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

Operative Neurosurgical Techniques: Indications, Methods, and Results

A decision making system, method and computer program product are provided. Initially, a plurality of attributes is defined. Thereafter, first information regarding the attributes is received from a receiving business. Second information is then received regarding proposed products or services in terms of the attributes. Such second information is received from a supplying business. In use, a decision process is executed based on the first information and the second information.

System, method and computer program product for a collaborative decision platform

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

/pdf/video-image-enhancement-and-machine-learning-pipeline-for-3tpscs83mq.pdf

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

Coral reef image classification employing Improved LDP for feature extraction

Espiritualidad y Salud

The task of reef restoration is very challenging for volunteer SCUBA divers, if it has to be carried out at deep sea, 200 meters, and low temperatures This kind of task can be properly performed by an Autonomous Underwater Vehicle (AUV); able to detect the location of reef areas and approach them The aim of this study is the development of a vision system for coral detections based on supervised machine learning In order to achieve this, we use a bank of Gabor Wavelet filters to extract texture feature descriptors, we use learning classifiers, from OpenCV library, to discriminate coral from non-coral reef We compare: running time, accuracy, specificity and sensitivity of nine different learning classifiers We select Decision Trees algorithm because it shows the fastest and the most accurate performance For the evaluation of this system, we use a database of 621 images (developed for this purpose), that represents the coral reef located in Belize: 110 for training the classifiers and 511 for testing the coral detector

/pdf/implementation-of-a-fast-coral-detector-using-a-supervised-387zc4svlu.pdf

Implementation of a fast coral detector using a supervised machine learning and Gabor Wavelet feature descriptors

OBJECTIVES: Currently in Venezuela the management of cancer patients is focused on chemotherapy, radiotherapy and surgery. In some fortunate cases interven...

/pdf/la-conexion-mente-cuerpo-espiritu-y-su-efecto-en-la-42td39qcra.pdf

La conexión mente-cuerpo-espíritu y su efecto en la promoción de la salud en pacientes oncológicos

DICOMDIR directory files are useful in medical software applications because they allow organized access to images and information sets that come from radiological studies that are stored in conformance with the digital imaging and communication in medicine (DICOM) standard. During the medical application software development, specialized programming libraries are commonly used in order to solve the requirements of computation and scientific visualization. However, these libraries do not provide suitable tools for reading DICOMDIR files, making necessary the implementation of a flexible tool for reading these files, which can be also easily integrated into applications under development. To solve this problem, this work introduces an object-oriented design and an open-source implementation for such reading tool. It produces an output data tree containing the information of the DICOM images and their related radiological studies, which can be browsed easily in a structured way through navigation interfaces coupled to it.

A Software Tool for Reading DICOM Directory Files

An overview is presented of different constraints in conventional medical decision-making for improving collaborative reasoning. When designing and implementing an effective assisted diagnoses system it is imperative to make a complete study of reasoning processes used by physicians every day. We are particularly interested in studying the process of conventional decision-making when, at the first stage, a physician faces a diagnosis decision and, later, when this decision is made by several physicians, that is, when collaborative medical reasoning is involved. The design of an architecture that supports the requirements of medical practices in this framework is presented.

Medical decision-making and collaborative reasoning

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