machine learning IEEE PAPER 2022


Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values







Data preparation for machine learning
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We cannot fit and evaluate machine learning algorithms on raw data; instead, we must transform the data to meet the requirements of individual machine learning algorithms. More than

Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning
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of machine learning models. However, current studies focus on supervised learning tasks, data reconstruction, representation learning and contrastive learning . Our results show

OPSSAT Spacecraft Autonomy with TensorFlow Lite, Unsupervised Learning and Online Machine Learning
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is achieved with unsupervised learning by running a k- learning and dives into a use case that reads photodiode sensor data as a stream of training data for online machine learning to

Using a business simulator with elements of machine learning to develop personal finance management skills
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an attempt was made to apply machine learning elements to make this in machine learning namely reinforcement training. Reinforcement learning (RL) is a type of machine learning

Semi-Automatic Assessment of Modeling Exercises using Supervised Machine Learning
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Objectives: This paper describes a semi-automatic assessment approach based on supervised machine learning . It aims to increase the fairness and efficiency of grading and improve

To seed or not to seed an empirical analysis of usage of seeds for testing in machine learning projects
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We describe previous research on flaky tests in Machine Learning projects. Hence, we select projects from the domains of Machine Learning and Probabilistic Programming. For

Geothermal, oil and gas well subsurface temperature prediction employing machine learning
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vast number of deep learning and machine learning techniques ranging learning experiments, generative adversarial networks, etc. On amalgamating these available machine learning

Machine learning approaches to detect DoS and their effect on WSNs lifetime
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of the use machine learning algorithms in WSN machine learning classification categories such as K-Nearest Neighbour (KNN), Logistic Regression (LR), Support Vector Machine (SVM [HTML]

A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data
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to train supervised machine learning models for predicting or In this study, 13 popular supervised machine learning superior to individual machine learning methods. IncludingThe popularization of electronic clinical medical records makes it possible to use automated methods to extract high-value information from medical records quickly. As essential

Machine learning and physician prescribing: a path to reduced antibiotic use
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is one leading example where machine learning is hoped to In this paper, we investigate how machine learning provides data, we find that machine learning methods are capable of added into the field of Machine Learning and Intelligent Systems, Test can be passed by a machine if it is made capable of However, it is the expression of emotions by a machine that

Electricity demand forecasting in industrial and residential facilities using ensemble machine learning
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This work explores the use of machine learning techniques, mainly those based on model ensembles. Feature selection is commonly applied in this kind of problems due to several

Leveraging machine learning to automatically derive robust decision strategies from imperfect knowledge of the real world
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it possible to leverage machine learning to discover optimal problem that the machine learning methods were applied to. step towards leveraging machine learning to improve human

Detecting causes of spatial variation in crop yield with interpretive machine learning
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Many machine learning models have in-built methods for Interpretive machine learning (IML) techniques are an different fields to create a machine learning model with satellite imagery

Reconstruction of human protein-coding gene functional association network based on machine learning
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interests include machine learning and deep learning . interaction data, training machine learning classifiers with various supervised learning strategy to select unlabeled interactions to

medoutcon: Nonparametric efficient causal mediation analysis with machine learning inR
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from machine learning . Neither concerns itself with opening the door to applying these estimators in real-world data analyses. Moreover, the implementation of open source software

Online machine learning algorithms to optimize performances of complex wireless communication systems
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In this study, we discuss the application of machine learning technologies to wireless complex wireless communication systems using machine learning technologies. Another scheme,

Blockchain and Machine Learning for Intelligent Multiple Factor-Based Ride-Hailing Services
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This research aims to improve the taxi demand service performance and security by applying machine learning and blockchain integration. This section has six parts: machine learning

Guest Editorial: Graph-powered machine learning in future-generation computing systems
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Learning from big graph data in future-generation computing systems considers the effectiveness of graph learning [22], and machine learning methods and big graph data analytics ap-

Supervised machine learning for automatic classification of in vivo burn injuries using the terahertz Portable Handheld Spectral Reflection (PHASR) scanner
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In the results section, we compare the performance of various machine learning techniques based on the area under the receiver operating characteristic (ROC) curves, ie, the trueSince we propose to use machine learning models on our emphasized and correctly labeled values of web services instances, we require to train on the correctly labeled information of techniques of machine learning . It explains the different classes of machine learning approaches In this chapter, machine learning approaches are employed to model the blast furnace

Government Construction Project Budget Prediction Using Machine Learning
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Big data technology includes Machine Learning (ML). The present study attempts to predict over-budget construction projects using an ML algorithm. Data were collected from the

Artificial Intelligence and Machine Learning in Food Industries: A Study
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Enlarging data quantities, computer resources along with improved learning algorithms in of machine learning . With a joint effort, both artificial intelligence and machine learning are

A machine learning framework for LES closure terms
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[25] used a multi-agent reinforcement learning approach to infer local LES model parameters for the HIT test case. In this work, we derive a general machine learning framework for LES

Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms
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teaching- learningbased optimization (TLBO) and machine learning algorithms using k- California, Irvine (UCI) Machine Learning Repository for comparative study and experimentation.

Destination Image of DMO and UGC on Instagram: A Machine Learning Approach
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Specifically, through the use of netnography supported by machine learning approaches, This study advances methodological approaches by using machine learning techniques to

The quenching of galaxies, bulges, and disks since cosmic noon: A machine learning approach for identifying causality in astronomical data
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We establish that our machine learning machine learning approach, and to aid in the interpretation of our observational results. Additionally, we demonstrate that our machine learning

Application of machine learning techniques to predict haul truck fuel consumption in open-pit mines
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is machine learning which aims to deploy computer programs for automatic learning and This research work aimed to investigate the various machine learning models feasibility machine learning approach and neural network model to calculate recurrence chances of BC. We used the Wisconsin dataset of the UCI machine learning support vector machine . Our

Machine Learning with Graphs: Paper Presentations
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As part of this course, you are expected to present one research paper on machine learning with graphs. Presentations will take place at the end of the semester and can be done in

Machine Learning for Scientific Data Analysis
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Abstract Over the last few years, machine learning has revolutionized countless areas and to adapt machine learning techniques to new requirements. We investigate machine learning

Method of monitoring and detection of failures in PV system based on machine learning
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ABSTRACT: Machine learning methods have been used to solve complicated practical supervision of measurement sensors, through Machine learning and data mining in response to

NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
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the implementation of distributed training for large machine learning tasks. In this paper, we of magnitude and provided up to linear scalability across multiple machine learning tasks.

Machine Learning for Modeling and Control of Industrial Clarifier Process
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In sugar production, model parameter estimation and controller tuning of the nonlinear clarification process are major concerns. Because the sugar industry s clarification process is

Machine Learning for Single and Complex 3D Head Gestures: Classification in Human-Computer Interaction
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This paper presents a new Hidden Markov Model based approach for fast and automatic detection and classification of head movements in real time dynamic videos. The model has

An innovative approach to sales lead generation using machine learning
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Working with a leading US staffing firm, we developed a machine learning solution to enable the powerful combination of automation and relationships to gain a competitive

Automated Testing of Software that Uses Machine Learning APIs
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It is challenging to efficiently and effectively test software containing machine learning components We evaluate Keeper with a variety of open-source machine learning applications and Machine Learning and Deep Learning Methods to Power System Problems aims to evaluate the application of machine learning /deep learning and applying machine learning to power

Shale Brittleness Prediction using Machine Learning A Middle East Basin Case Study 2
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This paper presents a machine learning (ML) approach to predict the BI using readily 20 available well logs. Well log data were collected from three different wells that encompass a

A Machine Learning based Study on Classical Arabic Authorship Identification
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In this contribution, we present a machine learningbased study on using different types of style markers for classical Arabic. Our aim is to compare the effectiveness of machine learning

Evaluation of Supervised machine learning classifiers for detecting the degrees of possibility of Coronavirus disease infection
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a comparative analysis of machine learning and soft computing 140 Among a wide range of 142 machine learning models 144 Also, Machine learning and deep learning can replace

Drilling Fluid Properties Prediction: A Machine Learning Approach to Automate Laboratory Experiments
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This study describes a novel methodology to train a machine learning model derived from over 878 drilling fluid formulations to successfully predict water-based drilling fluids

Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning
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This paper focuses on detecting diseased signals and arrhythmias classification into two classes: ventricular tachycardia and premature ventricular contraction. The sole purpose of the

Survival prediction among heart patients using machine learning techniques
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algorithms for heart failure prediction and evaluate the machine learning algorithms on a machine learning algorithms on the benchmark dataset by selecting various machine learning

Reconstructing production networks using machine learning
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using a typical supervised learning framework. We train a machine learning model on a portion of the network and study its capacity to predict links in the unobserved part. We validate

Mathematical modeling and machine learning for public health decision-making: the case of breast cancer in Benin
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errors thanks to the use of Machine Learning . Others have also According to [15], the KNN algorithm is a supervised learning The principal target of this study is to use machine learning

Automatic Debiased Machine Learning in Presence of Endogeneity
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We apply our approach to learning the conditional demand derivative functional in the First, we demonstrate that applying machine learning allows to uncover more complicatedWe review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL) approaches, and hybrid machine learning and deep learning models (primarily including artificial neural networks). Some important developments propelled by the use of these techniques and its applications

Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing. Electronics 202 1 325
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To achieve this, the dataset provided by the Sentinel-2 time series is transformed into information through the combination of classifications with machine learning using random forest

Analysis of COVID-19 Prediction Models: Design Analysis of New Machine Learning Approach
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Hence the use of soft computing techniques such as data mining, machine learning (ML), artificial intelligence (AI), deep learning (DL) plays a critical role in the contamination and

Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data. Diagnostics 202 1 203
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This study aimed to utilize clinical laboratory data and machine learning methods to build machine learning models, including decision tree, random forest, support vector machine

Depression Prediction using Machine Learning : A Review
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machine learning algorithms. This paper reviews publications from online electronic databases from to that use machine learning the latest machine learning algorithms used

Safe Havens, Machine Learning and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data
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two machine learning approaches. First, we use the least absolute shrinkage and selection operator (Lasso), proposed by Tibshirani (1996). The Lasso belongs to the spectrum of linear

Development of a simulator to determine personal financial strategies using machine learning
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for determining personal financial strategies using machine learning . The development of a financial strategies, which uses reinforced learning opportunities. The direction of future

Using Machine Learning to Analyze Sudanese Opinions for Political Decision Making
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application of machine learning algorithms machine learning classifiers such as SVM, multinomial Naïve Bayes, K-Nearest Neighbors (KNN), Decision Tree (DT) and ensemble learning

Hybrid Machine Learning Models for Storm Surge Prediction
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Statistical and machine learning (ML) approaches can be an effective way to accelerate the Our work is a combination of machine learning and simulation, which uses data and

Machine Learning based Health Care in Neurology
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and how it has been handled by the Machine Learning Community. In this paper we have We can see that the Machine Learning Community is very enthusiastically participating

machine learning IEEE PAPER 2021




River: machine learning for streaming data in Python
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River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result

A first encounter with Machine Learning
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In winter quarter I taught an undergraduate course in machine learning at UC Irvine. While I had been teaching machine learning at a graduate level it became soon clear that teaching the same material to an undergraduate class was a whole new challenge. Much of

Fake Reviews Detection using Supervised Machine Learning
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With the continuous evolve of E-commerce systems, online reviews are mainly considered as a crucial factor for building and maintaining a good reputation. Moreover, they have an effective role in the decision making process for end users. Usually, a positive review for a

giotto-tda:: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration.
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We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn compatible API and state-of-the-art C++ implementations. The librarys ability to handle various types of data is rooted in a wide

A new multi-agent feature wrapper machine learning approach for heart disease diagnosis
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Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently prevent the manifestation of the debilitating

Learning Best Practices: Can Machine Learning Improve Human Decision-Making
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Workers spend a significant amount of time learning how to make good decisions. Evaluating the efficacy of a given decision, however, is quite complicated. For one, decision outcomes are often long-term and relate to the original decision in complex ways. The goal

How Does Data Augmentation Affect Privacy in Machine Learning
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It is observed in the literature that data augmentation can significantly mitigate membership inference (MI) attack. However, in this work, we challenge this observation by proposing new MI attacks to utilize the information of augmented data. MI attack is widely used to measure

Fusion of text and graph information for machine learning problems on networks
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Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other This research intends to use machine learning approaches to predict tunnel geology and its construction time and costs. For this purpose, the Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree (DT) have been utilized. An estimation

Estimating identifiable causal effects through double machine learning
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Identifying causal effects from observational data is a pervasive challenge found throughout the empirical sciences. Very general methods have been developed to decide the identifiability of a causal quantity from a combination of observational data and causal

The machine learning embedded method of parameters determination in the constitutive models and potential applications for hydrogels
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We propose a machine learning embedded method of parameters determination in the constitutional models of hydrogels. It is found that the developed logistic regression liked algorithm for hydrogel swelling allows us to determine the fitting parameters based on

A new segmentation framework for arabic handwritten text using machine learning techniques
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The writer identification (WI) of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist organizations. It is also a useful instrument for the digitalization and attribution of old text to

A Comprehensive Survey on Machine Learning
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The objective of this briefing is to present an overview of the topic, machine learning techniques currently in use or in consideration at statistical agencies worldwide. It is important to know the main reason why real-world scenario should start exploring the use of

mikropml: user-friendly R package for supervised machine learning pipelines
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Machine learning (ML) for classification and prediction based on a set of features is used to make decisions in healthcare, economics, criminal justice and more. However, implementing an ML pipeline including preprocessing, model selection, and evaluation can

Survey on graph embeddings and their applications to machine learning problems on graphs
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Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques

Machine learning techniques in drug discovery and development
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The advancement and progress in technology and related techniques have created an opportunity for progress in many scientific fields and various industries. Machine learning has become important tool for drug designs and discovery with the availability of bit data

Product Reviews Sentiment Analysis using Machine Learning : A Systematic Literature Review
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ABSTRACT A huge amount of data is available on the internet and social media platforms. It is necessary to use this in data in an efficient way. The growth of the companies depends on the satisfaction of their customers. This makes the sentiment analysis an important and One of the most challenging issues in the big data research area is the inability to process a large volume of information in a reasonable time. Hadoop and Spark are two frameworks for distributed data processing. Hadoop is a very popular and general platform for big data

Brain lipidomics as a rising field in neurodegenerative contexts: Perspectives with Machine Learning approaches
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Lipids are essential for cellular functioning considering their role in membrane composition, signaling, and energy metabolism. The brain is the second most abundant organ in terms of lipid concentration and diversity only after adipose tissue. However, in the central system

Adversarial Machine Learning : A Comparative Study on Contemporary Intrusion Detection Datasets.
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Studies have shown the vulnerability of machine learning algorithms against adversarial samples in image classification problems in deep neural networks. However, there is a need for performing comprehensive studies of adversarial machine learning in the intrusion

Simulation of the present and future projection of permafrost on the Qinghai-Tibet Plateau with statistical and machine learning models
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The comprehensive understanding of the occurred changes of permafrost, including the changes of mean annual ground temperature (MAGT) and active layer thickness (ALT), on the Qinghai-Tibet Plateau (QTP) is critical to project permafrost changes due to climate

Review on emerging trends in detection of plant diseases using image processing with machine learning
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In India, about 70% of the population is involved in agriculture and farming. Today plant diseases are significant concern as it reduces the production and quality of agriculture produce. Most plant diseases are caused by bacteria, fungi and virus. Manual detection and

Development and Assessment of Physics-guided Machine Learning Framework for Prognosis System.
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Page 1. ABSTRACT GURGEN, ANIL. Development and Assessment of Physics-guided Machine Learning Framework for Prognosis System. (Under the direction of Dr. Nam T. Dinh). Nearly Autonomous Management and Control (NAMAC) is a system that provides recommendations

Nowcasting Earthquakes: Imaging the Earthquake Cycle in California with Machine Learning
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We propose a new machine learningbased method for nowcasting earthquakes to image the time-dependent earthquake cycle. The result is a timeseries which correspond to the process of stress accumulation and release. The timeseries is constructed by using

New directions in machine learning analyses of administrative data to prevent suicide-related behaviors
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This issue contains a thoughtful report by Gradus et al. on a machine learning (ML) analysis of administrative variables to predict suicide attempts over two decades throughout Denmark. This is one of numerous recent studies that document strong concentration of risk

Anemia estimation for covid-19 patients using a machine learning model
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Hemoglobin (Hbg) is considered to possess a necessary role in the human body. It lugs air from bronchitis to the body systems tissues and comes back to carbon dioxide from cells back to bronchitis. As more is being discovered about this virus, we aim to report findings of

Detection of schizophrenia: a machine learning algorithm for potential early detection and prevention based on event-related potentials
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We show that event-related potentials can be used to detect schizophrenia with a high degree of precision. With our machine learning algorithm we achieve a balanced accuracy of 96.4%, which exceeds all results with comparable approaches. For this we use additional

Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study
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ABSTRACT Responsible usage of Machine Learning (ML) systems in practice does not only require enforcing high prediction quality, but also accounting for other constraints, such as fairness, privacy, or execution time. One way to address multiple user-specified constraints

Introduction to Machine Learning with Robots and Playful Learning
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Inspired by explanations of machine learning concepts in childrens books, we developed an approach to introduce supervised, unsupervised, and reinforcement learning using a block- based programming language in combination with the benefits of educational robotics Health informatics and biomedical computing have introduced the use of computer methods to analyze clinical information and provide tools to assist clinicians during the diagnosis and treatment of diverse clinical conditions. With the amount of information that can be obtained

DC2: Delay-aware Compression Control for Distributed Machine Learning
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Distributed training performs data-parallel training of DNN models which is a necessity for increasingly complex models and large datasets. Recent works are identifying major communication bottlenecks in distributed training. These works seek possible opportunities

Machine learningbased direct solver for one-to-many problems of temporal shaping of electron bunches
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To control the temporal profile of a charged beam to meet requirements of various accelerator applications, a widely-used technique is bunch compression via 4-dipole chicanes that sometimes have a one-to-many map. Current approaches based on

Machine Learning and Central Banks: Ready for Prime Time
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In this article we review what machine learning might have to offer central banks as an analytical approach to support monetary policy decisions. After describing the central banks problem and providing a brief introduction to machine learning we propose to use the

Machine learning enabled early detection of breast cancer by structural analysis of mammograms
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Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an images accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS)

A comparative study of using various machine learning and deep learningā€based fraud detection models for universal health coverage schemes
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Fraud detection is an important area of research in the healthcare systems due to its financial consequences arising mainly from investigation costs, revenue losses, and reputational risk. To mitigate this, most of the companies adopt Machine Learning and/or

Introduction To Machine Learning
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Page 1. Introduction to Machine Learning PhD advanced course on Bryan Zaldívar UAM, IFT CSIC, IFIC April-Zoomland Page 2. About format and scope Theory lectures (except first half of this one) will be, a priori, on the blackboard Hands-on sessions (26th and 29th April) require




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