MACHINE LEARNING IEEE PAPERS AND PROJECTS-2020
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning .
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When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper. In particular, we propose a fast, and Machine learning in finance sits at the intersection of a number of emergent and established disciplines including pattern recognition, financial econometrics, statistical computing, probabilistic programming, and dynamic programming. With the trend towards increasingThe applications of machine learning cannot be under estimated in the current scenario, Every sectors have applied the concept of machine learning and artificial intelligence. The world has began to work smartly with the use of information tools. In the present study, the
Multiscale Modeling Meets Machine Learning : What Can We Learn
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Abstract Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in Efficient solid-state refrigeration techniques have drawn increasing attention due to their potential for improving energy efficiency of refrigeration temperature control systems without using harmful gas as in conventional gas compression techniques. Research on
Assessment of supervised machine learning methods for fluid flows
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We apply supervised machine learning techniques to a number of regression problems in fluid dynamics. Four machine learning architectures are examined in terms of their characteristics, accuracy, computational cost, and robustness for canonical flow problems Experimental chemistry and the younger discipline of computational chemistry have always aspired to increase data volume, velocity, and variety. The recent software developments in machine learning databases and automation and hardware advances in fast co-processors As one of the important research topics in machine learning loss function plays an important role in the construction of machine learning algorithms and the improvement of their performance, which has been concerned and explored by many researchers. But it still has
H2o automl: Scalable automatic machine learning
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H2O is an open source, distributed machine learning platform designed to scale to very large datasets, with APIs in R, Python, Java and Scala. We present H2O AutoML, a highly scalable, fully-automated, supervised learning algorithm which automates the process of
Machine learning for clinical outcome prediction
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Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with
Artificial intelligence and machine learning : What managers need to know
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In 200 Ken Jennings set a record for the largest total winnings and longest undefeated streak on the television game show Jeopardy ($2.5 million and 74 days, respectively). In 201 IBM invited Jeopardy to present the show from its artificial intelligence (AI) base inBackground The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using sequence Accurate prediction of airflow distribution and aerosol transport in the human lungs, which are difficult to be measured in vivo but important to understand the structure and function relationship, is challenging. It is because the interplay between them spans more than two
Mapping the risk terrain for crime using machine learning
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Objectives We illustrate how a machine learning algorithm, Random Forests, can provide accurate long-term predictions of crime at micro places relative to other popular techniques. We also show how recent advances in model summaries can help to open the black boxof
Machine Learning in Additive Manufacturing: A Review
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In this review article, the latest applications of machine learning (ML) in the additive manufacturing (AM) field are reviewed. These applications, such as parameter optimization and anomaly detection, are classified into different types of ML tasks, including regression
Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning
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Machine learning has started to be deployed in fields such as healthcare and finance, which involves dealing with a lot of sensitive data. This propelled the need for and growth of privacy-preserving machine learning (PPML). We propose an actively secure four-party Within the past few years, we have witnessed the rising of quantum machine learning (QML) models which infer electronic properties of molecules and materials, rather than solving approximations to the electronic Schr dinger equation. The increasing availability of large Materials informatics is increasingly finding ways to exploit machine learning algorithms. Techniques such as decision trees, ensemble methods, support vector machines, and a variety of neural network architectures are used to predict likely material characteristics and Purpose Hyponatremia after pituitary surgery is a frequent finding with potential severe complications and the most common cause for readmission. Several studies have found parameters associated with postoperative hyponatremia, but no reliable specific predictor
Machine Learning Classifiers for Android Malware Detection
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With the growing popularity of Android devices, it is also more prone to malware attacks. There are many malware scanning tools available for scanning the Android Malware but most of them perform static analysis and also require a lot of resources and manual
A path for translation of machine learning products into healthcare delivery
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Despite enormous enthusiasm, machine learning models are rarely translated into clinical care and there is minimal evidence of clinical or economic impact. New conference venues and academic journals have emerged to promote the proliferating research; however, the
Enhancing a machine learning binarization framework by perturbation operators: Analysis on the multidimensional knapsack problem
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Solving combinatorial optimization problems is of great interest in the areas of computer science and operations research. Optimization algorithms and particularly metaheuristics are constantly improved in order to reduce execution times, increase the quality of solutions
Gradient tracking and variance reduction for decentralized optimization and machine learning
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Decentralized methods to solve finite-sum minimization problems are important in many signal processing and machine learning tasks where the data is distributed over a network of nodes and raw data sharing is not permitted due to privacy and/or resource constraints. In Permeability prediction is crucial in shale gas and CO 2 geological sequestration. However, the intricate pore structure complicates the prediction of permeability. Machine learning (ML) is a promising approach for predicting inherent correlations in large data sets. In this paper
Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation
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Introduction Occupational rehabilitation often involves functional capacity evaluations (FCE) that use simulated work tasks to assess work ability. Currently, there exists no single, streamlined solution to simulate all or a large number of standard work tasks. Such a system
Machine learning of atomic-scale properties based on physical principles
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We briefly summarize the kernel regression approach, as used recently in materials modeling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the With the influx of technological advancements and the increased simplicity in communication, especially through emails, the upsurge in the volume of unsolicited bulk emails (UBEs) has become a severe threat to global security and economy. Spam emails
A Perspective on Using Machine Learning in 3D Bioprinting
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Recently, three-dimensional (3D) printing technologies have been widely applied in industry and our daily lives. The term 3D bioprinting has been coined to describe 3D printing at the biomedical level. Machine learning is currently becoming increasingly active and has been
Machine learningbased reduced-order modeling for unsteady flows around bluff bodies of various shapes
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We propose a method to construct a reduced order model with machine learning for unsteady flows. The present machinelearned reduced order model (ML-ROM) is constructed by combining a convolutional neural network autoencoder (CNN-AE) and a long
Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning
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Abstract Coronavirus disease (COVID-19) is a major threat worldwide due to its fast spreading. As yet, there are no established drugs or vaccines available. Speeding up drug discovery is urgently required. We applied a workflow of combined in silico methods (virtual
Machine Learning in Ethnobotany
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We describe new opportunities created by bring AI to the field of ethnobotany. In particular we describe a novel approach to ethnobotany documentation that harnesses machine learning opportunities, specifically for the documentation of traditional ecological knowledge
Editorial overview: Analytical biotechnology in the era of high-performance omicrmance omics, synthetic biology, and machine learning
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High-resolution and high-throughput analytical methods are advancing new insights in life science research, biomanufacturing, biomedicine, and biological engineering. Recently, an enormous amount of platform omics technologies, synthetic biology and genome editing Background and purpose Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features could improve
Machine Learning Regression Models, and Prediction of Claims Reserves
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The current paper introduces regression based reserving models that allow for separate RBNS and IBNR reserves based on aggregated discrete time data containing information about accident years, reporting years, and payment delay, since reporting. All introduced
Tslearn, A Machine Learning Toolkit for Time Series Data
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Abstract tslearn is a general-purpose Python machine learning library for time series that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression. It follows scikit-learns Application Programming
Computational creativity beyond machine learning
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IDyOT, Information Dynamics of Thinking, is a minimal cognitive architecture motivated by human cognition and creativity (Wiggins 2018). From an artificial intelligence point of view, where a lot of research is invested into automating problem solving, it is interesting how
Machine learningbased lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations
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Background The clinical utility of machine learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk
A Novel Machine Learning Approach for High-Performance Diagnosis of Premature Internet Addiction Using the Unfolded EEG Spectra
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Due to the increasing presence of internet-based applications in our private and professional environments, internet addiction (IA) has emerged as a universal issue todays society. Clinical diagnosis of IA is still in its beginnings, resulting in delays or failures inAbstract Machine learning methods have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchmarked and Background Despite high success rates, flap failure remains an inherent risk in microvascular breast reconstruction. Identifying patients who are at high risk for flap failure would enable us to recommend alternative reconstructive techniques. However, as flap
Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learningbased prognostic model with clinical data in
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Methods: We screened the electronic records of 799 patients admitted in Tongji Hospital from January 10th to February 18th. There were 375 discharged patients including 201 survivors. We built a prognostic prediction model based on XGBoost machine learning In this work we propose a new Unsupervised Deep Self-Organizing Map (UDSOM) algorithm for feature extraction, quite similar to the existing multi-layer SOM architectures. The principal underlying idea of using SOMs is that if a neuron is wins n times, these n Two different types of approaches:(a) approaches that combine quantitative structure activity relationships, quantum mechanical electronic structure methods, and machine learning and,(b) electronic structure vertical solvation approaches, were used to predict the logP
Introduction to Machine Learning
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By the end of 20 ~31 Billion IoT devices. The estimated size of the entire digital universe will be a whopping 44 zettabytes (21 Zeroes in a ZB!) ▶ What to do with all these Data Source- Statista, IEEE, TechJury, ILS, Raconteur, NPR, Sendpulse 2 Page 3. Machine Learning The Art and ScienceIn the following article, we introduce a novel workflow, which we subsume under the term explainable cooperative machine learning and show its practical application in a data annotation and model training tool called NOVA. The main idea of our approach is to The present study attempted to predict groundwater levels (GWL) obtained from precipitation and temperature data based on various temporal delays. The radial basis function (RBF) neural network whale algorithm (WA) model, the multilayer perception (MLP WA) model
Enhancement of shock-capturing methods via machine learning
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In recent years, machine learning has been used to create data-driven solutions to problems for which an algorithmic solution is intractable, as well as fine-tuning existing algorithms. This research applies machine learning to the development of an improved finite-volume Since its beginning, optimization has played a vital role in data science. The analysis and solution methods for many statistical and machine learning models rely on optimization. The recent surge of interest in optimization for computational data analysis also comes with a few Monitoring hourly river flows is indispensable for flood forecasting and disaster risk management. The objective of the present study is to develop a suite of hourly river flow forecasting models for the Albert river, located in Queensland, Australia using variousAbstract Machine learning (ML) has the potential to improve the dermatologists practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (eg, electronic medical records, image databases, omics), faster computing, and cheaper
Impact of machine learning and feature selection on type 2 diabetes risk prediction
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This survey summarizes the state of the art for type 2 diabetes mellitus (T2DM) prediction and compares the prediction accuracies obtained by conventional statistical regression and machine learning methods, including deep learning . The impact of feature selection and
Next-generation radiogenomics sequencing for prediction of EGFR and KRAS mutation status in NSCLC patients using multimodal imaging and machine learning
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Purpose Considerable progress has been made in the assessment and management of non- small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the same time
Ergonomic risk assessment based on computer vision and machine learning
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We develop a novel method that performs accurate ergonomic risk assessment, automatically computing Rapid Upper Limb Assessment (RULA) scores from snapshots or digital video using computer vision and machine learning techniques. Our method
Machine learning and artificial intelligence: definitions, applications, and future directions
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Just as the business of medicine was once considered outside the domain of the orthopedic surgeon, we report evidence that demonstrates these emerging applications of AI warrant ownership, leverage, and application by the orthopedic surgeon to better serve their patients Automatic multi-classification of breast cancer histopathological images has remained one of the top-priority research areas in the field of biomedical informatics, due to the great clinical significance of multi-classification in providing diagnosis and prognosis of breast
Detection of Excessive Daytime Sleepiness in Resting-State EEG Recordings: A Novel Machine Learning Approach Using Specific EEG Sub-Bands and
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Abstract Excessive Daytime Sleepiness (EDS) is a threat to the safety of the patient as well as others and can lead to higher rates of mortality. While current tests for EDS are time- consuming, expensive, and inaccurate, modern data-driven approaches can significantly
Ethical considerations of using machine learning for decision support in occupational health: An example involving periodic workers health assessments
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Abstract Purpose Computer algorithms and Machine Learning (ML) will be integrated into clinical decision support within occupational health care. This will change the interaction between health care professionals and their clients, with unknown consequences. The aim
Supervised machine learning
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Machine learning gives computers the ability to learn without being explicitly programmed for the task at hand. The learning happens when data is combined with mathematical models, for example by finding suitable values of unknown variables in the model. The most
Machine Learning Based Diagnosis of Binge Eating Disorder Using EEG Recordings.
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Binge eating disorder is the most common eating disorder and therefore an important health problem worldwide often resulting in obesity. Current investigations on binge eating disorders impact on the human brain regarding electroencephalography data are limited to
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