Machine Learning Revolution Research Articles

High School and Undergraduate Student Volunteers as an Imperfect Solution to Machine Learning Geoscience Research Needs

AbstractThe machine learning revolution presents geoscientists with exciting new opportunities for research, but is constrained by the need for large, high quality training data sets. Simultaneously, undergraduate and graduate program admissions have become increasingly competitive, pressuring high school and undergraduate students to differentiate themselves through involvement in research at earlier stages. Aligning these two interests provides mutually beneficial opportunities for both geoscientists and early‐stage students. We describe our experiences working with 20 early‐stage students to build a large training data set digitized from satellite images of meltwater drainage patterns on ice sheets. The intent of this Perspective is to share our experience and lessons learned with other machine learning researchers who, like us, may have minimal experience mentoring young volunteer researchers but may seek such partnerships for the first time in response to their machine learning training data set needs. These partnerships enabled creation of a powerful new machine learning model that would have otherwise been infeasible. Student benefits varied with their commitment and proactiveness, ranging from exposure to geoscience research and a resume line item to strong letters of recommendation and ongoing connections with geoscience researchers at an elite university lab. Many students were attracted to the project solely out of interest in machine learning, so the opportunity reached students who would not otherwise have conducted research in geoscience. Still, without incentives for researchers to engage less‐privileged students, our experience suggests that mutually beneficial partnerships between researchers and early‐stage students may exacerbate issues of inequality and lack of diversity within the geosciences.

Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications

Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications

Detecting Financial Fraud in the Digital Age: The AI and ML Revolution

Along with innovations in technology, swindlers are becoming smarter. Fraud is a major concern for the financial services industry. Earlier frauds in this industry were limited to robbery and loan defaults as the customers had limited ways to interact with their financial institutions. The technology innovation opened up various ways of interacting and transacting with financial services providers, which has led to a significant increase in several types of financial fraud. Technology is making a positive impact across many industries, including the financial sector. Technology is redefining what is possible in combating fraud and financial crimes. Artificial Intelligence (AI) and Machine Learning (ML) have grown drastically from being mere technology buzzwords to the commercial reality it is today. The aim of the present study is to understand the types of financial fraud and the role of AI and ML in financial fraud detection. Secondary data collected from websites and reports are used for the study.

Application of Optimization and Machine Learning for Sentiment Analysis

Sentiment analysis is called detecting emotions extracted from text features and is known as one of the most important parts of opinion extraction. Through this process, we can determine if a script is positive, negative or neutral. In this research, sentiment analysis is performed with textual data. A text feeling analyzer combines natural language processing (NLP) and machine learning techniques to assign weighted assessment scores to entities, subjects, subjects, and categories within a sentence or phrase. In expressing mood, the polarity of text reviews could be graded on a negative to positive scale using a learning algorithm.
 The current decade has seen significant developments in artificial intelligence, and the machine learning revolution has changed the entire AI industry. After all, machine learning techniques have become an integral part of any model in today's computing world. However, the ensemble to learning techniques is promise a high level of automation with the extraction of generalized rules for text and sentiment classification activities. This thesis aims to design and implement an optimized functionality matrix using to the ensemble learning for the sentiment classification and its applications.

Machine learning and applications in microbiology.

ABSTRACTTo understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell.

The perceptron learning algorithm and its multiple-layer extension, the backpropagation algorithm, are the foundations of the present-day machine learning revolution. However, these algorithms utilize a highly simplified mathematical abstraction of a neuron; it is not clear to what extent real biophysical neurons with morphologically-extended non-linear dendritic trees and conductance-based synapses can realize perceptron-like learning. Here we implemented the perceptron learning algorithm in a realistic biophysical model of a layer 5 cortical pyramidal cell with a full complement of non-linear dendritic channels. We tested this biophysical perceptron (BP) on a classification task, where it needed to correctly binarily classify 100, 1,000, or 2,000 patterns, and a generalization task, where it was required to discriminate between two “noisy” patterns. We show that the BP performs these tasks with an accuracy comparable to that of the original perceptron, though the classification capacity of the apical tuft is somewhat limited. We concluded that cortical pyramidal neurons can act as powerful classification devices.

Machine Learning for Molecular Simulation.

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into ML structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation.

A self-learning fall detection system for elderly persons using depth camera

The machine learning revolution is redesigning modern healthcare, and with the growth of the elderly population, fall detection has become an important research topic in healthcare. This paper surveys advances in machine learning-based fall detection technologies and reviews sensor-based, image processing-based, and wearable sensor-based fall detection systems and applications. In addition, this paper proposes a self-learning posture analysis and eye status-based fall detection system to solve the issue of mis-detections in fall detection systems, which have not been addressed in past works. Furthermore, this work proposes an image-feature-separation system that can use image processing with a low risk of privacy disclosure. Moreover, this work establishes a dataset, which includes 36 non-fall/fall cases comprising 25,200 images that can be used not only for this research but also in related studies. Experimental results show that this system can detect a fall with high accuracy and solve mis-detections in machine learning-based fall detection systems.

A self-learning fall detection system for elderly persons using depth camera

The machine learning revolution is redesigning modern healthcare, and with the growth of the elderly population, fall detection has become an important research topic in healthcare. This paper surveys advances in machine learning-based fall detection technologies and reviews sensor-based, image processing-based, and wearable sensor-based fall detection systems and applications. In addition, this paper proposes a self-learning posture analysis and eye status-based fall detection system to solve the issue of mis-detections in fall detection systems, which have not been addressed in past works. Furthermore, this work proposes an image-feature-separation system that can use image processing with a low risk of privacy disclosure. Moreover, this work establishes a dataset, which includes 36 non-fall/fall cases comprising 25,200 images that can be used not only for this research but also in related studies. Experimental results show that this system can detect a fall with high accuracy and solve mis-detections in machine learning-based fall detection systems.

The machine learning revolution in materials?

Abstract

Deep Learning usage by Large Experiments

The modern machine learning revolution holds great promise for high energy physics. Enormous detectors have been designed to search for rare signals in order to expose the smallest distance scales in nature. Modern tools like deep neural networks will be required to fully explore the sub-nuclear universe. Using the radiation pattern inside high energy jets as an example, we show how neural networks are being used in new ways to produce a class of techniques that were not previously possible.

A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revolution

The end of Moores law and Dennard scaling has led to the end of rapid improvement in general-purpose program performance. Machine learning (ML), and in particular deep learning, is an attractive alternative for architects to explore. It has recently revolutionized vision, speech, language understanding, and many other fields, and it promises to help with the grand challenges facing our society. The computation at its core is low-precision linear algebra. Thus, ML is both broad enough to apply to many domains and narrow enough to benefit from domain-specific architectures, such as Googles Tensor Processing Unit (TPU). Moreover, the growth in demand for ML computing exceeds Moores law at its peak, just as it is fading. Hence, ML experts and computer architects must work together to design the computing systems required to deliver on the potential of ML. This article offers motivation, suggestions, and warnings to computer architects on how to best contribute to the ML revolution.

Preface

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML ...Read More

Preface

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML ...Read More

Preface

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML ...Read More

Preface

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML ...Read More

Imaging Techniques for the Study of Chemical Reaction Dynamics

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML ...Read More

Ultrafast Time-Resolved Spontaneous and Coherent Raman Spectroscopy: The Structure and Dynamics of Photogenerated Transient Species

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML ...Read More

Intramolecular Dynamics from Eigenstate-Resolved Infrared Spectra

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML ...Read More

Cluster Reactions

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML ...Read More