다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

비만아 부모의 돌봄 경험: q 방법론

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

/pdf/review-of-deep-learning-concepts-cnn-architectures-hpbk62qzc2.pdf

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.

Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques

Ransomware is a recent type of malware which makes inaccessible the files or the device of the victim. The only way to unlock the infected device or to have the keys for decrypting the files is to pay a ransom to the attacker. Commercial solutions for removing ransomware and restoring the infected devices and files are ineffective, since this malware uses a very robust form of asymmetric cryptography and erases shadow copies and recovery points of the operating system. Literature does not count many solutions for effectively detecting and blocking ransomware and, at the best knowledge of the authors, formal methods were never applied to identify ransomware. In this paper we propose a methodology based on formal methods that is able to detect the ransomware and to identify in the malware's code the instructions that implement the characteristic instructions of the ransomware. The results of the experimentation are strongly encouraging and suggest that the proposed methodology could be the right way to follow for developing commercial solutions that could successful intercept the ransomware and blocking the infections it provokes.

/pdf/ransomware-steals-your-phone-formal-methods-rescue-it-25hg6tup2b.pdf

Antonella Santone

Papers

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.

Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques

Ransomware Steals Your Phone. Formal Methods Rescue It

Human behavior characterization for driving style recognition in vehicle system

An ensemble learning approach for brain cancer detection exploiting radiomic features.