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Applied Sciences
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Machine Learning Techniques for the Exploration and Understanding of Complex...
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Machine Learning Techniques for the Exploration and Understanding of Complex Systems II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 6285

Special Issue Editors

Dr. Alfonso Monaco

E-Mail Website
Guest Editor
Section of Bari, National Institute for Nuclear Physics (INFN), 70125 Bari, Italy
Interests: machine learning; deep learning; complex networks; big data analysis; neurodegeneratve diseases; imaging; complex systems
Special Issues, Collections and Topics in MDPI journals
Dr. Loredana Bellantuono

E-Mail Website
Guest Editor
Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, 70124 Bari, Italy
Interests: complex networks; machine learning; deep learning; natural language processing; neuroscience; social physics; genomics; complex systems

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the application of machine learning techniques for the study of complex systems, which are composed of several units that interact with each other through relationships that are difficult to detect and interpret using conventional statistical approaches. In particular, complex systems are characterized by the emergence of collective behaviors of constituents that cannot be inferred from their individual properties. Nowadays, the investigation of complexity patterns represents a primary need to unveil the mechanisms behind the evolution of complex systems related to different domains, such as biology, medicine, economics, and social science. Moreover, complex systems science can help to bridge the gap between science, humanities, engineering, and policy, providing a robust approach to address crucial challenges such as the sustainaibility, climate change, health, and social equity.

Machine learning methods provide effective strategies to deal with complex systems, thanks to their ability to extract relevant information from large and heterogeneous datasets. In this Special Issue, we aim to collect research works concerning the usage of machine learning methods to find paths and highlight relationships between the constituent parts of complex systems. We particularly welcome articles in biological, clinical, physical, and social fields, in which it is emphasized how machine learning techniques are able to solve problems more efficiently than traditional statistical methods.

This Special Issue is the second volume; the first volume is available at https://www.mdpi.com/journal/applsci/special_issues/Machine_Learning_Techniques_for_the_Study_of_Complex_Systems)

Dr. Alfonso Monaco
Dr. Loredana Bellantuono
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • complex networks
  • complex systems
  • data mining
  • data science
  • natural language processing
  • neurodegeneratve diseases
  • imaging
  • genomics
  • social physics

Published Papers (3 papers)

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Research

22 pages, 6124 KiB  
Article
Detection of Cyberbullying Patterns in Low Resource Colloquial Roman Urdu Microtext using Natural Language Processing, Machine Learning, and Ensemble Techniques
by Amirita Dewani, Mohsin Ali Memon, Sania Bhatti, Adel Sulaiman, Mohammed Hamdi, Hani Alshahrani, Abdullah Alghamdi and Asadullah Shaikh
Appl. Sci. 2023, 13(4), 2062; https://doi.org/10.3390/app13042062 - 05 Feb 2023
Cited by 4 | Viewed by 2092
Abstract
Social media platforms have become a substratum for people to enunciate their opinions and ideas across the globe. Due to anonymity preservation and freedom of expression, it is possible to humiliate individuals and groups, disregarding social etiquette online, inevitably proliferating and diversifying the [...] Read more.
Social media platforms have become a substratum for people to enunciate their opinions and ideas across the globe. Due to anonymity preservation and freedom of expression, it is possible to humiliate individuals and groups, disregarding social etiquette online, inevitably proliferating and diversifying the incidents of cyberbullying and cyber hate speech. This intimidating problem has recently sought the attention of researchers and scholars worldwide. Still, the current practices to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the recent prevalence of regional languages in social media, the dearth of language resources, and flexible detection approaches, specifically for low-resource languages. In this context, most existing studies are oriented towards traditional resource-rich languages and highlight a huge gap in recently embraced resource-poor languages. One such language currently adopted worldwide and more typically by South Asian users for textual communication on social networks is Roman Urdu. It is derived from Urdu and written using a Left-to-Right pattern and Roman scripting. This language elicits numerous computational challenges while performing natural language preprocessing tasks due to its inflections, derivations, lexical variations, and morphological richness. To alleviate this problem, this research proposes a cyberbullying detection approach for analyzing textual data in the Roman Urdu language based on advanced preprocessing methods, voting-based ensemble techniques, and machine learning algorithms. The study has extracted a vast number of features, including statistical features, word N-Grams, combined n-grams, and BOW model with TFIDF weighting in different experimental settings using GridSearchCV and cross-validation techniques. The detection approach has been designed to tackle users’ textual input by considering user-specific writing styles on social media in a colloquial and non-standard form. The experimental results show that SVM with embedded hybrid N-gram features produced the highest average accuracy of around 83%. Among the ensemble voting-based techniques, XGboost achieved the optimal accuracy of 79%. Both implicit and explicit Roman Urdu instances were evaluated, and the categorization of severity based on prediction probabilities was performed. Time complexity is also analyzed in terms of execution time, indicating that LR, using different parameters and feature combinations, is the fastest algorithm. The results are promising with respect to standard assessment metrics and indicate the feasibility of the proposed approach in cyberbullying detection for the Roman Urdu language. Full article
(This article belongs to the Special Issue Machine Learning Techniques for the Exploration and Understanding of Complex Systems II)
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15 pages, 2684 KiB  
Communication
Developing and Preliminary Testing of a Machine Learning-Based Platform for Sales Forecasting Using a Gradient Boosting Approach
by Antonio Panarese, Giuseppina Settanni, Valeria Vitti and Angelo Galiano
Appl. Sci. 2022, 12(21), 11054; https://doi.org/10.3390/app122111054 - 31 Oct 2022
Cited by 3 | Viewed by 1999
Abstract
Organizations engaged in business, regardless of the industry in which they operate, must be able to extract knowledge from the data available to them. Often the volume of customer and supplier data is so large, the use of advanced data mining algorithms is [...] Read more.
Organizations engaged in business, regardless of the industry in which they operate, must be able to extract knowledge from the data available to them. Often the volume of customer and supplier data is so large, the use of advanced data mining algorithms is required. In particular, machine learning algorithms make it possible to build predictive models in order to forecast customer demand and, consequently, optimize the management of supplies and warehouse logistics. We base our analysis on the use of the XGBoost as a predictive model, since this is now considered to provide the more efficient implementation of gradient boosting, shown with a numerical comparison. Preliminary tests lead to the conclusion that the XGBoost regression model is more accurate in predicting future sales in terms of various error metrics, such as MSE (Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and WAPE (Weighted Absolute Percentage Error). In particular, the improvement measured in tests using WAPE metric is in the range 15–20%. Full article
(This article belongs to the Special Issue Machine Learning Techniques for the Exploration and Understanding of Complex Systems II)
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20 pages, 5291 KiB  
Article
Territorial Development as an Innovation Driver: A Complex Network Approach
by Francesco De Nicolò, Alfonso Monaco, Giuseppe Ambrosio, Loredana Bellantuono, Roberto Cilli, Ester Pantaleo, Sabina Tangaro, Flaviano Zandonai, Nicola Amoroso and Roberto Bellotti
Appl. Sci. 2022, 12(18), 9069; https://doi.org/10.3390/app12189069 - 09 Sep 2022
Cited by 1 | Viewed by 1512
Abstract
Rankings are a well-established tool to evaluate the performance of actors in different sectors of the economy, and their use is increasing even in the context of the startup ecosystem, both on a regional and on a global scale. Although rankings meet the [...] Read more.
Rankings are a well-established tool to evaluate the performance of actors in different sectors of the economy, and their use is increasing even in the context of the startup ecosystem, both on a regional and on a global scale. Although rankings meet the demand for measurability and comparability, they often provide an oversimplified picture of the status quo, which, in particular, overlooks the variability of the socio-economic conditions in which the quantified results are achieved. In this paper, we describe an approach based on constructing a network of world countries, in which links are determined by mutual similarity in terms of development indicators. Through the instrument of community detection, we perform an unsupervised partition of the considered set of countries, aimed at interpreting their performance in the StartupBlink rankings. We consider both the global ranking and the specific ones (quality, quantity, business). After verifying if community membership is predictive of the success of a country in the considered ranking, we rate country performances in terms of the expectation based on community peers. We are thus able to identify cases in which performance is better than expected, providing a benchmark for countries in similar conditions, and cases in which performance is below the expectation, highlighting the need to strengthen the innovation ecosystem. Full article
(This article belongs to the Special Issue Machine Learning Techniques for the Exploration and Understanding of Complex Systems II)
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