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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

A morphable model for the synthesis of 3D faces

The Internet of Things (IoT) provides billions of Internet-enabled devices connections across the globe. In the IoT era, the healthcare industry has grown up from Healthcare 1.0 to Healthcare 4.0. As in Healthcare 3.0, patients visit hospital for their regular checkups, which increases the overall expenditure on medical treatment. However, with the recent technological advancements such as UAVs and blockchain, healthcare problems are solved with minimum payments and security of shared medical data. Motivated by these facts, this article provides blockchain-based UAV path planning to provide privacy-preservation in Healthcare 4.0. We propose a three-layered architecture for real-time collection, processing, and transmission of medical data. The proposed architecture provides a distributed platform for UAVs that ensures security and privacy for the transfer of medical data from one location to another with the proof of work (PoW) consensus mechanism. The simulation results show that the proposed model provides an effective solution for reliable data transmission and privacy-preserving sensitive healthcare information against potential threats. The comparative analysis also demonstrates the performance improvements in sharing medical data with patients and doctors. In summary, this article provides insights into the end users for the use of blockchain with UAVs in Healthcare 4.0 for future research directions.

Blockchain-Based UAV Path Planning for Healthcare 4.0: Current Challenges and the Way Ahead

In this paper, we propose a system for environment recognition using selected MPEG-7 audio low level descriptors together with conventional mel-frequency cepstral coefficients (MFCC). The MPEG-7 descriptors are first ranked based on Fisher’s discriminant ratio. Then principal component analysis is applied on top ranked 30 MPEG-7 descriptors to obtain 13 features. These 13 features are appended with MFCC features to complete the feature set of the proposed system. Gaussian mixture models (GMMs) are used as classifier. The system is evaluated using ten different environment sounds. The experimental results show a significant improvement in recognition performance of the proposed system over MFCC or full MPEG-7 descriptor based systems. For example, the best performance is achieved in Restaurant environment where MFCC, full MPEG-7, and the proposed method give 90%, 94%, and 96% accuracy, respectively.

Environment Recognition Using Selected MPEG-7 Audio Features and Mel-Frequency Cepstral Coefficients

The issue of the verification of the authenticity and integrity of digital images is increasingly being important. Copy move forgery is one type of tempering that is commonly used for manipulating the digital contents, in this case, a part of an image is copied and is pasted on another region of the image. The non-intrusive approach for this problem is becoming attractive because it does not need any embedded information, but it is still far from being satisfactory. In this paper, an efficient non-intrusive method for copy-move forgery detection is presented. This method is based on image segmentation and similarity detection using dyadic wavelet transform (DyWT). Copied and pasted regions are structurally similar and this structural similarity is detected using DyWT and statistical measures. The results show that the proposed method outperforms the stat-of-the-art methods.

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Copy-Move Forgery Detection Using Dyadic Wavelet Transform

The human-centered body sensor network (BSN) becomes one of the Internet of Things (IoT) development directions in the future due to interdisciplinary advantages in the fields of wireless communication technology, embedded microelectronic technology, and mobile Internet technology Only depending on the terminal perception and transmission technology is not enough to make up the weak data storage, computing, and analysis capacity Thus, in combination with green sensors, smart clouds computing, artificial intelligence technology, and BSN, this paper proposes a concept of green cognitive BSN (Green-CBSN) Starting from the three aspects including green active sensor, energy harvesting and efficient data collection, and health big data recognition and interaction, this paper introduces the architecture and thought of Green-CBSN in detail Then, we invite some volunteers equipped with wearable devices, such as smart clothing and smartphone, and carry out the physiological signal collection, heart rate monitoring, and physiological data emotion analysis experiments of electrocardiograph and photoplethysmography At last, this paper summarizes the open issues and research directions about IoT security, health big data recognition algorithm optimization, and energy saving and green energy harvesting, to provide the reference for Green-CBSN

Green Cognitive Body Sensor Network: Architecture, Energy Harvesting, and Smart Clothing-Based Applications

In this paper, we propose an automatic voice disorder classification system using first two formants of vowels. Five types of voice disorder, namely, cyst, GERD, paralysis, polyp and sulcus, are used in the experiments. Spoken Arabic digits from the voice disordered people are recorded for input. First formant and second formant are extracted from the vowels [Fatha] and [Kasra], which are present in Arabic digits. These four features are then used to classify the voice disorder using two types of classification methods: vector quantization (VQ) and neural networks. In the experiments, neural network performs better than VQ. For female and male speakers, the classification rates are 67.86% and 52.5%, respectively, using neural networks. The best classification rate, which is 78.72%, is obtained for female sulcus disorder.

Ghulam Muhammad

Papers

Blockchain-Based UAV Path Planning for Healthcare 4.0: Current Challenges and the Way Ahead

Environment Recognition Using Selected MPEG-7 Audio Features and Mel-Frequency Cepstral Coefficients

Copy-Move Forgery Detection Using Dyadic Wavelet Transform

Green Cognitive Body Sensor Network: Architecture, Energy Harvesting, and Smart Clothing-Based Applications

Automatic voice disorder classification using vowel formants