scholarly journals As Uncanny As a Body

2021 ◽  
Author(s):  
Irini Kalaitzidi
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Visual Data ◽  
Model Affect

‘As uncanny as a body’ is a video work which documents the constant transition of a dancing body from an abled human state into a glitched, injured, nonhuman one. Based on the development and use of a GAN model, the visual data of a dancer are processed, and figures that manifest this in-between state are produced. What happens when AI is not used in order to optimise the performance of the dancer but rather to generate body types and movements lying beyond fixed standards and classifications? How does a poorly trained AI model affect our perception of the body and the body itself? Does it remain human in all its smudges, cracks and distortions?Dance, as a practice that entails the act of change, offers the ground to study a body's possible becomings while undergoing the application of machine learning. 'As uncanny as a body' speculates upon AI-induced injuries in order to discuss trauma, to connect familiarity to uncanniness, and to raise questions about the witnessing and acknowledgment of our gaze.

Predicting the citation and impact factor of terms for scientific publications using machine learning algorithms

2020 ◽  
Author(s):  
Aleksey Klokov ◽  
Evgenii Slobodyuk ◽  
Michael Charnine
Keyword(s):  
Machine Learning ◽  
Semantic Processing ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Scientific Publications ◽  
Text Data ◽  
Semantic Relationships ◽  
Subject Areas ◽  
The Subject ◽  
Scientific Environment

The object of the research when writing the work was the body of text data collected together with the scientific advisor and the algorithms for processing the natural language of analysis. The stream of hypotheses has been tested against computer science scientific publications through a series of simulation experiments described in this dissertation. The subject of the research is algorithms and the results of the algorithms, aimed at predicting promising topics and terms that appear in the course of time in the scientific environment. The result of this work is a set of machine learning models, with the help of which experiments were carried out to identify promising terms and semantic relationships in the text corpus. The resulting models can be used for semantic processing and analysis of other subject areas.

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

scholarly journals Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Marcos Fabietti ◽  
Mufti Mahmud ◽  
Ahmad Lotfi
Keyword(s):  
Machine Learning ◽  
Open Access ◽  
Short Term Memory ◽  
The Body ◽  
Data Sets ◽  
Additional Information ◽  
Machine Learning Model ◽  
Signal Characteristics ◽  
Wide Range ◽  
Memory Network

AbstractAcquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.

scholarly journals System to assist in the Diagnosis of Diabetes using Ontology and Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 9971-9975
Keyword(s):  
Machine Learning ◽  
Public Health Problem ◽  
Medical Data ◽  
The Body ◽  
Machine Learning Method ◽  
Learning Method ◽  
Automatic Learning ◽  
Early Diabetes ◽  
Data Set ◽  
Medical Tests

Diabetes mellitus has become a public health problem in both developed and developing countries. If it is not treated early, diabetes-related complications in many vital organs of the body can become fatal. Its early detection is very important for early treatment that can prevent the disease from progressing to such complications. This article focuses on designing a system to assist in the diagnosis of diabetes disease based on medical ontology and automatic learning. The proposed method uses automatic learning algorithms as a classifier for the diagnosis of diabetes based on a medical data set. The ontology suggests a pre-processing of a coherent, consistent, interoperable and shareable knowledge basis of data and the machine learning method focuses on classification based on symptoms and medical tests. Based on the experimental results, DDAS not only offers better performance in predicting and diagnosing diabetes in individuals, but also has better accuracy in recommending useful treatment to patients.

scholarly journals Discovering Business Processes from Email Logs using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
sina zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

scholarly journals Detecting Controversial Articles on Citizen Journalism

2018 ◽  
Vol 11 (1) ◽  
pp. 34
Author(s):  
Alfan Farizki Wicaksono ◽  
Sharon Raissa Herdiyana ◽  
Mirna Adriani
Keyword(s):  
Machine Learning ◽  
Structural Information ◽  
Structural Features ◽  
The Body ◽  
Supervised Machine Learning ◽  
Citizen Journalism ◽  
Learning Approach ◽  
Daily News ◽  
Machine Learning Approach ◽  
Controversial Topic

Someone's understanding and stance on a particular controversial topic can be influenced by daily news or articles he consume everyday. Unfortunately, readers usually do not realize that they are reading controversial articles. In this paper, we address the problem of automatically detecting controversial article from citizen journalism media. To solve the problem, we employ a supervised machine learning approach with several hand-crafted features that exploits linguistic information, meta-data of an article, structural information in the commentary section, and sentiment expressed inside the body of an article. The experimental results shows that our proposed method manages to perform the addressed task effectively. The best performance so far is achieved when we use all proposed feature with Logistic Regression as our model (82.89\% in terms of accuracy). Moreover, we found that information from commentary section (structural features) contributes most to the classification task.

A Comparison of Machine Learning Methods to Predict Hospital Readmission of Diabetic Patient

2021 ◽  
Vol 7 (2) ◽  
pp. 164-168
Author(s):  
Cuong Le Dinh Phu ◽  
Dong Wang
Keyword(s):  
Machine Learning ◽  
Hospital Readmission ◽  
Random Search ◽  
Learning Model ◽  
The Body ◽  
Hyperparameter Optimization ◽  
Health Records ◽  
Search Optimization ◽  
Machine Learning Model ◽  
Public Dataset

Diabetes is a chronic disease whereby blood glucose is not metabolized in the body. Electronic health records (EHRs) (Yadav, P. et al., 2018). for each individual or a population have become important to standing developing trends of diseases. Machine learning helps provide accurate predictions higher than actual assessments. The main problem that we are trying to apply machine learning model and using EHRs that combines the strength of a machine learning model with various features and hyperparameter optimization or tuning. The hyperparameter optimization (Feurer, M., 2019) uses the random search optimization which minimizes a predefined loss function on given independent data. The evaluation on the method comparisons indicated that machine learning models has increased the ratio of metrics compared to previous models (Accuracy, Recall, F1 and AUC score) on the same public dataset that is reprocessed.

Tremor Identification Using Machine Learning in Parkinson's Disease

2019 ◽  
pp. 128-151
Author(s):  
Angana Saikia ◽  
Vinayak Majhi ◽  
Masaraf Hussain ◽  
Sudip Paul ◽  
Amitava Datta
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Discriminant Analysis ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

scholarly journals Machine learning for digital try-on: Challenges and progress

2020 ◽  
Author(s):  
Junbang Liang ◽  
Ming C. Lin
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Economic Benefits ◽  
The Body ◽  
Future Directions ◽  
Recent Advances ◽  
Practical Constraints

Abstract Digital try-on systems for e-commerce have the potential to change people's lives and provide notable economic benefits. However, their development is limited by practical constraints, such as accurate sizing of the body and realism of demonstrations. We enumerate three open challenges remaining for a complete and easy-to-use try-on system that recent advances in machine learning make increasingly tractable. For each, we describe the problem, introduce state-of-the-art approaches, and provide future directions.

scholarly journals Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ritaban Dutta ◽  
Cherry Chen ◽  
David Renshaw ◽  
Daniel Liang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Shape Memory ◽  
Video Data ◽  
The Body ◽  
Restricted Boltzmann Machine ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Boltzmann Machine ◽  
High Level

AbstractExtraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45,000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation.

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