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 integration of artificial intelligence (AI) and the Internet of Things (IoT) has tremendous prospects in smart healthcare. The advancement of AI in the form of deep learning brought a revolution in automatic classification and detection systems. In addition, next-generation wireless communications such as 5G networking brought speed and the seamless transmission of data. With the convergence of these elements, the smart healthcare sector is currently booming. Particularly during the post-COVID-19 pandemic, the necessity of smart healthcare has come to light more than before. A significant number of people suffer from voice pathology. This pathology can be easily cured if detected early. In this study, a voice pathology detection system within a smart healthcare framework is proposed. The inputs are obtained by the IoT, namely microphones and electroglottography (EGG) devices to capture voice and EGG signals, respectively. Spectrograms are obtained from these signals and fed into a pretrained convolutional neural network (CNN). The features extracted from the CNN are fused and processed using a bi-directional long short-term memory network. The proposed system is evaluated using a publicly available database, called the Saarbruecken voice database. The experimental results show that bimodal input performs better than a single input. An accuracy of 95.65% is obtained for the proposed system.

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Convergence of Artificial Intelligence and Internet of Things in Smart Healthcare: A Case Study of Voice Pathology Detection

The use of social networks has grown exponentially in recent years. The large amount of data available in these networks can be effectively utilized in many machine learning applications. This paper proposes a framework of an emotion recognition system that fetches huge amount of face images from the social networks into a cloud. In the cloud, the problem of the unlabeled facial images is handled by applying an active learning approach. For the feature extraction, an interlaced derivative pattern is used, while for a base classifier, an extreme learning machine is utilized. Once the emotion is recognized in the cloud, it can be shared with the end users to meet their interests. Several experiments were performed using some publicly available databases and heterogeneous images from the social networks. Experimental results showed that the proposed framework may effectively be used in the emotion recognition.

User emotion recognition from a larger pool of social network data using active learning

/pdf/practical-aspects-of-treatment-of-organophosphate-and-2fl91opcb2.pdf

Practical Aspects Of Treatment Of Organophosphate And Carbamate Insecticide Poisoning In Animals

Diluted chemical bath deposition of CdZnS as prospective buffer layer in CIGS solar cell

In the 5G-envisioned Internet of vehicles (IoV), a significant volume of data is exchanged through networks between intelligent transport systems (ITS) and clouds or fogs. With the introduction of Software-Defined Networking (SDN), the problems mentioned above are resolved by high-speed flow-based processing of data in network systems. To classify flows of packets in the SDN network, high throughput packet classification systems are needed. Although software packet classifiers are cheaper and more flexible than hardware classifiers, they could only deliver limited performance. A key idea to resolve this problem is parallelizing packet classification on graphical processing units (GPUs). In this paper, we study parallel forms of Tuple Space Search and Pruned Tuple Space Search algorithms for the flow classification suitable for GPUs using CUDA (Compute Unified Device Architecture). The key idea behind the offered methodology is to transfer the stream of packets from host memory to the global memory of the CUDA device, then assigning each of them to a classifier thread. To evaluate the proposed method, the GPU-based versions of the algorithms were implemented on two different CUDA devices, and two different CPU-based implementations of the algorithms were used as references. Experimental results showed that GPU computing enhances the performance of Pruned Tuple Space Search remarkably more than Tuple Space Search. Moreover, results evinced the computational efficiency of the proposed method for parallelizing packet classification algorithms.

Ghulam Muhammad

Papers

Convergence of Artificial Intelligence and Internet of Things in Smart Healthcare: A Case Study of Voice Pathology Detection

User emotion recognition from a larger pool of social network data using active learning

Practical Aspects Of Treatment Of Organophosphate And Carbamate Insecticide Poisoning In Animals

Diluted chemical bath deposition of CdZnS as prospective buffer layer in CIGS solar cell

Efficient Flow Processing in 5G-Envisioned SDN-Based Internet of Vehicles Using GPUs