16(3), 154; https://doi.org/10.3390/a16030154 - 10 Mar 2023

Cited by 2 | Viewed by 2071

Abstract

Crowd congestion is one of the main causes of modern public safety issues such as stampedes. Conventional crowd congestion monitoring using closed-circuit television (CCTV) video surveillance relies on manual observation, which is tedious and often error-prone in public urban spaces where crowds are [...] Read more.

Crowd congestion is one of the main causes of modern public safety issues such as stampedes. Conventional crowd congestion monitoring using closed-circuit television (CCTV) video surveillance relies on manual observation, which is tedious and often error-prone in public urban spaces where crowds are dense, and occlusions are prominent. With the aim of managing crowded spaces safely, this study proposes a framework that combines spatial and temporal information to automatically map the trajectories of individual occupants, as well as to assist in real-time congestion monitoring and prediction. Through exploiting both features from CCTV footage and spatial information of the public space, the framework fuses raw CCTV video and floor plan information to create visual aids for crowd monitoring, as well as a sequence of crowd mobility graphs (CMGraphs) to store spatiotemporal features. This framework uses deep learning-based computer vision models, geometric transformations, and Kalman filter-based tracking algorithms to automate the retrieval of crowd congestion data, specifically the spatiotemporal distribution of individuals and the overall crowd flow. The resulting collective crowd movement data is then stored in the CMGraphs, which are designed to facilitate congestion forecasting at key exit/entry regions. We demonstrate our framework on two video data, one public from a train station dataset and the other recorded at a stadium following a crowded football game. Using both qualitative and quantitative insights from the experiments, we demonstrate that the suggested framework can be useful to help assist urban planners and infrastructure operators with the management of congestion hazards. Full article

(This article belongs to the Special Issue Recent Advances in Algorithms for Computer Vision Applications)

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18 pages, 5327 KiB

Open AccessArticle

Speaker-Independent Spectral Enhancement for Bone-Conducted Speech

by Liangliang Cheng, Yunfeng Dou, Jian Zhou, Huabin Wang and Liang Tao

Algorithms 2023, 16(3), 153; https://doi.org/10.3390/a16030153 - 09 Mar 2023

Viewed by 1531

Abstract

Because of the acoustic characteristics of bone-conducted (BC) speech, BC speech can be enhanced to better communicate in a complex environment with high noise. Existing BC speech enhancement models have weak spectral recovery capability for the high-frequency part of BC speech and have [...] Read more.

Because of the acoustic characteristics of bone-conducted (BC) speech, BC speech can be enhanced to better communicate in a complex environment with high noise. Existing BC speech enhancement models have weak spectral recovery capability for the high-frequency part of BC speech and have poor enhancement and robustness for the speaker-independent BC speech datasets. To improve the enhancement effect of BC speech for speaker-independent speech enhancement, we use a GANs method to establish the feature mapping between BC and air-conducted (AC) speech to recover the missing components of BC speech. In addition, the method adds the training of the spectral distance constraint model and, finally, uses the enhanced model completed by the training to reconstruct the BC speech. The experimental results show that this method is superior to the comparison methods such as CycleGAN, BLSTM, GMM, and StarGAN in terms of speaker-independent BC speech enhancement and can obtain higher subjective and objective evaluation results of enhanced BC speech. Full article

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15 pages, 2122 KiB

Open AccessArticle

Unsupervised Transformer-Based Anomaly Detection in ECG Signals

by Abrar Alamr and Abdelmonim Artoli

Algorithms 2023, 16(3), 152; https://doi.org/10.3390/a16030152 - 09 Mar 2023

Cited by 7 | Viewed by 5657

Abstract

Anomaly detection is one of the basic issues in data processing that addresses different problems in healthcare sensory data. Technology has made it easier to collect large and highly variant time series data; however, complex predictive analysis models are required to ensure consistency [...] Read more.

Anomaly detection is one of the basic issues in data processing that addresses different problems in healthcare sensory data. Technology has made it easier to collect large and highly variant time series data; however, complex predictive analysis models are required to ensure consistency and reliability. With the rise in the size and dimensionality of collected data, deep learning techniques, such as autoencoder (AE), recurrent neural networks (RNN), and long short-term memory (LSTM), have gained more attention and are recognized as state-of-the-art anomaly detection techniques. Recently, developments in transformer-based architecture have been proposed as an improved attention-based knowledge representation scheme. We present an unsupervised transformer-based method to evaluate and detect anomalies in electrocardiogram (ECG) signals. The model architecture comprises two parts: an embedding layer and a standard transformer encoder. We introduce, implement, test, and validate our model in two well-known datasets: ECG5000 and MIT-BIH Arrhythmia. Anomalies are detected based on loss function results between real and predicted ECG time series sequences. We found that the use of a transformer encoder as an alternative model for anomaly detection enables better performance in ECG time series data. The suggested model has a remarkable ability to detect anomalies in ECG signal and outperforms deep learning approaches found in the literature on both datasets. In the ECG5000 dataset, the model can detect anomalies with 99% accuracy, 99% F1-score, 99% AUC score, 98.1% recall, and 100% precision. In the MIT-BIH Arrhythmia dataset, the model achieved an accuracy of 89.5%, F1 score of 92.3%, AUC score of 93%, recall of 98.2%, and precision of 87.1%. Full article

(This article belongs to the Special Issue Artificial Intelligence and Signal Processing Methods in Medicine and Life Sciences)

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18 pages, 4223 KiB

Open AccessArticle

Modelling and Analysis of Neuro Fuzzy Employee Ranking System in the Public Sector

by Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Aristeidis Karras, Christos Karras and Spyros Sioutas

Algorithms 2023, 16(3), 151; https://doi.org/10.3390/a16030151 - 09 Mar 2023

Cited by 2 | Viewed by 1908

Abstract

Human resource management has a significant influence on the performance of any public body. Employee classification and ranking are definitely time-consuming processes, which in many cases lead to controversial results. In addition, assessing employee efficiency through a variety of skills could lead to [...] Read more.

Human resource management has a significant influence on the performance of any public body. Employee classification and ranking are definitely time-consuming processes, which in many cases lead to controversial results. In addition, assessing employee efficiency through a variety of skills could lead to never-ending calculations and error-prone statistics. On the other hand, hard skill selection is proven to formulate a base for further investigation since subjectivity is not included in the performance equation. This research proposes a ranking model of employee selection based on certain criteria and attributes. The proposed prototype shows a series of results with a low error rate using ANFIS as the base methodology approach. This research was explanatory, and the population of this study consisted of employees with the majority of the sample in the wider region of Western Greece. The results showed a harmonic co-existence of the factors that proportionally affect the productivity of the employees in public service. Therefore, it provides the HR department with valuable information regarding the overall productivity of the public body, as well as significant material based on each profile separately. Therefore, efficiency was achieved through an automated time-saving procedure. The final output will enhance any personnel selection system with data extracted directly from the system, ensuring that the current method outperformed traditional approaches and secured a non-subjective procedure on employee management applied to the public sector. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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26 pages, 10336 KiB

Open AccessArticle

A Machine Learning Study of High Robustness Quantum Walk Search Algorithm with Qudit Householder Coins

by Hristo Tonchev and Petar Danev

Algorithms 2023, 16(3), 150; https://doi.org/10.3390/a16030150 - 09 Mar 2023

Cited by 1 | Viewed by 1426

Abstract

In this work, the quantum random walk search algorithm with a walk coin constructed by generalized Householder reflection and phase multiplier has been studied. The coin register is one qudit with an arbitrary dimension. Monte Carlo simulations, in combination with supervised machine learning, [...] Read more.

In this work, the quantum random walk search algorithm with a walk coin constructed by generalized Householder reflection and phase multiplier has been studied. The coin register is one qudit with an arbitrary dimension. Monte Carlo simulations, in combination with supervised machine learning, are used to find walk coins that make the quantum algorithm more robust to deviations in the coin’s parameters. This is achieved by introducing functional dependence between these parameters. The functions that give the best performance of the algorithm are studied in detail by numerical statistical methods. A thorough comparison between our modification and an algorithm, with coins made using only Householder reflection, shows significant advantages of the former. By applying a deep neural network, we make a prediction for the parameters of an optimal coin with an arbitrary size and estimate the algorithm’s stability for such a coin. Full article

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21 pages, 2129 KiB

Open AccessArticle

Electromyography Gesture Model Classifier for Fault-Tolerant-Embedded Devices by Means of Partial Least Square Class Modelling Error Correcting Output Codes (PLS-ECOC)

by Pablo Sarabia, Alvaro Araujo, Luis Antonio Sarabia and María de la Cruz Ortiz

Algorithms 2023, 16(3), 149; https://doi.org/10.3390/a16030149 - 07 Mar 2023

Viewed by 1735

Abstract

Surface electromyography (sEMG) plays a crucial role in several applications, such as for prosthetic controls, human–machine interfaces (HMI), rehabilitation, and disease diagnosis. These applications are usually occurring in real-time, so the classifier tends to run on a wearable device. This edge processing paradigm [...] Read more.

Surface electromyography (sEMG) plays a crucial role in several applications, such as for prosthetic controls, human–machine interfaces (HMI), rehabilitation, and disease diagnosis. These applications are usually occurring in real-time, so the classifier tends to run on a wearable device. This edge processing paradigm imposes strict requirements on the complexity classifier. To date, research on hand gesture recognition (GR) based on sEMG uses discriminant classifiers, such as support vector machines and neural networks. These classifiers can achieve good precision; they cannot detect when an error in classification has happened. This paper proposes a novel hand gesture multiclass model based on partial least square (PLS) class modelling that uses an encoding matrix called error correcting output codes (ECOC). A dataset of eight different gestures was classified using this method where all errors were detected, proving the feasibility of PLS-ECOC as a fault-tolerant classifier. Considering the PLS-ECOC model as a classifier, its accuracy, precision, and F1 are 87.5, 91.87, and 86.34%, respectively, similar to those obtained by other authors. The strength of our work lies in the extra information provided by the PLS-ECOC that allows the application to be fault tolerant while keeping a small-size model and low complexity, making it suitable for embedded real-time classification. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing)

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20 pages, 1598 KiB

Open AccessArticle

Properties of the Quadratic Transformation of Dual Variables

by Vladimir Krutikov, Elena Tovbis, Anatoly Bykov, Predrag Stanimirovic, Ekaterina Chernova and Lev Kazakovtsev

Algorithms 2023, 16(3), 148; https://doi.org/10.3390/a16030148 - 07 Mar 2023

Viewed by 1190

Abstract

We investigate a solution of a convex programming problem with a strongly convex objective function based on the dual approach. A dual optimization problem has constraints on the positivity of variables. We study the methods and properties of transformations of dual variables that [...] Read more.

We investigate a solution of a convex programming problem with a strongly convex objective function based on the dual approach. A dual optimization problem has constraints on the positivity of variables. We study the methods and properties of transformations of dual variables that enable us to obtain an unconstrained optimization problem. We investigate the previously known method of transforming the components of dual variables in the form of their modulus (modulus method). We show that in the case of using the modulus method, the degree of the degeneracy of the function increases as it approaches the optimal point. Taking into account the ambiguity of the gradient in the boundary regions of the sign change of the new dual function variables and the increase in the degree of the function degeneracy, we need to use relaxation subgradient methods (RSM) that are difficult to implement and that can solve non-smooth non-convex optimization problems with a high degree of elongation of level surfaces. We propose to use the transformation of the components of dual variables in the form of their square (quadratic method). We prove that the transformed dual function has a Lipschitz gradient with a quadratic method of transformation. This enables us to use efficient gradient methods to find the extremum. The above properties are confirmed by a computational experiment. With a quadratic transformation compared to a modulus transformation, it is possible to obtain a solution of the problem by relaxation subgradient methods and smooth function minimization methods (conjugate gradient method and quasi-Newtonian method) with higher accuracy and lower computational costs. The noted transformations of dual variables were used in the program module for calculating the maximum permissible emissions of enterprises (MPE) of the software package for environmental monitoring of atmospheric air (ERA-AIR). Full article

(This article belongs to the Collection Feature Papers in Algorithms)

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13 pages, 26313 KiB

Open AccessArticle

Detectron2 for Lesion Detection in Diabetic Retinopathy

by Farheen Chincholi and Harald Koestler

Algorithms 2023, 16(3), 147; https://doi.org/10.3390/a16030147 - 07 Mar 2023

Cited by 3 | Viewed by 2248

Abstract

Hemorrhages in the retinal fundus are a common symptom of both diabetic retinopathy and diabetic macular edema, making their detection crucial for early diagnosis and treatment. For this task, the aim is to evaluate the performance of two pre-trained and additionally fine-tuned models [...] Read more.

Hemorrhages in the retinal fundus are a common symptom of both diabetic retinopathy and diabetic macular edema, making their detection crucial for early diagnosis and treatment. For this task, the aim is to evaluate the performance of two pre-trained and additionally fine-tuned models from the Detectron2 model zoo, Faster R-CNN (R50-FPN) and Mask R-CNN (R50-FPN). Experiments show that the Mask R-CNN (R50-FPN) model provides highly accurate segmentation masks for each detected hemorrhage, with an accuracy of 99.34%. The Faster R-CNN (R50-FPN) model detects hemorrhages with an accuracy of 99.22%. The results of both models are compared using a publicly available image database with ground truth marked by experts. Overall, this study demonstrates that current models are valuable tools for early diagnosis and treatment of diabetic retinopathy and diabetic macular edema. Full article

(This article belongs to the Special Issue Deep Learning for Healthcare Applications and Analysis)

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25 pages, 3370 KiB

Open AccessReview

Transfer Learning and Analogical Inference: A Critical Comparison of Algorithms, Methods, and Applications

by Kara Combs, Hongjing Lu and Trevor J. Bihl

Algorithms 2023, 16(3), 146; https://doi.org/10.3390/a16030146 - 07 Mar 2023

Cited by 3 | Viewed by 2701

Abstract

Artificial intelligence and machine learning (AI/ML) research has aimed to achieve human-level performance in tasks that require understanding and decision making. Although major advances have been made, AI systems still struggle to achieve adaptive learning for generalization. One of the main approaches to [...] Read more.

Artificial intelligence and machine learning (AI/ML) research has aimed to achieve human-level performance in tasks that require understanding and decision making. Although major advances have been made, AI systems still struggle to achieve adaptive learning for generalization. One of the main approaches to generalization in ML is transfer learning, where previously learned knowledge is utilized to solve problems in a different, but related, domain. Another approach, pursued by cognitive scientists for several decades, has investigated the role of analogical reasoning in comparisons aimed at understanding human generalization ability. Analogical reasoning has yielded rich empirical findings and general theoretical principles underlying human analogical inference and generalization across distinctively different domains. Though seemingly similar, there are fundamental differences between the two approaches. To clarify differences and similarities, we review transfer learning algorithms, methods, and applications in comparison with work based on analogical inference. Transfer learning focuses on exploring feature spaces shared across domains through data vectorization while analogical inferences focus on identifying relational structure shared across domains via comparisons. Rather than treating these two learning approaches as synonymous or as independent and mutually irrelevant fields, a better understanding of how they are interconnected can guide a multidisciplinary synthesis of the two approaches. Full article

(This article belongs to the Special Issue Machine Learning Algorithms for Big Data Analysis)

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Algorithms, Volume 16, Issue 3 (March 2023) – 51 articles

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