Subset-binding: A novel algorithm to detect paired itemsets from heterogeneous data including biological datasets

Learning Techniques ◽

Wide Range ◽

Fuzzy Association Rule ◽

Consensus Information ◽

Fuzzy Association Rule Mining ◽

The Relationship ◽

Novel Algorithm

Abstract The integration of heterogeneous data to infer latent relationships across them and find the factors in the relationship is a challenging task. In this regard, various machine learning techniques have provided novel insights through data integration. However, concerns remain regarding their application to biological datasets because the latent consensus information across all views is often limited to partial components that do not have a significant impact on the mutual agreement across views. Advocating the idea of “subset-binding,” which focuses on finding inter-related attributes in heterogeneous data according to their co-occurrence, this study developed a novel algorithm to perform subset-binding by extending fuzzy association rule mining techniques. Our method could detect genes related to liver toxicity caused by acetaminophen in a data-driven manner; the results are consistent with those reported in the literature. This technology paves the way for a wide range of applications, including biomarker detection and patient stratification.

Delay Prediction of Aircrafts Based on Health Monitoring Data

International Journal of Business Analytics and Intelligence ◽

10.21863/ijbai/2016.4.1.015 ◽

2016 ◽

Vol 4 (1) ◽

Author(s):

B. A. Dattaram ◽

N. Madhusudanan

Keyword(s):

Machine Learning ◽

Health Monitoring ◽

Monitoring Data ◽

Learning Techniques ◽

Flight Delay ◽

Delay Prediction ◽

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

Journal Of Big Data ◽

10.1186/s40537-021-00539-2 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Majid Amirfakhrian ◽

Mahboub Parhizkar

Keyword(s):

Machine Learning ◽

Image Processing ◽

Machine Vision ◽

Traffic Accidents ◽

Fuzzy Theory ◽

Industrial Setting ◽

Technological Advances ◽

Learning Techniques ◽

Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

Near real-time air quality forecasts using the NASA GEOS model

10.5194/egusphere-egu21-13587 ◽

2021 ◽

Author(s):

K. Emma Knowland ◽

Christoph Keller ◽

Krzysztof Wargan ◽

Brad Weir ◽

Pamela Wales ◽

...

Keyword(s):

Machine Learning ◽

Air Quality ◽

Real Time ◽

Weather Forecasting ◽

Atmospheric Composition ◽

Learning Techniques ◽

Wide Range ◽

Reactive Trace Gases ◽

Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

Weather Prediction using Advanced Machine Learning Techniques

Journal of Physics Conference Series ◽

10.1088/1742-6596/2089/1/012059 ◽

2021 ◽

Vol 2089 (1) ◽

pp. 012059

Author(s):

G. Hemalatha ◽

K. Srinivasa Rao ◽

D. Arun Kumar

Keyword(s):

Linear Relationship ◽

Weather Prediction ◽

Weather Condition ◽

Weather Conditions ◽

Weather Data ◽

Learning Techniques ◽

Proposed Model ◽

Non Linear ◽

Abstract Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..

Big Data Analytics for Tourism Destinations

Encyclopedia of Information Science and Technology, Fourth Edition ◽

10.4018/978-1-5225-2255-3.ch031 ◽

2018 ◽

pp. 349-363 ◽

Author(s):

Wolfram Höpken ◽

Matthias Fuchs ◽

Maria Lexhagen

Keyword(s):

Data Analysis ◽

Big Data Analytics ◽

Heterogeneous Data ◽

Implicit Information ◽

Heterogeneous Data Sources ◽

Learning Techniques ◽

Exploratory Data ◽

Tourism Destinations ◽

User Ratings

The objective of this chapter is to address the above deficiencies in tourism by presenting the concept of the tourism knowledge destination – a specific knowledge management architecture that supports value creation through enhanced supplier interaction and decision making. Information from heterogeneous data sources categorized into explicit feedback (e.g. tourist surveys, user ratings) and implicit information traces (navigation, transaction and tracking data) is extracted by applying semantic mapping, wrappers or text mining (Lau et al., 2005). Extracted data are stored in a central data warehouse enabling a destination-wide and all-stakeholder-encompassing data analysis approach. By using machine learning techniques interesting patterns are detected and knowledge is generated in the form of validated models (e.g. decision trees, neural networks, association rules, clustering models). These models, together with the underlying data (in the case of exploratory data analysis) are interactively visualized and made accessible to destination stakeholders.

Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19

Briefings in Bioinformatics ◽

10.1093/bib/bbaa259 ◽

2020 ◽

Cited By ~ 2

Author(s):

Anjali Dhall ◽

Sumeet Patiyal ◽

Neelam Sharma ◽

Salman Sadullah Usmani ◽

Gajendra P S Raghava

Keyword(s):

Scientific Community ◽

Prediction Models ◽

Vital Role ◽

Validation Dataset ◽

Independent Validation ◽

Immune Epitope ◽

Learning Techniques ◽

Wide Range ◽

Immune Epitope Database

Abstract Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

Using AI Methods to Evaluate a Minimal Model for Perception

Open Philosophy ◽

10.1515/opphil-2019-0034 ◽

2019 ◽

Vol 2 (1) ◽

pp. 503-524 ◽

Author(s):

Robert Prentner ◽

Chris Fields

Keyword(s):

Conceptual Understanding ◽

Single Agent ◽

Psychological Problem ◽

Distributed Network ◽

Perceptual Inference ◽

Interacting Agents ◽

Learning Techniques ◽

AbstractThe relationship between philosophy and research on artificial intelligence (AI) has been difficult since its beginning, with mutual misunderstanding and sometimes even hostility. By contrast, we show how an approach informed by both philosophy and AI can be productive. After reviewing some popular frameworks for computation and learning, we apply the AI methodology of “build it and see” to tackle the philosophical and psychological problem of characterizing perception as distinct from sensation. Our model comprises a network of very simple, but interacting agents which have binary experiences of the “yes/no”-type and communicate their experiences with each other. When does such a network refer to a single agent instead of a distributed network of entities? We apply machine learning techniques to address the following related questions: i) how can the model explain stability of compound entities, and ii) how could the model implement a single task such as perceptual inference? We thereby find consistency with previous work on “interface” strategies from perception research.While this reflects some necessary conditions for the ascription of agency, we suggest that it is not sufficient. Here, AI research, if it is intended to contribute to conceptual understanding, would benefit from issues previously raised by philosophy. We thus conclude the article with a discussion of action-selection, the role of embodiment, and consciousness to make this more explicit. We conjecture that a combination of AI research and philosophy allows general principles of mind and being to emerge from a “quasi-empirical” investigation.

Age of the entrepreneurial decision: Differences among developed, developing, and non- developed countries

Economics and Business Letters ◽

10.17811/ebl.7.1.2018.36-46 ◽

2018 ◽

Vol 7 (1) ◽

pp. 36 ◽

Cited By ~ 2

Author(s):

Alicia Coduras ◽

Jorge Velilla ◽

Raquel Ortega

Keyword(s):

Machine Learning ◽

Social Norms ◽

Survey Data ◽

Population Survey ◽

Adult Population ◽

Developed Countries ◽

Learning Techniques ◽

Entrepreneurial Decision ◽

Although entrepreneurship is widely considered an engine of growth, it is not clear whether policies, de facto, promote it, and knowing which individuals are willing to become entrepreneurs could help in the design of those policies. In this paper, we study how individuals become entrepreneurs at different ages, according to the degree of development of the country of residence. We make use of the GEM 2014 Adult Population Survey data, against a background where social norms are controlled, to find that the relationship between entrepreneurship and age follows an inverted U-shape, according to machine learning techniques, and that younger individuals are the most willing to become entrepreneurs.

Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology

Sensors ◽

10.3390/s21041322 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1322

Author(s):

Wilfredo Graterol ◽

Jose Diaz-Amado ◽

Yudith Cardinale ◽

Irvin Dongo ◽

Edmundo Lopes-Silva ◽

...

Keyword(s):

Language Processing ◽

Application Programming Interface ◽

Social Robots ◽

Classification Model ◽

Emotional States ◽

Emotion Detection ◽

Learning Techniques ◽

Wide Range ◽

Application Programming

For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.

Emotional Decision-Making Biases Prediction in Cyber-Physical Systems

Big Data and Cognitive Computing ◽

10.3390/bdcc3030049 ◽

2019 ◽

Vol 3 (3) ◽

pp. 49 ◽

Author(s):

Alberto Corredera ◽

Marta Romero ◽

Jose M. Moya

Keyword(s):

Decision Making ◽

Cyber Physical Systems ◽

Specific Context ◽

Physical Systems ◽

Area Of Interest ◽

Learning Techniques ◽

External Stimuli ◽

The Relationship ◽

Decision Making Biases

This article faces the challenge of discovering the trends in decision-making based on capturing emotional data and the influence of the possible external stimuli. We conducted an experiment with a significant sample of the workforce and used machine-learning techniques to model the decision-making process. We studied the trends introduced by the emotional status and the external stimulus that makes these personnel act or report to the supervisor. The main result of this study is the production of a model capable of predicting the bias to act in a specific context. We studied the relationship between emotions and the probability of acting or correcting the system. The main area of interest of these issues is the ability to influence in advance the personnel to make their work more efficient and productive. This would be a whole new line of research for the future.