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 (4 papers)

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All Algorithms Applied Sciences Future Internet Information Mathematics

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27 pages, 4374 KiB  

Open AccessArticle

Quantifying Temporal Dynamics in Global Cyber Threats: A GPT-Driven Framework for Risk Forecasting and Strategic Intelligence

by Fahim Sufi and Musleh Alsulami

Mathematics 2025, 13(10), 1670; https://doi.org/10.3390/math13101670 - 20 May 2025

Abstract

Despite the exponential rise in cybersecurity incidents worldwide, existing analytical approaches often fail to detect subtle temporal dynamics in cyber threats, particularly on a quarterly scale. This paper addresses a critical research gap in the domain of temporal cyber risk analysis by introducing [...] Read more.

Despite the exponential rise in cybersecurity incidents worldwide, existing analytical approaches often fail to detect subtle temporal dynamics in cyber threats, particularly on a quarterly scale. This paper addresses a critical research gap in the domain of temporal cyber risk analysis by introducing a mathematically rigorous and AI-augmented framework capable of identifying, validating, and forecasting quarterly shifts in global cyber-attack patterns. The methodology integrates a hybrid data acquisition pipeline with GPT-based AI classification to construct a structured, high-dimensional dataset comprising 11,497 cybersecurity incidents spanning from October 2023 to March 2025. These incidents cover 106 attack types, 29 industries, and 257 countries. The framework decomposes the dataset into quarterly intervals and applies mathematical formulations to compute frequency shifts across categorical variables (attack types, industries, countries) and numerical variables (attack significance), followed by robust statistical validations (Chi-square and ANOVA tests), time-series forecasting via ARIMA, and the computation of a Quarterly Composite Index (QCI). Key results reveal dominant attack types—Social Engineering (ing1733) and Zero-Day Exploits (1657)—and highlight sectoral vulnerabilities in IT (5959) and Government (2508). Statistically significant quarterly variations were confirmed (${\chi }^2=2319.13$, $F=3.78$, $p<0.001$). ARIMA forecasts predict 1782–2080 incidents per quarter for 2025–2026, while QCI trends average around 0.75, signifying sustained volatility. The research delivers both theoretical and practical advancements by combining generative AI, temporal segmentation, and statistical modeling to create an operationalizable intelligence system. This contribution enhances strategic cybersecurity preparedness and policymaking in a complex, evolving threat landscape. Full article

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

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15 pages, 983 KiB  

Open AccessArticle

Task-Oriented Local Feature Rectification Network for Few-Shot Image Classification

by Ping Li and Xiang Zhu

Mathematics 2025, 13(9), 1519; https://doi.org/10.3390/math13091519 - 5 May 2025

Viewed by 232

Abstract

Few-shot image classification aims to classify unlabeled samples when only a small number of labeled samples are available for each class. Recently, local feature-based few-shot learning methods have made significant progress. However, existing methods often treat all local descriptors equally, without considering the [...] Read more.

Few-shot image classification aims to classify unlabeled samples when only a small number of labeled samples are available for each class. Recently, local feature-based few-shot learning methods have made significant progress. However, existing methods often treat all local descriptors equally, without considering the importance of each local descriptor in different tasks. Therefore, the few-shot learning model is easily disturbed by class-irrelevant features, which results in a decrease in accuracy. To address this issue, we propose a task-oriented local feature rectification network (TLFRNet) with two feature rectification modules (support rectification module and query rectification module). The former module uses the relationship between each local descriptor and prototypes within the support set to rectify the support features. The latter module uses a CNN to rectify the similarity tensors between the query and support local features and then models the importance of the query local features. Through these two modules, our model can effectively reduce the intra-class variation of class-relevant features, thus obtaining more accurate image-to-class similarity for classification. Extensive experiments on five datasets show that TLFRNet achieves more superior classification performance than the related methods. Full article

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

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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 519

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 1170

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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