Academic Articles & Research Papers

Machine Learning into Metaheuristics

During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-qua...

Machine Learning in Medicine

Interview with Dr. Isaac Kohane on machine learning in medicine. (16:31)Download In this view of the future of medicine, patient–provider interactions are informed and supported by massive amounts of data from interactions with similar pati...

How Machine Learning Will Transform Biomedicine

A survey on machine learning for data fusion

Machine Learning in Medicine

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular va...

Machine Learning, a Probabilistic Perspective

Kevin P. MurphyHardcover: 1104 pagesYear: 2012Publisher: The MIT PressISBN-13: 978-0262018029I have to admit the rather embarrassing fact that Machine Learning, a Probabilistic Perspective is the f......

Introduction to Machine Learning (Adaptive Computation and Machine Learning)

Automated Machine Learning

This open access book gives the first comprehensive overview of general methods in Automatic Machine Learning, AutoML, collects descriptions of existing AutoML systems based on these methods, and discusses the first international challenge ...

Adversarial machine learning

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a...

Machine Learning and Its Applications to Biology

The term machine learning refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. Two facets of mechan...

Machine Learning for Neural Decoding

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve deco...

Machine Learning and the Profession of Medicine

This Viewpoint discusses the opportunities and ethical implications of using machine learning technologies, which can rapidly collect and learn from large amounts of personal data, to provide individalized patient care....

Introduction to machine learning

The machine learning approach is to train an algorithm using a dataset for which the people do know the outcome, and then apply this algorithm in the future to make a prediction when they don't know the outcome. In this chapter, the authors...

Information disclosure and funding success of green crowdfunding campaigns: a study on GoFundMe

Abstract Crowdfunding has become important in increasing financial support for the development of green technologies. Self-disclosed information significantly affects supporters’ decisions and is important for the success of green project f...

Midbrain endocannabinoids actuate dopamine-based action selection

Summary: Quickly adapting behavior to changing environments is key for survival. Brain dopamine signals reinforce actions that procure rewards and avoid adverse outcomes. However, the molecular mechanisms within the circuits that actuate th...

Using generalized random forests to characterize vulnerability to adverse health outcomes following wildfire smoke exposure in California

Background: As the health burden attributable to wildfire activity increases under climate change, it is crucial to determine which subgroups face heightened vulnerability to wildfire smoke. Marginalized communities may experience dispropor...

A machine learning based scheme for enhancing the detection of position falsification attacks in vehicular ad hoc networks

Abstract Vehicular Ad Hoc Networks (VANETs) are wireless networks established between vehicles and their surrounding infrastructure, enabling the exchange of information. Consequently, many applications that can enhance passengers’ safety a...

A Deep Dive into a Groundbreaking Approach to Machine Learning-Powered E-Learning

Information retrieval aims to find the most important data for specific queries. The challenge is retrieving relevant data efficiently due to the large search area. Existing solutions lead to unnecessary processing costs. Additionally, iden...

Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening

In this study, we employed a novel approach to improve the serotonin-responsive ssDNA-wrapped single-walled carbon nanotube (ssDNA-SWCNT) nanosensors, combining directed evolution and machine learning-based prediction. Our iterative optimiz...

Data on Vulnerability Detection in Android

The data in this article have been collaborated from mainly four sources- Google Playstore,11 Google Playstore https://play.google.com/store/apps. Wandoujia22 Wandoujia apps http://www.wandoujia.com/apps. (third party app store market), AMD...