作者: BRAND 時(shí)間: 2025-3-21 22:59
Breast Cancer Detection and Localization Using MobileNet Based Transfer Learning for Mammograms,diagnosis. As opposed to biopsy, mammography helps in early detection of cancer and hence saves lives. Mass classification in mammograms remains a major challenge and plays a vital role in helping radiologists in accurate diagnosis. In this work, we propose a MobileNet based architecture for early b作者: diabetes 時(shí)間: 2025-3-22 03:35
,U-Net Based Glioblastoma Segmentation with Patient’s Overall Survival Prediction, This task can be simplified by computer aided automatic segmentation of brain MRI volumes into sub-regions. The MRI volumes segmentation can be achieved by deep learning methods but due to highly imbalance data, it becomes very challenging. In this article, we propose deep learning based solutions 作者: vanquish 時(shí)間: 2025-3-22 05:21 作者: Obliterate 時(shí)間: 2025-3-22 09:11 作者: Tortuous 時(shí)間: 2025-3-22 12:58
Blockchain-Based Authentication Approach for Securing Transportation System,h other to obtain services and perform updates via the internet. However, this will emerge new vulnerabilities which in turn will emerge attack vectors like Advanced Persistent Threat attack (APT) to gain unauthorized access to the transportation system. APT causes disruption and data loss as well a作者: probate 時(shí)間: 2025-3-22 19:19
A Classification Model for Modeling Online Articles,pic of interest to many categories of people ranging from marketing personnel to politicians. In this paper, we focus on comparing four classification algorithms on a dataset consisting of 39000 news articles taken from Mashable website. The articles were classified into two classes: Popular and not作者: 不幸的人 時(shí)間: 2025-3-22 22:45 作者: 匍匐前進(jìn) 時(shí)間: 2025-3-23 03:41 作者: 褪色 時(shí)間: 2025-3-23 07:05 作者: 沉積物 時(shí)間: 2025-3-23 10:08
Convolutional Neural Network U-Net for Trypanosoma cruzi Segmentation, get an accurate diagnosis of the disease but takes a long time and requires too much effort from the experts to analyze blood samples in the search of the parasites presence. Therefore, it is very useful to have an automatic system to detect the parasite in blood sample microscopic images. In this 作者: heirloom 時(shí)間: 2025-3-23 16:23
Segmentation of Echocardiographic Images in Murine Model of Chagas Disease,urine model for Chagas disease. The methodology presented is based on the active contour model with shape prior. We will show through experimental results the good performance of the model and discuss pros and cons of the methodology.作者: 收集 時(shí)間: 2025-3-23 18:32
,Lung Cancer Patient’s Survival Prediction Using GRNN-CP,ncer, it is not well known to the time, which sorts of techniques would generate more imminent information, and which data attributes should be employed in order to prepare this information. In this study, a supervised learning technique is implemented to analyze lung cancer patients in terms of sur作者: Humble 時(shí)間: 2025-3-23 22:41 作者: Morose 時(shí)間: 2025-3-24 04:54
Zahra Sadeghiise auf wesentliche Rahmenbedingungen der Etablierung von Arbeitsbeziehungen in der Kinder- und Jugendarbeit, die bereits in Kapitel 12 ausführlicher zusammengefasst wurden. Hier sollen nur wenige Aspekte thematisiert werden, die sich auf den an dieser Stelle nur angerissenen Sonderfall beziehen, da作者: left-ventricle 時(shí)間: 2025-3-24 09:30 作者: Frequency-Range 時(shí)間: 2025-3-24 13:02 作者: 脫毛 時(shí)間: 2025-3-24 16:33 作者: radiograph 時(shí)間: 2025-3-24 20:30 作者: legacy 時(shí)間: 2025-3-25 03:04
Maher Salemchten konnten — im p?dagogischen Alltag regelm??ig vorzufinden sind, werden sie in der Regel — unseres Erachtens vorschnell — im Falle ihrer Erw?hnung als Indiz für das Scheitern der Kinder- und Jugendarbeit angesehen. Dafür gibt es viele Gründe, die sich aus den normativen Pr?missen des jeweiligen 作者: incubus 時(shí)間: 2025-3-25 07:13 作者: landfill 時(shí)間: 2025-3-25 10:12 作者: encomiast 時(shí)間: 2025-3-25 14:33 作者: AGONY 時(shí)間: 2025-3-25 15:50 作者: 聾子 時(shí)間: 2025-3-25 23:49 作者: 發(fā)源 時(shí)間: 2025-3-26 03:13 作者: dissolution 時(shí)間: 2025-3-26 04:20
Wajeeha Ansar,Ahmad Raza Shahid,Basit Raza,Amir Hanif Dar zentrale Kernprobleme und Paradoxien professionellen Handelns in der Kinder- und Jugendarbeit bearbeitet werden. Damit wird an professionsempirische Studien angeschlossen, die sich für die ?Irritationen der professionellen Identit?t durch das Gefangensein in die systematischen Fehler bei der Arbeit作者: Debility 時(shí)間: 2025-3-26 10:11 作者: SAGE 時(shí)間: 2025-3-26 15:53
Kristian Dokic,Bojan Radisic,Mirko Cobovicinder- und Jugendarbeit allm?hlich entwickeln. Ausgew?hlt wurden insbesondere Interviewpassagen, die die Anf?nge solcher Arbeitsbeziehungen fokussieren. Dabei wird insbesondere auch berücksichtigt, welche dominanten Formen des working consensus und welche Bearbeitungsmodi für die jeweiligen Einricht作者: entail 時(shí)間: 2025-3-26 19:31
Nedaa Baker Al Barghuthi,Madeleine Togherinder- und Jugendarbeit allm?hlich entwickeln. Ausgew?hlt wurden insbesondere Interviewpassagen, die die Anf?nge solcher Arbeitsbeziehungen fokussieren. Dabei wird insbesondere auch berücksichtigt, welche dominanten Formen des working consensus und welche Bearbeitungsmodi für die jeweiligen Einricht作者: BRAVE 時(shí)間: 2025-3-26 21:51 作者: deciduous 時(shí)間: 2025-3-27 05:05
Rula Alhalaseh,Ali Rodan,Azmi Alazzamzum anderen in Bezug auf den zu realisierenden Auftrag widerstreitende Interessen artikulieren. Mit anderen Worten: W?hrend die Jugendarbeiterinnen teils von den Jugendlichen mehr über die lebensweltlichen Hintergründe der Fallentwicklung erfahren wollen, verschlie?en sich die Jugendlichen gegenüber作者: conceal 時(shí)間: 2025-3-27 08:28
Muhammad Irfan,Jiangbin Zheng,Muhammad Iqbal,Muhammad Hassan Arifinder- und Jugendarbeit allm?hlich entwickeln. Ausgew?hlt wurden insbesondere Interviewpassagen, die die Anf?nge solcher Arbeitsbeziehungen fokussieren. Dabei wird insbesondere auch berücksichtigt, welche dominanten Formen des working consensus und welche Bearbeitungsmodi für die jeweiligen Einricht作者: GLOOM 時(shí)間: 2025-3-27 12:56 作者: 不感興趣 時(shí)間: 2025-3-27 15:58 作者: 自傳 時(shí)間: 2025-3-27 19:44
,U-Net Based Glioblastoma Segmentation with Patient’s Overall Survival Prediction, on intensity and shape are extracted from the MRI volumes and segmented tumor for OS prediction task. We further eliminate the low variance features using Recursive Features Elimination (RFE). The Random Forest Regression is used to predict OS time. By using intensities of peritumoral edema-label 2作者: 墊子 時(shí)間: 2025-3-27 23:27
Analysis of Frameworks for Traffic Agent Simulations,ar. A smart city targets the efficient and sustainable use of its resources and services by using intelligent and interactive management. With the rapid growth of population density, the use of private cars and public transport, scientists thrive on trying to reduce urban traffic congestion in order作者: 玉米棒子 時(shí)間: 2025-3-28 02:21 作者: Flinch 時(shí)間: 2025-3-28 08:17
A Novel Feature Extraction Model to Enhance Underwater Image Classification,roposed system can accurately classify large-size underwater images with promising accuracy and outperforms state-of-the-art deep CNN methods. With the proposed network, we expect to advance underwater image classification research and its applications in many areas like ocean biology, sea explorati作者: CYN 時(shí)間: 2025-3-28 14:14 作者: gait-cycle 時(shí)間: 2025-3-28 18:00 作者: 柔軟 時(shí)間: 2025-3-28 18:57 作者: 軍火 時(shí)間: 2025-3-29 00:12
Rafael Viana-Camara,Carlos Brito-Loeza,Anabel Martin-Gonzalez,Nidiyare Hevia-Montiel作者: consolidate 時(shí)間: 2025-3-29 06:41 作者: Grasping 時(shí)間: 2025-3-29 10:14 作者: GROSS 時(shí)間: 2025-3-29 14:15
Breast Cancer Detection and Localization Using MobileNet Based Transfer Learning for Mammograms,reast cancer detection and further classify mass into malignant and benign. It requires less memory space and provides faster computations with 86.8% and 74.5% accurate results for DDSM and CBIS-DDSM, respectively. We have achieved better results than other deep CNN models such as AlexNet, VGG16, GoogleNet, and ResNet.作者: 大量 時(shí)間: 2025-3-29 18:40 作者: Melatonin 時(shí)間: 2025-3-29 22:00 作者: 寬宏大量 時(shí)間: 2025-3-30 02:11 作者: Abominate 時(shí)間: 2025-3-30 07:37 作者: hypnogram 時(shí)間: 2025-3-30 10:10
Convolutional Neural Network U-Net for Trypanosoma cruzi Segmentation,-Net model and we trained it with different loss functions to get accurate results. We report an F2 value of 0.8013, a recall value of 0.8702, a precision value of 0.6304 and a Dice score value of 0.6825.作者: LOPE 時(shí)間: 2025-3-30 14:06
