Search Results (102)

The growing complexity and risk profile of fire and emergency incidents necessitate advanced training methodologies that go beyond traditional approaches. Live-fire drills and classroom-based instruction, while foundational, often fall short in providing safe, repeatable, and scalable training environments that accurately reflect the dynamic nature of real-world emergencies. Recent advancements in immersive technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and simulation-based systems, offer promising alternatives to address these challenges. This review provides a comprehensive overview of the integration of VR, AR, MR, XR, and simulation technologies into firefighter and incident commander training. It examines current practices across fire services and emergency response agencies, highlighting the capabilities of immersive and interactive platforms to enhance operational readiness, decision-making, situational awareness, and team coordination. This paper analyzes the benefits of these technologies, such as increased safety, cost-efficiency, data-driven performance assessment, and personalized learning pathways, while also identifying persistent challenges, including technological limitations, realism gaps, and cultural barriers to adoption. Emerging trends, such as AI-enhanced scenario generation, biometric feedback integration, and cloud-based collaborative environments, are discussed as future directions that may further revolutionize fire service education. This review aims to support researchers, training developers, and emergency service stakeholders in understanding the evolving landscape of digital training solutions, with the goal of fostering more resilient, adaptive, and effective emergency response systems. Full article

Cloud computing is widely used by organizations and individuals to outsource computation and data storage. With the growing adoption of machine learning as a service (MLaaS), machine learning models are being increasingly deployed on cloud platforms. However, operating MLaaS on the cloud raises significant privacy concerns, particularly regarding the leakage of sensitive personal data and proprietary machine learning models. This paper proposes a privacy-preserving decision tree (PPDT) framework that enables secure predictions on sensitive inputs through homomorphic matrix multiplication within a three-party setting involving a data holder, a model holder, and an outsourced server. Additionally, we introduce a leaf node pruning (LNP) algorithm designed to identify and retain the most informative leaf nodes during prediction with a decision tree. Experimental results show that our approach reduces prediction computation time by approximately 85% compared to conventional protocols, without compromising prediction accuracy. Furthermore, the LNP algorithm alone achieves up to a 50% reduction in computation time compared to approaches that do not employ pruning. Full article

Digital transformation is reshaping business strategies and driving innovation across various industries including Cultural Heritage (CH) and tourism. Digital technologies, such as eXtended Reality (XR) and the Internet of Things (IoT), are increasingly being adopted to enhance visitors’ experiences, foster interactive engagement, and promote cultural knowledge. Despite the growing number of digital solutions proposed in the CH sector, several challenges remain in differentiating digital products and services, including matching industry needs and user expectations. This aspect is of particular interest when dealing with small and medium enterprises (SMEs), which often suffer from limited resources. Therefore, to design an effective digital solution, like a cloud-based platform for tourism and heritage applications, it is essential to first identify the key requirements, expectations, and preferences of SMEs and customers. This study presents the findings of a survey-based analysis conducted among 122 CH and tourism professionals, focusing on the most relevant features, services, and functionalities that such platforms should integrate. Results indicate a strong demand for cloud-based solutions that incorporate XR, IoT, sensors, and smart devices to collect context data and deliver personalized, immersive, and context-aware experiences. These insights suggest valuable practices for the development of digital tools that effectively support cultural organizations in engaging visitors. Full article

The European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service (CLMS), provides free pan-European ground motion data to support local and regional ground deformation analyses. To enhance the accessibility and usability of EGMS products, a new webapp, EGMStream, has been developed using Python and JavaScript for downloading and converting EGMS data. This revised and updated version improves the functionality and performance of the original R-based desktop tool, avoiding the need for a standalone software installation. Users can now simply access the webapp with an internet connection. In addition, the web version enhances data processing by leveraging high-performance server-side computing without relying on personal computer resources. The EGMStream webapp offers advanced features, including the parallel processing of large datasets and extraction of converted EGMS data for areas of interest (AoI) in various GIS-compatible formats. The transition from standalone software to a cloud-based system streamlines the integration of EGMS data into existing workflows, broadens user accessibility, and supports large-scale geospatial analysis. Consequently, this shift promotes the dissemination of these relevant and free available measurement data to a wider audience, including non-expert users. Full article

As the use of personal mobility (PM) devices continues to rise, regulatory violations have become more frequent, highlighting the need for technological solutions to ensure efficient enforcement. This study addresses these challenges by proposing an AI-based enforcement platform. The system integrates the You Only Look Once version 5 (YOLOv5) object detection model, a deep-learning-based framework, with Global Positioning System (GPS) location data, Raspberry Pi 5, and Amazon Web Services (AWS) for data processing and web-based implementation. The YOLOv5 model was deployed in two configurations: one for detecting electric scooter usage and another for identifying legal violations. The system utilized AWS Relational Database Service (RDS), Simple Storage Service (S3), and Elastic Compute Cloud (EC2) to store violation records and host web applications. The detection performance was evaluated using mean average precision (mAP) metrics. The electric scooter detection model achieved mAP50 and mAP50-95 scores of 99.5 and 99.457, respectively. Meanwhile, the legal violation detection model attained mAP50 and mAP50-95 scores of 99.5 and 81.813, indicating relatively lower accuracy for fine-grained violation detection. This study presents a practical technological platform for monitoring regulatory compliance and automating fine enforcement for shared electric scooters. Future improvements in object detection accuracy and real-time processing capabilities are expected to enhance the system’s overall reliability. Full article

Cloud manufacturing represents a pioneering service paradigm that provides flexible, personalized manufacturing services to customers via the Internet. Service composition plays a crucial role in cloud manufacturing, which focuses on integrating dispersed manufacturing services in the cloud platform into a complete composite service to form an efficient and collaborative manufacturing solution that fulfills the customer’s requirements, having the highest service quality. This research presents the multi-strategy improved artificial rabbit optimization (MIARO) technique, designed to overcome the limitations with the original method, which often risks converging to local optima and have poor solution quality when dealing with optimization problems. MIARO helps the algorithm escape local optimality with Lévy flights, extends local search with the golden sine mechanism, and expands variability with Archimedean spiral mutations. MIARO is experimented on 23 benchmark functions, 3 engineering design problems, and QoS-aware cloud service composition (QoS-CSC) issues at various sizes, and the experimental findings indicate that MIARO delivers outstanding performance and offers a viable solution to the QoS-CSC problem. Full article

With the rapid development of the Internet of Things (IoT), the scope of personal data sharing has significantly increased, enhancing convenience in daily life and optimizing resource management. However, this also poses challenges related to data privacy breaches and holdership threats. Typically, blockchain technology and cloud storage provide effective solutions. Nevertheless, the centralized storage architecture of traditional cloud servers is susceptible to single points of failure, potentially leading to system outages. To achieve secure data sharing, access control, and verification auditing, we propose a data security sharing scheme based on blockchain technology and attribute-based encryption, applied within the InterPlanetary File System (IPFS). This scheme employs multi-agent systems and attribute-based signcryption algorithms to process data, thereby enhancing privacy protection and verifying data holdership. The encrypted data are then stored in the distributed IPFS, with the returned hash values and access control policies uploaded to smart contracts, facilitating automated fine-grained access control services. Finally, blockchain data auditing is performed to ensure data integrity and accuracy. The results indicate that this scheme is practical and effective compared to existing solutions. Full article

Background/Objectives: This study aimed to investigate the actual situation of individuals with unknown health conditions (UHCs) and those indifferent to health (IH) among old-old adults (OOAs) aged 75 years and above using the National Health Insurance Database (KDB) system. Methods: A total of 102 individuals with no history of medical examinations were selected from the KDB system in a city in Japan. Data were collected through home visit interviews and blood pressure monitors distributed by public health nurses (PHNs) from Community Comprehensive Support Centers (CCSCs). The collected data included personal attributes, health concern levels, and responses to a 15-item OOA questionnaire. Semi-structured interviews were conducted with seven PHNs. The control group consisted of 76 users of the “Kayoinoba” service (Kayoinoba users: KUs). Results: Of the 83 individuals who could be interviewed, 50 (49.0%) were classified as UHCs and 11 (10.8%) were classified as IH, including 5 from the low health concern group and 6 who refused to participate. In the word cloud generated from the PHNs’ interviews, the words and phrases “community welfare commissioner”, “community development”, “blood pressure monitor”, “troublesome”, “suspicious”, and “young” were highlighted. In the comparison of health assessments between UHCs and KUs, “body weight loss” and “cognitive function” were more prevalent among KUs, and “smoking” and “social participation” were more prevalent among UHCs. Conclusions: The home visit activities of CCSCs utilizing the KDB system may contribute to an understanding of the actual situation of UHCs, including IHs, among OOAs. UHCs (including patients with IH status) had a higher proportion of risk factors related to smoking and lower social participation than KUs. Full article

Exer-games are interactive experiences in which participants engage in physical exercises to achieve specific goals. Some of these games have a collaborative nature, wherein the actions and achievements of one participant produce immediate effects on the experiences of others. Music serves as a stimulus that can be integrated into these games to influence players’ emotions and, consequently, their actions. In this paper, a framework of music services designed to enhance collaborative exer-games is presented. These services provide the necessary functionality to generate personalized musical stimuli that regulate players’ affective states, induce changes in their physical performance, and improve the game experience. The solution requires to determine the emotions that each song may evoke in players. These emotions are considered when recommending the songs that are used as part of stimuli. Personalization seeds based on players’ listening histories are also integrated in the recommendations in order to foster the effects of those stimuli. Emotions and seeds are computed from the information available in Spotify data services, one of the most popular commercial music providers. Two small-scale experiments present promising preliminary results on how the players’ emotional responses match the affective information included in the musical elements of the solution. The added value of these affective services is that they are integrated into an ecosystem of Internet of Things (IoT) devices and cloud computing resources to support the development of a new generation of emotion-based exer-games. Full article

The healthcare sector is undergoing a significant transformation due to the rapid expansion of data and advancements in digital technologies. The increasing complexity of healthcare data, including electronic health records (EHRs), medical imaging, and patient monitoring, underscores the necessity of big data technologies. These technologies are essential for enhancing decision-making, personalizing treatments, and optimizing operations. Digitalization further revolutionizes healthcare by improving accessibility and convenience through technologies such as EHRs, telemedicine, and wearable health devices. Cloud computing, with its scalable resources and cost efficiency, plays a crucial role in managing large-scale healthcare data and supporting remote treatment. However, integrating cloud computing in healthcare, especially in remote areas with limited network infrastructure, presents challenges. These include difficulties in accessing cloud services and concerns over data security. This article proposes a smart healthcare system utilizing the edge-cloud continuum to address these issues. The proposed system aims to enhance data accessibility and security while maintaining high prediction accuracy for disease management. The study includes foundational knowledge of relevant technologies, a detailed system architecture, experimental design, and discussions on conclusions and future research directions. Full article

This study investigates the readiness of manufacturing industries in the Iraqi sector to adopt and implement Industry 4.0 (I4.0) technologies. The research focuses on manufacturing industries, including automotive, electronics, textiles, food processing, etc. The study’s main objective is to investigate the relationship between adopting I4.0 technologies and performance benefits in these sectors. A structured survey was conducted across 240 manufacturing companies, including specific I4.0 technologies (IoT, Big Data Analytics, Cloud Computing, Artificial Intelligence, etc.), usage levels, operations, products/services, and sustainability. Data were collected through telephone interviews and personal contacts, where the respondents rated the benefits of I4.0 technology adoption and performance benefit dimensions on a five-point Likert scale. The study utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS 3.2.9 software for data analysis. Findings show a positive relationship between I4.0 technology adoption and industrial performance benefits, emphasizing productivity and production efficiency improvements more than sustainability improvements and resource benefits. This research contributes to the understanding of I4.0 readiness in emerging economies and provides insight for policymakers and industry leaders in Iraq’s manufacturing sector. Full article

Therapeutic efficacy is affected by adherence failure as also demonstrated by WHO clinical studies that 50–70% of patients follow a treatment plan properly. Patients’ failure to follow prescribed drugs is the main reason for morbidity and mortality and more cost of healthcare services. Adherence to medication could be improved with the use of patient engagement systems. Such engagement systems can include a patient’s preferences and beliefs in the treatment plans, resulting in more responsive and customized treatments. However, one key limitation of the existing engagement systems is their generic applications. We propose a personalized framework for patient medication engagement using AI methods such as Reinforcement Learning (RL) and Deep Learning (DL). The proposed Personalized Medication Engagement System (PMES) has two major components. The first component of the PMES is based on an RL agent, which is trained on adherence reports and later utilized to engage a patient. The RL agent, after training, can identify each patient’s patterns of responsiveness by observing and learning their response to signs and then optimize for each individual. The second component of the proposed system is based on DL and is used to monitor the medication process. The additional feature of the PMES is that it is cloud-based and can be utilized anywhere remotely. Moreover, the system is personalized as the RL component of PMES can be trained for each patient separately, while the DL part of the PMES can be trained for a given medication plan. Thus, the advantage of the proposed work is two-fold, i.e., RL component of the framework improves adherence to medication while the DL component minimizes medication errors. Full article

In recent years, with the rapid development of blockchain technology, the issues of storage load and data security have attracted increasing attention. Due to the immutable nature of data on the blockchain, where data can only be added and not deleted, there is a significant increase in storage pressure on blockchain nodes. In order to alleviate this burden, this paper proposes a blockchain data storage strategy based on a hot and cold block mechanism. It employs a block heat evaluation algorithm to assess the historical and correlation-based heat indicators of blocks, enabling the identification of frequently accessed block data for storage within the blockchain nodes. Conversely, less frequently accessed or “cold” block data are offloaded to cloud storage systems. This approach effectively reduces the overall storage pressure on blockchain nodes. Furthermore, in applications such as healthcare and government services that utilize blockchain technology, it is essential to encrypt stored data to safeguard personal privacy and enforce access control measures. To address this need, we introduce a blockchain data encryption storage mechanism based on threshold secret sharing. Leveraging threshold secret sharing technology, the encryption key for blockchain data is fragmented into multiple segments and distributed across network nodes. These encrypted key segments are further secured through additional encryption using public keys before being stored. This method serves to significantly increase attackers’ costs associated with accessing blockchain data. Additionally, our proposed encryption scheme ensures that each block has an associated encryption key that is stored alongside its corresponding block data. This design effectively mitigates vulnerabilities such as weak password attacks. Experimental results demonstrate that our approach achieves efficient encrypted storage of data while concurrently reducing the storage pressure experienced by blockchain nodes. Full article

Today, crop suggestions and necessary guidance have become a regular need for a farmer. Farmers generally depend on their local agriculture officers regarding this, and it may be difficult to obtain the right guidance at the right time. Nowadays, crop datasets are available on different websites in the agriculture sector, and they play a crucial role in suggesting suitable crops. So, a decision support system that analyzes the crop dataset using machine learning techniques can assist farmers in making better choices regarding crop selections. The main objective of this research is to provide quick guidance to farmers with more accurate and effective crop recommendations by utilizing machine learning methods, global positioning system coordinates, and crop cloud data. Here, the recommendation can be more personalized, which enables the farmers to predict crops in their specific geographical context, taking into account factors like climate, soil composition, water availability, and local conditions. In this regard, an existing historical crop dataset that contains the state, district, year, area-wise production rate, crop name, and season was collected for 246,091 sample records from the Dataworld website, which holds data on 37 different crops from different areas of India. Also, for better analysis, a dataset was collected from the agriculture offices of the Rayagada, Koraput, and Gajapati districts in Odisha state, India. Both of these datasets were combined and stored using a Firebase cloud service. Thirteen different machine learning algorithms have been applied to the dataset to identify dependencies within the data. To facilitate this process, an Android application was developed using Android Studio (Electric Eel | 2023.1.1) Emulator (Version 32.1.14), Software Development Kit (SDK, Android SDK 33), and Tools. A model has been proposed that implements the SMOTE (Synthetic Minority Oversampling Technique) to balance the dataset, and then it allows for the implementation of 13 different classifiers, such as logistic regression, decision tree (DT), K-Nearest Neighbor (KNN), SVC (Support Vector Classifier), random forest (RF), Gradient Boost (GB), Bagged Tree, extreme gradient boosting (XGB classifier), Ada Boost Classifier, Cat Boost, HGB (Histogram-based Gradient Boosting), SGDC (Stochastic Gradient Descent), and MNB (Multinomial Naive Bayes) on the cloud dataset. It is observed that the performance of the SGDC method is 1.00 in accuracy, precision, recall, F1-score, and ROC AUC (Receiver Operating Characteristics–Area Under the Curve) and is 0.91 in sensitivity and 0.54 in specificity after applying the SMOTE. Overall, SGDC has a better performance compared to all other classifiers implemented in the predictions. Full article

This paper investigates the challenge of finding and analyzing security vulnerabilities among widely available low-budget Internet of Things smart home appliances. It considers the identification of security vulnerabilities within the appliances’ embedded software and connectivity functions over wired and wireless channels in local networks and external communications with manufacturers’ cloud services. To analyze the security of these appliances, a universal laboratory test bench is proposed and a set of methodologies for testing the security of smart home devices is described. The proposed testing platform offers a practical solution for security analysis of Internet of Things smart home devices and it can serve as a reference approach for future research. The results from the research indicated varying levels of susceptibility across different types of devices. A list of recommendations for manufacturers and others to improve the security level of these appliances is provided. The findings emphasize the need for regular security assessments of smart home devices, to maintain the protection of personal and sensitive information. Full article