scholarly journals Creative AI: From Expressive Mimicry to Critical Inquiry

Artnodes ◽  
2020 ◽  
Author(s):  
Angus Forbes
Keyword(s):  
Machine Learning ◽  
New Media ◽  
Feature Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Media Arts ◽  
Generative Art ◽  
Applied Machine Learning ◽  
Insight Into

The nascent field of what has come to be known as “creative AI” consists of a range of activities at the intersections of new media arts, human-computer interaction, and artificial intelligence. This article provides an overview of recent projects that emphasise the use of machine learning algorithms as a means to identify, replicate, and modify features in existing media, to facilitate new multimodal mappings between user inputs and media outputs, to push the boundaries of generative art experiences, and to critically investigate the role of feature detection and pattern identification technologies in contemporary life. Despite the proliferation of such projects, recent advances in applied machine learning have not yet been incorporated into or interrogated by creative AI projects, and this article also highlights opportunities for computational artists working in this area. The article concludes by envisioning how creative AI practice could include delineating the boundaries of what can and cannot be learned by extracting features from artefacts and experiences, exploring how new forms of interpretation can be encoded into neural networks, and articulating how the interaction of multiple machine learning algorithms can be used to generate new insight into the intertwining sociotechnical systems that encompass our lives.

Role of machine learning algorithms over heart diseases prediction

2020 ◽  
Author(s):  
Siva Kumar Jonnavithula ◽  
Abhilash Kumar Jha ◽  
Modepalli Kavitha ◽  
Singaraju Srinivasulu
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

The Model Selection Curse

2019 ◽  
Vol 1 (2) ◽  
pp. 127-140 ◽  
Author(s):  
Kfir Eliaz ◽  
Ran Spiegler
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Learning Algorithms ◽  
Personal Data ◽  
Estimation Procedure ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Truth Telling ◽  
Agent Based

A statistician takes an action on behalf of an agent, based on the agent’s self-reported personal data and a sample involving other people. The action that he takes is an estimated function of the agent’s report. The estimation procedure involves model selection. We ask the following question: Is truth-telling optimal for the agent given the statistician’s procedure? We analyze this question in the context of a simple example that highlights the role of model selection. We suggest that our simple exercise may have implications for the broader issue of human interaction with machine learning algorithms. (JEL C52)

scholarly journals Machine Learning Algorithms Help Scientists Explore Mars

Eos ◽  
2022 ◽  
Vol 103 ◽  
Author(s):  
JoAnna Wendel
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Chemical Compositions ◽  
Applied Machine Learning

Researchers applied machine learning algorithms to several distinct chemical compositions of Mars and suggest that these algorithms could be a powerful tool to map the planet’s surface on a large scale.

An Insight into Machine Learning Algorithms to Map the Occurrence of the Soil Mattic Horizon in the Northeastern Qinghai-Tibetan Plateau

Pedosphere ◽  
2018 ◽  
Vol 28 (5) ◽  
pp. 739-750 ◽  
Author(s):  
Junjun ZHI ◽  
Ganlin ZHANG ◽  
Renmin YANG ◽  
Fei YANG ◽  
Chengwei JIN ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tibetan Plateau ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Insight Into

scholarly journals Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1111 ◽  
Author(s):  
Anita Rácz ◽  
Dávid Bajusz ◽  
Károly Héberger
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Performance Parameters ◽  
Factorial Anova ◽  
Applied Machine Learning ◽  
Dataset Size ◽  
Best Parameter

Applied datasets can vary from a few hundred to thousands of samples in typical quantitative structure-activity/property (QSAR/QSPR) relationships and classification. However, the size of the datasets and the train/test split ratios can greatly affect the outcome of the models, and thus the classification performance itself. We compared several combinations of dataset sizes and split ratios with five different machine learning algorithms to find the differences or similarities and to select the best parameter settings in nonbinary (multiclass) classification. It is also known that the models are ranked differently according to the performance merit(s) used. Here, 25 performance parameters were calculated for each model, then factorial ANOVA was applied to compare the results. The results clearly show the differences not just between the applied machine learning algorithms but also between the dataset sizes and to a lesser extent the train/test split ratios. The XGBoost algorithm could outperform the others, even in multiclass modeling. The performance parameters reacted differently to the change of the sample set size; some of them were much more sensitive to this factor than the others. Moreover, significant differences could be detected between train/test split ratios as well, exerting a great effect on the test validation of our models.

scholarly journals Prospects and Challenges for Clinical Decision Support in the Era of Big Data

2018 ◽  
pp. 1-12 ◽  
Author(s):  
Issam El Naqa ◽  
Michael R. Kosorok ◽  
Judy Jin ◽  
Michelle Mierzwa ◽  
Randall K. Ten Haken
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Learning Algorithms ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Current Status ◽  
Analytical Approaches

Recently, there has been burgeoning interest in developing more effective and robust clinical decision support systems (CDSSs) for oncology. This has been primarily driven by the demands for more personalized and precise medical practice in oncology in the era of so-called big data (BD), an era that promises to harness the power of large-scale data flow to revolutionize cancer treatment. This interest in BD analytics has created new opportunities as well as new unmet challenges. These include: routine aggregation and standardization of clinical data, patient privacy, transformation of current analytical approaches to handle such noisy and heterogeneous data, and expanded use of advanced statistical learning methods on the basis of confluence of modern statistical methods and machine learning algorithms. In this review, we present the current status of CDSSs in oncology, the prospects and current challenges of BD analytics, and the promising role of integrated modern statistics and machine learning algorithms in predicting complex clinical end points, individualizing treatment rules, and optimizing dynamic personalized treatment regimens. We discuss issues pertaining to these topics and present application examples from an aggregate of experiences. We also discuss the role of human factors in improving the use and acceptance of such enhanced CDSSs and how to mitigate possible sources of human error to achieve optimal performance and wider acceptance.

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