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Prognosticating colorectal cancer recurrence using machine learning techniques 22

Prognosticating colorectal cancer recurrence using machine l...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Ho, Danliang Chong, Dawn Qingqing Tay, Brenda Tan, Iain Beehuat Motani, Mehul NUS Graduate School National University of Singapore Singapore National Cancer Center Singapore Division of Medical Oncology Singapore National University of Singapore Dept. of Electrical and Computer Engineering Singapore

ISBN: (纸本)9781728162676

Colorectal cancer is among the top three most commonly occurring cancers worldwide, and around 30-40% of patients treated by curative intent surgery will experience cancer recurrence. Proactive prognostication would enable clinicians to better plan treatment modality and intensity, and follow-up frequency to reduce recurrence. Here, we study the application of machine learning models to predict cancer recurrence in a cohort of 904 post-resection colorectal cancer patients. We employ heterogeneous structured and temporal clinical features including demographic and diagnostic information, tumour stage and location details, biochemistry and molecular typing results, as well as surgical details and treatment parameters. We characterize the performance of multiple machine learning classifiers including logistic regression, support vector machine, gradient boosting and multi-layer perceptron on structured data. Our best model achieved a sensitivity of 80.7% and a specificity of 88.2%. This is comparable to and even exceeding the performance of carcinoembryonic antigen (CEA), a tumour marker commonly used in the clinic for colorectal cancer monitoring. We also demonstrate feasibility for accurate forecasting of recurrence up to 4 months in advance, as well as the possibility of predicting recurrence as early as 6 months post-surgery. Our results have positive implications for better management of colorectal cancer patients in the post-resection setting. © 2021 IEEE.

关键词： Surgery

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Model-based data augmentation to improve the performance of machine-learning diagnostic systems

Model-based data augmentation to improve the performance of ...

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22nd international Symposium on High Voltage engineering (ISH 2021)

作者： J. N. Kahlen A. Würde M. Andres A. Moser RWTH Aachen University Schinkelstraβe 6 52062 Aachen Germany Fraunhofer Institute for Applied Information Technology FIT Digital Energy Schloss Birlinghoven 1 Sankt Augustin Germany

Machine-learning diagnostic systems are already being used to detect abnormal conditions in electrical equipment. The challenge in machine-learning diagnostic approaches lies in dealing with small training databases of abnormal-condition samples available as the performance of the machine-learning diagnostic systems relies on the quality and quantity of the training data. One possible solution to this problem are data augmentation techniques generating synthetic data from the available data to augment the training data. However, these generic methods do not consider domain-specific knowledge about the diagnosis task. In this paper, a model-based data augmentation approach using computer-implementable, electromechanical models is presented. This approach uses statistical information extracted from the available data to sample model parameters and input variables to generate synthetic normal- and abnormal-condition data. The model-based data augmentation is showcased for detecting a distribution transformer fault. Machine-learning diagnostic systems are trained utilizing the model-based data augmentation and benchmarked with state-of-the-art diagnostic systems. The number of abnormal-condition measurements for training is limited and the performance of all diagnostic systems is analysed. It is shown that the model-based data augmentation is capable of improving the diagnosis accuracy for the analysed diagnostic task especially when only a very small amount of abnormal-condition data is available.

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A Survey on Randomized Mechanisms for Statistical learning under Local Differential Privacy 22

A Survey on Randomized Mechanisms for Statistical Learning u...

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22nd IEEE international conference on High Performance Computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Shen, Lingling Wu, Xiaotong Wu, Datong Xu, Xiaolong Qi, Lianyong Nanjing Normal University School of Computer and Electronic Information Nanjing China Changzhou University School of Petrochemical Engineering Changzhou China Qufu Normal University School of Information Science and Engineering Qufu China

ISBN: (纸本)9781728176499

High confidence computing is an emerging computing paradigm, which supports the next-generation information system. Statistical learning is a basic research direction of this paradigm, which has been widely applied to various services and applications, such as data mining and data analysis. However, data privacy is one of the most concerned challenges by multiple participants and has an important influence on the development of statistical learning. Local differential privacy is a strong privacy metric in a local setting, which has a rigorous mathematical model. There are a series of research works to propose efficient randomized mechanisms for statistical learning. However, there are few literatures to provide a systematic overview of the existing *** this paper, we present the existing literatures for randomized mechanisms under local differential privacy. At first, we introduce the basic perturbation mechanisms satisfying local differential privacy. Then, we give a framework and the main components of locally private mechanisms. Next, we describe the detailed content of randomized mechanisms for frequency estimation, heavy hitter identification and distribution estimation. Finally, we discuss the possible trends and challenges of the future research. Our survey provides a systematic and comprehensive understanding about randomized mechanisms for statistical learning under local differential privacy. © 2020 IEEE.

关键词： Surveys

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Automatic Modulation Classification Using Combination of Variational Mode Decomposition and Multifractal Analysis 22

Automatic Modulation Classification Using Combination of Var...

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作者： Li, Bingyang Wang, Wen Zhang, Xiaofei Zhang, Meng Institute of Information Engineering Chinese Academy of Sciences Beijing China State Radio Monitoring Center Beijing China

ISBN: (纸本)9781728176499

Automatic modulation classification (AMC) plays a key role in intelligent wireless communication. AMC is widely used in civil and military field for its ability of identifying the modulation type of unknown signal without any prior knowledge. To improve the classification accuracy of feature based AMC at low signal-To-noise (SNR), a novel modulation classification scheme based on Variational Mode Decomposition (VMD) and multifractal analysis is proposed in this paper. The proposed scheme applies VMD to decompose the original signal into multiple intrinsic mode functions(IMFs). Then, multifractal analysis is used to extract multifractal features from each IMF, which combine with statistical features extracted from each IMF as the input of Extreme learning Machine (ELM). In this paper, we use SMW200A Vector Signal Generator to generate real RF data to verify the effectiveness of the proposed scheme, and simulate the additive white Gaussian noise (AWGN) to verify the robustness of the scheme to noise. The results show that the proposed scheme can reach an average accuracy of 95% when the SNR is 0dB. © 2020 IEEE.

关键词： Variational mode decomposition

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A Game-Based learning Assessment Framework for learning Ubiquitous Computational Thinking 2nd

A Game-Based Learning Assessment Framework for Learning Ubiq...

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Wong, Yoke Seng Hayati, Mohamad Yatim Maizatul Tan, Wee Hoe Yap, Li Chen KDU Univ Coll Utropolis Glenmarie Shah Alam Selangor Malaysia Sultan Idris Educ Univ Tanjong Malian Perak Malaysia

ISBN: (纸本)9783030402716;9783030402709

At present there is no Game-based learning (GBL) assessment framework that could assess the student performance and understanding on Ubiquitous Computational Thinking (UCT) accurately. Therefore this paper aims to discuss the development of a novel assessment framework based on GBL to assess the student understanding toward UCT properly. The model is very crucial to aid in designing UCT learning content to improve the performance and understanding of student. This research is divided into two main stages, which are quasi-experiment and assessment framework modelling. In stage one, systematic literature review of assessment framework for learning UCT will be conduct to identify the research gap for this research. The systematic reviewed result will be adapting GBL approach to develop a computer game as the main assessment tool. To ensure the effectiveness of the developed game, quasi-experiment will be conducted. In stage two, data obtained from stage one will also be further analyzed to establish the assessment framework to access the student understanding towards UCT that adapting GBL approach. The newly constructed assessment framework will then be used to assess student understanding and performance towards UCT. This will involve quasi-experiment where target students will be participated to this research by playing the game while assessing the understanding towards UCT. Moreover the knowledge gained could contribute to determining the important of GBL approach that could construct an appropriate assessment framework for learning UCT.

关键词： Game-based learning Assessment framework learning computational thinking Ubiquitous computational thinking

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A deep metric learning algorithm for similarity measure of the gene expression profile 22

A deep metric learning algorithm for similarity measure of t...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Peng, Shaoliang Zhang, Lei Yang, Yaning Liu, Wei Li, Fei Hong, Hao Li, Kenli Wang, Shulin College of Computer Science and Electronic Engineering Hunan University China National Supercomputing Centre in Changsha Hunan University China Department of Biotechnology Beijing Institute of Radiation Medicine China School of Computer Science National University of Defense Technology China Peng Cheng Lab China

ISBN: (纸本)9781728162676

Clustering gene expression profiles is a fundamental task in the genome and biomedical research. With the development of RNA-seq and gene chip technology, mass gene expression profile data has been generated, which puts forward two requirements for related research of gene expression profile: i) accurate analysis of drug RD requires high accuracy of similarity analysis, ii) large-scale analysis of data requires as little running time as possible. We propose a faster, more accurate method called DeepCDNet, which is based on the framework of the Siamese network. DeepCDNet uses the DenseNet structure and optimized loss function to achieve rapid convergence, and the similarity between expression spectra is calculated by a cosine function. The experiment results show that: i) our method breaks through the limitation of high dimensions of gene expression profile and can quickly and accurately learn the required gene characteristics, ii) The accuracy of our method in similarity analysis is greatly improved, iii) as the dimension of data increases, the advantage of our method on time cost gradually becomes more prominent, and time consumption is less. © 2021 IEEE.

关键词： learning algorithms

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Method of Thematic Immersion in the Information Educational Environment as a Tool for the Formation and Assessment of Professional Competence of Future engineering Teachers 2nd

Method of Thematic Immersion in the Information Educational ...

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作者： Bondarenko, Tetiana Kovalenko, Denys Briukhanova, Nataliia Iagupov, Vasyl Ukrainian Engn Pedag Acad Kharkiv Ukraine Natl Def Univ Ukraine Kiev Ukraine

ISBN: (纸本)9783030402747;9783030402730

The paper describes the technique of thematic immersion in the information educational environment for the formation and assessment of professional competence of future engineering teachers. The theme of immersion is the development of electronic educational resources by future engineering teachers. The development of electronic educational resources is carried out in an environment as close as possible to the professional environment of the engineering teacher. In this environment, future engineering teachers are included in the pedagogical activity related to the themes of immersion. At the same time, learning acquires an activity character, that is, the formation and assessment of competence are carried out in the course of students' practical activity: in the process of creating electronic educational resources. In order to take into account the various options for the manifestation of competence under the thematic immersion, a system of particular and synthesis indicators has been formed and a model for the integrated assessment of competence has been developed. The results of the technique experimental verification have been considered and directions for its further improvement have been outlined.

关键词： engineering teachers Immersion Information educational environment Assessment of professional competence Electronic educational resources

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Building a Community of STEM Educators in Nigeria Using the TeachAKid2Code Program 2nd

Building a Community of STEM Educators in Nigeria Using the ...

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作者： Emembolu, Itoro Charles Emembolu, Charles Umechukwu, Kelvin Sulaiman, Abdulkabir Aderinwale, Olumide Northumbria Univ Newcastle Upon Tyne Tyne & Wear England TechQuest STEM Acad Lagos Nigeria

ISBN: (纸本)9783030402716;9783030402709

Research studies show insufficient number of STEM educators needed to meet the young population in Nigeria's STEM skills needs. This paper outlines the design of a learning and teaching framework using online technologies for STEM volunteers to enable delivery of STEM outreach activities across 7 states in Nigeria. The study addresses the barrier of insufficient number of STEM educators and lack of access to meet the demand of training young people requiring STEM skills. Using a constructionist approach and a connectivism learning theory, the study adopts a combination of a MOOC model and direct instruction using online resources to teach and train volunteers to deliver and facilitate STEM outreach activities to young children in primary and secondary schools in Nigeria. 215 volunteers were trained and 69 STEM volunteers were successfully paired in 63 schools to deliver STEM outreach activities to 5252 young people over the course of 3 months.

关键词： STEM outreach K-12 Education Volunteering Technologies for teaching engineering education

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Image Fraud Detection Application Using Convolutional Neural Networks (CNNs) - “ImageGuard”

Image Fraud Detection Application Using Convolutional Neural...

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international Workshop on Research and Education in Mechatronics (REM)

作者： Charbel Boustany Ali Wehbe Faculty of Engineering and Computer Science American University of Science and Technology Beirut Lebanon

ISBN: (数字)9798331505974

ISBN: (纸本)9798331505981

A surge in digital manipulation necessitates robust methods to combat image tampering, particularly the prevalent technique of image splicing. This paper addresses this challenge by proposing a real-time image splicing detection system leveraging a pre-trained convolutional neural network (CNN) model, specifically ResNet50, adapted for the task. The CASIA V2 dataset, containing both authentic and spliced images, serves as the foundation for model training and evaluation. To enhance performance, data augmentation techniques and meticulous hyperparameter tuning are employed, along with the exploration of alternative model architectures. The trained model interacts with a user-friendly Android mobile application through an API endpoint hosted on a web server, enabling real-time image splicing detection directly on the mobile device. Initial testing yielded an accuracy of 78%, subsequently improved to 83% through further analysis and model modifications, with continuous training expected to further enhance accuracy. The Android application seamlessly displays the splicing detection results in real-time. This paper demonstrates a real-time image splicing detection system utilizing a modified ResNet50 architecture and an Android application, offering significant potential across multiple domains, such as aiding forensic investigations by analyzing digital evidence, verifying the authenticity of visual content in journalism, and combating the spread of manipulated imagery on social media platforms to ensure the integrity of digital media. At the end of this paper, we include a comparative study and analysis between our proposed CNN model with the Support Vector Machine (SVM) tuned by a Histogram of Oriented Gradients (HOG). SVM is a powerful supervised learning algorithm effective for high-dimensional spaces and commonly used for image classification tasks, so it will provide us with an adequate insight about the proposed model.

关键词： Training Support vector machines Adaptation models Accuracy Social networking (online) Splicing Real-time systems Convolutional neural networks Smart phones Residual neural networks

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Conceptual Framework on League learning Management 2nd

Conceptual Framework on League Learning Management

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作者： Chutipascharoen, Anutchai Krootjohn, Soradech King Mongkuts Univ Technol North Bangkok Fac Tech Educ Dept Comp Educ Bangkok Thailand

ISBN: (纸本)9783030402747;9783030402730

This article proposes a novel learning model called "League learning" based on a combination of cooperative learning methods and various competition principles including TGT, STAD, Gamification, and League Competition in sports such as British Premier League, La Liga, Bundesliga, etc. League learning is designed to meet the needs of students in this generation. It employs Round-Robin format for scheduling competitions at weekly basis. The learning process consists of 5 stages, including Coaching, Training, Competing, Declaring, and Pairing. League learning is suitable for classrooms of Middle School and High School students on the subjects concerning Mathematics, Computations, Analytical Thinking, Logical Thinking, and other related subjects. It raises student's awareness to always be ready for the upcoming competition, while simultaneously promotes cooperation and collaboration with other students. With all the aforementioned, League learning should be able to make students become more proactive and yield better learning performance resulting in higher learning achievement. Finally, with the support from today internet technologies, League learning can be implemented online. This can publicly extend knowledge sharing among students worldwide and possibly result in reducing inequity in global learning societies as well.

关键词： Cooperative learning Teaching method League learning

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