Computers

13 pages, 1736 KiB

Open AccessArticle

Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

by Christos Troussas, Akrivi Krouska, Antonios Koliarakis and Cleo Sgouropoulou

Computers 2023, 12(5), 109; https://doi.org/10.3390/computers12050109 - 22 May 2023

Cited by 3 | Viewed by 1605

Abstract

Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. [...] Read more.

Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. In greater detail, we introduce an enhanced collaborative filtering (CF) approach in order to develop hybrid recommender systems that personalize search results for users. The proposed CF enhancement incorporates user actions beyond explicit ratings to collect data and alleviate the issue of sparse data, resulting in high-quality recommendations. As a testbed for our research, a web-based digital library, incorporating the proposed algorithm, has been developed. Examples of operation of the use of the system are presented using cognitive walkthrough inspection, which demonstrates the effectiveness of the approach in producing personalized recommendations and improving user experience. Thus, the hybrid recommender system, which is incorporated in the digital library, has been evaluated, yielding promising results. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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15 pages, 2050 KiB

Open AccessArticle

Investigating the Cultural Impact on Predicting Crowd Behavior

by Fatima Jafar Muhdher, Osama Ahmed Abulnaja and Fatmah Abdulrahman Baothman

Computers 2023, 12(5), 108; https://doi.org/10.3390/computers12050108 - 21 May 2023

Viewed by 1251

Abstract

The Cultural Crowd–Artificial Neural Network (CC-ANN) takes the cultural dimensions of a crowd into account, based on Hofstede Cultural Dimensions (HCDs), to predict social and physical behavior concerning cohesion, collectivity, speed, and distance. This study examines the impact of applying the CC-ANN learning [...] Read more.

The Cultural Crowd–Artificial Neural Network (CC-ANN) takes the cultural dimensions of a crowd into account, based on Hofstede Cultural Dimensions (HCDs), to predict social and physical behavior concerning cohesion, collectivity, speed, and distance. This study examines the impact of applying the CC-ANN learning model on more cultures to test the effect of predicting crowd behavior and the relationships among their characteristics. Our previous work which applied the CC-ANN only included eight nations using the six HCDs. In this paper, we including the United Arab Emirates (UAE) in the CC-ANN as a new culture which aided a comparative study with four HCDs, with and without the UAE, using Mean Squared Error (MSE) for evaluation. The results indicated that most of the best-case experiments involved the UAE having the lowest MSE: 0.127, 0.014, and 0.010, which enhanced the CC-ANN model’s ability to predict crowd behavior. Moreover, the links between the cultural, sociological, and physical properties of crowds can be seen from a broader perspective with stronger correlations using the CC-ANN in more countries with diverse cultures. Full article

(This article belongs to the Special Issue Human Understandable Artificial Intelligence)

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18 pages, 934 KiB

Open AccessArticle

Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence

by Moez Krichen

Computers 2023, 12(5), 107; https://doi.org/10.3390/computers12050107 - 18 May 2023

Cited by 10 | Viewed by 4526

Abstract

Smart contracts (SCs) are digital agreements that execute themselves and are stored on a blockchain. Despite the fact that they offer numerous advantages, such as automation and transparency, they are susceptible to a variety of assaults due to their complexity and lack of [...] Read more.

Smart contracts (SCs) are digital agreements that execute themselves and are stored on a blockchain. Despite the fact that they offer numerous advantages, such as automation and transparency, they are susceptible to a variety of assaults due to their complexity and lack of standardization. In this paper, we investigate the use of artificial intelligence (AI) to improve SC security. We provide an overview of Smart Contracts (SCs) and blockchain technology, as well as a discussion of possible SC-based attacks. Then, we introduce various AI categories and their applications in cybersecurity, followed by a thorough analysis of how AI can be used to enhance SC security. We also highlight the open questions and future directions of research in this field. Our research demonstrates that AI can provide an effective defense against assaults on SCs and contribute to their security and dependability. This article lays the groundwork for future research in the field of AI for SC security. Full article

(This article belongs to the Special Issue Using New Technologies on Cyber Security Solutions)

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15 pages, 3539 KiB

Open AccessArticle

Peer-to-Peer Federated Learning for COVID-19 Detection Using Transformers

by Mohamed Chetoui and Moulay A. Akhloufi

Computers 2023, 12(5), 106; https://doi.org/10.3390/computers12050106 - 17 May 2023

Cited by 3 | Viewed by 1656

Abstract

The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. The intrinsic heterogeneity of [...] Read more.

The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. The intrinsic heterogeneity of IoT devices, or non-independent and identically distributed (Non-I.I.D.) data, combined with the unstable communication network environment, causes a bottleneck that slows convergence and degrades learning efficiency. Additionally, the majority of weight averaging-based model aggregation approaches raise questions about learning fairness. In this paper, we propose a peer-to-peer federated learning (P2PFL) framework based on Vision Transformers (ViT) models to help solve some of the above issues and classify COVID-19 vs. normal cases on Chest-X-Ray (CXR) images. Particularly, clients jointly iterate and aggregate the models in order to build a robust model. The experimental results demonstrate that the proposed approach is capable of significantly improving the performance of the model with an Area Under Curve (AUC) of 0.92 and 0.99 for hospital-1 and hospital-2, respectively. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain)

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22 pages, 4355 KiB

Open AccessArticle

Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

by Tarik El Lel, Mominul Ahsan and Julfikar Haider

Computers 2023, 12(5), 105; https://doi.org/10.3390/computers12050105 - 16 May 2023

Cited by 2 | Viewed by 1925

Abstract

Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the global reaction to [...] Read more.

Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the global reaction to it severely affected the world economy, causing a significant increase in global inflation rates, unemployment, and the cost of energy commodities. To stop the spread of the virus and dampen its global effect, it is imperative to detect infected patients early on. Convolutional neural networks (CNNs) can effectively diagnose a patient’s chest X-ray (CXR) to assess whether they have been infected. Previous medical image classification studies have shown exceptional accuracies, and the trained algorithms can be shared and deployed using a computer or a mobile device. CNN-based COVID-19 detection can be employed as a supplement to reverse transcription-polymerase chain reaction (RT-PCR). In this research work, 11 ensemble networks consisting of 6 CNN architectures and a classifier layer are evaluated on their ability to differentiate the CXRs of patients with COVID-19 from those of patients that have not been infected. The performance of ensemble models is then compared to the performance of individual CNN architectures. The best ensemble model COVID-19 detection accuracy was achieved using the logistic regression ensemble model, with an accuracy of 96.29%, which is 1.13% higher than the top-performing individual model. The highest F1-score was achieved by the standard vector classifier ensemble model, with a value of 88.6%, which was 2.06% better than the score achieved by the best-performing individual model. This work demonstrates that combining a set of top-performing COVID-19 detection models could lead to better results if the models are integrated together into an ensemble. The model can be deployed in overworked or remote health centers as an accurate and rapid supplement or back-up method for detecting COVID-19. Full article

(This article belongs to the Special Issue Uncertainty-Aware Artificial Intelligence)

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11 pages, 1083 KiB

Open AccessReview

Pressure-Based Posture Classification Methods and Algorithms: A Systematic Review

by Luís Fonseca, Fernando Ribeiro and José Metrôlho

Computers 2023, 12(5), 104; https://doi.org/10.3390/computers12050104 - 15 May 2023

Cited by 1 | Viewed by 1195

Abstract

There are many uses for machine learning in everyday life and there is a steady increase in the field of medicine; the use of such technologies facilitates the tiresome work of health professionals by either automating repetitive tasks or making them simpler. Bed-related [...] Read more.

There are many uses for machine learning in everyday life and there is a steady increase in the field of medicine; the use of such technologies facilitates the tiresome work of health professionals by either automating repetitive tasks or making them simpler. Bed-related disorders are a great example where tedious tasks could be facilitated by machine learning algorithms, as suggested by many authors, by providing information on the posture of a particular bedded patient to health professionals. To assess the already existing studies in this field, this study provides a systematic review where the literature is analyzed to find correlations between the various factors involved in the making of such a system and how they perform. The overall findings suggest that there is only a significant relationship between the postures considered for classification and the resulting accuracy, despite some other factors such as the amount of data available providing some differences according to the type of algorithm used, with neural networks needing larger datasets. This study aims to increase awareness in this field and give future researchers information based on previous works’ strengths and limitations while giving some suggestions based on the literature review. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain)

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21 pages, 1418 KiB

Open AccessArticle

EEGT: Energy Efficient Grid-Based Routing Protocol in Wireless Sensor Networks for IoT Applications

by Nguyen Duy Tan, Duy-Ngoc Nguyen, Hong-Nhat Hoang and Thi-Thu-Huong Le

Computers 2023, 12(5), 103; https://doi.org/10.3390/computers12050103 - 12 May 2023

Cited by 3 | Viewed by 1276

Abstract

The Internet of Things (IoT) integrates different advanced technologies in which a wireless sensor network (WSN) with many smart micro-sensor nodes is an important portion of building various IoT applications such as smart agriculture systems, smart healthcare systems, smart home or monitoring environments, [...] Read more.

The Internet of Things (IoT) integrates different advanced technologies in which a wireless sensor network (WSN) with many smart micro-sensor nodes is an important portion of building various IoT applications such as smart agriculture systems, smart healthcare systems, smart home or monitoring environments, etc. However, the limited energy resources of sensors and the harsh properties of the WSN deployment environment make routing a challenging task. To defeat this routing quandary, an energy-efficient routing protocol based on grid cells (EEGT) is proposed in this study to improve the lifespan of WSN-based IoT applications. In EEGT, the whole network region is separated into virtual grid cells (clusters) at which the number of sensor nodes is balanced among cells. Then, a cluster head node (CHN) is chosen according to the residual energy and the distance between the sink and nodes in each cell. Moreover, to determine the paths for data delivery inside the cell with small energy utilization, the Kruskal algorithm is applied to connect nodes in each cell and their CHN into a minimum spanning tree (MST). Further, the ant colony algorithm is also used to find the paths of transmitting data packets from CHNs to the sink (outside cell) to reduce energy utilization. The simulation results show that the performance of EEGT is better than the three existing protocols, which are LEACH-C (low energy adaptive clustering hierarchy), PEGASIS (power-efficient gathering in sensor information systems), and PEGCP (maximizing WSN life using power-efficient grid-chain routing protocol) in terms of improved energy efficiency and extended the lifespan of the network. Full article

(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems 2023)

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16 pages, 1370 KiB

Open AccessArticle

Info-Autopoiesis and the Limits of Artificial General Intelligence

by Jaime F. Cárdenas-García

Computers 2023, 12(5), 102; https://doi.org/10.3390/computers12050102 - 07 May 2023

Cited by 1 | Viewed by 2326

Abstract

Recent developments, begun by the ascending spiral of the anticipated endless prospects of ChatGPT, promote artificial intelligence (AI) as an indispensable tool and commodity whose time has come. Yet the sinister specter of a technology that has hidden and unmanageable attributes that might [...] Read more.

Recent developments, begun by the ascending spiral of the anticipated endless prospects of ChatGPT, promote artificial intelligence (AI) as an indispensable tool and commodity whose time has come. Yet the sinister specter of a technology that has hidden and unmanageable attributes that might be harmful to society looms in the background, as well as the likelihood that it will never deliver on the purported promise of artificial general intelligence (AGI). Currently, the prospects for the development of AI and AGI are more a matter of opinion than based on a consistent methodological approach. Thus, there is a need to take a step back to develop a general framework from which to evaluate current AI efforts, which also permits the determination of the limits to its future prospects as AGI. To gain insight into the development of a general framework, a key question needs to be resolved: what is the connection between human intelligence and machine intelligence? This is the question that needs a response because humans are at the center of AI creation and realize that, without an understanding of how we become what we become, we have no chance of finding a solution. This work proposes info-autopoiesis, the self-referential, recursive, and interactive process of self-production of information, as the needed general framework. Info-autopoiesis shows how the key ingredient of information is fundamental to an insightful resolution to this crucial question and allows predictions as to the present and future of AGI. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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17 pages, 3686 KiB

Open AccessArticle

Application of GNS3 to Study the Security of Data Exchange between Power Electronic Devices and Control Center

by Ivan Nedyalkov

Computers 2023, 12(5), 101; https://doi.org/10.3390/computers12050101 - 05 May 2023

Cited by 1 | Viewed by 1952

Abstract

This paper proposes the use of the GNS3 IP network modeling platform to study/verify whether the exchanged information between power electronic devices and a control center (Monitoring and Control Centre) is secure. For the purpose of this work, a power distribution unit (PDU) [...] Read more.

This paper proposes the use of the GNS3 IP network modeling platform to study/verify whether the exchanged information between power electronic devices and a control center (Monitoring and Control Centre) is secure. For the purpose of this work, a power distribution unit (PDU) and a UPS (Uninterruptable Power Supply) that are used by internet service providers are studied. Capsa Free network analyzer and Wireshark network protocol analyzer were used as supporting tools. A working model of an IP network in GNS3 has been created through which this research has been carried out. In addition to checking whether the exchanged information is secure, a characterization of the generated traffic has been made, showing results for the generated traffic and which ports generate the most traffic. These carried-outstudies show that the exchanged information is not secure. As a way to secure the exchanged information, the use of VPN (Virtual Private Network) technology is proposed; thanks to a VPN, the exchange of information is secure. The obtained results confirm this and validate the applicability of GNS3 to test/study whether data exchange between power electronic devices and a control center is secure. Full article

(This article belongs to the Special Issue Advances in Energy-Efficient Computer and Network Systems)

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30 pages, 5403 KiB

Open AccessFeature PaperArticle

A Study on Energy Efficiency of a Distributed Processing Scheme for Image-Based Target Recognition for Internet of Multimedia Things

by Adel Soudani, Manal Alsabhan and Manan Almusallam

Computers 2023, 12(5), 99; https://doi.org/10.3390/computers12050099 - 04 May 2023

Viewed by 1446

Abstract

A growing number of services and applications are developed using multimedia sensing low-cost wireless devices, thus creating the Internet of Multimedia Things (IoMT). Nevertheless, energy efficiency and resource availability are two of the most challenging issues to overcome when developing image-based sensing applications. [...] Read more.

A growing number of services and applications are developed using multimedia sensing low-cost wireless devices, thus creating the Internet of Multimedia Things (IoMT). Nevertheless, energy efficiency and resource availability are two of the most challenging issues to overcome when developing image-based sensing applications. In depth, image-based sensing and transmission in IoMT significantly drain the sensor energy and overwhelm the network with redundant data. Event-based sensing schemes can be used to provide efficient data transmission and an extended network lifetime. This paper proposes a novel approach for distributed event-based sensing achieved by a cluster of processing nodes. The proposed scheme aims to balance the processing load across the nodes in the cluster. This study demonstrates the adequacy of distributed processing to extend the lifetime of the IoMT platform and compares the efficiency of Haar wavelet decomposition and general Fourier descriptors (GFDs) as a feature extraction module in a distributed features-based target recognition system. The results show that the distributed processing of the scheme based on the Haar wavelet transform of the image outperforms the scheme based on a general Fourier shape descriptor in recognition accuracy of the target as well as the energy consumption. In contrast to a GFD-based scheme, the recognition accuracy of a Haar-based scheme was increased by 26%, and the number of sensing cycles was increased from 40 to 70 cycles, which attests to the adequacy of the proposed distributed Haar-based processing scheme for deployment in IoMT devices. Full article

(This article belongs to the Section Internet of Things (IoT) and Industrial IoT)

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25 pages, 46929 KiB

Open AccessArticle

Impact of Image Compression on In Vitro Cell Migration Analysis

by Ehsaneddin Jalilian, Michael Linortner and Andreas Uhl

Computers 2023, 12(5), 98; https://doi.org/10.3390/computers12050098 - 04 May 2023

Viewed by 1313

Abstract

Collective cell movement is an indication of phenomena such as wound healing, embryonic morphogenesis, cancer invasion, and metastasis. Wound healing is a complicated cellular and biochemical procedure in which skin cells migrate from the wound boundaries into the wound area to reconstruct the [...] Read more.

Collective cell movement is an indication of phenomena such as wound healing, embryonic morphogenesis, cancer invasion, and metastasis. Wound healing is a complicated cellular and biochemical procedure in which skin cells migrate from the wound boundaries into the wound area to reconstruct the injured skin layer(s). In vitro analysis of cell migration is an effective assay for measuring changes in cell migratory complement in response to experimental inspections. Open-source segmentation software (e.g., an ImageJ plug-in) is available to analyze images of in vitro scratch wound healing assays; however, often, these tools are error-prone when applied to, e.g., low-contrast, out-of-focus, and noisy images, and require manual tuning of various parameters, which is imprecise, tedious, and time-consuming. We propose two algorithmic methods (namely log gradient segmentation and entropy filter segmentation) for cell segmentation and the subsequent measurement of the collective cell migration in the corresponding microscopic imagery. We further investigate the effects of image compression on the algorithms’ measurement accuracy, applying lossy compression algorithms (the current ISO standards JPEG2000, JPEG, JPEG-XL and AV1, BPG, and WEBP). We aim to identify the most suitable compression algorithm that can be used for this purpose, relating rate–distortion performance as measured in terms of peak signal-to-noise ratio (PSNR) and the multiscale structural similarity index (MS-SSIM) to the segmentation accuracy obtained by the segmentation algorithms. The experimental results show that the log gradient segmentationalgorithm provides robust performance for segmenting the wound area, whereas the entropy filter segmentation algorithm is unstable for this purpose under certain circumstances. Additionally, the best-suited compression strategy is observed to be dependent on (i) the segmentation algorithm used and (ii) the actual data sequence being processed. Full article

(This article belongs to the Special Issue Computational Science and Its Applications 2022)

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12 pages, 1109 KiB

Open AccessArticle

Theoretical Models Explaining the Level of Digital Competence in Students

by Marcos Cabezas-González, Sonia Casillas-Martín and Ana García-Valcárcel Muñoz-Repiso

Computers 2023, 12(5), 100; https://doi.org/10.3390/computers12050100 - 04 May 2023

Cited by 2 | Viewed by 2912

Abstract

In the new global scene, digital skills are a key skill for students to seize new learning opportunities, train to meet the demands of the labor market, and compete in the global market, while also communicating effectively in their everyday and academic lives. [...] Read more.

In the new global scene, digital skills are a key skill for students to seize new learning opportunities, train to meet the demands of the labor market, and compete in the global market, while also communicating effectively in their everyday and academic lives. This article presents research aimed at relating the impact of personal variables on the digital competence of technical problem solving in Spanish students from 12 to 14 years old. A quantitative methodology with a cross-sectional design was employed. A sample of 772 students from 18 Spanish educational institutions was used. For data collection, an assessment test was designed (ECODIES^®) based on a validated indicator model to evaluate learners’ digital competence (INCODIES^®), taking as a model the European framework for the development of digital competence. Mediation models were used and theoretical reference models were created. The results allowed us to verify the influence of personal, technology use, and attitudinal variables in the improvement of digital skill in technical problem solving. The findings lead to the conclusion that gender, acquisition of digital devices, and regular use do not determine a better level of competence. Full article

(This article belongs to the Special Issue Immersive Virtual Learning Environments: Connecting Spaces, Time, Technologies, Curriculum, Assessment, and Innovation)

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33 pages, 3184 KiB

Open AccessArticle

The Applicability of Automated Testing Frameworks for Mobile Application Testing: A Systematic Literature Review

by Natnael Gonfa Berihun, Cyrille Dongmo and John Andrew Van der Poll

Computers 2023, 12(5), 97; https://doi.org/10.3390/computers12050097 - 03 May 2023

Viewed by 2273

Abstract

Mobile applications are developed and released to the market every day. Due to the intense usage of mobile applications, their quality matters. End users’ rejection of mobile apps increases from time to time due to their low quality and lack of proper mobile [...] Read more.

Mobile applications are developed and released to the market every day. Due to the intense usage of mobile applications, their quality matters. End users’ rejection of mobile apps increases from time to time due to their low quality and lack of proper mobile testing. This indicates that the role of mobile application testing is crucial in the acceptance of a given software product. Test engineers use automation frameworks for testing their mobile applications. Automated testing brings several advantages to the development team. For example, automated checks are used for regression testing, fast execution of test scripts, and providing quick feedback for the development team. A systematic literature review has been used to identify and collect evidence on automated testing frameworks for mobile application testing. A total of 56 relevant research papers were identified that were published in prominent journals and conferences until February 2023. The results were summarized and tabulated to provide insights into the suitability of the existing automation testing framework for mobile application testing. We identified the major test concerns and test challenges in performing mobile automation testing. The results showed that the keyword-driven testing framework is the widely used approach, but recently, hybrid approaches have been adopted for mobile test automation. On the other hand, this review indicated that the existing frameworks need to be customized using reusable and domain-specific keywords to make them suitable for mobile application testing. Considering this, this study proposes an architecture, the mobile-based automation testing framework (MATF). In the future, to address the mobile application testing challenges, the authors will work on implementing the proposed framework (MATF). Full article

(This article belongs to the Special Issue Best Practices, Challenges and Opportunities in Software Engineering)

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16 pages, 2832 KiB

Open AccessArticle

Supporting the Conservation and Restoration OpenLab of the Acropolis of Ancient Tiryns through Data Modelling and Exploitation of Digital Media

by Efthymia Moraitou, Markos Konstantakis, Angeliki Chrysanthi, Yannis Christodoulou, George Pavlidis, George Alexandridis, Konstantinos Kotsopoulos, Nikolaos Papastamatiou, Alkistis Papadimitriou and George Caridakis

Computers 2023, 12(5), 96; https://doi.org/10.3390/computers12050096 - 02 May 2023

Cited by 3 | Viewed by 1570

Abstract

Open laboratories (OpenLabs) in Cultural Heritage institutions are an effective way to provide visibility into the behind-the-scenes processes and promote documentation data collected and produced by domain specialists. However, presenting these processes without proper explanation or communication with specialists may cause issues in [...] Read more.

Open laboratories (OpenLabs) in Cultural Heritage institutions are an effective way to provide visibility into the behind-the-scenes processes and promote documentation data collected and produced by domain specialists. However, presenting these processes without proper explanation or communication with specialists may cause issues in terms of visitors’ understanding. To support OpenLabs and disseminate information, digital media and efficient data management can be utilized. The CAnTi (Conservation of Ancient Tiryns) project seeks to design and implement virtual and mixed reality applications that visualize conservation and restoration data, supporting OpenLab operations at the Acropolis of Ancient Tiryns. Semantic Web technologies will be used to model the digital content, facilitating organization and interoperability with external sources in the future. These applications will be part of the OpenLab activities on the site, enhancing visitors’ experiences and understanding of current and past conservation and restoration practices. Full article

(This article belongs to the Special Issue Artificial Intelligence Models, Tools and Applications with A Social and Semantic Impact)

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19 pages, 3763 KiB

Open AccessArticle

Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

by Prajoy Podder, Fatema Binte Alam, M. Rubaiyat Hossain Mondal, Md Junayed Hasan, Ali Rohan and Subrato Bharati

Computers 2023, 12(5), 95; https://doi.org/10.3390/computers12050095 - 02 May 2023

Cited by 4 | Viewed by 2069

Abstract

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep [...] Read more.

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model’s performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The model’s ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain)

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16 pages, 2492 KiB

Open AccessArticle

Disparity of Density in the Age of Mobility: Analysis by Opinion Formation Model

by Shiro Horiuchi

Computers 2023, 12(5), 94; https://doi.org/10.3390/computers12050094 - 01 May 2023

Viewed by 1405

Abstract

High mobility has promoted the concentration of people’s aggregation in urban areas. As people pursue areas with higher density, gentrification and sprawl become more serious. Disadvantaged people are then pushed out of urban centers. Conversely, as mobility increases, the disadvantaged may also migrate [...] Read more.

High mobility has promoted the concentration of people’s aggregation in urban areas. As people pursue areas with higher density, gentrification and sprawl become more serious. Disadvantaged people are then pushed out of urban centers. Conversely, as mobility increases, the disadvantaged may also migrate in pursuit of their desired density. As a result, disparities relative to density and housing may shrink. Hence, migration is a complex system. Understanding the effects of migration on disparities intuitively is difficult. This study explored the effects of mobility on disparity using an agent-based model of opinion formation. We find that as mobility increases, disparities between agents in density and diversity widen, but as mobility increases further, the disparities shrink, and then widen again. Our results present possibilities for a just city in the age of mobility. Full article

(This article belongs to the Special Issue Computational Modeling of Social Processes and Social Networks)

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23 pages, 3686 KiB

Open AccessArticle

Abstract Entity Patterns for Sensors and Actuators

by Bijayita Thapa, Eduardo B. Fernandez, Ionut Cardei and Maria M. Larrondo-Petrie

Computers 2023, 12(5), 93; https://doi.org/10.3390/computers12050093 - 30 Apr 2023

Cited by 1 | Viewed by 1241

Abstract

Sensors and actuators are fundamental units in Cyber–Physical and Internet of Things systems. Because they are included in a variety of systems, using many technologies, it is very useful to characterize their functions abstractly by describing them as Abstract Entity Patterns (AEPs), which [...] Read more.

Sensors and actuators are fundamental units in Cyber–Physical and Internet of Things systems. Because they are included in a variety of systems, using many technologies, it is very useful to characterize their functions abstractly by describing them as Abstract Entity Patterns (AEPs), which are patterns that describe abstract conceptual entities. From AEPs, we can derive concrete patterns; a structure combining related AEPs is an Entity Solution Frame (ESF). This paper concentrates on the functional aspects of these devices and defines conceptual units that can be used to design any system that requires sensors and actuators; that is, almost any Cyber–Physical system. For concreteness, we explore them in this study in the context of autonomous cars. An autonomous car is a complex system because, in addition to its own complex design, it interacts with other vehicles and with the surrounding infrastructure. To handle these functions, it must incorporate various technologies from different sources. An autonomous car is an example of a Cyber–Physical System, where some of its functions are performed via Internet of Things units. Sensors are extensively used in autonomous cars to measure physical quantities; actuators are commanded by controllers to perform appropriate physical actions. Both sensors and actuators are susceptible to malicious attacks due to the large attack surface of the system in which they are used. Our work is intended to make autonomous cars more secure, which also increases their safety. Our final objective is to build a Security Solution Frame for sensors and actuators of autonomous cars that will facilitate their secure design. A Security Solution Frame is a solution structure that groups together and organizes related security patterns. Full article

(This article belongs to the Special Issue Cooperative Vehicular Networking 2023)

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19 pages, 543 KiB

Open AccessArticle

An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder

by Md. Jamal Uddin, Md. Martuza Ahamad, Prodip Kumar Sarker, Sakifa Aktar, Naif Alotaibi, Salem A. Alyami, Muhammad Ashad Kabir and Mohammad Ali Moni

Computers 2023, 12(5), 92; https://doi.org/10.3390/computers12050092 - 30 Apr 2023

Cited by 6 | Viewed by 2151

Abstract

Autism Spectrum Disorder (ASD) is a neurological impairment condition that severely impairs cognitive, linguistic, object recognition, interpersonal, and communication skills. Its main cause is genetic, and early treatment and identification can reduce the patient’s expensive medical costs and lengthy examinations. We developed a [...] Read more.

Autism Spectrum Disorder (ASD) is a neurological impairment condition that severely impairs cognitive, linguistic, object recognition, interpersonal, and communication skills. Its main cause is genetic, and early treatment and identification can reduce the patient’s expensive medical costs and lengthy examinations. We developed a machine learning (ML) architecture that is capable of effectively analysing autistic children’s datasets and accurately classifying and identifying ASD traits. We considered the ASD screening dataset of toddlers in this study. We utilised the SMOTE method to balance the dataset, followed by feature transformation and selection methods. Then, we utilised several classification techniques in conjunction with a hyperparameter optimisation approach. The AdaBoost method yielded the best results among the classifiers. We employed ML and statistical approaches to identify the most crucial characteristics for the rapid recognition of ASD patients. We believe our proposed framework could be useful for early diagnosis and helpful for clinicians. Full article

(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain)

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26 pages, 2472 KiB

Open AccessReview

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

by Mohammad Mustafa Taye

Computers 2023, 12(5), 91; https://doi.org/10.3390/computers12050091 - 25 Apr 2023

Cited by 71 | Viewed by 44996

Abstract

In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance. Deep learning technology, which grew out of [...] Read more.

In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance. Deep learning technology, which grew out of artificial neural networks (ANN), has become a big deal in computing because it can learn from data. The ability to learn enormous volumes of data is one of the benefits of deep learning. In the past few years, the field of deep learning has grown quickly, and it has been used successfully in a wide range of traditional fields. In numerous disciplines, including cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, deep learning has outperformed well-known machine learning approaches. In order to provide a more ideal starting point from which to create a comprehensive understanding of deep learning, also, this article aims to provide a more detailed overview of the most significant facets of deep learning, including the most current developments in the field. Moreover, this paper discusses the significance of deep learning and the various deep learning techniques and networks. Additionally, it provides an overview of real-world application areas where deep learning techniques can be utilised. We conclude by identifying possible characteristics for future generations of deep learning modelling and providing research suggestions. On the same hand, this article intends to provide a comprehensive overview of deep learning modelling that can serve as a resource for academics and industry people alike. Lastly, we provide additional issues and recommended solutions to assist researchers in comprehending the existing research gaps. Various approaches, deep learning architectures, strategies, and applications are discussed in this work. Full article

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27 pages, 810 KiB

Open AccessArticle

A Knowledge Representation System for the Indian Stock Market

by Bikram Pratim Bhuyan, Vaishnavi Jaiswal and Amar Ramdane Cherif

Computers 2023, 12(5), 90; https://doi.org/10.3390/computers12050090 - 24 Apr 2023

Viewed by 3610

Abstract

Investors at well-known firms are increasingly becoming interested in stock forecasting as they seek more effective methods to predict market behavior using behavioral finance tools. Accordingly, studies aimed at predicting stock performance are gaining popularity in both academic and business circles. This research [...] Read more.

Investors at well-known firms are increasingly becoming interested in stock forecasting as they seek more effective methods to predict market behavior using behavioral finance tools. Accordingly, studies aimed at predicting stock performance are gaining popularity in both academic and business circles. This research aims to develop a knowledge graph-based model for representing stock price movements using fundamental ratios of well-known corporations in India. The paper uses data from 15 ratios taken from the top 50 companies according to market capitalization in India. The data were processed, and different algorithms were used to extract tuples of knowledge from the data. Our technique involves guiding a domain expert through the process of building a knowledge graph. The scripts of the proposed knowledge representation and data could be found here: GitHub. The work can be integrated with a deep learning model for explainable forecasting of stock price. Full article

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14 pages, 1268 KiB

Open AccessArticle

Classification of Arabic Poetry Emotions Using Deep Learning

by Sakib Shahriar, Noora Al Roken and Imran Zualkernan

Computers 2023, 12(5), 89; https://doi.org/10.3390/computers12050089 - 22 Apr 2023

Cited by 3 | Viewed by 2200

Abstract

The automatic classification of poems into various categories, such as by author or era, is an interesting problem. However, most current work categorizing Arabic poems into eras or emotions has utilized traditional feature engineering and machine learning approaches. This paper explores deep learning [...] Read more.

The automatic classification of poems into various categories, such as by author or era, is an interesting problem. However, most current work categorizing Arabic poems into eras or emotions has utilized traditional feature engineering and machine learning approaches. This paper explores deep learning methods to classify Arabic poems into emotional categories. A new labeled poem emotion dataset was developed, containing 9452 poems with emotional labels of joy, sadness, and love. Various deep learning models were trained on this dataset. The results show that traditional deep learning models, such as one-dimensional Convolutional Neural Networks (1DCNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) networks, performed with F1-scores of 0.62, 0.62, and 0.53, respectively. However, the AraBERT model, an Arabic version of the Bidirectional Encoder Representations from Transformers (BERT), performed best, obtaining an accuracy of 76.5% and an F1-score of 0.77. This model outperformed the previous state-of-the-art in this domain. Full article

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Computers, Volume 12, Issue 5 (May 2023) – 21 articles

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