Acta Universitatis Sapientiae Informatica最新文献_第2页

IF 0.3

Acta Universitatis Sapientiae Informatica Pub Date : 2023-08-01 DOI: 10.2478/ausi-2023-0001

James Joseph, Mayamma Joseph

引用次数: 0

Explainable patch-level histopathology tissue type detection with bag-of-local-features models and data augmentation 可解释的斑块级组织病理学组织类型检测与局部特征袋模型和数据增强

IF 0.3

Acta Universitatis Sapientiae Informatica Pub Date : 2023-08-01 DOI: 10.2478/ausi-2023-0006

Gergő Galiger, Z. Bodó

{"title":"Explainable patch-level histopathology tissue type detection with bag-of-local-features models and data augmentation","authors":"Gergő Galiger, Z. Bodó","doi":"10.2478/ausi-2023-0006","DOIUrl":"https://doi.org/10.2478/ausi-2023-0006","url":null,"abstract":"Abstract Automatic detection of tissue types on whole-slide images (WSI) is an important task in computational histopathology that can be solved with convolutional neural networks (CNN) with high accuracy. However, the black-box nature of CNNs rightfully raises concerns about using them for this task. In this paper, we reformulate the task of tissue type detection to multiple binary classification problems to simplify the justification of model decisions. We propose an adapted Bag-of-local-Features interpretable CNN for solving this problem, which we train on eight newly introduced binary tissue classification datasets. The performance of the model is evaluated simultaneously with its decision-making process using logit heatmaps. Our model achieves better performance than its non-interpretable counterparts, while also being able to provide human-readable justification for decisions. Furthermore, the problem of data scarcity in computational histopathology is accounted for by using data augmentation techniques to improve both the performance and even the validity of model decisions. The source code and binary datasets can be accessed at: https://github.com/galigergergo/BolFTissueDetect.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"51 1","pages":"60 - 80"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90962474","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}

引用次数: 0

Hourly electricity price forecast for short-and long-term, using deep neural networks 利用深度神经网络进行短期和长期的小时电价预测

IF 0.3

Acta Universitatis Sapientiae Informatica Pub Date : 2022-12-01 DOI: 10.2478/ausi-2022-0013

Gergely Dombi, T. Dulai

引用次数: 0

Computational complexity of network vulnerability analysis 网络漏洞分析的计算复杂度

IF 0.3

Acta Universitatis Sapientiae Informatica Pub Date : 2022-12-01 DOI: 10.2478/ausi-2022-0012

M. Berberler

引用次数: 0

Rendering automatic bokeh recommendation engine for photos using deep learning algorithm 使用深度学习算法为照片绘制自动散景推荐引擎

IF 0.3

Acta Universitatis Sapientiae Informatica Pub Date : 2022-12-01 DOI: 10.2478/ausi-2022-0015

Rakesh Kumar, Meenu Gupta, Jaismeen, Shreya Dhanta, Nishant Kumar Pathak, Yukti Vivek, Ayush Sharma, Deepak, Gaurav Ramola, S. Velusamy

{"title":"Rendering automatic bokeh recommendation engine for photos using deep learning algorithm","authors":"Rakesh Kumar, Meenu Gupta, Jaismeen, Shreya Dhanta, Nishant Kumar Pathak, Yukti Vivek, Ayush Sharma, Deepak, Gaurav Ramola, S. Velusamy","doi":"10.2478/ausi-2022-0015","DOIUrl":"https://doi.org/10.2478/ausi-2022-0015","url":null,"abstract":"Abstract Automatic bokeh is one of the smartphone’s essential photography effects. This effect enhances the quality of the image where the subject background gets out of focus by providing a soft (i.e., diverse) background. Most smartphones have a single rear camera that is lacking to provide which effects need to be applied to which kind of images. To do the same, smartphones depend on different software to generate the bokeh effect on images. Blur, Color-point, Zoom, Spin, Big Bokeh, Color Picker, Low-key, High-Key, and Silhouette are the popular bokeh effects. With this wide range of bokeh types available, it is difficult for the user to choose a suitable effect for their images. Deep Learning (DL) models (i.e., MobileNetV2, InceptionV3, and VGG16) are used in this work to recommend high-quality bokeh effects for images. Four thousand five hundred images are collected from online resources such as Google images, Unsplash, and Kaggle to examine the model performance. 85% accuracy has been achieved for recommending different bokeh effects using the proposed model MobileNetV2, which exceeds many of the existing models.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"10 1","pages":"248 - 272"},"PeriodicalIF":0.3,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78672850","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}

引用次数: 0

Bounds on Nirmala energy of graphs 图的Nirmala能的界

IF 0.3

Acta Universitatis Sapientiae Informatica Pub Date : 2022-12-01 DOI: 10.2478/ausi-2022-0017

N. Yalçın

引用次数: 0

A two-stage U-net approach to brain tumor segmentation from multi-spectral MRI records 基于多谱MRI记录的两阶段U-net脑肿瘤分割方法

IF 0.3

Acta Universitatis Sapientiae Informatica Pub Date : 2022-12-01 DOI: 10.2478/ausi-2022-0014

Ágnes Győrfi, L. Kovács, L. Szilágyi

{"title":"A two-stage U-net approach to brain tumor segmentation from multi-spectral MRI records","authors":"Ágnes Győrfi, L. Kovács, L. Szilágyi","doi":"10.2478/ausi-2022-0014","DOIUrl":"https://doi.org/10.2478/ausi-2022-0014","url":null,"abstract":"Abstract The automated segmentation of brain tissues and lesions represents a widely investigated research topic. The Brain Tumor Segmentation Challenges (BraTS) organized yearly since 2012 provided standard training and testing data and a unified evaluation framework to the research community, which provoked an intensification in this research field. This paper proposes a solution to the brain tumor segmentation problem, which is built upon the U-net architecture that is very popular in medical imaging. The proposed procedure involves two identical, cascaded U-net networks with 3D convolution. The first stage produces an initial segmentation of a brain volume, while the second stage applies a post-processing based on the labels provided by the first stage. In the first U-net based classification, each pixel is characterized by the four observed features (T1, T2, T1c, and FLAIR), while the second identical U-net works with four features extracted from the volumetric neighborhood of the pixels, representing the ratio of pixels with positive initial labeling within the neighborhood. Statistical accuracy indexes are employed to evaluate the initial and final segmentation of each MRI record. Tests based on BraTS 2019 training data set led to average Dice scores over 87%. The postprocessing step can increase the average Dice scores by 0.5%, it improves more those volumes whose initial segmentation was less successful.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"45 1","pages":"223 - 247"},"PeriodicalIF":0.3,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73865741","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}

引用次数: 0

Experiments with holographic associative memory 全息联想记忆实验

IF 0.3

Acta Universitatis Sapientiae Informatica Pub Date : 2022-12-01 DOI: 10.2478/ausi-2022-0010

G. Román

引用次数: 0

A feature selection strategy using Markov clustering, for the optimization of brain tumor segmentation from MRI data 一种基于马尔可夫聚类的特征选择策略，用于从MRI数据中优化脑肿瘤分割

IF 0.3

Acta Universitatis Sapientiae Informatica Pub Date : 2022-12-01 DOI: 10.2478/ausi-2022-0018

Ioan-Marius Pisak-Lukáts, L. Kovács, Szilágyi László

{"title":"A feature selection strategy using Markov clustering, for the optimization of brain tumor segmentation from MRI data","authors":"Ioan-Marius Pisak-Lukáts, L. Kovács, Szilágyi László","doi":"10.2478/ausi-2022-0018","DOIUrl":"https://doi.org/10.2478/ausi-2022-0018","url":null,"abstract":"Abstract The automatic segmentation of medical images stands at the basis of modern medical diagnosis, therapy planning and follow-up studies after interventions. The accuracy of the segmentation is a key element in assisting the work of the physician, but the efficiency of the process is also relevant. This paper introduces a feature selection strategy that attempts to define reduced feature sets for ensemble learning methods employed in brain tumor segmentation based on MRI data such a way that the segmentation outcome hardly suffers any damage. Initially, the full set of observed and generated features are deployed in ensemble training and prediction on testing data, which provide us information on all couples of features from the full feature set. The extracted pairwise data is fed to a Markov clustering (MCL) algorithm, which uses a graph structure to characterize the relation between features. MCL produces connected subgraphs that are totally separated from each other. The largest such subgraph defines the group of features which are selected for evaluation. The proposed technique is evaluated using the high-grade and low-grade tumor records of the training dataset of the BraTS 2019 challenge, in an ensemble learning framework relying on binary decision trees. The proposed method can reduce the set of features to 30%ofits initial size without losing anything in terms of segmentation accuracy, significantly contributing to the efficiency of the segmentation process. A detailed comparison of the full set of 104 features and the reduced set of 41 features is provided, with special attention to highly discriminative and redundant features within the MRI data.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"5 1","pages":"316 - 337"},"PeriodicalIF":0.3,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79186347","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}

引用次数: 0

Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method 调查YouTube频道的算法:评论词频率比较社交媒体分析方法

IF 0.3

Acta Universitatis Sapientiae Informatica Pub Date : 2022-12-01 DOI: 10.2478/ausi-2022-0016

Pálma Rozália Osztián, Z. Kátai, Ágnes Sántha, Erika Osztián

{"title":"Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method","authors":"Pálma Rozália Osztián, Z. Kátai, Ágnes Sántha, Erika Osztián","doi":"10.2478/ausi-2022-0016","DOIUrl":"https://doi.org/10.2478/ausi-2022-0016","url":null,"abstract":"Abstract In this paper we investigate the comments from the AlgoRythmics YouTube channel using the Comment Term Frequency Comparison social media analytics method. Comment Term Frequency Comparison can be a useful tool to understand how a social media platform, such as a Youtube channel is being discussed by users and to identify opportunities to engage with the audience. Understanding viewer opinions and reactions to a video, identifying trends and patterns in the way people are discussing a particular topic, and measuring the effectiveness of a video in achieving its intended goals is one of the most important points of view for a channel to develop. Youtube comment analytics can be a valuable tool looking to understand how the AlgoRythmics channel videos are being received by viewers and to identify opportunities for improvement. Our study focuses on the importance of user feedback based on ten algorithm visualization videos from the AlgoRythmics channel. In order to find evidence how our channel works and new ideas to improve we used the so-called comment term frequency comparison social media analytics method to investigate the main characteristics of user feedback. We analyzed the comments using both Youtube Studio Analytics and Mozdeh Big Data Analysis tool.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"6 1","pages":"273 - 301"},"PeriodicalIF":0.3,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76319973","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}

引用次数: 0