A Machine Learning Approach to Characterize the Modulation of the Hippocampal Rhythms Via Optogenetic Stimulation of the Medial Septum

2019 ◽  
Vol 29 (10) ◽  
pp. 1950020 ◽  
Author(s):  
Sang-Eon Park ◽  
Nealen G. Laxpati ◽  
Claire-Anne Gutekunst ◽  
Mark J. Connolly ◽  
Jack Tung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Control ◽  
Field Potential ◽  
Medial Septum ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Optimal Parameters ◽  
Optogenetic Stimulation ◽  
Stimulation Parameters ◽  
Machine Learning Approach

The medial septum (MS) is a potential target for modulating hippocampal activity. However, given the multiple cell types involved, the changes in hippocampal neural activity induced by MS stimulation have not yet been fully characterized. We combined MS optogenetic stimulation with local field potential (LFP) recordings from the hippocampus and leveraged machine learning techniques to explore how activating or inhibiting multiple MS neuronal subpopulations using different optical stimulation parameters affects hippocampal LFP biomarkers. First, of the seven different optogenetic viral vectors used for modulating different neuronal subpopulations, only two induced a substantial change in hippocampal LFP. Second, we found hippocampal low-gamma band to be most effectively modulated by the stimulation. Third, the hippocampal biomarkers were sensitive to the optogenetic virus type and the stimulation frequency, establishing those parameters as the critical ones for the regulation of hippocampal biomarker activity. Last, we built a Gaussian process regression model to describe the relationship between stimulation parameters and activity of the biomarker as well as to identify the optimal parameters for biomarker modulation. This new machine learning approach can further our understanding of the effects of neural stimulation and guide the selection of optimal parameters for neural control.

scholarly journals Predicting Future Service Use in Dutch Mental Healthcare: A Machine Learning Approach

2021 ◽  
Author(s):  
Kasper van Mens ◽  
Sascha Kwakernaak ◽  
Richard Janssen ◽  
Wiepke Cahn ◽  
Joran Lokkerbol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Service Use ◽  
Visual Analysis ◽  
Mental Healthcare ◽  
Machine Learning Techniques ◽  
Individual Treatment ◽  
Learning Approach ◽  
Group Level ◽  
Starting Point ◽  
Machine Learning Approach

AbstractA mental healthcare system in which the scarce resources are equitably and efficiently allocated, benefits from a predictive model about expected service use. The skewness in service use is a challenge for such models. In this study, we applied a machine learning approach to forecast expected service use, as a starting point for agreements between financiers and suppliers of mental healthcare. This study used administrative data from a large mental healthcare organization in the Netherlands. A training set was selected using records from 2017 (N = 10,911), and a test set was selected using records from 2018 (N = 10,201). A baseline model and three random forest models were created from different types of input data to predict (the remainder of) numeric individual treatment hours. A visual analysis was performed on the individual predictions. Patients consumed 62 h of mental healthcare on average in 2018. The model that best predicted service use had a mean error of 21 min at the insurance group level and an average absolute error of 28 h at the patient level. There was a systematic under prediction of service use for high service use patients. The application of machine learning techniques on mental healthcare data is useful for predicting expected service on group level. The results indicate that these models could support financiers and suppliers of healthcare in the planning and allocation of resources. Nevertheless, uncertainty in the prediction of high-cost patients remains a challenge.

scholarly journals Arabic English Cross-Lingual Plagiarism Detection Based on Keyphrases Extraction, Monolingual and Machine Learning Approach

2019 ◽  
pp. 1-12
Author(s):  
Mokhtar Al-Suhaiqi ◽  
Muneer A. S. Hazaa ◽  
Mohammed Albared
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Approach ◽  
Plagiarism Detection ◽  
Machine Learning Approach ◽  
Cross Lingual ◽  
Cross Language

Due to rapid growth of research articles in various languages, cross-lingual plagiarism detection problem has received increasing interest in recent years. Cross-lingual plagiarism detection is more challenging task than monolingual plagiarism detection. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a method that combines keyphrases extraction, monolingual detection methods and machine learning approach. The research methodology used in this study has facilitated to accomplish the objectives in terms of designing, developing, and implementing an efficient Arabic – English cross lingual plagiarism detection. This paper empirically evaluates five different monolingual plagiarism detection methods namely i)N-Grams Similarity, ii)Longest Common Subsequence, iii)Dice Coefficient, iv)Fingerprint based Jaccard Similarity  and v) Fingerprint based Containment Similarity. In addition, three machine learning approaches namely i) naïve Bayes, ii) Support Vector Machine, and iii) linear logistic regression classifiers are used for Arabic-English Cross-language plagiarism detection. Several experiments are conducted to evaluate the performance of the key phrases extraction methods. In addition, Several experiments to investigate the performance of machine learning techniques to find the best method for Arabic-English Cross-language plagiarism detection. According to the experiments of Arabic-English Cross-language plagiarism detection, the highest result was obtained using SVM   classifier with 92% f-measure. In addition, the highest results were obtained by all classifiers are achieved, when most of the monolingual plagiarism detection methods are used. 

scholarly journals A Median-Based Machine-Learning Approach for Predicting Random Sampling Bernoulli Distribution Parameter

2019 ◽  
Vol 06 (01) ◽  
pp. 17-28 ◽  
Author(s):  
Hoang Pham ◽  
David H. Pham
Keyword(s):  
Machine Learning ◽  
Population Distribution ◽  
Real Life ◽  
Population Data ◽  
Correction Method ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Large Sample Size ◽  
Bernoulli Distribution ◽  
Machine Learning Approach

In real-life applications, we often do not have population data but we can collect several samples from a large sample size of data. In this paper, we propose a median-based machine-learning approach and algorithm to predict the parameter of the Bernoulli distribution. We illustrate the proposed median approach by generating various sample datasets from Bernoulli population distribution to validate the accuracy of the proposed approach. We also analyze the effectiveness of the median methods using machine-learning techniques including correction method and logistic regression. Our results show that the median-based measure outperforms the mean measure in the applications of machine learning using sampling distribution approaches.

A Hybrid Machine Learning Approach for Freeway Traffic Speed Estimation

2020 ◽  
Vol 2674 (10) ◽  
pp. 68-78
Author(s):  
Zhao Zhang ◽  
Yun Yuan ◽  
Xianfeng (Terry) Yang
Keyword(s):  
Machine Learning ◽  
Flow Model ◽  
Hybrid Approach ◽  
Traffic Monitoring ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Freeway Traffic ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Traffic Speed Estimation

Accurate and timely estimation of freeway traffic speeds by short segments plays an important role in traffic monitoring systems. In the literature, the ability of machine learning techniques to capture the stochastic characteristics of traffic has been proved. Also, the deployment of intelligent transportation systems (ITSs) has provided enriched traffic data, which enables the adoption of a variety of machine learning methods to estimate freeway traffic speeds. However, the limitation of data quality and coverage remain a big challenge in current traffic monitoring systems. To overcome this problem, this study aims to develop a hybrid machine learning approach, by creating a new training variable based on the second-order traffic flow model, to improve the accuracy of traffic speed estimation. Grounded on a novel integrated framework, the estimation is performed using three machine learning techniques, that is, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). All three models are trained with the integrated dataset including the traffic flow model estimates and the iPeMS and PeMS data from the Utah Department of Transportation (DOT). Further using the PeMS data as the ground truth for model evaluation, the comparisons between the hybrid approach and pure machine learning models show that the hybrid approach can effectively capture the time-varying pattern of the traffic and help improve the estimation accuracy.

scholarly journals Machine Learning for Epidemiological Analysis in The Industrial Area for a Sustainable Life

2019 ◽  
Vol 9 (2S2) ◽  
pp. 1017-1025
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Industrial Area ◽  
Epidemiological Studies ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Air Pollution Exposure ◽  
Pollution Exposure

Pollution exposure and human health in the industry contaminated area are always a concern. The need for industrialization urges to concentrate on sustainable life of residents in the vicinity of the industrial area rather than opposing the industrialists. Literature in epidemiological studies reveal that air pollution is one of the major problems for health risks faced by residents in the industrial area. Main pollutants in industry related air pollution are particulate matter (PM2.5, PM10), SO2 , NO2 , and other pollutants upon the industry. Data for epidemiological studies obtained from different sources which are limited to public access include residents’ sociodemographic characters, health problems, and air quality index for personal exposure to pollutants. This combined data and limited resources make the analysis more complex so that statistical methods cannot compensate. Our review finds that there is an increase in literature that evaluates the connection between ambient air pollution exposure and associated health events of residents in the industrially polluted area using statistical methods, mainly regression models. A very few applies machine learning techniques to figure out the impact of common air pollution exposure on human health. Most of the machine learning approach to epidemiological studies end up in air pollution exposure monitoring, not to correlate its association with diseases. A machine learning approach to epidemiological studies can automatically characterize the residents’ exposure to pollutants and its associated health effects. Uniqueness of the model depends on the appropriate exhaustive data that characterizes the features, and machine learning algorithm used to build the model. In this contribution, we discuss various existing approaches that evaluate residents’ health effects and the source of irritation in association with air pollution exposure, focuses machine learning techniques and mathematical background for epidemiological studies for residents’ sustainable life.

scholarly journals Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques

2020 ◽  
Vol 13 (9) ◽  
pp. 204
Author(s):  
Rodrigo A. Nava Lara ◽  
Jesús A. Beltrán ◽  
Carlos A. Brizuela ◽  
Gabriel Del Rio
Keyword(s):  
Machine Learning ◽  
Broad Spectrum ◽  
Human Microbiome ◽  
Antibiotic Activity ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Broad Spectrum Antibiotic ◽  
Approach Training ◽  
Learning Techniques ◽  
Machine Learning Approach

Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension). We previously reported a machine-learning approach to identify polyHAM from FDA-approved human targeted drugs using a heterologous approach (training with peptides and non-peptide compounds). Here we discover that polyHAM are more likely to be found among antimicrobials displaying a broad-spectrum antibiotic activity and that topological, but not chemical features, are most informative to classify this activity. A heterologous machine-learning approach was trained with broad-spectrum antimicrobials and tested with human metabolites; these metabolites were labeled as antimicrobials or non-antimicrobials based on a naïve text-mining approach. Human metabolites are not commonly recognized as antimicrobials yet circulate in the human body where microbes are found and our heterologous model was able to classify those with antimicrobial activity. These results provide the basis to develop applications aimed to design human diets that purposely alter metabolic compounds proportions as a way to control human microbiome.

A Comprehensive Review on Online News Popularity Prediction using Machine Learning Approach

2019 ◽  
Vol 5 (1) ◽  
pp. 7
Author(s):  
Priyanka Rathord ◽  
Dr. Anurag Jain ◽  
Chetan Agrawal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online News ◽  
Machine Learning Techniques ◽  
News Article ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
The World ◽  
Popularity Prediction

With the help of Internet, the online news can be instantly spread around the world. Most of peoples now have the habit of reading and sharing news online, for instance, using social media like Twitter and Facebook. Typically, the news popularity can be indicated by the number of reads, likes or shares. For the online news stake holders such as content providers or advertisers, it’s very valuable if the popularity of the news articles can be accurately predicted prior to the publication. Thus, it is interesting and meaningful to use the machine learning techniques to predict the popularity of online news articles. Various works have been done in prediction of online news popularity. Popularity of news depends upon various features like sharing of online news on social media, comments of visitors for news, likes for news articles etc. It is necessary to know what makes one online news article more popular than another article. Unpopular articles need to get optimize for further popularity. In this paper, different methodologies are analyzed which predict the popularity of online news articles. These methodologies are compared, their parameters are considered and improvements are suggested. The proposed methodology describes online news popularity predicting system.

scholarly journals Sentiment Analysis on Social Media using Machine Learning Approach

2021 ◽  
Author(s):  
Erick Omuya ◽  
George Okeyo ◽  
Michael Kimwele
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Machine Learning Approach

Social media has been embraced by different people as a convenient and official medium of communication. People write messages and attach images and videos on Twitter, Facebook and other social media which they share. Social media therefore generates a lot of data that is rich in sentiments from these updates. Sentiment analysis has been used to determine opinions of clients, for instance, relating to a particular product or company. Knowledge based approach and Machine learning approach are among the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is however distorted by noise, the curse of dimensionality, the data domains and size of data used for training and testing. This research aims at developing a model for sentiment analysis in which dimensionality reduction and the use of different parts of speech improves sentiment analysis performance. It uses natural language processing for filtering, storing and performing sentiment analysis on the data from social media. The model is tested using Naïve Bayes, Support Vector Machines and K-Nearest neighbor machine learning algorithms and its performance compared with that of two other Sentiment Analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.

Auditor Response to Estimated Misstatement Risk: A Machine Learning Approach

2021 ◽  
Author(s):  
Emily Hunt ◽  
Joshua O.S. Hunt ◽  
Vernon J. Richardson ◽  
David Rosser
Keyword(s):  
Machine Learning ◽  
Risk Premium ◽  
Audit Quality ◽  
Audit Fees ◽  
Risk Assessments ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Auditor Switching ◽  
Learning Techniques ◽  
Machine Learning Approach

In this paper, we investigate whether misstatement risk estimated using advanced machine learning techniques, hereafter referred to as estimated misstatement risk (EMR), approximates auditors' risk assessments in practice. We find that auditors price EMR and that auditor turnover is more likely to occur when EMR increases, indicating that EMR is associated with auditors' risk assessment. We also find evidence that EMR is positively and significantly associated with audit fees and auditor switching for companies with Big N auditors but not for other companies, suggesting that Big N auditors are more responsive to risks captured by EMR. Additional analyses reveal that companies switching auditors when EMR increases are more likely to engage non-Big N auditors. Surprisingly, we find little evidence that the association between audit quality and EMR differs by auditor type. Our findings are consistent with the notion that the documented association between audit fees and EMR primarily reflects a risk premium in our setting.

scholarly journals Machine Learning Approach

2022 ◽  
pp. 349-366
Author(s):  
Roopashree S. ◽  
Anitha J. ◽  
Madhumathy P.
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Medicinal Herbs ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Domain Experts ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Insufficient Knowledge ◽  
Complete Framework

Ayurveda medicines uses herbs for curing many ailments without side effects. The biggest concern related to Ayurveda medicine is extinction of many important medicinal herbs, which may be due to insufficient knowledge, weather conditions, and urbanization. Another reason consists of lack of online facts on Indian herbs because it is dependent on books and experts. This concern has motivated in utilizing the machine learning techniques to identify and reveal few details of Indian medicinal herbs because, until now, it is identified manually, which is cumbersome and may lead to errors. Many researchers have shown decent results in identifying and classifying plants with good accuracy and robustness. But no complete framework and strong evidence is projected on Indian medicinal herbs. Accordingly, the chapter aims to provide an outline on how machine learning techniques can be adopted to enrich the knowledge of Indian herbs, which advantages both common man and the domain experts with wide information on traditional herbs.

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