2021 6th International Conference on Computer Science and Engineering (UBMK)最新文献_第2页

Analyzing Deep Learning Models’ Generalization Ability Under Different Augmentations on Deepfake Datasets 深度学习模型在Deepfake数据集上不同增强的泛化能力分析

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558927

Ilkin Huseynli, Songül Varlı

引用次数: 1

A UML-Based Conceptual Model for Appointment Booking Systems 基于uml的预约系统概念模型

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558929

Ali Pişirgen, Serhat Peker

引用次数: 3

FPGA-based Minimal Latency HEFT Scheduler for Heterogeneous Computing 基于fpga的异构计算最小延迟HEFT调度程序

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558926

I. Aliyev, J. Mack, Nirmal Kumbhare, A. Akoglu, H. F. Ugurdag

{"title":"FPGA-based Minimal Latency HEFT Scheduler for Heterogeneous Computing","authors":"I. Aliyev, J. Mack, Nirmal Kumbhare, A. Akoglu, H. F. Ugurdag","doi":"10.1109/UBMK52708.2021.9558926","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558926","url":null,"abstract":"This paper proposes a new hardware scheduler. As heterogeneous computing becomes prevalent, mapping applications on to multiple processing elements (PEs) proves to be non-trivial. Heterogeneous Earliest Finish Time (HEFT) algorithm is an already existing scheduler that aims to minimize the total execution time of an application. The paradigm of HEFT is such that it accepts an acyclic task graph as input at run-time and assigns/schedules the precompiled atomic tasks to PEs. HEFT stands out among many such schedulers not only in terms of producing shorter schedules but also in terms of its own short execution time. However, in real-time applications, the lower the latency, the better it is. To the best of our knowledge, this work is the only work that implements HEFT in hardware (on FPGA) further lowering its latency from milliseconds to as much as less than a microsecond. Porting HEFT to hardware has been challenging as data dependencies limit the amount of parallelism. Design of an efficient memory access pattern as well as an “incremental sorter” were key enablers in reducing the latency of the hardware implementation. We also integrated our FPGA-HEFT into an ARM-based SoC and validated its functionality using a realistic workload.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134183426","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: 3

An Ensemble Approach for Automatic Artefact Detection on Gastroendoscopy Images 胃内窥镜图像伪影自动检测的集成方法

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558919

Furkan Artunc, I. Oksuz

{"title":"An Ensemble Approach for Automatic Artefact Detection on Gastroendoscopy Images","authors":"Furkan Artunc, I. Oksuz","doi":"10.1109/UBMK52708.2021.9558919","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558919","url":null,"abstract":"Endoscopy imaging is a clinical procedure for the early detection of numerous cancers as well as for therapeutic procedures and minimally invasive surgery. Using endoscopic examination data to detect diseases is very helpful for medical doctors and speeds up the diagnosis. Because of the very narrow area, captured frames during endoscopic examination include a variety of artefacts. Artefacts degrade diagnostic image quality, which in turn makes disease diagnosis difficult for both clinicians and computer aided disease detection algorithms. Therefore, it is very crucial to find and eliminate those artefacts from medical images. In this paper, a detection system which utilizes ensemble of deep learning models and data augmentation is proposed. A fast and accurate object detection model which is YOLOv5 (improved version of YOLOv4) is selected as a base model. The 3 separate models are trained with segregated and augmented data; then, the models are combined to make an ensemble. The EndoCV2020 dataset is utilized to benchmark the ensemble model. The model achieves state-of-the-art performance with 49.6 mAP. The final mAP is calculated averaging several APs for different IoU thresholds (starting from 0.25 IoU to 0.75 Iou with step size 0.05).","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130925919","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

Gender Determination from Pictures with CNN Models 从CNN模特的图片中确定性别

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558915

E. Bulus

引用次数: 0

Empirical Comparison of Deep Neural Networks for Brain Vessel Segmentation 深度神经网络用于脑血管分割的经验比较

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9559015

Tuğçe Koçak, M. Aydın, Berna Kiraz

{"title":"Empirical Comparison of Deep Neural Networks for Brain Vessel Segmentation","authors":"Tuğçe Koçak, M. Aydın, Berna Kiraz","doi":"10.1109/UBMK52708.2021.9559015","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559015","url":null,"abstract":"Examination, monitoring and analysis of structural changes in the blood vessels of the brain enable the observation of brain functions. Therefore, the segmentation of the entire cerebral vascular network (including the capillaries) is of great importance in terms of the relevant specialist’s opinion on the diagnosis and treatment of a disease. When performed manuall, segmentation of the vascular network of the brain is a long time-consuming and fault-tolerant process. The automatic segmentation of the brain microvascular structure with machine learning approaches eliminates the need for specialists, and provides a method for perfroming cerebral vessel segmentation in a short time. This study provides the empirical comparision of three different deep neural network models including autoencoder, U-Net and ResNet+U-Net for the vascular network segmentation of brain vessels. The experiments are conducted on vesseINN dataset, which is a volumetric cerebrovascular system dataset obtained by two-photon microscopy. The models are evaluated based on accuracy, f1-score, recall, and precision metrics. During the training phase, U-Net and ResNet+Unet achieve 98% accuracy. Auto-encoder, on the other hand, yields 95% accuracy. In the test phase, it is observed that U-Net and ResNet+U-Net models give better results than the autoencoder model, according to the results obtained with 97% accuracy for U-Net and ResNet+Unet networks and 95% accuracy for autoencoder.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132719121","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: 1

Anomaly Detection with Deep Long Short Term Memory Networks 深度长短期记忆网络异常检测

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9559034

M. B. Terzi

{"title":"Anomaly Detection with Deep Long Short Term Memory Networks","authors":"M. B. Terzi","doi":"10.1109/UBMK52708.2021.9559034","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559034","url":null,"abstract":"In this study, a robust anomaly detection technique for ECG signals is developed using deep gated recurrent neural networks (GRNN) with Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) unit. By training deep GRU and LSTM networks on normal ECG data acquired from healthy subjects, a robust prediction model that learns to predict future time steps of ECG time series is developed. The prediction errors are modeled using Multivariate Gaussian Distribution and the estimations of optimum parameters were performed via Maximum Likelihood Estimation (MLE) method. By using probability distributions of prediction errors and optimum threshold values, the classification of normal and abnormal time series is performed. The results of the study show that deep LSTM networks with stacked recurrent hidden layers can learn higher-level temporal features in ECG time series without prior knowledge of the data and can robustly model normal time series behaviors. The performance results of the proposed deep learning and Gaussian-based statistical anomaly detection technique over the European ST-T database show that the technique provides the reliable diagnosis of cardiovascular diseases by performing the robust detection of anomalies in ECG time series.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114959492","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

Comparison of Feature Selection Methods in Security Analysis of Android Android安全分析中特征选择方法的比较

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558984

R. Arslan

引用次数: 0

Demographic Targeting With Epsilon-greedy Exploration in Digital Advertising 数字广告中Epsilon-greedy探索的人口定位

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558951

Basak Esin Köktürk Güzel, Bora Mocan, Büsra Arslan, Gokce Polat, Tarık Kavuşan

引用次数: 1

Real-Time Puddle Detection Using Convolutional Neural Networks with Unmanned Aerial Vehicles 基于卷积神经网络的无人机实时水坑检测

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558907

Mehmet Bilge Han Tas, Muhammed Coskun Irmak, Sedat Turan, A. Hasiloglu

{"title":"Real-Time Puddle Detection Using Convolutional Neural Networks with Unmanned Aerial Vehicles","authors":"Mehmet Bilge Han Tas, Muhammed Coskun Irmak, Sedat Turan, A. Hasiloglu","doi":"10.1109/UBMK52708.2021.9558907","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558907","url":null,"abstract":"The study was carried out in order to enable systems with weak processing power and motion to detect objects using cloud services. In addition, the dataset is expanded by continuous labeling to create big data. In the study, it is aimed to detect objects using cloud-based deep learning methods with an unmanned aerial vehicle (UAV). In the study, training processes were carried out with Google Colaboratory, a cloud service provider. The training processes are a YOLO-based system, and a convolutional neural network was created by revising the parameters in line with the needs. The convolutional neural network model provides communication between neurons in the convolutional layers by bringing the image data to the desired pixel ranges. Unlabeled pictures are included in the training by being tagged. In this way, it is possible to continuously enlarge the data pool. Since the microcomputers used in UAVs are insufficient for these processes, a cloud-based training model has been created. As a result of the study, cloud-based deep learning models work as desired. It is possible to show the accuracy of the model with the low losses seen in the loss functions and the mAP value. Graphic cards with high processing power are needed to provide training. It is essential to use powerful graphics cards when working on image data. Cost reduced by using cloud services. The training was accelerated and high-rate object detections were made. YOLOv5x was used in the study. It is preferred because of its fast training and high frame rate. Recall 80% Precision 93% mAP 82.6% values were taken.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128473318","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