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Harnessing Artificial Intelligence for Social and Semantic Understanding

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A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 18224

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Special Issue Editors

Dr. Yorghos Voutos

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Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece
Interests: IoT; data and social mining; environmental monitoring; semantics; knowledge representation; spatio-temporal

Dr. Akrivi Krouska

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Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: web systems; artificial intelligence systems; social networking services; adaptive software and multimedia applications
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Dr. Christos Troussas

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Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: adaptive hypermedia systems; artificial intelligence in education; human-centered artificial intelligence; human–computer interaction; personalized software
Special Issues, Collections and Topics in MDPI journals

Dr. Phivos Mylonas

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Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
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Prof. Dr. Cleo Sgouropoulou

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Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: e-learning; open educational resources (OERs); learning outcomes; competence and skill modeling for strengthening LLL; mobility and training to employment pathways; open data and analytics for e-research
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Special Issue Information

Dear Colleagues,

In today's data-rich landscape, the demand for efficient artificial intelligence (AI) models, tools, and applications for data mining and machine learning is more pronounced than ever. The proliferation of social and semantic networks has generated a wealth of valuable information, presenting both opportunities and challenges. Researchers grapple with novel approaches to address emerging research questions, particularly in the realms of social information processing and semantic applications. The proposed Special Issue and the SMAP workshop series revolve around two central themes: first, the extraction, processing, and analysis of data, information, and knowledge using AI techniques; and second, the application of these results to construct effective models and tools. Ultimately, this interdisciplinary endeavor aims to foster diverse behaviors associated with AI activities, bridging the gap between social and semantic impact. Computer scientists are encouraged to contribute innovative AI solutions to tackle the inherent challenges posed by dynamic and semantically heterogeneous computational data. The call for relevant research manuscripts is open to all interested parties.

The content of the proposed Special Issue and the goals of the SMAP workshop series are organized around two main themes. The first theme focuses on efficiently extracting, processing, manipulating and analysing data, information and knowledge with the utilization of artificial intelligence in the process, while the second theme focuses on using the above results to effectively build models, tools and applications. The ultimate goal, of course, is to promote a variety of machine and/or human behaviours associated with related artificial intelligence activities.

In addition to the Open Call, selected papers that will be presented during SMAP 2024 will be invited to be submitted as extended versions to this Special Issue. In this case, the workshop paper should be cited and noted on the first page of the submitted paper; authors are asked to disclose that it is a workshop paper in their Cover Letter and include a statement on what has been changed compared to the original workshop paper. Each submission to this journal Special Issue should contain at least 50% new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases.

All submitted papers will undergo standard peer-review procedures. Accepted papers will be published in open access format in Computers and available on the Special Issue’s website.

Dr. Yorghos Voutos
Dr. Akrivi Krouska
Dr. Christos Troussas
Dr. Phivos Mylonas
Prof. Dr. Cleo Sgouropoulou
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. Computers is an international peer-reviewed open access monthly 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 1800 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

artificial intelligence (AI)
social network
semantic network

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Published Papers (10 papers)

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Research

30 pages, 1844 KiB

Open AccessArticle

Exploring Machine Learning Methods for Aflatoxin M1 Prediction in Jordanian Breast Milk Samples

by Abdullah Aref, Eman Omar, Eman Alseidi, Nour Elhuda A. Alqudah and Sharaf Omar

Computers 2024, 13(11), 288; https://doi.org/10.3390/computers13110288 - 7 Nov 2024

Viewed by 580

Abstract

The presence of aflatoxin M1 in breast milk poses a serious risk to the health of infants because of its potential to cause cancer and have negative effects on development. Detecting AFM1 in milk samples using conventional methods is often time-consuming and may not provide real-time monitoring capabilities. The use of machine learning techniques to forecast aflatoxin M1 levels in breast milk samples is examined in this study. To develop predictive models of aflatoxin M1 in breast milk, we employed well-known supervised machine learning algorithms such as Random Forest and Gradient Boosting. The findings show that machine learning can be used for the identification of aflatoxin M1 in breast milk. By actively monitoring breast quality, this research highlights the significance of machine learning in protecting babies’ health and advances the prediction skills in food safety. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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16 pages, 2121 KiB

Open AccessArticle

Enhancement of Named Entity Recognition in Low-Resource Languages with Data Augmentation and BERT Models: A Case Study on Urdu

by Fida Ullah, Alexander Gelbukh, Muhammad Tayyab Zamir, Edgardo Manuel Felipe Riverόn and Grigori Sidorov

Computers 2024, 13(10), 258; https://doi.org/10.3390/computers13100258 - 10 Oct 2024

Viewed by 1005

Abstract

Identifying and categorizing proper nouns in text, known as named entity recognition (NER), is crucial for various natural language processing tasks. However, developing effective NER techniques for low-resource languages like Urdu poses challenges due to limited training data, particularly in the nastaliq script. To address this, our study introduces a novel data augmentation method, “contextual word embeddings augmentation” (CWEA), for Urdu, aiming to enrich existing datasets. The extended dataset, comprising 160,132 tokens and 114,912 labeled entities, significantly enhances the coverage of named entities compared to previous datasets. We evaluated several transformer models on this augmented dataset, including BERT-multilingual, RoBERTa-Urdu-small, BERT-base-cased, and BERT-large-cased. Notably, the BERT-multilingual model outperformed others, achieving the highest macro F1 score of 0.982%. This surpassed the macro f1 scores of the RoBERTa-Urdu-small (0.884%), BERT-large-cased (0.916%), and BERT-base-cased (0.908%) models. Additionally, our neural network model achieved a micro F1 score of 96%, while the RNN model achieved 97% and the BiLSTM model achieved a macro F1 score of 96% on augmented data. Our findings underscore the efficacy of data augmentation techniques in enhancing NER performance for low-resource languages like Urdu. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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19 pages, 1495 KiB

Open AccessArticle

Deep Learning for Predicting Attrition Rate in Open and Distance Learning (ODL) Institutions

by Juliana Ngozi Ndunagu, David Opeoluwa Oyewola, Farida Shehu Garki, Jude Chukwuma Onyeakazi, Christiana Uchenna Ezeanya and Elochukwu Ukwandu

Computers 2024, 13(9), 229; https://doi.org/10.3390/computers13090229 - 11 Sep 2024

Viewed by 811

Abstract

Student enrollment is a vital aspect of educational institutions, encompassing active, registered and graduate students. All the same, some students fail to engage with their studies after admission and drop out along the line; this is known as attrition. The student attrition rate is acknowledged as the most complicated and significant problem facing educational systems and is caused by institutional and non-institutional challenges. In this study, the researchers utilized a dataset obtained from the National Open University of Nigeria (NOUN) from 2012 to 2022, which included comprehensive information about students enrolled in various programs at the university who were inactive and had dropped out. The researchers used deep learning techniques, such as the Long Short-Term Memory (LSTM) model and compared their performance with the One-Dimensional Convolutional Neural Network (1DCNN) model. The results of this study revealed that the LSTM model achieved overall accuracy of 57.29% on the training data, while the 1DCNN model exhibited lower accuracy of 49.91% on the training data. The LSTM indicated a superior correct classification rate compared to the 1DCNN model. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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21 pages, 4836 KiB

Open AccessArticle

Chef Dalle: Transforming Cooking with Multi-Model Multimodal AI

by Brendan Hannon, Yulia Kumar, J. Jenny Li and Patricia Morreale

Computers 2024, 13(7), 156; https://doi.org/10.3390/computers13070156 - 21 Jun 2024

Viewed by 3280

Abstract

In an era where dietary habits significantly impact health, technological interventions can offer personalized and accessible food choices. This paper introduces Chef Dalle, a recipe recommendation system that leverages multi-model and multimodal human-computer interaction (HCI) techniques to provide personalized cooking guidance. The application integrates voice-to-text conversion via Whisper and ingredient image recognition through GPT-Vision. It employs an advanced recipe filtering system that utilizes user-provided ingredients to fetch recipes, which are then evaluated through multi-model AI through integrations of OpenAI, Google Gemini, Claude, and/or Anthropic APIs to deliver highly personalized recommendations. These methods enable users to interact with the system using voice, text, or images, accommodating various dietary restrictions and preferences. Furthermore, the utilization of DALL-E 3 for generating recipe images enhances user engagement. User feedback mechanisms allow for the refinement of future recommendations, demonstrating the system’s adaptability. Chef Dalle showcases potential applications ranging from home kitchens to grocery stores and restaurant menu customization, addressing accessibility and promoting healthier eating habits. This paper underscores the significance of multimodal HCI in enhancing culinary experiences, setting a precedent for future developments in the field. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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9 pages, 275 KiB

Open AccessArticle

Mitigating Large Language Model Bias: Automated Dataset Augmentation and Prejudice Quantification

by Devam Mondal and Carlo Lipizzi

Computers 2024, 13(6), 141; https://doi.org/10.3390/computers13060141 - 4 Jun 2024

Cited by 1 | Viewed by 1526

Abstract

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics. Our code can be found on an online GitHub repository. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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27 pages, 4827 KiB

Open AccessArticle

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

by Uppugunduri Vijay Nikhil, Athiya M. Pandiyan, S. P. Raja and Zoran Stamenkovic

Computers 2024, 13(6), 137; https://doi.org/10.3390/computers13060137 - 29 May 2024

Cited by 4 | Viewed by 3291

Abstract

Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually predict crops based on the region, soil, weather conditions and the crop itself based on experience and estimates which may not be very accurate especially with the constantly changing and unpredictable climactic conditions of the present day. To solve this problem, we aim to predict the production and yield of various crops such as rice, sorghum, cotton, sugarcane and rabi using Machine Learning (ML) models. We train these models with the weather, soil and crop data to predict future crop production and yields of these crops. We have compiled a dataset of attributes that impact crop production and yield from specific states in India and performed a comprehensive study of the performance of various ML Regression Models in predicting crop production and yield. The results indicated that the Extra Trees Regressor achieved the highest performance among the models examined. It attained a R-Squared score of 0.9615 and showed lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 21.06 and 33.99. Following closely behind are the Random Forest Regressor and LGBM Regressor, achieving R-Squared scores of 0.9437 and 0.9398 respectively. Moreover, additional analysis revealed that tree-based models, showing a R-Squared score of 0.9353, demonstrate better performance compared to linear and neighbors-based models, which achieved R-Squared scores of 0.8568 and 0.9002 respectively. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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27 pages, 2040 KiB

Open AccessArticle

Machine Learning Decision System on the Empirical Analysis of the Actual Usage of Interactive Entertainment: A Perspective of Sustainable Innovative Technology

by Rex Revian A. Guste and Ardvin Kester S. Ong

Computers 2024, 13(6), 128; https://doi.org/10.3390/computers13060128 - 23 May 2024

Cited by 1 | Viewed by 1705

Abstract

This study focused on the impact of Netflix’s interactive entertainment on Filipino consumers, seamlessly combining vantage points from consumer behavior and employing data analytics. This underlines the revolutionary aspect of interactive entertainment in the quickly expanding digital media ecosystem, particularly as Netflix pioneers fresh content distribution techniques. The main objective of this study was to find the factors impacting the real usage of Netflix’s interactive entertainment among Filipino viewers, filling a critical gap in the existing literature. The major goal of using advanced data analytics techniques in this study was to understand the subtle dynamics affecting customer behavior in this setting. Specifically, the random forest classifier with hard and soft classifiers was assessed. The random forest compared to LightGBM was also employed, alongside the different algorithms of the artificial neural network. Purposive sampling was used to obtain responses from 258 people who had experienced Netflix’s interactive entertainment, resulting in a comprehensive dataset. The findings emphasized the importance of hedonic motivation, underlining the requirement for highly engaging and rewarding interactive material. Customer service and device compatibility, for example, have a significant impact on user uptake. Furthermore, behavioral intention and habit emerged as key drivers, revealing interactive entertainment’s long-term influence on user engagement. Practically, the research recommends strategic platform suggestions that emphasize continuous innovation, user-friendly interfaces, and user-centric methods. This study was able to fill in the gap in the literature on interactive entertainment, which contributes to a better understanding of consumer consumption and lays the groundwork for future research in the dynamic field of digital media. Moreover, this study offers essential insights into the intricate interaction of consumer preferences, technology breakthroughs, and societal influences in the ever-expanding environment of digital entertainment. Lastly, the comparative approach to the use of machine learning algorithms provides insights for future works to adopt and employ among human factors and consumer behavior-related studies. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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14 pages, 6899 KiB

Open AccessArticle

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

by Adnane Ait Nasser and Moulay A. Akhloufi

Computers 2024, 13(5), 116; https://doi.org/10.3390/computers13050116 - 7 May 2024

Cited by 2 | Viewed by 1517

Abstract

Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named “CTPlantNet”. This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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20 pages, 601 KiB

Open AccessArticle

Harnessing Machine Learning to Unveil Emotional Responses to Hateful Content on Social Media

by Ali Louati, Hassen Louati, Abdullah Albanyan, Rahma Lahyani, Elham Kariri and Abdulrahman Alabduljabbar

Computers 2024, 13(5), 114; https://doi.org/10.3390/computers13050114 - 29 Apr 2024

Viewed by 1757

Abstract

Within the dynamic realm of social media, the proliferation of harmful content can significantly influence user engagement and emotional health. This study presents an in-depth analysis that bridges diverse domains, from examining the aftereffects of personal online attacks to the intricacies of online trolling. By leveraging an AI-driven framework, we systematically implemented high-precision attack detection, psycholinguistic feature extraction, and sentiment analysis algorithms, each tailored to the unique linguistic contexts found within user-generated content on platforms like Reddit. Our dataset, which spans a comprehensive spectrum of social media interactions, underwent rigorous analysis employing classical statistical methods, Bayesian estimation, and model-theoretic analysis. This multi-pronged methodological approach allowed us to chart the complex emotional responses of users subjected to online negativity, covering a spectrum from harassment and cyberbullying to subtle forms of trolling. Empirical results from our study reveal a clear dose–response effect; personal attacks are quantifiably linked to declines in user activity, with our data indicating a 5% reduction after 1–2 attacks, 15% after 3–5 attacks, and 25% after 6–10 attacks, demonstrating the significant deterring effect of such negative encounters. Moreover, sentiment analysis unveiled the intricate emotional reactions users have to these interactions, further emphasizing the potential for AI-driven methodologies to promote more inclusive and supportive digital communities. This research underscores the critical need for interdisciplinary approaches in understanding social media’s complex dynamics and sheds light on significant insights relevant to the development of regulation policies, the formation of community guidelines, and the creation of AI tools tailored to detect and counteract harmful content. The goal is to mitigate the impact of such content on user emotions and ensure the healthy engagement of users in online spaces. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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14 pages, 1352 KiB

Open AccessArticle

MTL-AraBERT: An Enhanced Multi-Task Learning Model for Arabic Aspect-Based Sentiment Analysis

by Arwa Fadel, Mostafa Saleh, Reda Salama and Osama Abulnaja

Computers 2024, 13(4), 98; https://doi.org/10.3390/computers13040098 - 15 Apr 2024

Cited by 1 | Viewed by 1875

Abstract

Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis; it works on an aspect level. It mainly focuses on extracting aspect terms from text or reviews, categorizing the aspect terms, and classifying the sentiment polarities toward each aspect term and aspect category. Aspect term extraction (ATE) and aspect category detection (ACD) are interdependent and closely associated tasks. However, the majority of the current literature on Arabic aspect-based sentiment analysis (ABSA) deals with these tasks individually, assumes that aspect terms are already identified, or employs a pipeline model. Pipeline solutions employ single models for each task, where the output of the ATE model is utilized as the input for the ACD model. This sequential process can lead to the propagation of errors across different stages, as the performance of the ACD model is influenced by any errors produced by the ATE model. Therefore, the primary objective of this study was to investigate a multi-task learning approach based on transfer learning and transformers. We propose a multi-task learning model (MTL) that utilizes the pre-trained language model (AraBERT), namely, the MTL-AraBERT model, for extracting Arabic aspect terms and aspect categories simultaneously. Specifically, we focused on training a single model that simultaneously and jointly addressed both subtasks. Moreover, this paper also proposes a model integrating AraBERT, single pair classification, and BiLSTM/BiGRU that can be applied to aspect term polarity classification (APC) and aspect category polarity classification (ACPC). All proposed models were evaluated using the SemEval-2016 annotated dataset for the Arabic hotel dataset. The experiment results of the MTL model demonstrate that the proposed models achieved comparable or better performance than state-of-the-art works (F1-scores of 80.32% for the ATE and 68.21% for the ACD). The proposed SPC-BERT model demonstrated high accuracy, reaching 89.02% and 89.36 for APC and ACPC, respectively. These improvements hold significant potential for future research in Arabic ABSA. Full article

(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)

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