machine learning 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 learning‐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