Computers

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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35 pages, 633 KiB

Open AccessArticle

Set-Word Embeddings and Semantic Indices: A New Contextual Model for Empirical Language Analysis

by Pedro Fernández de Córdoba, Carlos A. Reyes Pérez, Claudia Sánchez Arnau and Enrique A. Sánchez Pérez

Computers 2025, 14(1), 30; https://doi.org/10.3390/computers14010030 - 20 Jan 2025

Abstract

We present a new word embedding technique in a (non-linear) metric space based on the shared membership of terms in a corpus of textual documents, where the metric is naturally defined by the Boolean algebra of all subsets of the corpus and a [...] Read more.

We present a new word embedding technique in a (non-linear) metric space based on the shared membership of terms in a corpus of textual documents, where the metric is naturally defined by the Boolean algebra of all subsets of the corpus and a measure

μ

defined on it. Once the metric space is constructed, a new term (a noun, an adjective, a classification term) can be introduced into the model and analyzed by means of semantic projections, which in turn are defined as indexes using the measure

μ

and the word embedding tools. We formally define all necessary elements and prove the main results about the model, including a compatibility theorem for estimating the representability of semantically meaningful external terms in the model (which are written as real Lipschitz functions in the metric space), proving the relation between the semantic index and the metric of the space (Theorem 1). Our main result proves the universality of our word-set embedding, proving mathematically that every word embedding based on linear space can be written as a word-set embedding (Theorem 2). Since we adopt an empirical point of view for the semantic issues, we also provide the keys for the interpretation of the results using probabilistic arguments (to facilitate the subsequent integration of the model into Bayesian frameworks for the construction of inductive tools), as well as in fuzzy set-theoretic terms. We also show some illustrative examples, including a complete computational case using big-data-based computations. Thus, the main advantages of the proposed model are that the results on distances between terms are interpretable in semantic terms once the semantic index used is fixed and, although the calculations could be costly, it is possible to calculate the value of the distance between two terms without the need to calculate the whole distance matrix. “Wovon man nicht sprechen kann, darüber muss man schweigen”. Tractatus Logico-Philosophicus. L. Wittgenstein. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling)

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33 pages, 19016 KiB

Open AccessArticle

Multitask Learning-Based Pipeline-Parallel Computation Offloading Architecture for Deep Face Analysis

by Faris S. Alghareb and Balqees Talal Hasan

Computers 2025, 14(1), 29; https://doi.org/10.3390/computers14010029 - 20 Jan 2025

Abstract

Deep Neural Networks (DNNs) have been widely adopted in several advanced artificial intelligence applications due to their competitive accuracy to the human brain. Nevertheless, the superior accuracy of a DNN is achieved at the expense of intensive computations and storage complexity, requiring custom [...] Read more.

Deep Neural Networks (DNNs) have been widely adopted in several advanced artificial intelligence applications due to their competitive accuracy to the human brain. Nevertheless, the superior accuracy of a DNN is achieved at the expense of intensive computations and storage complexity, requiring custom expandable hardware, i.e., graphics processing units (GPUs). Interestingly, leveraging the synergy of parallelism and edge computing can significantly improve CPU-based hardware platforms. Therefore, this manuscript explores levels of parallelism techniques along with edge computation offloading to develop an innovative hardware platform that improves the efficacy of deep learning computing architectures. Furthermore, the multitask learning (MTL) approach is employed to construct a parallel multi-task classification network. These tasks include face detection and recognition, age estimation, gender recognition, smile detection, and hair color and style classification. Additionally, both pipeline and parallel processing techniques are utilized to expedite complicated computations, boosting the overall performance of the presented deep face analysis architecture. A computation offloading approach, on the other hand, is leveraged to distribute computation-intensive tasks to the server edge, whereas lightweight computations are offloaded to edge devices, i.e., Raspberry Pi 4. To train the proposed deep face analysis network architecture, two custom datasets (HDDB and FRAED) were created for head detection and face-age recognition. Extensive experimental results demonstrate the efficacy of the proposed pipeline-parallel architecture in terms of execution time. It requires 8.2 s to provide detailed face detection and analysis for an individual and 23.59 s for an inference containing 10 individuals. Moreover, a speedup of 62.48% is achieved compared to the sequential-based edge computing architecture. Meanwhile, 25.96% speed performance acceleration is realized when implementing the proposed pipeline-parallel architecture only on the server edge compared to the sever sequential implementation. Considering classification efficiency, the proposed classification modules achieve an accuracy of 88.55% for hair color and style classification and a remarkable prediction outcome of 100% for face recognition and age estimation. To summarize, the proposed approach can assist in reducing the required execution time and memory capacity by processing all facial tasks simultaneously on a single deep neural network rather than building a CNN model for each task. Therefore, the presented pipeline-parallel architecture can be a cost-effective framework for real-time computer vision applications implemented on resource-limited devices. Full article

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18 pages, 731 KiB

Open AccessReview

Computational Methods for Information Processing from Natural Language Complaint Processes—A Systematic Review

by J. C. Blandón Andrade, A. Castaño Toro, A. Morales Ríos and D. Orozco Ospina

Computers 2025, 14(1), 28; https://doi.org/10.3390/computers14010028 - 20 Jan 2025

Abstract

Complaint processing is of great importance for companies because it allows them to understand customer satisfaction levels, which is crucial for business success. It allows them to show the real perceptions of users and thus visualize the problems, which are regularly processed from [...] Read more.

Complaint processing is of great importance for companies because it allows them to understand customer satisfaction levels, which is crucial for business success. It allows them to show the real perceptions of users and thus visualize the problems, which are regularly processed from oral or written natural language, derived from the provision of a service. In addition, the treatment of complaints is relevant because according to the laws of each country, companies have the obligation to respond to these complaints in a specified time. The specialized literature mentions that enterprises lost USD 75 billion due to poor customer service, highlighting that companies need to know and understand customer perceptions, especially emotions, and product reviews to gain insight and learn about customer feedback because of the importance of the voice of the customer for an organization. In general, it is evident that there is a need for research related to computational language processing to handle user requests. The authors show great interest in computational techniques for the processing of this information in natural language and how this could contribute to the improvement of processes within the productive sector. This work searches in indexed journals for information related to computational methods for processing relevant data from user complaints. It is proposed to apply a systematic literature review (SLR) method combining literature review guides by Kitchenham and the PRISMA statement. The systematic process allows the extraction of consistent information, and after applying it, 27 articles were obtained from which the analysis was conducted. The results show various proposals using linguistic, statistical, machine learning, and hybrid methods. We find that most authors combine Natural Language Processing (NLP) and Machine Learning (ML) to create hybrid methods. The methods extract relevant information from complaints of the customers in natural language in various domains, such as government, medical, banks, e-commerce, public services, agriculture, customer service, environmental, and tourism, among others. This work contributes as support for the creation of new systems that can give companies a significant competitive advantage due to their ability to reduce the response time of the complaints as established by law. Full article

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40 pages, 21233 KiB

Open AccessArticle

Large-Scale Cross-Cultural Tourism Analytics: Integrating Transformer-Based Text Mining and Network Analysis

by Dian Puteri Ramadhani, Andry Alamsyah, Mochamad Yudha Febrianta, Muhammad Nadhif Fajriananda, Mahira Shafiya Nada and Fathiyyah Hasanah

Computers 2025, 14(1), 27; https://doi.org/10.3390/computers14010027 - 16 Jan 2025

Abstract

The growth of the tourism industry in Southeast Asia, particularly in Indonesia, Thailand, and Vietnam, establishes the region as a leading global tourism destination. Numerous studies have explored tourist behavior within specific regions. However, the question of whether tourists’ experience perceptions differ based [...] Read more.

The growth of the tourism industry in Southeast Asia, particularly in Indonesia, Thailand, and Vietnam, establishes the region as a leading global tourism destination. Numerous studies have explored tourist behavior within specific regions. However, the question of whether tourists’ experience perceptions differ based on their cultural backgrounds is still insufficiently addressed. Previous articles suggest that an individual’s cultural background plays a significant role in shaping tourist values and expectations. This study investigates how tourists’ cultural backgrounds, represented by their geographical regions of origin, impact their entertainment experiences, sentiments, and mobility patterns across the three countries. We gathered 387,010 TripAdvisor reviews and analyzed them using a combination of advanced text mining techniques and network analysis to map tourist mobility patterns. Comparing sentiments and behaviors across cultural backgrounds, this study found that entertainment preferences vary by origin. The network analysis reveals distinct exploration patterns: diverse and targeted exploration. Vietnam achieves the highest satisfaction across the cultural groups through balanced development, while Thailand’s integrated entertainment creates cultural divides, and Indonesia’s generates moderate satisfaction regardless of cultural background. This study contributes to understanding tourism dynamics in Southeast Asia through a data-driven, comparative analysis of tourist behaviors. The findings provide insights for destination management, marketing strategies, and policy development, highlighting the importance of tailoring tourism offerings to meet the diverse preferences of visitors from different global regions. Full article

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

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