Computers

18 pages, 571 KiB

Open AccessArticle

Can ChatGPT Solve Undergraduate Exams from Warehousing Studies? An Investigation

by Sven Franke, Christoph Pott, Jérôme Rutinowski, Markus Pauly, Christopher Reining and Alice Kirchheim

Computers 2025, 14(2), 52; https://doi.org/10.3390/computers14020052 - 5 Feb 2025

Viewed by 247

Abstract

The performance of Large Language Models, such as ChatGPT, generally increases with every new model release. In this study, we investigated to what degree different GPT models were able to solve the exams of three different undergraduate courses on warehousing. We contribute to [...] Read more.

The performance of Large Language Models, such as ChatGPT, generally increases with every new model release. In this study, we investigated to what degree different GPT models were able to solve the exams of three different undergraduate courses on warehousing. We contribute to the discussion of ChatGPT’s existing logistics knowledge, particularly in the field of warehousing. Both the free version (GPT-4o mini) and the premium version (GPT-4o) completed three different warehousing exams using three different prompting techniques (with and without role assignments as logistics experts or students). The o1-preview model was also used (without a role assignment) for six runs. The tests were repeated three times. A total of 60 tests were conducted and compared with the in-class results of logistics students. The results show that the GPT models passed a total of 46 tests. The best run solved 93% of the exam correctly. Compared with the students from the respective semester, ChatGPT outperformed the students in one exam. In the other two exams, the students performed better on average than ChatGPT. Full article

(This article belongs to the Special Issue IT in Production and Logistics)

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28 pages, 2072 KiB

Open AccessReview

Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey

by Mohammed Ahmed Salem, Heng Siong Lim, Kah Seng Diong, Khaled A. Alaghbari, Charilaos C. Zarakovitis and Su Fong Chien

Computers 2025, 14(2), 51; https://doi.org/10.3390/computers14020051 - 5 Feb 2025

Viewed by 261

Abstract

The expansion of 5G infrastructure and the deployment of large antenna arrays are set to substantially influence electromagnetic field (EMF) exposure levels within mobile networks. As a result, the accurate measurement of EMF exposure and the integration of EMF exposure constraints into radio [...] Read more.

The expansion of 5G infrastructure and the deployment of large antenna arrays are set to substantially influence electromagnetic field (EMF) exposure levels within mobile networks. As a result, the accurate measurement of EMF exposure and the integration of EMF exposure constraints into radio resource management are expected to become increasingly important in future mobile communication systems. This paper provides a comprehensive review of EMF exposure evaluation frameworks for 5G networks, considering the impacts of high-energy beams, the millimeter wave spectrum, network densification and reconfigurable intelligent surfaces (RISs), while also examining EMF-aware radio resource management strategies for 5G networks and beyond, with RIS technology as an assistive factor. Furthermore, challenges and open research topics in the EMF evaluation framework and EMF-aware resource management for 5G mobile networks and beyond are highlighted. Despite the growing importance of RIS technology in enhancing mobile networks, a research gap remains in addressing specific EMF exposure considerations associated with RIS deployments. Additionally, the impact of EMF-aware radio resource allocation approaches on RIS-assisted 5G networks is still not fully understood. Full article

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22 pages, 1671 KiB

Open AccessArticle

Modeling Mobile Applications for Proximity-Based Promotion Delivery to Shopping Centers Using Petri Nets

by Julian Velazquez, Ruben Machucho, Jose F. Lopez, Hiram Herrera and Jorge-Arturo Hernandez-Almazan

Computers 2025, 14(2), 50; https://doi.org/10.3390/computers14020050 - 5 Feb 2025

Viewed by 433

Abstract

This article presents the design and implementation of an API that delivers real-time promotional notifications to mobile devices based on their proximity to shopping centers, calculated using the Haversine formula. Developed in Laravel, the API determines whether a mobile device is within a [...] Read more.

This article presents the design and implementation of an API that delivers real-time promotional notifications to mobile devices based on their proximity to shopping centers, calculated using the Haversine formula. Developed in Laravel, the API determines whether a mobile device is within a 600 m radius of any registered shopping center, such as Soriana, GranD, and HEB, and sends the relevant promotional information. The system uses Petri nets to model asynchronous behavior, enabling efficient concurrency management between the mobile application and the API. This structure ensures optimized message delivery, preventing communication collisions and delays. The mobile application, developed in Kotlin, integrates geolocation services to capture and update the user’s location in real time. The results indicate an improvement in response time and proximity detection accuracy, highlighting the effectiveness of the Petri net model for systems requiring concurrent interaction. The combination of Laravel, Kotlin, and formal modeling with Petri nets proves to be an effective and scalable solution for proximity-based mobile applications. Full article

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28 pages, 4277 KiB

Open AccessArticle

Analysing Cyber Attacks and Cyber Security Vulnerabilities in the University Sector

by Harjinder Singh Lallie, Andrew Thompson, Elzbieta Titis and Paul Stephens

Computers 2025, 14(2), 49; https://doi.org/10.3390/computers14020049 - 4 Feb 2025

Viewed by 869

Abstract

Universities hold and process vast amounts of financial, user, and research data, which makes them prime targets for cybercriminals. In addition to the usual external threat actors, universities face a unique insider threat from students, who—alongside staff—may lack adequate cyber security training despite [...] Read more.

Universities hold and process vast amounts of financial, user, and research data, which makes them prime targets for cybercriminals. In addition to the usual external threat actors, universities face a unique insider threat from students, who—alongside staff—may lack adequate cyber security training despite having access to various sensitive systems. This paper provides a focused assessment of the current cyber security threats facing UK universities, based on a comprehensive review of available information. A chronological timeline of notable cyber attacks against universities is produced, with incidents classified according to the CIA triad (Confidentiality, Integrity, Availability) and incident type. Several issues have been identified. Limited disclosure of attack details is a major concern, as full information is often withheld for security reasons, hindering institutions’ abilities to assess vulnerabilities thoroughly and respond effectively. Additionally, universities increasingly rely on third-party service providers for critical services, meaning that an attack on these external providers can directly impact university operations and data security. While SQL injection attacks, previously a significant issue, appear to have declined in frequency—perhaps reflecting improvements in defences—other threats continue to persist. Universities report lower levels of concern regarding DDoS attacks, potentially due to enhanced resilience and mitigation strategies; however, ransomware and phishing attacks remain prevalent. Insider threats, especially from students with varied IT skills, exacerbate these risks, as insiders may unknowingly or maliciously facilitate cyber attacks, posing ongoing challenges for university IT teams. This study recommends that universities leverage these insights, along with other available data, to refine their cyber security strategies. Developing targeted policies, strengthening training, and implementing international standards will allow universities to enhance their security posture and mitigate the complex and evolving threats they face. Full article

(This article belongs to the Special Issue Cyber Security and Privacy in IoT Era)

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13 pages, 14087 KiB

Open AccessArticle

From Data Surveying to the Geometrical Analysis of Historical Constructive Wooden Ceiling Structures: A Renaissance Villa in the North of Italy

by Daniela Oreni

Computers 2025, 14(2), 48; https://doi.org/10.3390/computers14020048 - 4 Feb 2025

Viewed by 346

Abstract

Villa Cicogna Mozzoni, located in Bisuschio near Varese and Lake Lugano, on the border between Lombardy and Switzerland, has origins dating back to the 1540s as a hunting lodge owned by the Mozzoni family. In the 16th century, significant renovations transformed it into [...] Read more.

Villa Cicogna Mozzoni, located in Bisuschio near Varese and Lake Lugano, on the border between Lombardy and Switzerland, has origins dating back to the 1540s as a hunting lodge owned by the Mozzoni family. In the 16th century, significant renovations transformed it into a “villa di delizia”, adding gardens and elaborate decorative features to the interior and exterior, many of which are still preserved today. This article focuses on a precise geometric analysis of the building’s wooden ceilings, based on laser scanning and photogrammetric data surveying. The ongoing research particularly examines the wooden coffered ceilings on the first floor and the camorcanna wooden fake vault of the Grand Staircase of Honor. By analyzing the geometric data and comparing it with historical, archival, and recent manuals, the study has provided valuable morphological, construction, and conservation insights, forming the basis for the diagnostic and restoration project. Full article

(This article belongs to the Special Issue Computational Science and Its Applications 2024 (ICCSA 2024))

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20 pages, 3107 KiB

Open AccessArticle

Computer Simulation and Speedup of Solving Heat Transfer Problems of Heating and Melting Metal Particles with Laser Radiation

by Arturas Gulevskis and Konstantin Volkov

Computers 2025, 14(2), 47; https://doi.org/10.3390/computers14020047 - 4 Feb 2025

Viewed by 266

Abstract

The study of the process of laser action on powder materials requires the construction of mathematical models of the interaction of laser radiation with powder particles that take into account the features of energy supply and are applicable in a wide range of [...] Read more.

The study of the process of laser action on powder materials requires the construction of mathematical models of the interaction of laser radiation with powder particles that take into account the features of energy supply and are applicable in a wide range of beam parameters and properties of the particle material. A model of the interaction of pulsed or pulse-periodic laser radiation with a spherical metal particle is developed. To find the temperature distribution in the particle volume, the non-stationary three-dimensional heat conductivity equation with a source term that takes into account the action of laser radiation is solved. In the plane normal to the direction of propagation of laser radiation, the change in the radiation intensity obeys the Gaussian law. It is possible to take into account changes in the intensity of laser radiation in space due to its absorption by the environment. To accelerate numerical calculations, a computational algorithm is used based on the use of vectorized data structures and parallel implementation of operations on general-purpose graphics accelerators. The features of the software implementation of the method for solving a system of difference equations that arises as a result of finite-volume discretization of the heat conductivity equation with implicit scheme by the iterative method are presented. The model developed describes the heating and melting of a spherical metal particle exposed by multi-pulsed laser radiation. The implementation of the computational algorithm developed is based on the use of vectorized data structures and GPU resources. The model and calculation results are of interest for constructing a two-phase flow model describing the interaction of test particles with laser radiation on the scale of the entire calculation domain. Such a model is implemented using a discrete-trajectory approach to modeling the motion and heat exchange of a dispersed admixture. Full article

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42 pages, 2946 KiB

Open AccessArticle

The Development of User-Centric Design Guidelines for Web3 Applications: An Empirical Study

by Polina Bobrova and Paolo Perego

Computers 2025, 14(2), 46; https://doi.org/10.3390/computers14020046 - 1 Feb 2025

Viewed by 380

Abstract

The design of Web3 applications presents unique challenges due to their complex technical requirements. Despite the increasing spread of this technology, there is a notable lack of comprehensive, empirically grounded design guidelines for developing user-friendly Web3 interfaces. This study addresses this gap through [...] Read more.

The design of Web3 applications presents unique challenges due to their complex technical requirements. Despite the increasing spread of this technology, there is a notable lack of comprehensive, empirically grounded design guidelines for developing user-friendly Web3 interfaces. This study addresses this gap through a systematic three-phase approach: (1) developing initial guidelines from a literature review and industry sources (n = 31), (2) conducting evaluations using a 14-point framework based on the initial guidelines to test its effectiveness across diverse Web3 applications (n = 25), and (3) validating refined guidelines through expert evaluation sessions (n = 7). Expert evaluations highlighted the need for task-oriented rather than category-based organization of design principles. Based on these findings, we developed a structured framework organizing guidelines into four key task flows, each with three implementation levels. The framework emphasizes progressive disclosure of blockchain concepts, integrated user education, and clear state visualization. Our findings contribute to academic discussion and industry practice by providing empirically validated patterns for Web3 interface design. This study lays a foundation for creating more accessible and user-friendly decentralized applications, though future work should focus on longitudinal validation and adaptation to emerging technologies. Full article

(This article belongs to the Special Issue Harnessing the Blockchain Technology in Unveiling Futuristic Applications)

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22 pages, 746 KiB

Open AccessArticle

Heart Rate Variability-Based Stress Detection and Fall Risk Monitoring During Daily Activities: A Machine Learning Approach

by Ines Belhaj Messaoud and Ornwipa Thamsuwan

Computers 2025, 14(2), 45; https://doi.org/10.3390/computers14020045 - 30 Jan 2025

Viewed by 304

Abstract

Impaired balance and mental stress are significant health concerns, particularly among older adults. This study investigated the relationship between the heart rate variability and fall risk during daily activities among individuals over 40 years old. This aimed to explore the potential of the [...] Read more.

Impaired balance and mental stress are significant health concerns, particularly among older adults. This study investigated the relationship between the heart rate variability and fall risk during daily activities among individuals over 40 years old. This aimed to explore the potential of the heart rate variability as an indicator of stress and balance loss. Data were collected from 14 healthy participants who wore a Polar H10 heart rate monitor and performed Berg Balance Scale activities as part of an assessment of functional balance. Machine learning techniques applied to the collected data included two phases: unsupervised clustering and supervised classification. K-means clustering identified three distinct physiological states based on HRV features, such as the high-frequency band power and the root mean square of successive differences between normal heartbeats, suggesting patterns that may reflect stress levels. In the second phase, integrating the cluster labels obtained from the first phase together with HRV features into machine learning models for fall risk classification, we found that Gradient Boosting performed the best, achieving an accuracy of 95.45%, a precision of 93.10% and a recall of 85.71%. This study demonstrates the feasibility of using the heart rate variability and machine learning to monitor physiological responses associated with stress and fall risks. By highlighting this potential biomarker of autonomic health, the findings contribute to developing real-time monitoring systems that could support fall prevention efforts in everyday settings for older adults. Full article

(This article belongs to the Special Issue Wearable Computing and Activity Recognition)

17 pages, 20545 KiB

Open AccessArticle

Optimizing Loss Functions for You Only Look Once Models: Improving Object Detection in Agricultural Datasets

by Atsuki Matsui, Ryuto Ishibashi and Lin Meng

Computers 2025, 14(2), 44; https://doi.org/10.3390/computers14020044 - 30 Jan 2025

Viewed by 298

Abstract

Japan faces a significant labor shortage due to an aging population, particularly in the agricultural sector. The rising average age of farmers and the declining participation of younger individuals threaten the sustainability of farming practices. These trends reduce the availability of agricultural labor [...] Read more.

Japan faces a significant labor shortage due to an aging population, particularly in the agricultural sector. The rising average age of farmers and the declining participation of younger individuals threaten the sustainability of farming practices. These trends reduce the availability of agricultural labor and pose a risk to lowering Japan’s food self-sufficiency rate. The reliance on food imports raises concerns regarding price fluctuations and sanitation standards. Moreover, the challenging working conditions in agriculture and a lack of technological innovation have hindered productivity and increased the burden on the existing workforce. To address these challenges, “smart agriculture” presents a promising solution. By leveraging advanced technologies such as sensors, drones, the Internet of Things (IoT), and automation, smart agriculture aims to optimize farm operations. Real-time data collection and AI-driven analysis play a crucial role in monitoring crop growth, assessing soil conditions, and improving overall efficiency. This study proposes enhancements to the YOLO (You Only Look Once) object detection model to develop an automated tomato harvesting system. This system uses a camera to detect tomatoes and assess their ripeness for harvest. Our objective is to streamline the harvesting process through AI technology. Our improved YOLO model integrates two novel loss functions to enhance detection accuracy. The first, “VSR”, refines the model’s ability to classify tomatoes and determine their harvest readiness. The second, “SBCE”, enhances the detection of small tomatoes by training the model to recognize a range of object sizes within the dataset. These improvements have significantly increased the system’s detection performance. Our experimental results demonstrate that the mean Average Precision (mAP) of YOLOv7-tiny improved from 61.81% to 70.21%. Additionally, the F1 score increased from 0.61 to 0.71 and the mean Intersection over Union (mIoU) rose from 65.03% to 66.44% on the tomato dataset. These findings underscore the potential of our proposed system to enhance efficiency in agricultural practices. Full article

(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)

16 pages, 1542 KiB

Open AccessArticle

Mitigation of Risks Associated with Distrustful Routers in OSPF Networks—An Enhanced Method

by Kvitoslava Obelovska, Yaromyr Snaichuk, Oleh Liskevych, Stergios-Aristoteles Mitoulis and Rostyslav Liskevych

Computers 2025, 14(2), 43; https://doi.org/10.3390/computers14020043 - 29 Jan 2025

Viewed by 359

Abstract

Packet routing in computer networks provides complex challenges in environments with distrustful routers due to security vulnerabilities or potential malicious behaviors. The literature offers solutions to the problem designed for different types of networks. This paper introduces a novel method to mitigate risks [...] Read more.

Packet routing in computer networks provides complex challenges in environments with distrustful routers due to security vulnerabilities or potential malicious behaviors. The literature offers solutions to the problem designed for different types of networks. This paper introduces a novel method to mitigate risks associated with distrustful routers by constructing secure and efficient routing paths in Open Shortest Path First (OSPF) networks. Networks in which routing is carried out based on OSPF protocols are currently the most widespread, hence ensuring the security of data transmission in such networks is urgently needed. In turn, distrustful routers can degrade the overall security and performance of the network, creating vulnerabilities that can be used for malicious purposes. The proposed method is based on the Dijkstra algorithm which is enhanced to identify and mitigate the risk connected with potential distrustful network nodes. Analysis of the proposed method shows its ability to build efficient routes exclusively through trusted routers if such paths exist. As a criterion for effectiveness, a metric such as the channel weight is used. The proposed method is validated using applications across networks of varying topologies and sizes, including large-scale networks. For networks containing post-distrustful routers to which there is no path without distrustful nodes, the proposed method is able to build the shortest paths that are marked as not secure but have a minimum number of distrustful nodes on their path. In scenarios with multiple compromised routers with different locations in the network, the proposed method significantly increases network resilience. Full article

(This article belongs to the Special Issue Multimedia Data and Network Security)

19 pages, 529 KiB

Open AccessReview

Redefining Event Detection and Information Dissemination: Lessons from X (Twitter) Data Streams and Beyond

by Harshit Srivastava and Ravi Sankar

Computers 2025, 14(2), 42; https://doi.org/10.3390/computers14020042 - 28 Jan 2025

Viewed by 504

Abstract

X (formerly known as Twitter), Reddit, and other social media forums have dramatically changed the way society interacts with live events in this day and age. The huge amount of data generated by these platforms presents challenges, especially in terms of processing speed [...] Read more.

X (formerly known as Twitter), Reddit, and other social media forums have dramatically changed the way society interacts with live events in this day and age. The huge amount of data generated by these platforms presents challenges, especially in terms of processing speed and the complexity of finding meaningful patterns and events. These data streams are generated in multiple formats, with constant updating, and are real-time in nature; thus, they require sophisticated algorithms capable of dynamic event detection in this dynamic environment. Event detection techniques have recently achieved substantial development, but most research carried out so far evaluates only single methods, not comparing the overall performance of these methods across multiple platforms and types of data. With that view, this paper represents a deep investigation of complex state-of-the-art event detection algorithms specifically customized for streams of data from X. We review various current techniques based on a thorough comparative performance test and point to problems inherently related to the detection of patterns in high-velocity streams with noise. We introduce some novelty to this research area, supported by appropriate robust experimental frameworks, to performed comparisons quantitatively and qualitatively. We provide insight into how those algorithms perform under varying conditions by defining a set of clear, measurable metrics. Our findings contribute new knowledge that will help inform future research into the improvement of event detection systems for dynamic data streams and enhance their capabilities for real-time and actionable insights. This paper will go a step further than the present knowledge of event detection and discuss how algorithms can be adapted and refined in view of the emerging demands imposed by data streams. Full article

(This article belongs to the Special Issue Recent Advances in Social Networks and Social Media)

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28 pages, 991 KiB

Open AccessReview

A Review of Vessel Time of Arrival Prediction on Waterway Networks: Current Trends, Open Issues, and Future Directions

by Abdullah Al Noman, Aaron Heuermann, Stefan Wiesner and Klaus-Dieter Thoben

Computers 2025, 14(2), 41; https://doi.org/10.3390/computers14020041 - 28 Jan 2025

Viewed by 336

Abstract

With the vast majority of global trade volume and value reliant on maritime transport, accurate prediction of vessel estimated time of arrival (ETA) is crucial for optimizing supply chain efficiency and managing logistical complexities in port operations. This review paper systematically examines the [...] Read more.

With the vast majority of global trade volume and value reliant on maritime transport, accurate prediction of vessel estimated time of arrival (ETA) is crucial for optimizing supply chain efficiency and managing logistical complexities in port operations. This review paper systematically examines the current state of research and practices in the field of vessel ETA prediction, highlighting significant trends, methodologies, and technologies. It explores various approaches, including classical methods, machine learning and deep learning algorithms, and hybrid methods, developed to enhance the accuracy and reliability of vessel travel time and arrival time predictions. Additionally, this paper categorizes key influencing factors and metrics, and identifies open issues and challenges within current prediction models. Concluding with proposed future research directions aimed at addressing the identified gaps and leveraging technological advancements, this review emphasizes the importance of fostering innovation in maritime ETA prediction systems, particularly within the framework of Intelligent Transportation Systems (ITSs) and maritime logistics. By applying a systematic literature review (SLR) methodology and conducting an in-depth evaluation, the results provide a comprehensive overview of vessel ETA prediction for researchers, practitioners, and policy makers involved in maritime transport and logistics, and offer insights into the potential for improved efficiency, safety, and environmental sustainability in waterway networks. Full article

(This article belongs to the Special Issue IT in Production and Logistics)

20 pages, 3245 KiB

Open AccessReview

Model Transformations Used in IT Project Initial Phases: Systematic Literature Review

by Oksana Nikiforova, Kristaps Babris, Uldis Karlovs-Karlovskis, Marta Narigina, Andrejs Romanovs, Anita Jansone, Janis Grabis and Oscar Pastor

Computers 2025, 14(2), 40; https://doi.org/10.3390/computers14020040 - 27 Jan 2025

Viewed by 465

Abstract

The paper emphasizes the critical importance of the initial phase in IT project development to avoid implementation errors. It argues that minimizing these errors can be achieved by developing project artifacts at the early stage using a model-driven engineering-based approach. Model transformation plays [...] Read more.

The paper emphasizes the critical importance of the initial phase in IT project development to avoid implementation errors. It argues that minimizing these errors can be achieved by developing project artifacts at the early stage using a model-driven engineering-based approach. Model transformation plays a basic role in that context. The goal of this paper is to survey publications in which the authors propose generating initial project elements through model-driven engineering and to analyze the level of model transformations offered in their solutions. As a result, the authors would highlight the necessity of understanding which elements of a project can be obtained through automatic transformations and which still require manual manipulation. This distinction is crucial, as it can significantly influence the efficiency and accuracy of the project’s early phases. In general, identifying the project components that can be reliably generated through model transformations helps streamline the project inception and elaboration process performed before IT product implementation. Full article

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16 pages, 2717 KiB

Open AccessArticle

Identification of Greek Orthodox Church Chants Using Fuzzy Entropy

by Lazaros Moysis, Konstantinos Karasavvidis, Dimitris Kampelopoulos, Achilles D. Boursianis, Sotirios Sotiroudis, Spiridon Nikolaidis, Christos Volos, Panagiotis Sarigiannidis, Mohammad Abdul Matin and Sotirios K. Goudos

Computers 2025, 14(2), 39; https://doi.org/10.3390/computers14020039 - 27 Jan 2025

Viewed by 298

Abstract

In this work, a comparison of Greek Orthodox religious chants is performed using fuzzy entropy. Using a dataset of chant performances, each recitation is segmented into overlapping time windows, and the fuzzy entropy of each window in the frequency domain is computed. We [...] Read more.

In this work, a comparison of Greek Orthodox religious chants is performed using fuzzy entropy. Using a dataset of chant performances, each recitation is segmented into overlapping time windows, and the fuzzy entropy of each window in the frequency domain is computed. We introduce a novel audio fingerprinting framework by comparing the variations in the resulting fuzzy entropy vector for the dataset. For this purpose, we use the correlation coefficient as a measure and dynamic time warping. Thus, it is possible to match the performances of the same chant with high probability. The proposed methodology provides a foundation for building an audio fingerprinting method based on fuzzy entropy. Full article

15 pages, 1916 KiB

Open AccessArticle

Cybercrime Resilience in the Era of Advanced Technologies: Evidence from the Financial Sector of a Developing Country

by Adeel Ali, Mahmood Shah, Monika Foster and Mansour Naser Alraja

Computers 2025, 14(2), 38; https://doi.org/10.3390/computers14020038 - 27 Jan 2025

Viewed by 603

Abstract

Technological advancements have helped all sectors to evolve. This advancement has widened the cyberspace and attack surface, which has led to a drastic increase in cyberattacks. Cybersecurity solutions have also evolved. The advancement is relatively slower in developing countries. However, the financial sector [...] Read more.

Technological advancements have helped all sectors to evolve. This advancement has widened the cyberspace and attack surface, which has led to a drastic increase in cyberattacks. Cybersecurity solutions have also evolved. The advancement is relatively slower in developing countries. However, the financial sector in developing countries has shown resistance to cyberattacks. This paper investigates the reasons for this resistance. Despite using legacy systems, the banking sector in Pakistan has demonstrated resistance to cyberattacks. The research used a qualitative approach. Semi-structured interviews were conducted with nine cybersecurity experts in the banking sector to illustrate the reasons for this cybersecurity resistance. The research focused on cybersecurity experts in the banking sector, recognizing that this industry is particularly prone to cyberattacks on a global scale. The study utilised a thematic analysis technique to find resistance factors. The analysis suggests that the opportunity cost of cyberattacks and lower attack surface in developing countries like Pakistan are the main reasons for the lower financial losses. The findings of this research will encourage the adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) for cybersecurity in developing countries’ banking and financial sectors. Full article

(This article belongs to the Special Issue Cyber Security and Privacy in IoT Era)

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26 pages, 11614 KiB

Open AccessArticle

Commutative Quaternion Algebra with Quaternion Fourier Transform-Based Alpha-Rooting Color Image Enhancement

by Artyom M. Grigoryan and Alexis A. Gomez

Computers 2025, 14(2), 37; https://doi.org/10.3390/computers14020037 - 26 Jan 2025

Viewed by 257

Abstract

In this paper, we describe the associative and commutative algebra or the (2,2)-model of quaternions with application in color image enhancement. The method of alpha-rooting, which is based on the 2D quaternion discrete Fourier transform (QDFT) is considered. In the (2,2)-model, the aperiodic [...] Read more.

In this paper, we describe the associative and commutative algebra or the (2,2)-model of quaternions with application in color image enhancement. The method of alpha-rooting, which is based on the 2D quaternion discrete Fourier transform (QDFT) is considered. In the (2,2)-model, the aperiodic convolution of quaternion signals can be calculated by the product of their QDFTs. The concept of linear convolution is simple, that is, it is unique, and the reduction of this operation to the multiplication in the frequency domain makes this model very attractive for processing color images. Note that in the traditional quaternion algebra, which is not commutative, the convolution can be chosen in many different ways, and the number of possible QDFTs is infinite. And most importantly, the main property of the traditional Fourier transform that states that the aperiodic convolution is the product of the transform in the frequency domain is not valid. We describe the main property of the (2,2)-model of quaternions, the quaternion exponential functions and convolution. Three methods of alpha-rooting based on the 2D QDFT are presented, and illustrative examples on color image enhancement are given. The image enhancement measures to estimate the quality of the color images are described. Examples of the alpha-rooting enhancement on different color images are given and analyzed with the known histogram equalization and Retinex algorithms. Our experimental results show that the alpha-rooting method in the quaternion space is one of the most effective methods of color image enhancement. Quaternions allow all colors in each pixel to be processed as a whole, rather than individually as is done in traditional processing methods. Full article

(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)

34 pages, 1690 KiB

Open AccessReview

Machine Learning Approaches for Speech-Based Alzheimer’s Detection: A Comprehensive Survey

by Ahmed Sharafeldeen, Justin Keowen and Ahmed Shaffie

Computers 2025, 14(2), 36; https://doi.org/10.3390/computers14020036 - 24 Jan 2025

Viewed by 605

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly impairs cognitive functions, leading to memory loss and other behavioral changes. It is the seventh leading cause of death worldwide, with millions of people affected. Early and accurate detection of AD is critical [...] Read more.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly impairs cognitive functions, leading to memory loss and other behavioral changes. It is the seventh leading cause of death worldwide, with millions of people affected. Early and accurate detection of AD is critical for improving patient outcomes and slowing disease progression. Recent advancements in machine learning (ML) and deep learning (DL) models have demonstrated significant potential for detecting AD using patient’s speech signals, as subtle changes in speech patterns, such as reduced fluency, pronunciation difficulties, and cognitive decline, can serve as early indicators of the disease, offering a non-invasive and cost-effective method for early diagnosis. This survey paper provides a comprehensive review of the current literature on the application of ML and DL techniques for AD detection through the analysis of a patient’s speech signal, utilizing various acoustic and textual features. Moreover, it offers an overview of the changes in the brain caused by the disease, associated risk factors, publicly available datasets, and future directions for leveraging ML and DL in the detection of AD. Full article

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29 pages, 8212 KiB

Open AccessArticle

ApproxGeoMap: An Efficient System for Generating Approximate Geo-Maps from Big Geospatial Data with Quality of Service Guarantees

by Reem Abdelaziz Alshamsi, Isam Mashhour Al Jawarneh, Luca Foschini and Antonio Corradi

Computers 2025, 14(2), 35; https://doi.org/10.3390/computers14020035 - 23 Jan 2025

Viewed by 452

Abstract

Timely, region-based geo-maps like choropleths are essential for smart city applications like traffic monitoring and urban planning because they can reveal statistical patterns in geotagged data. However, because data overloading is brought on by the quick inflow of massive geospatial data, creating these [...] Read more.

Timely, region-based geo-maps like choropleths are essential for smart city applications like traffic monitoring and urban planning because they can reveal statistical patterns in geotagged data. However, because data overloading is brought on by the quick inflow of massive geospatial data, creating these visualizations in real time presents serious difficulties. This paper introduces ApproxGeoMap, a novel system designed to efficiently generate approximate geo-maps from fast-arriving georeferenced data streams. ApproxGeoMap employs a stratified spatial sampling method, leveraging geohash tessellation and Earth Mover’s Distance (EMD) to maintain both accuracy and processing speed. We developed a prototype system and tested it on real-world smart city datasets, demonstrating that ApproxGeoMap meets time-based and accuracy-based quality of service (QoS) constraints. Results indicate that ApproxGeoMap significantly enhances efficiency in both running time and map accuracy, offering a reliable solution for high-speed data environments where traditional methods fall short. Full article

(This article belongs to the Special Issue Feature Papers in Computers 2024)

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31 pages, 8973 KiB

Open AccessArticle

Number Recognition Through Color Distortion Using Convolutional Neural Networks

by Christopher Henshaw, Jacob Dennis, Jonathan Nadzam and Alan J. Michaels

Computers 2025, 14(2), 34; https://doi.org/10.3390/computers14020034 - 22 Jan 2025

Viewed by 569

Abstract

Machine learning applied to image-based number recognition has made significant strides in recent years. Recent use of Large Language Models (LLMs) in natural language search and generation of text have improved performance for general images, yet performance limitations still exist for data subsets [...] Read more.

Machine learning applied to image-based number recognition has made significant strides in recent years. Recent use of Large Language Models (LLMs) in natural language search and generation of text have improved performance for general images, yet performance limitations still exist for data subsets related to color blindness. In this paper, we replicated the training of six distinct neural networks (MNIST, LeNet5, VGG16, AlexNet, and two AlexNet modifications) using deep learning techniques with the MNIST dataset and the Ishihara-Like MNIST dataset. While many prior works have dealt with MNIST, the Ishihara adaption addresses red-green combinations of color blindness, allowing for further research in color distortion. Through this research, we applied pre-processing to accentuate the effects of red-green and monochrome colorblindness and hyper-parameterized the existing architectures, ultimately achieving better overall performance than currently published in known works. Full article

(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)

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21 pages, 3679 KiB

Open AccessArticle

Use of IoT with Deep Learning for Classification of Environment Sounds and Detection of Gases

by Priya Mishra, Naveen Mishra, Dilip Kumar Choudhary, Prakash Pareek and Manuel J. C. S. Reis

Computers 2025, 14(2), 33; https://doi.org/10.3390/computers14020033 - 22 Jan 2025

Viewed by 522

Abstract

The need for safe and healthy air quality has become critical as urbanization and industrialization increase, leading to health risks and environmental concerns. Gas leaks, particularly of gases like carbon monoxide, methane, and liquefied petroleum gas (LPG), pose significant dangers due to their [...] Read more.

The need for safe and healthy air quality has become critical as urbanization and industrialization increase, leading to health risks and environmental concerns. Gas leaks, particularly of gases like carbon monoxide, methane, and liquefied petroleum gas (LPG), pose significant dangers due to their flammability and toxicity. LPG, widely used in residential and industrial settings, is especially hazardous because it is colorless, odorless, and highly flammable, making undetected leaks an explosion risk. To mitigate these dangers, modern gas detection systems employ sensors, microcontrollers, and real-time monitoring to quickly identify dangerous gas levels. This study introduces an IoT-based system designed for comprehensive environmental monitoring, with a focus on detecting LPG and butane leaks. Using sensors like the MQ6 for gas detection, MQ135 for air quality, and DHT11 for temperature and humidity, the system, managed by an Arduino Mega, collects data and sends these to the ThingSpeak platform for analysis and visualization. In cases of elevated gas levels, it triggers an alarm and notifies the user through IFTTT. Additionally, the system includes a microphone and a CNN model for analyzing audio data, enabling a thorough environmental assessment by identifying specific sounds related to ongoing activities, reaching an accuracy of 96%. Full article

(This article belongs to the Special Issue Wireless Sensor Network, IoT and Cloud Computing Technologies for Smart Cities)

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14 pages, 3057 KiB

Open AccessArticle

Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality

by Avisek Kundu, Seeboli Ghosh Kundu, Santosh Kumar Sahu and Nitesh Dhar Badgayan

Computers 2025, 14(2), 32; https://doi.org/10.3390/computers14020032 - 22 Jan 2025

Viewed by 453

Abstract

The importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, comprising five dimensions—reliability, assurance, tangibility, empathy, and responsiveness—provides [...] Read more.

The importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, comprising five dimensions—reliability, assurance, tangibility, empathy, and responsiveness—provides a measurable framework for evaluating the overall customer satisfaction. This study endeavors to ascertain whether all SERVQUAL dimensions carry equal weight in their effect on the overall service quality and to estimate the service quality based on various input features. To achieve this, questions were framed to assess the impact of variables such as gender, age, marital status, highest level of education, and frequency of hotel stays. The importance of each feature relative to the five SERVQUAL dimensions was investigated using machine learning models, specifically, CatBoost and Microsoft Azure Automated Machine Learning (AutoML) studio. This study revealed that both CatBoost and Azure AutoML identified the frequency of hotel stays and age group as the dominant predictors of service quality. Additionally, Azure AutoML highlighted the marital status as a more significant factor, suggesting its potential influence on customer preferences. The comparative modeling results demonstrated a strong alignment between the feature importance derived from CatBoost and Azure AutoML, enabling decision-makers to identify which dimensions are influenced by specific predictors and focus on targeted improvements. Full article

(This article belongs to the Special Issue AI in Its Ecosystem)

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20 pages, 2428 KiB

Open AccessArticle

Combining Smartphone Inertial Sensors and Machine Learning Algorithms to Estimate Power Variables in Standing Long Jump

by Beatrice De Lazzari, Giuseppe Vannozzi and Valentina Camomilla

Computers 2025, 14(2), 31; https://doi.org/10.3390/computers14020031 - 21 Jan 2025

Viewed by 425

Abstract

Standing long jump (SLJ) power is recognized as informative of the ability of lower limbs to exert power. The study aims to provide athletes/coaches with a simple and low-cost estimate of selected SLJ power features. A group of 150 trained young participants was [...] Read more.

Standing long jump (SLJ) power is recognized as informative of the ability of lower limbs to exert power. The study aims to provide athletes/coaches with a simple and low-cost estimate of selected SLJ power features. A group of 150 trained young participants was recruited and performed a SLJ task while holding a smartphone, whose inertial sensors were used to collect data. Considering the state-of-the-art in SLJ biomechanics, a set of features was extracted and then selected by Lasso regression and used as inputs to several different optimized machine learning architectures to estimate the SLJ power variables. A Multi-Layer Perceptron Regressor was selected as the best-performing model to estimate total and concentric antero-posterior mean power, with an RMSE of 0.37 W/kg, R² > 0.70, and test phase homoscedasticity (Kendall’s τ < 0.1) in both cases. Model performance was dependent on the dataset size rather than the participants’ sex. A Multi-Layer Perceptron Regressor was able to also estimate the antero-posterior peak power (RMSE = 2.34 W/kg; R² = 0.67), although affected by heteroscedasticity. This study proved the feasibility of combining low-cost smartphone sensors and machine learning to automatically and objectively estimate SLJ power variables in ecological settings. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)

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Computers, Volume 14, Issue 2 (February 2025) – 22 articles

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