scholarly journals Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2768 ◽  
Author(s):  
Victor Vlădăreanu ◽  
Valentin-Gabriel Voiculescu ◽  
Vlad-Alexandru Grosu ◽  
Luige Vlădăreanu ◽  
Ana-Maria Travediu ◽  
...  
Keyword(s):  
Comparative Evaluation ◽  
Anomalous Behavior ◽  
Neural Nets ◽  
Data Sources ◽  
Support Vector ◽  
Software Sensor ◽  
Software Infrastructure ◽  
Machine Learning Classifiers ◽  
Vector Machines ◽  
Logistic Regressions

This paper describes the steps involved in obtaining a set of relevant data sources and the accompanying method using software-based sensors to detect anomalous behavior in modern smartphones based on machine-learning classifiers. Three classes of models are investigated for classification: logistic regressions, shallow neural nets, and support vector machines. The paper details the design, implementation, and comparative evaluation of all three classes. If necessary, the approach could be extended to other computing devices, if appropriate changes were made to the software infrastructure, based upon mandatory capabilities of the underlying hardware.

scholarly journals Big Data: New Tricks for Econometrics

2014 ◽  
Vol 28 (2) ◽  
pp. 3-28 ◽  
Author(s):  
Hal R. Varian
Keyword(s):  
Big Data ◽  
Linear Models ◽  
Neural Nets ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Model Complex ◽  
Vector Machines ◽  
Sheer Size ◽  
Complex Relationships

Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.

scholarly journals A decision-theoretic approach to the evaluation of machine learning algorithms in computational drug discovery

2019 ◽  
Vol 35 (22) ◽  
pp. 4656-4663 ◽  
Author(s):  
Oliver P Watson ◽  
Isidro Cortes-Ciriano ◽  
Aimee R Taylor ◽  
James A Watson
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Ridge Regression ◽  
Machine Learning Algorithms ◽  
Neural Nets ◽  
Loss Functions ◽  
Support Vector ◽  
Activity Distribution ◽  
Structure Activity ◽  
Vector Machines

Abstract Motivation Artificial intelligence, trained via machine learning (e.g. neural nets, random forests) or computational statistical algorithms (e.g. support vector machines, ridge regression), holds much promise for the improvement of small-molecule drug discovery. However, small-molecule structure-activity data are high dimensional with low signal-to-noise ratios and proper validation of predictive methods is difficult. It is poorly understood which, if any, of the currently available machine learning algorithms will best predict new candidate drugs. Results The quantile-activity bootstrap is proposed as a new model validation framework using quantile splits on the activity distribution function to construct training and testing sets. In addition, we propose two novel rank-based loss functions which penalize only the out-of-sample predicted ranks of high-activity molecules. The combination of these methods was used to assess the performance of neural nets, random forests, support vector machines (regression) and ridge regression applied to 25 diverse high-quality structure-activity datasets publicly available on ChEMBL. Model validation based on random partitioning of available data favours models that overfit and ‘memorize’ the training set, namely random forests and deep neural nets. Partitioning based on quantiles of the activity distribution correctly penalizes extrapolation of models onto structurally different molecules outside of the training data. Simpler, traditional statistical methods such as ridge regression can outperform state-of-the-art machine learning methods in this setting. In addition, our new rank-based loss functions give considerably different results from mean squared error highlighting the necessity to define model optimality with respect to the decision task at hand. Availability and implementation All software and data are available as Jupyter notebooks found at https://github.com/owatson/QuantileBootstrap. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A systematic literature review of automatic Alzheimer’s disease detection from speech and language

2020 ◽  
Vol 27 (11) ◽  
pp. 1784-1797 ◽  
Author(s):  
Ulla Petti ◽  
Simon Baker ◽  
Anna Korhonen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machines ◽  
Literature Review ◽  
Decision Trees ◽  
Systematic Literature Review ◽  
Neural Nets ◽  
Support Vector ◽  
Speech And Language ◽  
Vector Machines

Abstract Objective In recent years numerous studies have achieved promising results in Alzheimer’s Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. Materials and Methods We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? Results and Discussion We identified 33 eligible studies and 5 main findings: participants’ demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups. Conclusion The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.

scholarly journals Musenet : Music Generation using Abstractive and Generative Methods

2020 ◽  
Vol 9 (6) ◽  
pp. 784-788
Keyword(s):  
Music Composition ◽  
Neural Nets ◽  
Support Vector ◽  
Learning Models ◽  
Art Form ◽  
Vector Machines ◽  
Music Generation ◽  
Recent Trends ◽  
Generative Methods ◽  
Machine Learning Models

Humans have been entertained by music for millennia. For ages it has been treated as an art form which requires a lot of imagination, creativity and accumulation of feelings and emotions. Recent trends in the field of Artificial Intelligence have been getting traction and Researchers have been developing and generating rudimentary forms of music through the use of AI. Our goal is to generate novel music, which will be non-repetitive and enjoyable. We aim to utilize a couple of Machine Learning models for the same. Given a seed bar of music, our first Discriminatory network consisting of Support Vector Machines and Neural Nets will choose a note/chord to direct the next bar. Based on this chord or note another network, a Generative Net consisting of Generative Pretrained Transformers(GPT2) and LSTMs will generate the entire bar of music. Our two fold method is novel and our aim is to make the generation method as similar to music composition in reality as possible. This in turn results in better concordant music. Machine generated music will be copyright free and can be generated conditioned on a few parameters for a given use.The paper presents several use cases and while the utilization will be for a niche audience, if a easy to use application can be built, almost anyone will be able to use deep learning to generate concordant music based on their needs.

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