Soft Computing and Machine Learning

Topic Information

Dear Colleagues,

Soft computing methodologies, techniques, and algorithms focus on approximate models and aim to provide solutions to complex problems. These algorithms aim to be tolerant of imprecision, uncertainty, partial truth, and approximation. Soft computing is the subject of both theoretical and practical research, and soft computing techniques are currently being applied in many applications in areas such as industrial systems, commercial, or domestic applications.

This Topic is open to receive submissions of high-quality papers regarding advances in soft computing and its applications. The themes of the papers include, but are not limited to, computational intelligence, computational learning, machine learning, intelligent control, fuzzy systems, neural networks, genetic algorithms, ant colony, particle swarm, other evolutionary algorithms, other probabilistic computing, rough sets, hybrid methods, wavelets, expert systems, optimization, modeling, estimation, prediction, simulation, control, big data, robotics, mobile robotics and intelligent vehicles, robot manipulator control, sensing, soft sensors, automation, industrial systems, embedded systems, and real-time systems.

The Topic aims to provide for the rapid dissemination of important research in soft computing technologies. It encourages the integration and cross-fertilization of soft computing techniques and other scientific areas, from both theoretical and practical points of view. It aims to link ideas and techniques from soft computing with other disciplines and with advanced applications.

Application areas include, but are not limited to: robotics, intelligent agents, signal and image processing, computer vision, system monitoring, fault detection and diagnosis, control systems, systems identification and modeling, optimization, process optimization, multi-objective optimization, decision support, autonomous reasoning, manufacturing systems, power systems, energy systems, mechatronics, nano- and microsystems, motion control and power electronics, industrial electronics, time series prediction, human–machine interfaces, virtual reality, intelligent agents, consumer electronics, bio-inspired algorithms, biomedical engineering, agricultural systems and production, data mining, and data visualization.

Dr. Rui Araújo
Dr. António Pedro Aguiar
Dr. Nuno Lau
Dr. Rodrigo Ventura
Dr. João Fabro
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	1.8	4.1	2008	18.9 Days	CHF 1600	Submit
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Future Internet futureinternet	2.8	7.1	2009	16.9 Days	CHF 1600	Submit
Information information	2.4	6.9	2010	16.4 Days	CHF 1600	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit

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Published Papers (2 papers)

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All Algorithms Applied Sciences Future Internet Information Mathematics

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28 pages, 29565 KiB  

Open AccessArticle

AI-Driven Global Disaster Intelligence from News Media

by Fahim Sufi and Musleh Alsulami

Mathematics 2025, 13(7), 1083; https://doi.org/10.3390/math13071083 - 26 Mar 2025

Viewed by 371

Abstract

Open-source disaster intelligence (OSDI) is crucial for improving situational awareness, disaster preparedness, and real-time decision-making. Traditional OSDI frameworks often rely on social media data, which are susceptible to misinformation and credibility issues. This study proposes a novel AI-driven framework utilizing automated data collection [...] Read more.

Open-source disaster intelligence (OSDI) is crucial for improving situational awareness, disaster preparedness, and real-time decision-making. Traditional OSDI frameworks often rely on social media data, which are susceptible to misinformation and credibility issues. This study proposes a novel AI-driven framework utilizing automated data collection from 444 large-scale online news portals, including CNN, BBC, CBS News, and The Guardian, to enhance data reliability. Over a 514-day period (27 September 2023 to 26 February 2025), 1.25 million news articles were collected, of which 17,884 were autonomously classified as disaster-related using Generative Pre-Trained Transformer (GPT) models. The analysis identified 185 distinct countries and 6068 unique locations, offering unprecedented geospatial and temporal intelligence. Advanced clustering and predictive analytics techniques, including K-means, DBSCAN, seasonal decomposition (STL), Fourier transform, and ARIMA, were employed to detect geographical hotspots, cyclical patterns, and temporal dependencies. The ARIMA (2, 1, 2) model achieved a mean squared error (MSE) of 823,761, demonstrating high predictive accuracy. Key findings highlight that the USA (6548 disasters), India (1393 disasters), and Australia (1260 disasters) are the most disaster-prone countries, while hurricanes/typhoons/cyclones (5227 occurrences), floods (3360 occurrences), and wildfires (2724 occurrences) are the most frequent disaster types. The framework establishes a comprehensive methodology for integrating geospatial clustering, temporal analysis, and multimodal data processing in OSDI. By leveraging AI automation and diverse news sources, this study provides a scalable, adaptable, and ethically robust solution for proactive disaster management, improving global resilience and preparedness. Full article

(This article belongs to the Topic Soft Computing and Machine Learning)

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19 pages, 1622 KiB  

Open AccessArticle

AI-Driven Chatbot for Real-Time News Automation

by Fahim Sufi and Musleh Alsulami

Mathematics 2025, 13(5), 850; https://doi.org/10.3390/math13050850 - 4 Mar 2025

Viewed by 988

Abstract

The rapid expansion of digital news sources has necessitated intelligent systems capable of filtering, analyzing, and deriving meaningful insights from vast amounts of information in real time. This study presents an AI-driven chatbot designed for real-time news automation, integrating advanced natural language processing [...] Read more.

The rapid expansion of digital news sources has necessitated intelligent systems capable of filtering, analyzing, and deriving meaningful insights from vast amounts of information in real time. This study presents an AI-driven chatbot designed for real-time news automation, integrating advanced natural language processing techniques, knowledge graphs, and generative AI models to improve news summarization and correlation analysis. The chatbot processes over 1,306,518 news reports spanning from 25 September 2023 to 17 February 2025, categorizing them into 15 primary event categories and extracting key insights through structured analysis. By employing state-of-the-art machine learning techniques, the system enables real-time classification, interactive query-based exploration, and automated event correlation. The chatbot demonstrated high accuracy in both summarization and correlation tasks, achieving an average F1 score of 0.94 for summarization and 0.92 for correlation analysis. Summarization queries were processed within an average response time of 9 s, while correlation analyses required approximately 21 s per query. The chatbot’s ability to generate real-time, concise news summaries and uncover hidden relationships between events makes it a valuable tool for applications in disaster response, policy analysis, cybersecurity, and public communication. This research contributes to the field of AI-driven news analytics by bridging the gap between static news retrieval platforms and interactive conversational agents. Future work will focus on expanding multilingual support, enhancing misinformation detection, and optimizing computational efficiency for broader real-world applicability. The proposed chatbot stands as a scalable and adaptive solution for real-time decision support in dynamic information environments. Full article

(This article belongs to the Topic Soft Computing and Machine Learning)

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