TY - GEN. T1 - Performance comparison of machine learning classification algorithms. AU - Veena, K. M.. AU - Manjula Shenoy, K.. AU - Ajitha Shenoy, K. B.. PY - 2018/1/1. Y1 - 2018/1/1. N2 - Classification of binary and multi-class datasets to draw meaningful decisions is the key in todays scientific world. Machine learning algorithms are known to effectively classify complex datasets. This paper attempts to study and compare the classification performance if four supervised machine learning classification algorithms, viz., Classification And Regression Trees, k-Nearest Neighbor, Support Vector Machines and Naive Bayes to five different types of data sets, viz., mushrooms, page-block, satimage, thyroid and wine. The classification accuracy of each algorithm is evaluated using the 10-fold cross-validation technique. The Classification And Regression Tree algorithm is found to give the best classification accuracy.. AB - Classification of binary and multi-class datasets to draw meaningful ...

Artificial intelligence can make doctors more efficient by diagnosing a patient quickly without any loss of accuracy, it can even interpret results rapidly. But despite all these benefits, we shouldnt forget that artificial intelligence is but another piece of technology. And like all pieces of technology, there will always be a risk of failure.. Now, the question at hand is whether artificial intelligence can be sued when it causes injury or death to a patient. How is justice meted in such a scenario, and who is liable for damages?. No Laws, No Precedents On Handling AI.. As of this writing, there are no known laws or precedents that govern the use of artificial intelligence as the technology has yet to be implemented into the medical field. However, this does not mean that when an injury is caused by the failure of artificial intelligence, that there are no liabilities to be determined.. However.. Artificial intelligence should be treated as a product rather than its own person, given the ...

Artificial Intelligence (AI) is a computer science algorithm and analytics-driven approach to replicate human intelligence in a machine and Machine learning (ML) is an enhanced application of artificial intelligence, which allows software applications to predict the resulted accurately. The development of powerful and affordable cloud computing infrastructure is having a substantial impact on the growth potential of artificial intelligence and advanced machine learning market. In addition, diversifying application areas of the technology, as well as a growing level of customer satisfaction by users of AI & ML services and products is another factor that is currently driving the Artificial Intelligence & Advanced Machine Learning market. Moreover, in the coming years, applications of machine learning in various industry verticals is expected to rise exponentially. Proliferation in data generation is another major driving factor for the AI & Advanced ML market. As natural learning develops, ...

Agent--Based Models (ABMs) are indispensable to studying the aggregated impacts of individual actions of heterogeneous interacting adaptive agents. Concurrently, artificial intelligence has been employed for decades to simulate autonomous actions of individual entities that react, learn and exchange information with an environment and one another. There are obvious synergies between the two computational approaches. For example, artificial intelligence is often used to enhance agents behaviour in ABMs. Artificial intelligence learning algorithms (AILAs) allow for a richer agents architecture for operationalization of more realistic learning decisions beyond a simplistic treatment of agents cognitive and sensory capacities. Firstly, we review recent socio--economic and spatial ABMs that employ different AILAs to create individually, socially and spatially intelligent agents. We provide a systematic structured analysis of the types of AILAs employed in various application domains, their specific

However, many different areas of artificial intelligence exist beyond machine learning. Resolution 6. , it consists of two parts, the first part x is the subject of the statement and second part is an integer, is known as a predicate. Logic and Artificial Intelligence research encompasses foundational studies in Logic and a variety of Artificial Intelligence disciplines. If the condition is true, then the action is taken, else not. The simple form of logic is Propositional Logic, also called Boolean Logic. Artificial intelligence (AI) is as much a branch of computer science as are its other branches, which include numerical methods, language theory, programming systems, and hardware systems. Module - 2 Artificial Intelligence Notes pdf (AI notes pdf) Logic Concepts and Logic Programming, Propositional Logic, Natural Deduction Systems, Axiomatic System,Semantic Tableau, System in Propositional logic and Knowledge Representation and more topics. In Existential quantifier, ∃x∃y is similar to ...

Chatbots are computer engineered programs to perform tasks. While Artificial Intelligence depends on what it has learnt previously and its current interactions, so it can adapt to the next scenario. Chatbots will be able to solve queries that are engineered through programs as they rely on limited parameters, on the other hand, artificial intelligence improves with time as it relies on historical patterns and evolves with data.. AI powered virtual assistants and chatbots are your intelligent assistants to customer service agents just like a calculator is for an accountant. Companies that focus on Artificial Intelligence rely on NLP, deep learning and machine learning. Using Artificial Intelligence computers can be trained to accomplish specific job functions and tasks by processing large amounts of data.. Artificial Intelligence will help improve productivity, lower costs, drive sales, and create new growth opportunities. In the not so distant future, AI will offer more personalized experiences ...

As a MarketResearchReports.Biz report, it covers all details inside analysis and opinion in Artificial Intelligence Market. This report mainly introduces volume and value market share by players, by regions, by product type, by consumers and also their price change details.. This report splits Artificial Intelligence Market by Product & Service, by Test Type, by Allergen, which covers the history data information from 2016 to 2024.. The Artificial Intelligence Market report provides analysis of the global artificial intelligence market for the period 2014-2024, wherein the years from 2016 to 2024 is the forecast period and 2015 is considered as the base year. The report precisely covers all the major trends and technologies playing a major role in the artificial intelligence markets growth over the forecast period. It also highlights the drivers, restraints, and opportunities expected to influence the market growth during this period. The study provides a holistic perspective on the ...

Get Free Sample Global Healthcare Artificial Intelligence Market Report 2019″ at: https://www.marketresearchfuture.com/sample_request/5681. Top Companies Operating in Healthcare Artificial Intelligence Market. The prominent players operating the Artificial Intelligence in Healthcare market are Koninklijke Philips N.V., IBM Watson Health, NVIDIA Corporation, CloudMedx Inc., Microsoft Corporation, Google, General Electric, Next IT Corp., Intel Corporation, DEEP GENOMICS, General Vision, and Stryker.. Healthcare Artificial Intelligence Market Potential and Pitfalls. Artificial Intelligence has developed several applications in the healthcare sector, such as delivery of health services, detection of diseases, drug discovery, and management of chronic diseases. It ensures effectiveness and efficiency which is likely to trigger its adoption rate in the coming years. AI has also strengthened its foothold in the healthcare research sector for delivering accurate results. This has further contributed ...

Purpose This paper proposes a real-time knowledge support framework for the development of an RFID-multi-agent based process knowledge-based system which has the ability to solve dynamic logistics process management problems. Design/methodology/approach The proposed system is developed with real-time process management capability which automatically identifies current process status, performs the process logic checking/reasoning, and, provides process knowledge support to staff members when they are tackling logistics activity problems. The unique feature of this on-line knowledge-based system, which enables it to enhance the performance of logistics organizations, is a process management engine incorporating radio-frequency identification (RFID) and multi-agent (MA) technologies.

With the great advancement in robot technology, smart human-robot interaction is considered to be the most wanted success by the researchers these days. If a robot can identify emotions and intentions of a human interacting with it, that would make robots more useful. Electroencephalography (EEG) is considered one effective way of recording emotions and motivations of a human using brain. Various machine learning techniques are used successfully to classify EEG data accurately. K-Nearest Neighbor, Bayesian Network, Artificial Neural Networks and Support Vector Machine are among the suitable machine learning techniques to classify EEG data. The aim of this thesis is to evaluate different machine learning techniques to classify EEG data associated with specific affective/emotional states. Different methods based on different signal processing techniques are studied to find a suitable method to process the EEG data. Various number of EEG data features are used to identify those which give best ...

TY - GEN. T1 - Weka machine learning classification in identifying autonomic dysfunction parameters associated with ACE insertion/deletion genotypes. AU - Ng, Ethan. AU - Hambly, Brett. AU - Matthews, Slade. AU - McLachlan, Craig S.. AU - Jelinek, Herbert F.. PY - 2012/7/16. Y1 - 2012/7/16. N2 - This study was designed to investigate parameters of autonomic dysfunction that may be under the influence of ACE ID genotypes. 136 patients with (47) and without type II diabetes were genotyped. Biomarkers such as HbAlc and eGFR, blood pressure, blood cholesterol are in part regulated by the autonomic nervous system and heart rate variability is an indicator of autonomic balance between the sympathetic and parasympathetic division. Several statistical methods were used, including the J48 decision tree machine learning algorithm to associate parameters of autonomic dysfunction and other biomarkers with ACE genotype. Non-parametric and machine learning methods detected more variables, which were able to ...

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Research of the Section for Artificial Intelligence and Decision Support (AID) focusses on Artificial Intelligence technology and computer-based decison support and its use in the medical domain. This includes the development of software systems for intelligent data analysis, knowledge-based systems technology for decision support and therapy planning, and natural language processing which deals with automatic processing of text and speech.. The department offers lectures in Artificial Intelligence on an introductory as well as an advanced level. With regard to particular study programmes, we offer courses for students of Medicine and of Medical Informatics, as well as for students of the Middle European Master Programme in Cognitive Science. Courses are also offered for computer science students in a cooperation with the University of Vienna.. The department was founded in 1977 as an institute of the Medical Faculty of the University of Vienna. In 2004, it became part of the Center for Brain ...

In recent years rapid progress has been made in neuroscience and artificial intelligence. The advancement of artificial intelligence leads to various applications. The brain is the most complex part of human and require thorough study of each part of brain that depicts a persons thoughts, behavior and response. The thoughts and imagination occurring or happening in a persons mind depicts the mental stability of the person, these thoughts and imaginations are converted into images with the help of artificial intelligence. A brain is an electrochemical organ, electrical activity emanating from the brain is displayed in the form of waves or brain waves .The human brain being a complex system is turning into attention catching term for developing technologies in the field of artificial intelligence. Electroencephalography (EEG) is an electrophysiological observation methodology to record electrical activity of brain. Clinically EEG refers to the recording of the brains spontaneous electrical ...

Artificial Intelligence (AI) Apps (Enterprise Level & Excluding - B2C) Market: Overview. The rising adoption of artificial intelligence in diverse industries and the growing demand to create apps are projected to enhance the growth of the global artificial intelligence apps market in the next few years. The market is expected to register a strong growth rate throughout the forecast period. The research study offers a detailed study of the market and highlights the major factors that are likely to support the growth of the overall market in the next few years. AI Apps (Enterprise Level & Excluding - B2C) Market: Trends A tremendous rise in the big data and the rising demand for intelligent virtual assistants are the key factors that are projected to encourage the growth of the global artificial intelligence apps market in the next few years. The growth in the adoption of cloud-based applications and services is another major factor that is likely to enhance market growth in the near future. On ...

Whats the first thing that comes to mind when you think of the term artificial intelligence? If youre a sci-fi junkie like me, you might immediately jump to thoughts of Cyberdyne Technologies or Marvin, the paranoid Android. But in reality, artificial intelligence already plays an active role in our everyday lives.. You may have already recognized it in phone assistants like Siri or Google Now. Or you may have identified AI when playing Chess against a virtual opponent, or when playing more sophisticated motion-tracking games with the Kinect™. But did you know that artificial intelligence is also present in Google Translate and spam blockers?. Studying artificial intelligence opens a world of opportunities. At a basic level, youll better understand the systems and tools that you interact with on a daily basis. And if you stick with the subject and study more, you can help create cutting edge AI applications, like the Google Self Driving Car, or IBMs Watson.. In the field of artificial ...

Leen dit bij een bibliotheek! Artificial intelligence in medicine : 14th Conference on Artificial Intelligence in Medicine, AIME 2013, Murcia, Spain, May 29-June 1, 2013 : proceedings. [Niels Peek; Roque Marín Morales; Mor Peleg;] -- This book constitutes the refereed proceedings of the 14th Conference on Artificial Intelligence in Medicine, AIME 2013, held in Murcia, Spain, in May/June 2013. The 43 revised full and short papers ...

Engineering & Artificial Intelligence Projects for $10 - $30. There are two artificial intelligence homework. Homework-3 has 6 questions and Homework-4 has 4 questions. The artificial intelligence homework will send with message. Payment will be done after home...

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In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible.. ...

mindtalks artificial intelligence: Keen Insight for Artificial Intelligence and Machine Learning Market Trend by 2027 - The Courier - The Courier - picked by ...

Artificial Intelligence (A.I.) is a rapidly growing topic. It has been one of the top technologies that companies and startups are looking for to increase the efficiency of their organization. A.I. has gained a lot of attention in the past years, mainly due to rapid progress in machine learning and other areas of artificial intelligence. However, many people misunderstand what A.I. is all about.. Artificial Intelligence (A.I.) is the branch of computer science that deals with making computers do things that look like intelligence, whether that is the ability to recognize objects and situations, understand human speech, or solve problems. The development of this technology has contributed to our ability to gather and utilize vast amounts of data, helping us make smarter decisions about our own health, our homes, and our world.. Machine learning is a technique that allows computers to learn how to solve problems without being explicitly programmed. It is not very different from the way humans ...

The challenge with artificial intelligence is that no single and agreed-upon definition exists. Nils Nilsson defined A.I. as activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment. But that definition isnt close to describing how A.I. evolved.. Artificial intelligence began with the Turing Test, proposed in 1950 by Alan Turing, the scientist, cryptanalyst and theoretical biologist. Since then, rapid progress has been made over the last 75 years, advancing A.I. capabilities.. Isaac Asimov proposed the Three Laws of Robotics in 1950. The first A.I. program was coded in 1951. In 1959, MIT began research in the field of artificial intelligence. GM introduced the first robot into its production assembly line in 1961. The 1960s were transformative, with the first machine learning program written and the first demonstration of an A.I. program which understood natural language, and the ...

On the basis of offering, the global artificial intelligence in healthcare market is segmented into hardware, software, and services. The market is further segmented by technology into speech recognition, machine learning, querying method, natural language processing, and context aware processing; by application into medical research, diagnosing diseases, electrocardiography, laparoscopy, medical imaging, personalized health assessments, drug discovery, personalized medicine, and others and by end-users into diagnostic labs, hospitals, clinics, and others.. The global artificial intelligence in healthcare market is anticipated to record a CAGR of 45.3% over the forecast period i.e. 2019-2027.. Artificial intelligence in healthcare is being used in radiology, imaging, electronic health records and others which can be attributed to its low error rate, high speed, accuracy, precision and ability to complete a hazardous task without the involvement of humans. For instance, mishandling of samples for ...

Applying Artificial Intelligence to Financial Investing: 10.4018/978-1-5225-2255-3.ch001: Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring

As part of a recent ESOMAR World article on Artificial Intelligence I was interviewed by Insites Consultings Annelies Verheghe for opinions on the topic and how it may affect marketing research, text and data mining. Today Im sharing part of that Q&A below. Would love your thoughts at the bottom, this is an area that is continually evolving for me as well, and which Ive also written about on the OdinText blog here.]. ESOMAR: What is your experience with Artificial Intelligence & Machine Learning (AI)? Would you describe yourself as a user of AI or a person with an interest in the matter but with no or limited experience?. TomHCA: I would describe myself as both a user of Artificial Intelligence as well as a person with a strong interest in the matter even though I have limited mathematical/algorithmic experience with AI. However, I have colleagues here at OdinText who have PhDs in Computer Science and are extremely knowledgeable as they studied AI extensively in school and used it elsewhere ...

Artificial Intelligence, or AI, transformed the digital landscape in more ways than one. Image-recognition software, semi-autonomous vehicles, medical robots and other AI-enabled technology are all possible with this revolutionary technology.. For marketers, AI also presents opportunities we never couldve dreamed of. With AI marketing, digital markets can improve personalization and generate better performance and profits, adding to an already strong data-driven focus.. With AI, the consumer experience can be personalized in a way thats easier and more cost-effective. This allows brands to achieve incredible gains through a deeper understanding of the customer base.. Learn more about the applications for AI in marketing and see how it can revolutionize your marketing campaigns.. What is Artificial Intelligence (AI)?. Artificial Intelligence is simulated intelligence in machines that are programmed to think like humans and mimic human behavior. At its best, AI can rationalize and take action ...

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in artificial intelligence is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of artificial intelligence in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data call for advancing the interoperability among artificial intelligence approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples

Technology has played an immense role in the evolution of healthcare delivery for the United States and on an international scale. Today, perhaps no innovation offers more potential than artificial intelligence. Utilizing machine intelligence as opposed to human intelligence for the purposes of planning, offering solutions, and providing insights, AI has the ability to alter traditional dynamics between doctors, patients, and administrators; this reality is now producing both elation at artificial intelligences medical promise and uncertainty regarding its capacity in current systems. Nevertheless, current trends reveal that interest in AI among healthcare stakeholders is continuously increasing, and with the current COVID-19 pandemic highlighting institutional flaws, it is reasonable to assume that many industry changes proposed by artificial intelligence will be further considered in the coming years. Therefore, this research aims to assess the changes proposed by AI and how they might impact

Peptides are routinely identified from mass spectrometry-based proteomics experiments by matching observed spectra to peptides derived from protein databases. The error rates of these identifications can be estimated by target-decoy analysis, which involves matching spectra to shuffled or reversed peptides. Besides estimating error rates, decoy searches can be used by semi-supervised machine learning algorithms to increase the number of confidently identified peptides. As for all machine learning algorithms, however, the results must be validated to avoid issues such as overfitting or biased learning, which would produce unreliable peptide identifications. Here, we discuss how the target-decoy method is employed in machine learning for shotgun proteomics, focusing on how the results can be validated by cross-validation, a frequently used validation scheme in machine learning. We also use simulated data to demonstrate the proposed cross-validation schemes ability to detect overfitting.. ...

Artificial intelligence promises to improve existing goods and services, and, by enabling automation of many tasks, to greatly increase the efficiency with which they are produced. But it may have an even larger impact on the economy by serving as a new general-purpose new method of invention that can reshape the nature of the innovation process and the organization of R&D. This exploratory essay considers this possibility in three interrelated ways. First, Cockburn, Henderson, and Stern review the history of artificial intelligence, focusing in particular on the distinction between automation-oriented applications such as robotics and the potential for recent developments in deep learning to serve as a general-purpose method of invention. The researchers then assess preliminary evidence of this differential impact in changing nature of measurable innovation outputs in artificial intelligence, including papers and patents. They find strong evidence of a shift in the importance of ...

Purpose: The study is conducted to evaluate the adaptability of artificial intelligence in recruitment and to assess the effect of this technology on the performance of the employees. Design/Methodology/Approach: Standard Multiple Linear regression model is used to predict the performance of the employees and one-way ANOVA is used to compare the artificial intelligence based recruitment with performance indicating variables namely reliability, productivity, Automation, Gamification & Training using SPSS. Snowball sampling method has been adopted for a sample size of 440 respondents working in leading recruitment consultancies in urban Bangalore. Findings: There is a greater association between the recruitment and performance variables when artificial intelligence is adopted as it is significant at 0.001 per cent level and productivity being the maximum. However, the impact of implementing gamification for recruitment doesnt have a significant impact on the output due to partial significant

Get information, facts, and pictures about artificial intelligence at Encyclopedia.com. Make research projects and school reports about artificial intelligence easy with credible articles from our FREE, online encyclopedia and dictionary.

Reporting from: https://exhibits.stanford.edu/ai/catalog?f%5Bcollection_with_title%5D%5B%5D=jb056mm1304-%7C-Stanford+Artificial+Intelligence+Laboratory+Records&f%5Bformat_main_ssim%5D%5B%5D=Image&f%5Btopic_facet%5D%5B%5D=Stanford+Artificial+Intelligence+Laboratory&f%5Btopic_facet%5D%5B%5D=Artificial+intelligence&per_page=24&sort=pub_year_isi+asc%2C+title_sort+asc&view= ...

Columbia Universitys first MicroMasters program in Artificial Intelligence on edX will launch on January 16. This MicroMasters Program from Columbia University will give participants a rigorous, advanced, professional, graduate-level foundation in Artificial Intelligence. The program represents 25 precent of the coursework toward a Masters degree in Computer Science at Columbia. The MicroMasters Program in Artificial Intelligence is intended for those who have a Bachelors degree in Computer Science or Mathematics and have a basic understanding of statistics, college level algebra, calculus and comfort with programming languages.. Learn more on edX. ...

European CEO: How are you analysing all of this genetic data?. Niels Iversen Møller: Well were using artificial intelligence and supercomputers to analyse the genomes of humans or bacteria. We have trained the artificial intelligence to identify components that are unique to cancer patients, and also the Achilles heel proteins of the pathogens that were analysing.. When we talk about cancer, there are certain components called epitopes that we identify by comparing the DNA from tumours to the DNA of healthy tissue. By looking at differences between cancer DNA and also healthy DNA, we can find the mutations that are critical. And also critical in terms of formulating into a vaccine.. When we talk about bacteria, we again analyse using our artificial intelligence. The DNA of those bacteria, finding the Achilles heel components really in the bacteria that we want to put into a vaccine to illicit a strong and protective immune response.. European CEO: So this is personalised healthcare right down ...

TUESDAY, April 25, 2017 (HealthDay News) -- It may be possible to use artificial intelligence to diagnose tuberculosis in people who live in developing nations, a new study suggests.. TB, among the top 10 causes of death worldwide, can be identified on X-rays. But, the expertise required to screen for and diagnose TB is often lacking in areas that have high rates of the lung disease, according to the studys authors.. The researchers from Thomas Jefferson University in Philadelphia used 1,007 X-rays of people with and without active TB to train artificial intelligence models to identify TB on X-rays. It proved highly accurate -- up to 96 percent-- in diagnosing cases.. The study findings were published online April 25 in the journal Radiology. An artificial intelligence solution that could interpret radiographs for presence of TB in a cost-effective way could expand the reach of early identification and treatment in developing nations, study co-author Dr. Paras Lakhani, an assistant professor ...

Artificial Intelligence is already impacting our lives in a major way. Be it getting driving instructions through our smart phone or getting daily reminders by our fitness device to increase our workouts, all these are manifestations of how Artificial Intelligence is changing the way we function. What is often less understood is the significant role Artificial Intelligence can play in the social sector.

View Notes - Homework1 from ENGL 135 at DeVry Addison. Ginger Binder Artificial Intelligence Homework #1 1. Researching and examining various artificial intelligence articles on the subject

How is Mcculloch-Pitts Neuron Model (artificial intelligence) abbreviated? M-P stands for Mcculloch-Pitts Neuron Model (artificial intelligence). M-P is defined as Mcculloch-Pitts Neuron Model (artificial intelligence) very rarely.

Currently, the risk of network information insecurity is increasing rapidly in number and level of danger. The methods mostly used by hackers today is to attack end-to-end technology and exploit human vulnerabilities. These techniques include social engineering, phishing, pharming, etc. One of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs). As a results, malicious URL detection is of great interest nowadays. There have been several scientific studies showing a number of methods to detect malicious URLs based on machine learning and deep learning techniques. In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behaviors and attributes. Moreover, bigdata technology is also exploited to improve the capability of detection malicious URLs based on abnormal behaviors. In short, the proposed detection system consists of a new set of URLs features and behaviors, a machine learning

BACKGROUND: Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity arthroplasty is uniquely positioned to most dramatically benefit from ML applications given its central role in alternative payment models and the value equation. METHODS: In this report, we discuss the origins and model specifics behind machine learning, consider its progression into healthcare, and present some of its most recent advances and applications in arthroplasty. RESULTS: A narrative review of artificial intelligence and ML developments is summarized with specific applications to lower extremity arthroplasty, with specific lessons learned from osteoarthritis gait models, joint-specific imaging analysis, and value-based payment models. CONCLUSION: The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the

Published in Knowledge-Based Systems, 2017. Recommended citation: María Pérez-Ortiz, Pedro Antonio Gutiérrez, M. D. Ayllón-Terán, N. Heaton, R. Ciria, J. Briceño, César Hervás-Martínez, Synthetic semi-supervised learning in imbalanced domains: Constructing a model for donor-recipient matching in liver transplantation. Knowledge-Based Systems, Vol. 123, 2017, pp.75--87. http://doi.org/10.1016/j.knosys.2017.02.020 ...

The mission of the CI group in our Department is to perform research on various aspects of computational intelligence and to contribute to the Business Mathematics and Informatics curriculum. Computational Intelligence belongs to the areas of evolutionary computing, fuzzy computing and neurocomputing. As opposed to traditional logic based artificial intelligence techniques, computational intelligence techniques are generally bottom-up, where order and structure emerges from an unstructured beginning ...

Intelligent systems can optimise their structure and properties in order to successfully function within a complex, partially changing environment. Three sub-areas - perception, learning and action - can be differentiated here. The scientists at the Max Planck Institute for Intelligent Systems are carrying out basic research and development of intelligent systems in all three sub-areas. Research expertise in the areas of computer science, material science and biology is brought together in one Institute, at two different sites.

An Analytical Study on Performance Factors of Automatic Classification based on Machine Learning - automatic classification;text categorization;performance factors;conference paper;rocchio algorithm;multi-label classification;machine learning;

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Artificial Intelligence can not only turn your customer data into business insights but can use that information to make intelligent decisions, predict outcomes, suggest actions and automate tasks based on machine learning. AI makes your business smarter about how and when it engages its customers. Artificial intelligence is a game-changing business technology thats transforming how we work.

Artificial Intelligence can not only turn your customer data into business insights but can use that information to make intelligent decisions, predict outcomes, suggest actions and automate tasks based on machine learning. AI makes your business smarter about how and when it engages its customers. Artificial intelligence is a game-changing business technology thats transforming how we work.

Defence Science and Technology Laboratory (Dstl) has awarded a contract of a £1 million investment to Roke Manor Research, an electronics engineering consultancy, to develop artificial intelligence software that is designed to help the Royal Navy handle the growing complexity of threats.. Named STARTLE, the machine situational awareness software continuously monitors and evaluates potential threats using a combination of artificial intelligence techniques. It is designed after the human brain, emulating the mammalian conditioned-fear response mechanism ...

By Dipayan Mitra. Scientists have developed a new artificial intelligence technology that can spot shipwrecks from the ocean surface and also from the air. The University of Texas collaborated with the United State Navys underwater archeology branch to develop this new artificial intelligence software capable of detecting shipwrecks with an accuracy rate of 92%. The newly developed computer model is now ready to be deployed in order to identify unmapped shipwrecks on the coasts of the United States and Puerto Rico. The artificial intelligence algorithm was fed with images of shipwrecks and underwater topology to enable it to recognize unknown wrecks. The platform uses images from publicly available databases of pictures collected from various parts of the globe and the National Oceanic and Atmospheric Administrations database of shipwrecks. It also uses lidar and sonar-based imageries of the seafloor to carry out its operations more accurately. The lead researcher of the project, Leila ...

The researchers used cutting-edge artificial intelligence to create a chatbot interventional radiologist that can automatically communicate with referring clinicians and quickly provide evidence-based answers to frequently asked questions.. This allows the referring physician to provide real-time information to the patient about the next phase of treatment, or basic information about an interventional radiology treatment.. We theorised that artificial intelligence could be used in a low-cost, automated way in interventional radiology as a way to improve patient care, said Edward W. Lee, M.D., Ph.D., assistant professor of radiology at UCLAs David Geffen School of Medicine and one of the authors of the study. Because artificial intelligence has already begun transforming many industries, it has great potential to also transform health care.. In this research, deep learning was used to understand a wide range of clinical questions and respond appropriately in a conversational manner similar ...

Microsoft Azure Machine Learning (MAML) is a fully managed service on Windows Azure which a developer can use to build a predictive analytics model using machine learning over data and then deploy her model as a web service. ML Studio is accessible through a web browser, with no software to purchase or install, and the authoring experience is through visual composition. There are modules in Azure ML to support the end-to-end data science workflow for constructing a predictive model, from ready access to common data sources, data exploration, feature selection and creation, building training and testing sets, machine learning over data, and final model evaluation and experimentation. In this talk I will present an overview of the basic data science workflow, with details on select machine learning algorithms, then build a predictive analytics model using real world data, evaluate several different machine learning algorithms, then deploy the finished model as a machine learning web service within ...

Get this from a library! Artificial intelligence research and development : proceedings of the 14th International Conference of the Catalan Association for Artificial Intelligence. [Cèsar Fernández; Hector Gaffner; Felip Manyà; IOS Press.;] -- This book is a collection of the papers accepted for presentation at the 14th International Conference of the Catalan Association for Artificial Intelligence (CCIA 2011), held at the University of ...

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.

Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly

The course introduces the students into the field of artificial intelligence and gives the necessary basis for designing machine control algorithms. It advances the knowledge of state space search algorithms by including uncertainty in state transition. Students are introduced into reinforcement learning for solving problems when the state transitions are unknown, which also connects the artificial intelligence and cybernetics fields. Bayesian decision task introduces supervised learning. Learning from data is demonstrated on a linear classifier. Students practice the algoritms in computer labs. ...

TY - JOUR. T1 - Detection of breast cancer with mammography. T2 - Effect of an artificial intelligence support system. AU - Rodríguez-Ruiz, Alejandro. AU - Krupinski, Elizabeth A. AU - Mordang, Jan Jurre. AU - Schilling, Kathy. AU - Heywang-Köbrunner, Sylvia H.. AU - Sechopoulos, Ioannis. AU - Mann, Ritse M.. PY - 2019/3/1. Y1 - 2019/3/1. N2 - Purpose: To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods: An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support. ...

Challenges for Socially-Beneficial Artificial Intelligence. Daniel S. Weld. University of Washington. Stephen Hawking, Bill Gates, and other luminaries warn that an intelligence explosion may lead to the extinction of humanity at the hands of rampant robots. At the same time, many pundits see a prosperous future in which self-driving cars reduce highway fatalities , while AI advisors improve medical care and minimize malpractice. Weld argues that the utopian outcome is more likely, but only if we address several key social and technical challenges.. Daniel S. Weld is Thomas J. Cable / WRF Professor of Computer Science & Engineering and Entrepreneurial Faculty Fellow at the University of Washington. After formative education at Phillips Academy, he received bachelors degrees in both Computer Science and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab in 1988, received a Presidential Young Investigators award in 1989, an Office of Naval ...

HVAC systems are used in buildings to provide a comfortable thermal environment to the occupants. They are one of the most significant sources of energy consumption in buildings. While thermal comfort level tends to be different among individuals, traditional HVAC systems are operated based on fixed setpoints and do not automatically consider the changing building conditions. With traditional HVAC systems, users have to adjust the thermal levels manually. Other than that, indoor activities also affect the proper optimization of these systems. More ventilation would be required in a kitchen space than in the workplace. Continually adjusting the settings is a difficult task. These situations can effectively be tackled by the use of Artificial Intelligence (AI). Artificial Intelligence can be integrated into the HVAC systems to improve their efficiency and minimize energy consumption. Here, we will discuss how AI can help boost the performance of HVAC systems.. ...

Other chapters in this book offer more technical discussions on some computational intelligence techniques including those that were not reviewed in this chapter. Computational intelligence is a collection of computational models and tools, whose classification, clusterization, optimization, prediction, reasoning, and approximation capabilities have been improved incrementally and continuously. There are already many computational intelligence techniques or combinations of the techniques. It is always possible to find alternative techniques to address a specific earth and environmental problem. Environ Manage 51:267-277 Riff MC, Alfaro T, Bonnaire X, Grandon C (2008) EA-MP: an evolutionary algorithm for a mine planning problem. In: Proceedings of IEEE congress on evolutionary computation, June 2008, pp 4011-4014 Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing: explorations in ...

The aim of this course is to build on Artificial Intelligence I, first by introducing more elaborate methods for planning within the symbolic tradition, but then by moving beyond the purely symbolic view of AI and presenting methods developed for dealing with the critical concept of uncertainty. The central tool used to achieve the latter is probability theory. The course continues to exploit the primarily algorithmic and computer science-centric perspective that informed Artificial Intelligence I. The course aims to provide further tools and algorithms required to produce AI systems able to exhibit limited human-like abilities, with an emphasis on the need to obtain better planning algorithms, and systems able to deal with the uncertainty inherent in the environments that most real agents might be expected to perform within. ...

A new study, published in Nature by German scientists from Jena and Hamburg, with lead author Prof. Markus Reichstein, managing director at the Max Planck Institute for Biogeochemistry (MPI-BGC) and co-author Prof. Bjorn Stevens, director and head of the department The Atmosphere in the Earth System at the Max Planck Institute for Meteorology (MPI-M), shows that artificial intelligence (AI) can help to better understand climate and the Earth system. The scientists show that specifically deep learning has thus far only partially exhausted its potential for understanding the Earth system. In particular, complex dynamic processes such as hurricanes, fire propagation, and vegetation dynamics can be better described with the help of AI. As a result, climate and Earth system models will be improved, with novel hybrid models combining artificial intelligence and physical modelling playing an important role. The scientists contend that detection and early warning of extreme events as well as seasonal ...

10 accredited schools offering Artificial Intelligence program in USA. Find complete list of Artificial Intelligence schools offers graduate and under graduate degree.

This book constitutes the refereed proceedings of the 16th Conference on Artificial Intelligence in Medicine, AIME 2017, held in Vienna, Austria, in June 2017.

The Artificial Intelligence Society is an independent and voluntary technological society formed in 2018 with the aim of promoting and disseminating Artificial Intelligence technology across the Kingdom. Main objectives of the society are as follows:. ...

Join Eminent Scientists, Entrepreneurs and Engineers from Europe, USA (America), Asia Pacific, Middle East to the Artificial Intelligence Conferences and Data Mining Conferences happening from April 16-17, 2018 Las Vegas,USA

In mathematics, casually speaking, a mixture of two functions. In machine learning, a convolution mixes the convolutional filter and the input matrix in order to train weights.. The term convolution in machine learning is often a shorthand way of referring to either convolutional operation or convolutional layer.. Without convolutions, a machine learning algorithm would have to learn a separate weight for every cell in a large tensor. For example, a machine learning algorithm training on 2K x 2K images would be forced to find 4M separate weights. Thanks to convolutions, a machine learning algorithm only has to find weights for every cell in the convolutional filter, dramatically reducing the memory needed to train the model. When the convolutional filter is applied, it is simply replicated across cells such that each is multiplied by the filter.. ...

TY - JOUR. T1 - Predicting diabetes mellitus using SMOTE and ensemble machine learning approach. T2 - The Henry Ford ExercIse Testing (FIT) project. AU - Alghamdi, Manal. AU - Al-Mallah, Mouaz. AU - Keteyian, Steven. AU - Brawner, Clinton. AU - Ehrman, Jonathan. AU - Sakr, Sherif. PY - 2017/7. Y1 - 2017/7. N2 - Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and ...

Abstract: In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between ...

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Causality has been a subject of study for a long time. Often causality is confused with correlation. Human intuition has evolved such that it has learned to identify causality through correlation. In this book, four main themes are considered and these are causality, correlation, artificial intelligence and decision making. A correlation machine is defined and built using multi-layer perceptron network, principal component analysis, Gaussian Mixture models, genetic algorithms, expectation maximization technique, simulated annealing and particle swarm optimization. Furthermore, a causal machine is defined and built using multi-layer perceptron, radial basis function, Bayesian statistics and Hybrid Monte Carlo methods. Both these machines are used to build a Granger non-linear causality model. In addition, the Neyman-Rubin, Pearl and Granger causal models are studied and are unified. The automatic relevance determination is also applied to extend Granger causality framework to the non-linear domain. The

TY - JOUR. T1 - Predictive vaccinology. T2 - 6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005. AU - Bozic, Ivana. AU - Zhang, Guang Lan. AU - Brusic, Vladimir. PY - 2005/1/1. Y1 - 2005/1/1. N2 - Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 supertype molecules with excellent accuracy, even for molecules where no binding data are currently available.. AB - Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial ...

Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support

Although supervised learning requires a labeled dataset, obtaining labels from experts is generally expensive. For this reason, crowdsourcing services are attracting attention in the field of machine learning as a way to collect labels at relatively low cost. However, the labels obtained by crowdsourcing, i.e., from non-expert workers, are often noisy. A number of methods have thus been devised for inferring true labels, and several methods have been proposed for learning classifiers directly from crowdsourced labels, referred to as learning from crowds. A more practical problem is learning from crowdsourced labeled data and unlabeled data, i.e., semi-supervised learning from crowds. This paper presents a novel generative model of the labeling process in crowdsourcing. It leverages unlabeled data effectively by introducing latent features and a data distribution. Because the data distribution can be complicated, we use a deep neural network for the data distribution. Therefore, our model can ...

Roy Labban is the director of computer modeling and simulation in the Information Systems Department at Consolidated Contractors Company (CCC), which is ranked among the top 20 international contractors in 2017 by ENR. Roy has 20+ years of experience in software engineering and database application development, business intelligence and analytics, and computer modeling and simulation. Roy is the cofounder and managing partner of a boutique consulting firm focused on delivering business intelligence and analytics for higher education enrollment management. Roy is also the founder and director of a postgraduate coding bootcamp diploma program focusing on new technologies such as the blockchain, artificial intelligence, machine learning, and mobile apps. Roy serves as a member of the Industry Advisory Board of the Computer Science Program (ABET Accredited) at the American University of Science and Technology. He is also a part-time university instructor teaching graduate level courses on computer

Eventbrite - Omni212 presents Colombo Prerequisites to Learning Artificial Intelligence | AI | Machine Learning | Deep Learning | IT Training | Disruptive Technologies - Saturday, December 16, 2017 | Sunday, January 14, 2018 at Instructor Led Online | Video Conference, Colombo, Colombo. Find event and ticket information.

TY - JOUR. T1 - Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network with Long Short-term Memory for the Automated Detection of Calcified Plaques from Coronary Computed Tomography Angiography. AU - Fischer, Andreas M.. AU - Eid, Marwen. AU - De Cecco, Carlo N.. AU - Gulsun, Mehmet A.. AU - Van Assen, Marly. AU - Nance, John. AU - Sahbaee, Pooyan. AU - De Santis, Domenico. AU - Bauer, Maximilian J.. AU - Jacobs, Brian E.. AU - Varga-Szemes, Akos. AU - Kabakus, Ismail M.. AU - Sharma, Puneet. AU - Jackson, Logan J.. AU - Schoepf, U. Joseph. PY - 2020/1/1. Y1 - 2020/1/1. N2 - Purpose: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data. Materials and Methods: Under an IRB waiver and in HIPAA compliance, a ...

Researchers proving how machine learning can help detect dangerous polyps.The South Australian researchers are finding new ways to better pinpoint suspicious polyps using artificial intelligence in the fight against deadly bowel cancer.

Ensemble learning can improve the performance of individual classifiers by combining their decisions. The sparseness of ensemble learning has attracted much attention in recent years. In this paper, a novel multiobjective sparse ensemble learning (MOSEL) model is proposed. Firstly, to describe the ensemble classifiers more precisely the detection error trade-off (DET) curve is taken into consideration. The sparsity ratio (sr) is treated as the third objective to be minimized, in addition to false positive rate (fpr) and false negative rate (fnr) minimization. The MOSEL turns out to be augmented DET (ADET) convex hull maximization problem. Secondly, several evolutionary multiobjective algorithms are exploited to find sparse ensemble classifiers with strong performance. The relationship between the sparsity and the performance of ensemble classifiers on the ADET space is explained. Thirdly, an adaptive MOSEL classifiers selection method is designed to select the most suitable ensemble classifiers ...

Title:New Computational Tool Based on Machine-learning Algorithms for the Identification of Rhinovirus Infection-Related Genes. VOLUME: 22 ISSUE: 10. Author(s):Yan Xu, Yu-Hang Zhang, JiaRui Li, Xiao Y. Pan, Tao Huang* and Yu-Dong Cai*. Affiliation:School of Life Sciences, Shanghai University, Shanghai 200444, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, School of Life Sciences, Shanghai University, Shanghai 200444, BASF & IDLab, Ghent University, Ghent, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, School of Life Sciences, Shanghai University, Shanghai 200444. Keywords:Human Rhinovirus, maximum relevance minimum redundancy, support vector machine, incremental feature selection, OTOF, SOCS1.. Abstract:. Background: Human rhinovirus has different identified serotypes and is the most common cause of cold in humans. To ...

Projects in the REU in Computational Sensing for Human-aware AI [Alm, Bailey, Geigel, Huenerfauth, Ptucha, Shinohara]: The REU Site in Computational Sensing for Human-centered Artificial Intelligence recognizes that as the boundaries between HCI and AI blur, and AI grows increasingly agile and pervasive, the next generation of computational scientists must be capable of responsibly and effectively leveraging a spectrum of sensing data from data-generating humans. With this focus, the REU Site will expand its trajectory as an attractor for highly diverse students who will gain experience with sensing hardware and software towards transformative advances in intelligent systems focused on human behaviors and cognitive processes. Enabling diverse stakeholders early in their careers to discover how to collect, fuse, make inference with, and visualize multimodal human data can transform how humans and machines engage and collaborate. The research in the REU Site will address two limitations in AI: ...

Tumor classification and segmentation from brain computed tomography image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. This paper deals with an efficient segmentation algorithm for extracting the brain tumors in computed tomography images using Support Vector Machine classifier. The objective of this work is to compare the dominant grey level run length feature extraction method with wavelet based texture feature extraction method and SGLDM method. A dominant gray level run length texture feature set is derived from the region of interest (ROI) of the image to be selected. The optimal texture features are selected using Genetic Algorithm. The selected optimal run length texture features are fed to the Support Vector Machine classifier (SVM) to classify and segment the tumor from brain

In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1 × N and N × N comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms ...

PhD thesis.. Dorian Šuc and Ivan Bratko (2005) Combining Learning Constraints and Numerical Regression. In: 19th Int. Joint Conf. on Artificial Intelligence, IJCAI-05, 30 July - 5 August 2005, Edinburgh, Scotland.. Nancy Van Driessche and Janez Demsar and Ezgi O Booth and Paul Hill and Peter Juvan and Blaz Zupan and Adam Kuspa and Gad Shaulsky (2005) Epistasis analysis with global transcriptional phenotypes. Nature Genetics, 37 (5). pp. 471-477.. Aleks Jakulin and Martin Možina and Janez Demšar and Ivan Bratko and Blaz Zupan (2005) Nomograms for Visualizing Support Vector Machines. In: SIGKDD05 Chicago, August 2005, Illinois, USA.. Gregor Leban and Minca Mramor and Ivan Bratko and Blaz Zupan (2005) Simple and Effective Visual Models for Gene Expression Cancer Diagnostics. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 21-24, 2005, Chicago, IL, USA.. Aleks Jakulin (2004) Modelling Modelled.. Aleks Jakulin and Wray Buntine (2004) Analyzing the US Senate ...

Description:. Pulmonary embolism is a common cardiovascular emergency with approximately 600,000 incidents and 200,000 deaths occurring annually in the United States. CT pulmonary angiography (CTPA) has become the reference standard for pulmonary embolism diagnosis, but this technique has several issues with interpretation of the intricate branching structure of the pulmonary vessels, artifacts that may obscure or mimic embolisms, suboptimal contrast, and inhomogeneities. To overcome these shortcomings, researchers at Arizona State University have developed a machine learning-based approach for automatically detecting the pulmonary trunk. By using a cascaded Adaptive Boosting machine learning algorithm with a large number of digital image object recognition features, this method automatically identifies the pulmonary trunk by sequentially scanning the CTPA images and classifying each encountered sub-image with the trained classifier. This approach outperforms existing anatomy-based approaches. ...

JONESBORO - The AI (Artificial Intelligence) Group at Arkansas State University finished in third place in the Multiple Myeloma DREAM Challenge, a machine-learning competition.. The research group was very pleased with the third-place performance in competition with the 290 teams participating internationally; in fact, their submission was statistically tied with the first place awardees.. Members of the A-State AI group are computer science faculty members Dr. Xiuzhen Huang, Dr. Jason Causey and Dr. Jake Qualls, along with senior scientist Dr. Wei Dong. Huang was introduced in December as an Arkansas Research Alliance Fellow for 2019.. The DREAM Challenge was organized by Celgene, the Multiple Myeloma Research Foundation (MMRF), and Sage Bionetworks. The A-State AI group learned about the competition from professional collaborators.. We entered the competition to train our team and to keep up with the most advanced AI and machine learning technologies, Qualls said. We were also interested in ...

The effective application of current decision tree and influence diagram software requires a relatively high level of sophistication in the theory and practice of decision analysis. Research on intelligent decision systems aims to lower the cost and amount of training required to use these methods through the use of knowledge-based systems; however, application prototypes implemented to date have required time-consuming and tedious handcrafting of knowledge bases. This paper describes the development of DDUCKS, an ?open architecture? problem-modeling environment that integrates components from Axotl, a knowledge-based decision analysis workbench, with those of Aquinas, a knowledge acquisition workbench based on personal construct theory. The knowledge base tools in Axotl can be configured with knowledge to provide guidance and help in formulating, evaluating, and refining decision models represented in influence diagrams. Knowledge acquisition tools in DDUCKS will allow the knowledge to be ...

Building a Software Pipeline for Developing and Evaluating Time-Series Machine Learning Models Using Electronic Health Record Data ...

Artificial intelligence, naturally: Smart agents technology. Smart agents technology is the only solution that overcomes the limits of the legacy machine learning technologies to allow personalization, adaptability and self-learning. Simply put, a smart agent can hold entire conversations using natural language technology that understands the intent and meaning of customer questions. But there is more: It creates a virtual representation of every entity that learns and builds a profile from the entitys actions and activities. For payments, a smart agent is associated with each individual cardholder, merchant or terminal. The smart agents associated to an entity (such as a card or a merchant) learn in real time from every transaction and collect specific and unique behaviors over time. There are as many smart agents as active entities in the system. Decision making becomes specific to each entity and no longer relies on universally applied logic, regardless of their individual characteristics. ...

Detrital monazite geochronology has been used in provenance studies. However, there are complexities in the interpretation of age spectra due to their wide occurrence in both igneous and metamorphic rocks. We use the multinomial logistic regression (MLR) and cross-validation (CV) techniques to establish a geochemical discrimination of monazite source rocks. The elemental abundance-based geochemical discrimination was tested by selecting 16 elements from granitic and metamorphic rocks. The MLR technique revealed that light rare earth elements (REEs), Eu, and some heavy REEs are important discriminators that reflect elemental fractionation during magmatism and/or metamorphism. The best model yielded a discrimination rate of ~97%, and the CV method validated this approach. We applied the discrimination model to detrital monazites from African rivers. The detrital monazites were mostly classified as granitic and of garnet-bearing metamorphic origins; however, their proportion of metamorphic origin was

The immune system has evolved to sense invading pathogens, control infection, and restore tissue integrity. Despite symptomatic variability in patients, unequivocal evidence that an individuals immune system distinguishes between different organisms and mounts an appropriate response is lacking. We here used a systematic approach to characterize responses to microbiologically well-defined infection in a total of 83 peritoneal dialysis patients on the day of presentation with acute peritonitis. A broad range of cellular and soluble parameters was determined in peritoneal effluents, covering the majority of local immune cells, inflammatory and regulatory cytokines and chemokines as well as tissue damage-related factors. Our analyses, utilizing machine-learning algorithms, demonstrate that different groups of bacteria induce qualitatively distinct local immune fingerprints, with specific biomarker signatures associated with Gram-negative and Gram-positive organisms, and with culture-negative ...

These data can be used in the area of artificial intelligence (machine learning and deeplearning) and among all obtain more efficient and faster results. Is now available on CRAN (v0.1.0). The package provides a tidy format of the daily cases by type (confirmed, recovered, and death). While the CRAN version will be updated on a monthly cadence, the Github version (v0.1.0.9000) is getting updated on a daily bases. For non-R users a CSV format of the data is available (please see links below). More details available here: CRAN: https://cran.r-project.org/web/packages/coronavirus/index.html Github: https://github.com/RamiKrispin/coronavirus Package site: https://ramikrispin.github.io/coronavirus/ Vignette: https://ramikrispin.github.io/coronavirus/articles/intro_coronavirus_dataset.html CSV format: https://github.com/RamiKrispin/coronavirus-csv/blob/master/coronavirus_dataset.csv Author, please: Rami Krispin (Data Scientist at Apple iCloud) . External link (Linkedin): ...

Objective To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (< 4 cm) renal angiomyolipoma without visible fat...

Dermatology researchers in the US have developed an artificial intelligence program that uses histology samples to identify which patients with melanoma will respond to immunotherapy. They say the system offers a better performance than genomic biomarkers such as PD-L1, which may be modified during the course of immune checkpoint inhibitor therapy. Instead, their machine learning .... ...

IBM Research and the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology have joined forces to further develop the scientific field of machine vision - a core aspect of artificial intelligence. Big Blue and MIT will build the IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension, or BM3C, in Cambridge, Mass. Together they plan to develop cognitive computing systems that mimic the human ability to understand and integrate input from multiple sources for use in a variety of computer applications in industries such as healthcare, education, and entertainment. MIT researchers will collaborate with IBM scientists and engineers who will provide technology expertise and advances from the IBM Watson platform. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine ...

Novel embryo development parameters analyzed with the use of time-lapse technology were used as input data for a predictive model of implantation potential based on artificial intelligence.

Lo and behold, the system began performing as advertised. The lucky break was a symptom of a troubling trend, according to Pineau. Neural networks, the technique thats given us Go-mastering bots and text generators that craft classical Chinese poetry, are often called black boxes because of the mysteries of how they work. Getting them to perform well can be like an art, involving subtle tweaks that go unreported in publications. The networks also are growing larger and more complex, with huge data sets and massive computing arrays that make replicating and studying those models expensive, if not impossible for all but the best-funded labs.. Is that even research anymore? asks Anna Rogers, a machine-learning researcher at the University of Massachusetts. Its not clear if youre demonstrating the superiority of your model or your budget.. Pineau is trying to change the standards. Shes the reproducibility chair for NeurIPS, a premier artificial intelligence conference. Under her watch, the ...

The field of computer-aided diagnosis has recently made progress in the diagnosing of Alzheimers disease (AD) from magnetic resonance images (MRI) of the brain. Lahmiri and Boukadoum (2013) have research this topic since 2011, and in 2013 they presented a system for automatic detection of AD based on machine learning classification. Their proposed system achieved a classification accuracy of 100% (2013, p. 1507) using support vector machines with quadratic kernel classifiers. The MRI scans were first translated to 1-dimensional signals, from which three features were extracted to measure the signals self-affinity. These three features were Hursts exponent, the total fluctuation energy of a detrended fluctuational analysis and the same analysis scaling exponent. The results of their study were validated using a dataset of 23 MRI scans from brains with AD and normal brains.. This report makes an attempt at implementing the method proposed by Lahmiri and Boukadoum in 2013 and evaluating its ...