Theodoros Tzelepis, George Matlis, Nikos Dimokas, Petros Karvelis, P. Malliou, A. Beneka
In recent years the number of people who exercise every day has increased dramatically. More precisely, due to COVID period many people have become recreational runners. Recreational running is a regular way to keep active and healthy at any age. Additionally, running is a popular physical exercise that offers numerous health advantages. However, recreational runners report a high incidence of musculoskeletal injuries due to running. The healthcare industry has been compelled to use information technology due to the quick rate of growth and developments in electronic systems, the internet, and telecommunications. Our proposed intelligent system uses data mining algorithms for the rehabilitation guidance of recreational runners with musculoskeletal discomfort. The system classifies recreational runners based on a questionnaire that has been built according to the severity, irritability, nature, stage, and stability model and advise them on the appropriate treatment plan/exercises to follow. Through rigorous testing across various case studies, our method has yielded highly promising results, underscoring its potential to significantly contribute to the well-being and rehabilitation of recreational runners facing musculoskeletal challenges.
{"title":"An Intelligent Injury Rehabilitation Guidance System for Recreational Runners Using Data Mining Algorithms","authors":"Theodoros Tzelepis, George Matlis, Nikos Dimokas, Petros Karvelis, P. Malliou, A. Beneka","doi":"10.3390/a16110523","DOIUrl":"https://doi.org/10.3390/a16110523","url":null,"abstract":"In recent years the number of people who exercise every day has increased dramatically. More precisely, due to COVID period many people have become recreational runners. Recreational running is a regular way to keep active and healthy at any age. Additionally, running is a popular physical exercise that offers numerous health advantages. However, recreational runners report a high incidence of musculoskeletal injuries due to running. The healthcare industry has been compelled to use information technology due to the quick rate of growth and developments in electronic systems, the internet, and telecommunications. Our proposed intelligent system uses data mining algorithms for the rehabilitation guidance of recreational runners with musculoskeletal discomfort. The system classifies recreational runners based on a questionnaire that has been built according to the severity, irritability, nature, stage, and stability model and advise them on the appropriate treatment plan/exercises to follow. Through rigorous testing across various case studies, our method has yielded highly promising results, underscoring its potential to significantly contribute to the well-being and rehabilitation of recreational runners facing musculoskeletal challenges.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"46 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139270918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the utility of side information in the context of machine learning and, in particular, in supervised neural networks. Side information can be viewed as expert knowledge, additional to the input, that may come from a knowledge base. Unlike other approaches, our formalism can be used by a machine learning algorithm not only during training but also during testing. Moreover, the proposed approach is flexible as it caters for different formats of side information, and we do not constrain the side information to be fed into the input layer of the network. A formalism is presented based on the difference between the neural network loss without and with side information, stating that it is useful when adding side information reduces the loss during the test phase. As a proof of concept we provide experimental results for two datasets, the MNIST dataset of handwritten digits and the House Price prediction dataset. For the experiments we used feedforward neural networks containing two hidden layers, as well as a softmax output layer. For both datasets, side information is shown to be useful in that it improves the classification accuracy significantly.
{"title":"A General Model for Side Information in Neural Networks","authors":"Tameem Adel, Mark Levene","doi":"10.3390/a16110526","DOIUrl":"https://doi.org/10.3390/a16110526","url":null,"abstract":"We investigate the utility of side information in the context of machine learning and, in particular, in supervised neural networks. Side information can be viewed as expert knowledge, additional to the input, that may come from a knowledge base. Unlike other approaches, our formalism can be used by a machine learning algorithm not only during training but also during testing. Moreover, the proposed approach is flexible as it caters for different formats of side information, and we do not constrain the side information to be fed into the input layer of the network. A formalism is presented based on the difference between the neural network loss without and with side information, stating that it is useful when adding side information reduces the loss during the test phase. As a proof of concept we provide experimental results for two datasets, the MNIST dataset of handwritten digits and the House Price prediction dataset. For the experiments we used feedforward neural networks containing two hidden layers, as well as a softmax output layer. For both datasets, side information is shown to be useful in that it improves the classification accuracy significantly.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"53 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data. Some relational learning methods have been proposed, but those with discriminative relationship analysis are lacking yet, as important supervisory information is usually ignored. In this paper, we propose a novel and general framework, called relational Fisher analysis (RFA), which successfully integrates relational information into the dimensionality reduction model. For nonlinear data representation learning, we adopt the kernel trick to RFA and propose the kernelized RFA (KRFA). In addition, the convergence of the RFA optimization algorithm is proved theoretically. By leveraging suitable strategies to construct the relational matrix, we conduct extensive experiments to demonstrate the superiority of our RFA and KRFA methods over related approaches.
{"title":"Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence","authors":"Lina Wang, Guoqiang Zhong, Yaxin Shi, Mohamed Cheriet","doi":"10.3390/a16110522","DOIUrl":"https://doi.org/10.3390/a16110522","url":null,"abstract":"Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data. Some relational learning methods have been proposed, but those with discriminative relationship analysis are lacking yet, as important supervisory information is usually ignored. In this paper, we propose a novel and general framework, called relational Fisher analysis (RFA), which successfully integrates relational information into the dimensionality reduction model. For nonlinear data representation learning, we adopt the kernel trick to RFA and propose the kernelized RFA (KRFA). In addition, the convergence of the RFA optimization algorithm is proved theoretically. By leveraging suitable strategies to construct the relational matrix, we conduct extensive experiments to demonstrate the superiority of our RFA and KRFA methods over related approaches.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"48 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139273548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-intensive processes in the plastics industry: the plastic injection molding process. Concerning the industrial setting, the energy consumption of the injection molding machine was monitored across multiple injection molding cycles. The collected data were then analyzed to establish patterns and trends in the energy consumption of the injection molding process. To this end, we considered mixtures of regression models given their flexibility in modeling heterogeneous time series and clustering time series in an unsupervised machine learning framework. Given the assumption of autocorrelated data and exogenous variables in the mixture model, we implemented an algorithm for model fitting that combined autocorrelated observations with spline and polynomial regressions. Our results demonstrate an accurate grouping of energy-consumption profiles, where each cluster is related to a specific production schedule. The clustering method also provides a unique profile of energy consumption for each cluster, depending on the production schedule and regression approach (i.e., spline and polynomial). According to these profiles, information related to the shape of energy consumption was identified, providing insights into reducing the electrical demand of the plant.
{"title":"Utilizing Mixture Regression Models for Clustering Time-Series Energy Consumption of a Plastic Injection Molding Process","authors":"Massimo Pacella, Matteo Mangini, G. Papadia","doi":"10.3390/a16110524","DOIUrl":"https://doi.org/10.3390/a16110524","url":null,"abstract":"Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-intensive processes in the plastics industry: the plastic injection molding process. Concerning the industrial setting, the energy consumption of the injection molding machine was monitored across multiple injection molding cycles. The collected data were then analyzed to establish patterns and trends in the energy consumption of the injection molding process. To this end, we considered mixtures of regression models given their flexibility in modeling heterogeneous time series and clustering time series in an unsupervised machine learning framework. Given the assumption of autocorrelated data and exogenous variables in the mixture model, we implemented an algorithm for model fitting that combined autocorrelated observations with spline and polynomial regressions. Our results demonstrate an accurate grouping of energy-consumption profiles, where each cluster is related to a specific production schedule. The clustering method also provides a unique profile of energy consumption for each cluster, depending on the production schedule and regression approach (i.e., spline and polynomial). According to these profiles, information related to the shape of energy consumption was identified, providing insights into reducing the electrical demand of the plant.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"70 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamad Abou Ali, F. Dornaika, Ignacio Arganda-Carreras
Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigated the transfer learning process of the Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results showed that the Google ViT is an excellent DL neural solution for data scarcity. The BCCD results showed that the Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead.
随着视觉转换器（ViTs）的出现，深度学习（DL）在计算机视觉领域取得了重大进展。与卷积神经网络（CNNs）不同，ViTs 利用自我注意从图像数据中提取局部和全局特征，然后应用残差连接将这些特征直接输入完全网络化的多层感知器头。在医院里，血液学专家要制备外周血涂片（PBS），并在医用显微镜下进行读取，以检测血细胞计数的异常情况，如白血病。然而，这项工作既耗时又容易出现人为错误。本研究调查了 Google ViT 和 ImageNet CNN 的迁移学习过程，以实现 PBS 读取的自动化。研究使用了 PBC 和 BCCD 两个在线 PBS 数据集，并将它们转移到平衡数据集中，以研究数据量和抗噪性对两个神经网络的影响。PBC 结果表明，Google ViT 是一种出色的数据稀缺性 DL 神经解决方案。BCCD 结果表明，Google ViT 在处理不干净、有噪声的图像数据方面优于 ImageNet CNN，因为它能够提取全局和局部特征，并使用残差连接，尽管需要额外的时间和计算开销。
{"title":"White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope","authors":"Mohamad Abou Ali, F. Dornaika, Ignacio Arganda-Carreras","doi":"10.3390/a16110525","DOIUrl":"https://doi.org/10.3390/a16110525","url":null,"abstract":"Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigated the transfer learning process of the Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results showed that the Google ViT is an excellent DL neural solution for data scarcity. The BCCD results showed that the Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"43 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emmanouil Koutoulakis, Louis Marage, Emmanouil Markodimitrakis, L. Aubignac, Catherine Jenny, I. Bessières, Alain Lalande
MR-Linac is a recent device combining a linear accelerator with an MRI scanner. The improved soft tissue contrast of MR images is used for optimum delineation of tumors or organs at risk (OARs) and precise treatment delivery. Automatic segmentation of OARs can contribute to alleviating the time-consuming process for radiation oncologists and improving the accuracy of radiation delivery by providing faster, more consistent, and more accurate delineation of target structures and organs at risk. It can also help reduce inter-observer variability and improve the consistency of contouring while reducing the time required for treatment planning. In this work, state-of-the-art deep learning techniques were evaluated based on 2D and 2.5D training strategies to develop a comprehensive tool for the accurate segmentation of pelvic OARs dedicated to 0.35 T MR-Linac. In total, 103 cases with 0.35 T MR images of the pelvic region were investigated. Experts considered and contoured the bladder, rectum, and femoral heads as OARs and the prostate as the target volume. For the training of the neural network, 85 patients were randomly selected, and 18 were used for testing. Multiple U-Net-based architectures were considered, and the best model was compared using both 2D and 2.5D training strategies. The evaluation of the models was performed based on two metrics: the Dice similarity coefficient (DSC) and the Hausdorff distance (HD). In the 2D training strategy, Residual Attention U-Net (ResAttU-Net) had the highest scores among the other deep neural networks. Due to the additional contextual information, the configured 2.5D ResAttU-Net performed better. The overall DSC were 0.88 ± 0.09 and 0.86 ± 0.10, and the overall HD was 1.78 ± 3.02 mm and 5.90 ± 7.58 mm for 2.5D and 2D ResAttU-Net, respectively. The 2.5D ResAttU-Net provides accurate segmentation of OARs without affecting the computational cost. The developed end-to-end pipeline will be merged with the treatment planning system for in-time automatic segmentation.
{"title":"Automatic Multiorgan Segmentation in Pelvic Region with Convolutional Neural Networks on 0.35 T MR-Linac Images","authors":"Emmanouil Koutoulakis, Louis Marage, Emmanouil Markodimitrakis, L. Aubignac, Catherine Jenny, I. Bessières, Alain Lalande","doi":"10.3390/a16110521","DOIUrl":"https://doi.org/10.3390/a16110521","url":null,"abstract":"MR-Linac is a recent device combining a linear accelerator with an MRI scanner. The improved soft tissue contrast of MR images is used for optimum delineation of tumors or organs at risk (OARs) and precise treatment delivery. Automatic segmentation of OARs can contribute to alleviating the time-consuming process for radiation oncologists and improving the accuracy of radiation delivery by providing faster, more consistent, and more accurate delineation of target structures and organs at risk. It can also help reduce inter-observer variability and improve the consistency of contouring while reducing the time required for treatment planning. In this work, state-of-the-art deep learning techniques were evaluated based on 2D and 2.5D training strategies to develop a comprehensive tool for the accurate segmentation of pelvic OARs dedicated to 0.35 T MR-Linac. In total, 103 cases with 0.35 T MR images of the pelvic region were investigated. Experts considered and contoured the bladder, rectum, and femoral heads as OARs and the prostate as the target volume. For the training of the neural network, 85 patients were randomly selected, and 18 were used for testing. Multiple U-Net-based architectures were considered, and the best model was compared using both 2D and 2.5D training strategies. The evaluation of the models was performed based on two metrics: the Dice similarity coefficient (DSC) and the Hausdorff distance (HD). In the 2D training strategy, Residual Attention U-Net (ResAttU-Net) had the highest scores among the other deep neural networks. Due to the additional contextual information, the configured 2.5D ResAttU-Net performed better. The overall DSC were 0.88 ± 0.09 and 0.86 ± 0.10, and the overall HD was 1.78 ± 3.02 mm and 5.90 ± 7.58 mm for 2.5D and 2D ResAttU-Net, respectively. The 2.5D ResAttU-Net provides accurate segmentation of OARs without affecting the computational cost. The developed end-to-end pipeline will be merged with the treatment planning system for in-time automatic segmentation.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"3 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139274570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model’s detection head (IDetect) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model’s effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that mAP@0.5:0.95 and mAP@0.5 increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection.
在无人机照片中，目标检测算法在提高不同尺寸目标的速度和准确性方面遇到了挑战，主要是由于复杂的背景和小目标。本研究引入了基于YOLOv7-tiny模型的PDWT-YOLO算法，以提高各种尺寸目标检测的有效性。该方法通过引入专用的小目标检测层来增强对小目标的检测，同时通过将YOLOv7-tiny模型的检测头(IDetect)替换为解耦头来减少分类任务与回归任务之间的冲突。通过在损失函数中使用WIoU (Wise Intersection over Union)聚焦机制取代CIoU (Complete Intersection over Union)损失函数，加快了网络收敛速度，提高了回归精度。为了评估所提出的模型的有效性，在VisDrone-2019数据集上对其进行了训练和测试，该数据集包括各种无人机在不同场景、天气条件和照明条件下捕获的图像。实验表明，mAP@0.5:0.95和mAP@0.5分别比原来的YOLOv7-tiny模型提高了5%和6.7%，运行速度可以接受。此外，该方法在其他数据集上也有改进，证实了PDWT-YOLO对多尺度目标检测的有效性。
{"title":"Improved Object Detection Method Utilizing YOLOv7-Tiny for Unmanned Aerial Vehicle Photographic Imagery","authors":"Linhua Zhang, Ning Xiong, Xinghao Pan, Xiaodong Yue, Peng Wu, Caiping Guo","doi":"10.3390/a16110520","DOIUrl":"https://doi.org/10.3390/a16110520","url":null,"abstract":"In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model’s detection head (IDetect) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model’s effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that mAP@0.5:0.95 and mAP@0.5 increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"5 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134992028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The maximum sum subarray problem is to find a contiguous subarray with the largest sum. The history of algorithms to address this problem is recounted, culminating in what is known as Kadane’s algorithm. However, that algorithm is not the algorithm Kadane intended. Nonetheless, the algorithm known as Kadane’s has found many uses, some of which are recounted here. The algorithm Kadane intended is reported here, and compared to the algorithm attributed to Kadane. They are both linear in time, employ just a few words of memory, and use a dynamic programming structure. The results proved here show that these two algorithms differ only in the case of an input consisting of only negative numbers. In that case, the algorithm Kadane intended is more informative than the algorithm attributed to him.
{"title":"Two Kadane Algorithms for the Maximum Sum Subarray Problem","authors":"Joseph B. Kadane","doi":"10.3390/a16110519","DOIUrl":"https://doi.org/10.3390/a16110519","url":null,"abstract":"The maximum sum subarray problem is to find a contiguous subarray with the largest sum. The history of algorithms to address this problem is recounted, culminating in what is known as Kadane’s algorithm. However, that algorithm is not the algorithm Kadane intended. Nonetheless, the algorithm known as Kadane’s has found many uses, some of which are recounted here. The algorithm Kadane intended is reported here, and compared to the algorithm attributed to Kadane. They are both linear in time, employ just a few words of memory, and use a dynamic programming structure. The results proved here show that these two algorithms differ only in the case of an input consisting of only negative numbers. In that case, the algorithm Kadane intended is more informative than the algorithm attributed to him.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"21 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134993745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The continuous development of quantum computing necessitates the development of quantum-resistant cryptographic algorithms. In response to this demand, the National Institute of Standards and Technology selected standardized algorithms including Crystals-Dilithium, Falcon, and Sphincs+ for digital signatures. This paper provides a comparative evaluation of these algorithms across key metrics. The results indicate varying strengths and weaknesses for each algorithm, underscoring the importance of context-specific deployments. Our findings indicate that Dilithium offers advantages in low-power scenarios, Falcon excels in signature verification speed, and Sphincs+ provides robust security at the cost of computational efficiency. These results underscore the importance of context-specific deployments in specific and resource-constrained technological applications, like IoT, smart cards, blockchain, and vehicle-to-vehicle communication.
量子计算的不断发展要求开发抗量子密码算法。为了满足这一需求，美国国家标准与技术研究所(National Institute of Standards and Technology)为数字签名选择了包括crystals - diliium、Falcon和sphins +在内的标准化算法。本文提供了跨关键指标的这些算法的比较评估。结果表明了每种算法的不同优点和缺点，强调了特定于上下文的部署的重要性。我们的研究结果表明，diliium在低功耗场景中具有优势，Falcon在签名验证速度方面表现出色，而sphins +以计算效率为代价提供了强大的安全性。这些结果强调了在特定和资源受限的技术应用中，如物联网、智能卡、区块链和车对车通信，具体部署的重要性。
{"title":"Performance and Applicability of Post-Quantum Digital Signature Algorithms in Resource-Constrained Environments","authors":"Marin Vidaković, Kruno Miličević","doi":"10.3390/a16110518","DOIUrl":"https://doi.org/10.3390/a16110518","url":null,"abstract":"The continuous development of quantum computing necessitates the development of quantum-resistant cryptographic algorithms. In response to this demand, the National Institute of Standards and Technology selected standardized algorithms including Crystals-Dilithium, Falcon, and Sphincs+ for digital signatures. This paper provides a comparative evaluation of these algorithms across key metrics. The results indicate varying strengths and weaknesses for each algorithm, underscoring the importance of context-specific deployments. Our findings indicate that Dilithium offers advantages in low-power scenarios, Falcon excels in signature verification speed, and Sphincs+ provides robust security at the cost of computational efficiency. These results underscore the importance of context-specific deployments in specific and resource-constrained technological applications, like IoT, smart cards, blockchain, and vehicle-to-vehicle communication.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"33 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jöran Rixen, Nico Blass, Simon Lyra, Steffen Leonhardt
Breast cancer is the leading cause of cancer-related death among women. Early prediction is crucial as it severely increases the survival rate. Although classical X-ray mammography is an established technique for screening, many eligible women do not consider this due to concerns about pain from breast compression. Electrical Impedance Tomography (EIT) is a technique that aims to visualize the conductivity distribution within the human body. As cancer has a greater conductivity than surrounding fatty tissue, it provides a contrast for image reconstruction. However, the interpretation of EIT images is still hard, due to the low spatial resolution. In this paper, we investigated three different classification models for the detection of breast cancer. This is important as EIT is a highly non-linear inverse problem and tends to produce reconstruction artifacts, which can be misinterpreted as, e.g., tumors. To aid in the interpretation of breast cancer EIT images, we compare three different classification models for breast cancer. We found that random forests and support vector machines performed best for this task.
{"title":"Comparison of Machine Learning Classifiers for the Detection of Breast Cancer in an Electrical Impedance Tomography Setup","authors":"Jöran Rixen, Nico Blass, Simon Lyra, Steffen Leonhardt","doi":"10.3390/a16110517","DOIUrl":"https://doi.org/10.3390/a16110517","url":null,"abstract":"Breast cancer is the leading cause of cancer-related death among women. Early prediction is crucial as it severely increases the survival rate. Although classical X-ray mammography is an established technique for screening, many eligible women do not consider this due to concerns about pain from breast compression. Electrical Impedance Tomography (EIT) is a technique that aims to visualize the conductivity distribution within the human body. As cancer has a greater conductivity than surrounding fatty tissue, it provides a contrast for image reconstruction. However, the interpretation of EIT images is still hard, due to the low spatial resolution. In this paper, we investigated three different classification models for the detection of breast cancer. This is important as EIT is a highly non-linear inverse problem and tends to produce reconstruction artifacts, which can be misinterpreted as, e.g., tumors. To aid in the interpretation of breast cancer EIT images, we compare three different classification models for breast cancer. We found that random forests and support vector machines performed best for this task.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"37 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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