Big Data and Artificial Intelligence, 2nd Volume

Topic Information

Dear Colleagues,

The evolution of research in Big Data and artificial intelligence in recent years challenges almost all domains of human activity. The potential of artificial intelligence to act as a catalyst for all given business models, and the capacity of Big Data research to provide sophisticated data and services ecosystems at a global scale, provide a challenging context for scientific contributions and applied research. This Topic section promotes scientific dialogue for the added value of novel methodological approaches and research in the specified areas. Our interest is on the entire end-to-end spectrum of Big Data and artificial intelligence research, from social sciences to computer science including, strategic frameworks, models, and best practices, to sophisticated research related to radical innovation. The topics include, but are not limited to, the following indicative list:

• Enabling Technologies for Big Data and AI research:
• Data warehouses;
• Business intelligence;
• Machine learning;
• Neural networks;
• Natural language processing;
• Image processing;
• Bot technology;
• AI agents;
• Analytics and dashboards;
• Distributed computing;
• Edge computing.
• Methodologies, frameworks, and models for artificial intelligence and Big Data research:
• Towards sustainable development goals;
• As responses to social problems and challenges;
• For innovations in business, research, academia industry, and technology;
• For theoretical foundations and contributions to the bodyf knowledgef AI and Big Data research.
• Best practices and use cases;
• Outcomesf R&D projects;
• Advanced data science analytics;
• Industry-government collaboration;
• Systemsf information systems;
• Interoperability issues;
• Security and privacy issues;
• Ethicsn Big Data and AI;
• Social impactf AI;
• Open data.

Prof. Dr. Miltiadis D. Lytras
Prof. Dr. Andreea Claudia Serban
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Big Data and Cognitive Computing BDCC	3.7	4.9	2017	18.2 Days	CHF 1800
Economies economies	2.6	3.2	2013	21.4 Days	CHF 1800
Information information	3.1	5.8	2010	18 Days	CHF 1600
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400

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Published Papers (3 papers)

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All BDCC Economies Information Remote Sensing Sustainability

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16 pages, 3440 KiB  

Open AccessArticle

Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand

by Akkarapon Chaiyana, Ratchawatch Hanchoowong, Neti Srihanu, Haris Prasanchum, Anongrit Kangrang, Rattana Hormwichian, Siwa Kaewplang, Werapong Koedsin and Alfredo Huete

Sustainability 2024, 16(6), 2260; https://doi.org/10.3390/su16062260 - 08 Mar 2024

Viewed by 615

Abstract

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aims to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive [...] Read more.

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aims to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive model for predicting crop production for an internal crop growth season prior to harvest at the province scale for fourteen provinces in Thailand’s Chi basin between 2011 and 2019. We provide approaches for reducing redundant variables and multicollinearity in remotely sensed (RS) and meteorological data to avoid overfitting models using correlation analysis (CA) and the variance inflation factor (VIF). The temperature condition index (TCI), the normalized difference vegetation index (NDVI), land surface temperature (LST_nighttime), and mean temperature (Tmean) were the resulting variables in the prediction model with a p-value < 0.05 and a VIF < 5. The baseline data (2011–2017: June to November) were used to train four regression models, which revealed that eXtreme Gradient Boosting (XGBoost), random forest (RF), and XGBoost achieved R2 values of 0.95, 0.94, and 0.93, respectively. In addition, the testing dataset (2018–2019) displayed a minimum root-mean-square error (RMSE) of 0.18 ton/ha for the optimal solution by integrating variables and applying the XGBoost model. Accordingly, it is estimated that between 2020 and 2022, the total crop production in the Chi basin region will be 7.88, 7.64, and 7.72 million tons, respectively. The results demonstrated that the proposed model is proficient at greatly improving crop yield prediction accuracy when compared to a conventional regression method and that it may be deployed in different regions to assist farmers and policymakers in making more informed decisions about agricultural practices and resource allocation. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)

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22 pages, 2151 KiB  

Open AccessArticle

Enhancing Supervised Model Performance in Credit Risk Classification Using Sampling Strategies and Feature Ranking

by Niwan Wattanakitrungroj, Pimchanok Wijitkajee, Saichon Jaiyen, Sunisa Sathapornvajana and Sasiporn Tongman

Big Data Cogn. Comput. 2024, 8(3), 28; https://doi.org/10.3390/bdcc8030028 - 06 Mar 2024

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Abstract

For the financial health of lenders and institutions, one important risk assessment called credit risk is about correctly deciding whether or not a borrower will fail to repay a loan. It not only helps in the approval or denial of loan applications but [...] Read more.

For the financial health of lenders and institutions, one important risk assessment called credit risk is about correctly deciding whether or not a borrower will fail to repay a loan. It not only helps in the approval or denial of loan applications but also aids in managing the non-performing loan (NPL) trend. In this study, a dataset provided by the LendingClub company based in San Francisco, CA, USA, from 2007 to 2020 consisting of 2,925,492 records and 141 attributes was experimented with. The loan status was categorized as “Good” or “Risk”. To yield highly effective results of credit risk prediction, experiments on credit risk prediction were performed using three widely adopted supervised machine learning techniques: logistic regression, random forest, and gradient boosting. In addition, to solve the imbalanced data problem, three sampling algorithms, including under-sampling, over-sampling, and combined sampling, were employed. The results show that the gradient boosting technique achieves nearly perfect $Accuracy$, $Precision$, $Recall$, and $F1score$ values, which are better than 99.92%, but its $MCC$ values are greater than 99.77%. Three imbalanced data handling approaches can enhance the model performance of models trained by three algorithms. Moreover, the experiment of reducing the number of features based on mutual information calculation revealed slightly decreasing performance for 50 data features with $Accuracy$ values greater than 99.86%. For 25 data features, which is the smallest size, the random forest supervised model yielded 99.15% $Accuracy$. Both sampling strategies and feature selection help to improve the supervised model for accurately predicting credit risk, which may be beneficial in the lending business. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)

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23 pages, 4267 KiB  

Open AccessArticle

Nowcasting Unemployment Using Neural Networks and Multi-Dimensional Google Trends Data

by Andrius Grybauskas, Vaida Pilinkienė, Mantas Lukauskas, Alina Stundžienė and Jurgita Bruneckienė

Economies 2023, 11(5), 130; https://doi.org/10.3390/economies11050130 - 25 Apr 2023

Cited by 2 | Viewed by 2635

Abstract

This article forms an attempt to expand the ability of online search queries to predict initial jobless claims in the United States and further explore the intricacies of Google Trends. In contrast to researchers who used only a small number of search queries [...] Read more.

This article forms an attempt to expand the ability of online search queries to predict initial jobless claims in the United States and further explore the intricacies of Google Trends. In contrast to researchers who used only a small number of search queries or limited themselves to job agency explorations, we incorporated keywords from the following six dimensions of Google Trends searches: job search, benefits, and application; mental health; violence and abuse; leisure search; consumption and lifestyle; and disasters. We also propose the use of keyword optimization, dimension reduction techniques, and long-short memory neural networks to predict future initial claims changes. The findings suggest that including Google Trends keywords from other dimensions than job search leads to the improved forecasting of errors; however, the relationship between jobless claims and specific Google keywords is unstable in relation to time. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)

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