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18 pages, 4559 KiB

Open AccessArticle

Trends of Social Anxiety in University Students of Pakistan Post-COVID-19 Lockdown: A Healthcare Analytics Perspective

by Ikram E. Khuda, Azeem Aftab, Sajid Hasan, Samar Ikram, Sadique Ahmad, Abdelhamied Ashraf Ateya and Muhammad Asim

Information 2024, 15(7), 373; https://doi.org/10.3390/info15070373 (registering DOI) - 28 Jun 2024

Abstract

This paper disseminates our research findings that we conducted on university students in the years 2021, 2022, and 2023, with the year 2021 taken as the base year. Our research mined and excavated a concealed prevalence of social anxiety as an important and [...] Read more.

This paper disseminates our research findings that we conducted on university students in the years 2021, 2022, and 2023, with the year 2021 taken as the base year. Our research mined and excavated a concealed prevalence of social anxiety as an important and crucial facet of study anxiety in the university students of Pakistan. Using the Liebowitz Social Anxiety Scale (LSAS), we found a significant increase in the social anxiety level among university students in the past three years after the COVID-19 lockdown. Our data showed that the ‘very severe anxiety’ level soared up to 52.94% in the year 2023 from just 5.98% in the year 2021, showing a net increase of 47.06%. Statistical analyses demonstrate noteworthy differences in the overall social anxiety levels among the students, reaching significance at the 5% level and a discernable upward trend in the social anxiety levels as study anxiety. We also employed predictive analytics, including binary classifiers and generalized linear models with a 95% confidence interval, to identify individuals at risk. This study highlights a dynamic shift with escalating social anxiety levels among the university students and thus emphasizing its awareness, which is significantly important for the timely intervention, potentially preventing symptom escalation and improving outcomes. Full article

(This article belongs to the Special Issue Health Data Information Retrieval)

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20 pages, 1066 KiB

Open AccessArticle

Mining Spatial-Temporal Frequent Patterns of Natural Disasters in China Based on Textual Records

by Aiai Han, Wen Yuan, Wu Yuan, Jianwen Zhou, Xueyan Jian, Rong Wang and Xinqi Gao

Information 2024, 15(7), 372; https://doi.org/10.3390/info15070372 - 27 Jun 2024

Viewed by 69

Abstract

Natural disasters pose serious threats to human survival. With global warming, disaster chains related to extreme weather are becoming more common, making it increasingly urgent to understand the relationships between different types of natural disasters. However, there remains a lack of research on [...] Read more.

Natural disasters pose serious threats to human survival. With global warming, disaster chains related to extreme weather are becoming more common, making it increasingly urgent to understand the relationships between different types of natural disasters. However, there remains a lack of research on the frequent spatial-temporal intervals between different disaster events. In this study, we utilize textual records of natural disaster events to mine frequent spatial-temporal patterns of disasters in China. We first transform the discrete spatial-temporal disaster events into a graph structure. Due to the limit of computing power, we reduce the number of edges in the graph based on domain expertise. We then apply the GraMi frequent subgraph mining algorithm to the spatial-temporal disaster event graph, and the results reveal frequent spatial-temporal intervals between disasters and reflect the spatial-temporal changing pattern of disaster interactions. For example, the pattern of sandstorms happening after gales is mainly concentrated within 50 km and rarely happens at farther spatial distances, and the most common temporal interval is 1 day. The statistical results of this study provide data support for further understanding disaster association patterns and offer decision-making references for disaster prevention efforts. Full article

22 pages, 1425 KiB

Open AccessArticle

Towards Reliable Healthcare LLM Agents: A Case Study for Pilgrims during Hajj

by Hanan M. Alghamdi and Abeer Mostafa

Information 2024, 15(7), 371; https://doi.org/10.3390/info15070371 - 26 Jun 2024

Viewed by 195

Abstract

There is a pressing need for healthcare conversational agents with domain-specific expertise to ensure the provision of accurate and reliable information tailored to specific medical contexts. Moreover, there is a notable gap in research ensuring the credibility and trustworthiness of the information provided [...] Read more.

There is a pressing need for healthcare conversational agents with domain-specific expertise to ensure the provision of accurate and reliable information tailored to specific medical contexts. Moreover, there is a notable gap in research ensuring the credibility and trustworthiness of the information provided by these healthcare agents, particularly in critical scenarios such as medical emergencies. Pilgrims come from diverse cultural and linguistic backgrounds, often facing difficulties in accessing medical advice and information. Establishing an AI-powered multilingual chatbot can bridge this gap by providing readily available medical guidance and support, contributing to the well-being and safety of pilgrims. In this paper, we present a comprehensive methodology aimed at enhancing the reliability and efficacy of healthcare conversational agents, with a specific focus on addressing the needs of Hajj pilgrims. Our approach leverages domain-specific fine-tuning techniques on a large language model, alongside synthetic data augmentation strategies, to optimize performance in delivering contextually relevant healthcare information by introducing the HajjHealthQA dataset. Additionally, we employ a retrieval-augmented generation (RAG) module as a crucial component to validate uncertain generated responses, which improves model performance by 5%. Moreover, we train a secondary AI agent on a well-known health fact-checking dataset and use it to validate medical information in the generated responses. Our approach significantly elevates the chatbot’s accuracy, demonstrating its adaptability to a wide range of pilgrim queries. We evaluate the chatbot’s performance using quantitative and qualitative metrics, highlighting its proficiency in generating accurate responses and achieve competitive results compared to state-of-the-art models, in addition to mitigating the risk of misinformation and providing users with trustworthy health information. Full article

(This article belongs to the Special Issue Applications of Information Extraction, Knowledge Graphs, and Large Language Models)

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13 pages, 1520 KiB

Open AccessArticle

A Study of Delayed Competitive Influence Propagation Based on Shortest Path Calculation

by Yang Li and Zhiqiang Wang

Information 2024, 15(7), 370; https://doi.org/10.3390/info15070370 - 26 Jun 2024

Viewed by 139

Abstract

In social network applications, competitive influence propagation often exhibits a certain degree of time lag. In the scenario of positive and negative competitive propagation studied in this paper, under the premise that negative nodes are activated first, how to find a set of [...] Read more.

In social network applications, competitive influence propagation often exhibits a certain degree of time lag. In the scenario of positive and negative competitive propagation studied in this paper, under the premise that negative nodes are activated first, how to find a set of positive seed nodes to participate in competitive propagation is studied, aiming to minimize the spread of negative influence. In the current study, the time complexity of the improved algorithms based on greedy strategies is high, which limits their scope of application in practical scenarios; some heuristic algorithms achieve better scalability, but there is still much room for improvement. Therefore, this paper proposes a new method to solve the influence propagation problem of delayed competition, also known as a heuristic propagation factor evaluation algorithm (HeuPFE). The main process is as follows: (1) We build a shortest path snapshot for the nearest propagation region of negative nodes and reduce the search space of competing nodes. (2) Then, we construct a node propagation factor evaluation method based on this shortest negative-oriented node path snapshot to reduce computational complexity. Comparing results with those of traditional heuristic algorithms, we carry out experiments on real datasets and verify the effectiveness of the proposed method. Full article

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23 pages, 1189 KiB

Open AccessArticle

A Data-Driven Approach to Set-Theoretic Model Predictive Control for Nonlinear Systems

by Francesco Giannini and Domenico Famularo

Information 2024, 15(7), 369; https://doi.org/10.3390/info15070369 - 23 Jun 2024

Viewed by 367

Abstract

In this paper, we present a data-driven model predictive control (DDMPC) framework specifically designed for constrained single-input single-output (SISO) nonlinear systems. Our approach involves customizing a set-theoretic receding horizon controller within a data-driven context. To achieve this, we translate model-based conditions into data [...] Read more.

In this paper, we present a data-driven model predictive control (DDMPC) framework specifically designed for constrained single-input single-output (SISO) nonlinear systems. Our approach involves customizing a set-theoretic receding horizon controller within a data-driven context. To achieve this, we translate model-based conditions into data series of available input and output signals. This translation process leverages recent advances in data-driven control theory, enabling the controller to operate effectively without relying on explicit system models. The proposed framework incorporates a robust methodology for managing system constraints, ensuring that the control actions remain within predefined bounds. By means of time sequences, the controller learns the underlying system dynamics and adapts to changes in real time, providing enhanced performance and reliability. The integration of set-theoretic methods allows for the systematic handling of uncertainties and disturbances, which are common when the trajectory of a nonlinear system is embedded inside a linear trajectory state tube. To validate the effectiveness of our DDMPC framework, we conduct extensive simulations on a nonlinear DC motor system. The results demonstrate significant improvements in control performance, highlighting the robustness and adaptability of our approach compared to traditional model-based MPC techniques. Full article

(This article belongs to the Special Issue Second Edition of Predictive Analytics and Data Science)

16 pages, 857 KiB

Open AccessArticle

Applying Machine Learning in Marketing: An Analysis Using the NMF and k-Means Algorithms

by Victor Gallego, Jessica Lingan, Alfons Freixes, Angel A. Juan and Celia Osorio

Information 2024, 15(7), 368; https://doi.org/10.3390/info15070368 - 22 Jun 2024

Viewed by 295

Abstract

The integration of machine learning (ML) techniques into marketing strategies has become increasingly relevant in modern business. Utilizing scientific manuscripts indexed in the Scopus database, this article explores how this integration is being carried out. Initially, a focused search is undertaken for academic [...] Read more.

The integration of machine learning (ML) techniques into marketing strategies has become increasingly relevant in modern business. Utilizing scientific manuscripts indexed in the Scopus database, this article explores how this integration is being carried out. Initially, a focused search is undertaken for academic articles containing both the terms “machine learning” and “marketing” in their titles, which yields a pool of papers. These papers have been processed using the Supabase platform. The process has included steps like text refinement and feature extraction. In addition, our study uses two key ML methodologies: topic modeling through NMF and a comparative analysis utilizing the k-means clustering algorithm. Through this analysis, three distinct clusters emerged, thus clarifying how ML techniques are influencing marketing strategies, from enhancing customer segmentation practices to optimizing the effectiveness of advertising campaigns. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)

17 pages, 3327 KiB

Open AccessArticle

Automated Knowledge Extraction in the Field of Wheat Sharp Eyespot Control

by Keyi Liu and Yunpeng Cui

Information 2024, 15(7), 367; https://doi.org/10.3390/info15070367 - 21 Jun 2024

Viewed by 352

Abstract

Wheat sharp eyespot is a soil-borne fungal disease commonly found in wheat areas in China, which can occur throughout the entire reproductive period of wheat and has a great impact on the yield and quality of wheat in China. By constructing a domain [...] Read more.

Wheat sharp eyespot is a soil-borne fungal disease commonly found in wheat areas in China, which can occur throughout the entire reproductive period of wheat and has a great impact on the yield and quality of wheat in China. By constructing a domain ontology for wheat sharp eyespot control and modeling the domain knowledge, we aim to integrate and share the knowledge in the field of wheat sharp eyespot control, which can provide important support and guidance for agricultural decision-making and disease control. In this study, the literature in the field of wheat sharp eyespot control was used as a data source, the KeyBERT keyword extraction algorithm was used to mine the core concepts of the ontology, and the hierarchical relationships among the ontology concepts were extracted through clustering. Based on the constructed ontology of wheat sharp eyespot control, the schema of knowledge extraction was formed, and the knowledge extraction model was trained using the ERNIE 3.0 knowledge enhancement pretraining model. This study proposes a model and algorithm to realize knowledge extraction based on domain ontology, describes the construction method and process framework of wheat sharp eyespot control domain ontology, and details the training and reasoning effect of the knowledge extraction model. The knowledge extraction model constructed in this study for wheat sharp eyespot control contains a more complete conceptual system of wheat sharp eyespot. The F1 value of the model reaches 91.26%, which is a 17.86% improvement compared with the baseline model, and it can satisfy the knowledge extraction needs in the field of wheat sharp eyespot control. This study can provide a reference for domain knowledge extraction and provide strong support for knowledge discovery and downstream applications such as intelligent Q&A and intelligent recommendation in the field of wheat sharp eyespot control. Full article

(This article belongs to the Special Issue Text Mining: Challenges, Algorithms, Tools and Applications)

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