Search Results (189)

Urban areas are facing increasing traffic congestion, pollution, and infrastructure strain. Traditional urban transportation systems are often fragmented. They require users to plan, pay, and travel across multiple disconnected services. Mobility-as-a-Service (MaaS) integrates these services into a single digital platform, simplifying access and improving the user experience. This review critically examines the role of MaaS in fostering sustainable mobility ecosystems. MaaS aims to enhance user-friendliness, service variety, and sustainability by adopting a customer-centric approach to transportation. The findings reveal that successful MaaS systems consistently align with multimodal transport infrastructure, equitable access policies, and strong public-private partnerships. MaaS enhances the management of routes and traffic, effectively mitigating delays and congestion while concurrently reducing energy consumption and fuel usage. In this study, the authors examine MaaS as a new mobility paradigm for a sustainable transportation system in smart cities, observing the challenges and opportunities associated with its implementation. To assess the environmental impact, a sustainability index is calculated based on the use of different modes of transportation. Significant findings indicate that MaaS systems are proliferating in both quantity and complexity, increasingly integrating capabilities such as real-time multimodal planning, dynamic pricing, and personalized user profiles. Full article

Cloud computing offers a broad spectrum of services to users, particularly in multi-cloud environments where service-centric features are introduced to support users from multiple endpoints. To improve service availability and optimize the utilization of required services, cloud brokerage has been integrated into multi-cloud systems. The primary objective of a cloud broker is to ensure the quality and outcomes of services offered to customers. However, traditional cloud brokers face limitations in measuring service trust, ensuring validity, and anticipating future enhancements of services across different cloud platforms. To address these challenges, the proposed intelligent cloud broker integrates an intelligence mechanism that enhances decision-making within a multi-cloud environment. This broker performs a comprehensive validation and verification of service trustworthiness by analyzing various trust factors, including service response time, sustainability, suitability, accuracy, transparency, interoperability, availability, reliability, stability, cost, throughput, efficiency, and scalability. Customer feedback is also incorporated to assess these trust factors prior to service recommendation. The proposed model calculates service ranking (SR) values for available cloud services and dynamically includes newly introduced services during the validation process by mapping them with existing entries in the Service Collection Repository (SCR). Performance evaluation using the Google cluster-usage traces dataset demonstrates that the ICB outperforms existing approaches such as the Clustering-Based Trust Degree Computation (CBTDC) algorithm and the Service Context-Aware QoS Prediction and Recommendation (SCAQPR) model. Results confirm that the ICB significantly enhances the effectiveness and reliability of cloud service recommendations for users. Full article

Manual assembly remains essential in modern manufacturing, yet the increasing complexity of customised production imposes significant cognitive burdens and error rates on workers. Existing Spatial Augmented Reality (SAR) systems often operate passively, lacking adaptive interaction, real-time feedback and a control system with gesture. In response, we present Gest-SAR, a SAR framework that integrates a custom MediaPipe-based gesture classification model to deliver adaptive light-guided pick-to-place assembly instructions and real-time error feedback within a closed-loop interaction instance. In a within-subject study, ten participants completed standardised Duplo-based assembly tasks using Gest-SAR, paper-based manuals, and tablet-based instructions; performance was evaluated via assembly cycle time, selection and placement error rates, cognitive workload assessed by NASA-TLX, and usability test by post-experimental questionnaires. Quantitative results demonstrate that Gest-SAR significantly reduces cycle times with an average of 3.95 min compared to Paper (Mean = 7.89 min, p < 0.01) and Tablet (Mean = 6.99 min, p < 0.01). It also achieved 7 times less average error rates while lowering perceived cognitive workload (p < 0.05 for mental demand) compared to conventional modalities. In total, 90% of the users agreed to prefer SAR over paper and tablet modalities. These outcomes indicate that natural hand-gesture interaction coupled with real-time visual feedback enhances both the efficiency and accuracy of manual assembly. By embedding AI-driven gesture recognition and AR projection into a human-centric assistance system, Gest-SAR advances the collaborative interplay between humans and machines, aligning with Industry 5.0 objectives of resilient, sustainable, and intelligent manufacturing. Full article

The adoption of artificial intelligence (AI) in marketing and social media is gaining scholarly interest. While AI technologies offer significant potential for enhancing customer engagement (CE), their effectiveness depends on an industry’s level of digital and AI readiness. This is especially relevant for people-centric sectors such as tourism and hospitality, where digital maturity remains relatively low. This study aims to understand how AI supports CE and social media marketing (SMM), and to identify the key antecedents and consequences of its use. Using the PRISMA approach, we conduct a systematic review of 55 peer-reviewed empirical studies on AI-based CE and SMM. Our analysis identifies the main contributing theories and AI technologies in the field, and uncovers four central themes: (1) AI in customer service and user experience design, (2) AI-based customer relationships with brands, (3) AI-driven development of customer trust, and (4) cultural differences and varying levels of AI readiness. We also develop a conceptual framework that outlines the determinants and outcomes of AI-based CE, including relevant moderators and mediators. The study concludes with directions for future research and provides theoretical and managerial implications, particularly for the tourism and hospitality industries. Full article

Face recognition systems often falter when deployed in uncontrolled settings, grappling with low light, unexpected occlusions, motion blur, and the degradation of sensor signals. Most contemporary algorithms chase raw accuracy yet overlook the pragmatic need for uncertainty estimation and multispectral reasoning rolled into a single framework. This study introduces HUE-Net—a Human-centric, Uncertainty-aware, Event-fused Network—designed specifically to thrive under severe environmental stress. HUE-Net marries the visible RGB band with near-infrared (NIR) imagery and high-temporal-event data through an early-fusion pipeline, proven more responsive than serial approaches. A custom hybrid backbone that couples convolutional networks with transformers keeps the model nimble enough for edge devices. Central to the architecture is the perturbed multi-branch variational module, which distills probabilistic identity embeddings while delivering calibrated confidence scores. Complementing this, an Adaptive Spectral Attention mechanism dynamically reweights each stream to amplify the most reliable facial features in real time. Unlike previous efforts that compartmentalize uncertainty handling, spectral blending, or computational thrift, HUE-Net unites all three in a lightweight package. Benchmarks on the IJB-C and N-SpectralFace datasets illustrate that the system not only secures state-of-the-art accuracy but also exhibits unmatched spectral robustness and reliable probability calibration. The results indicate that HUE-Net is well-positioned for forensic missions and humanitarian scenarios where trustworthy identification cannot be deferred. Full article

This review article examines the role of additive manufacturing (AM) in increasing energy efficiency and sustainability within the evolving framework of Industry 5.0 and 6.0. This review highlights the unique ability of additive manufacturing to deliver mass-customized products while minimizing material waste and reducing energy consumption. The integration of smart technologies such as AI and IoT is explored to optimize AM processes and support decentralized, on-demand manufacturing. Thisarticle discusses different AM techniques and materials from an environmental and life-cycle perspective, identifying key benefits and constraints. This review also examines the potential of AM to support circular economy practices through local repair, remanufacturing, and material recycling. The net energy efficiency of AM depends on the type of process, part complexity, and production scale, but the energy savings per component can be significant if implemented strategically.AM significantly improves energy efficiency in certain manufacturing contexts, often reducing energy consumption by 25–50% compared to traditional subtractive methods. The results emphasize the importance of innovation in both hardware and software to overcome current energy and sustainability challenges. This review highlights AM as a key tool in achieving a human-centric, intelligent, and ecological manufacturing paradigm. Full article

Machine learning (ML) algorithms are widely used in various fields, including cyber threat intelligence (CTI), financial technology (Fintech), and intrusion detection systems (IDSs). They automate security alert data analysis, enhancing attack detection, incident response, and threat mitigation. Fintech is particularly vulnerable to cyber-attacks and cyber espionage due to its data-centric nature. Because of this, it is essential to give priority to the classification of cyber-attacks to accomplish the most crucial attack detection. Improving ML models for superior prioritized recognition requires a comprehensive strategy that includes data preprocessing, enhancement, algorithm refinement, and customized assessment. To improve cyber-attack detection in the Fintech, CTI, and IDS sectors, it is necessary to develop an ML model that better recognizes the prioritized classes, thereby enhancing security against important types of threats. This research introduces adaptive incremental learning, which enables ML models to keep learning new information by looking at changing data from a data stream, improving their ability to accurately identify types of cyber-attacks with high priority. The Analytical Hierarchy Process (AHP) is suggested to help make the best decision by evaluating model performance based on prioritized classes using real multi-class datasets instead of artificially improved ones. The findings demonstrate that the ML model improved its ability to identify prioritized classes of cyber-attacks utilizing the ToN_IoT network dataset. The recall value for the “injection” class rose from 59.5% to 61.8%, the recall for the “password” class increased from 86.7% to 88.6%, and the recall for the “ransomware” class improved from 0% to 23.6%. Full article

A company’s competitive advantage largely depends on the longevity and quality of its customer relationships, making it essential to understand which tools best support these interactions. In particular, identifying the factors that shape the impact of Customer Relationship Management (CRM) systems on business performance is crucial. This study examines the influence of CRM on the business performance of Portuguese companies by employing a conceptual model structured around five dimensions: customer-centric management (CCM), CRM organization (CRMO), operational CRM (OCRM), customer service quality (CSQ), and technological turbulence (TT). Data were gathered via a questionnaire completed by employees of Portuguese firms using CRM systems, yielding a total of 228 valid responses. Of the nine hypotheses tested, eight were confirmed. The results indicate that CRM organization (CRMO) exerts the strongest positive influence on business performance (0.457), followed by customer service quality (CSQ), operational CRM (OCRM), and customer-centric management (CCM). The study also confirms that technological turbulence (TT) moderates the relationship between the CRM dimensions and business performance. These findings suggest that the proposed model is well-suited to the context of Portuguese SMEs and provide valuable insights for managers aiming to enhance competitiveness through the strategic use of CRM systems. Additionally, the results offer a relevant contribution to the academic literature on CRM and business performance. Full article

Non-Intrusive Load Monitoring (NILM) technology, enabled by high-precision electrical data acquisition sensors at household entry points, facilitates real-time monitoring of electricity consumption, enhancing user interaction with smart home systems and reducing electrical safety risks. However, the growing diversity of household appliances and limitations in NILM accuracy and robustness necessitate innovative solutions. Additionally, outdated public datasets fail to capture the rapid evolution of modern appliances. To address these challenges, we constructed a high-sampling-rate voltage–current dataset, measuring 15 common household appliances across diverse scenarios in a controlled laboratory environment tailored to regional grid standards (220 V/50 Hz). We propose an AI-driven NILM method that integrates power-mapped, color-coded voltage–current (V–I) trajectories with frequency-domain features to significantly improve load recognition accuracy and robustness. By leveraging deep learning frameworks, this approach enriches temporal feature representation through chromatic mapping of instantaneous power and incorporates frequency-domain spectrograms to capture dynamic load behaviors. A novel channel-wise attention mechanism optimizes multi-dimensional feature fusion, dynamically prioritizing critical information while suppressing noise. Comparative experiments on the custom dataset demonstrate superior performance, particularly in distinguishing appliances with similar load profiles, underscoring the method’s potential for advancing smart home energy management, user-centric energy feedback, and social informatics applications in complex electrical environments. Full article

This systematic literature review explores how conflict management strategies (CMS) impact supply chain performance (SCP), focusing on the mediating roles of supply chain operational processes (SCOP) and customer-centric green supply chain management (CCGSCM) within Jordan’s food manufacturing sector. Framed within smart city initiatives and sustainable development goals (SDGs 9, 11, and 12), this study addresses critical gaps identified in the literature, particularly the lack of integrated examination of CMS impacts in emerging markets like Jordan. Utilizing thematic analysis, this review consolidates key findings across relevant studies from 2010 to 2025 sourced from top-tier databases. The results reveal that collaboration emerges as the most effective CMS strategy, enhancing stakeholder interactions, operational coordination, and resilience. SCOP significantly mediate CMS–SCP relationships, with logistics and inventory management notably vital in mitigating disruptions. Additionally, CCGSCM is highlighted as pivotal for sustainability and operational efficiency in post-COVID market conditions. The findings offer valuable insights for practitioners and policymakers, providing strategic recommendations for integrating technology-driven and relationship-focused CMS tailored to Jordan’s unique socio-economic context, thereby aligning operational practices with global sustainability goals (SDGs 9, 11, and 12). Full article

This study explores the transformative role of financial technology (fintech) in advancing sustainability, financial inclusion, and customer engagement in Jordan’s banking sector. Utilizing a quantitative descriptive survey design, data were collected from 400 participants—comprising 300 bank customers and 100 banking professionals—through a structured bilingual questionnaire distributed via digital platforms. The study aims to evaluate how fintech innovations align with sustainable finance practices, extend banking access to underserved populations, and influence customer satisfaction. The results reveal strong evidence of fintech’s positive impact across all three domains. Regression analysis confirmed a statistically significant relationship between fintech innovation and the adoption of sustainable finance practices (β = 0.6498, p < 0.001), explaining 42.2% of the variance in sustainability outcomes. Similarly, fintech adoption was found to significantly improve financial inclusion among underserved populations (β = 0.6842, p < 0.001), accounting for 46.85% of the variance in access to services. One-way ANOVA analysis further showed that increased fintech integration significantly enhances customer engagement, with mean satisfaction scores rising progressively with higher fintech usage levels (F = 24.49, p < 0.001). The study underscores that fintech is a critical enabler of ethical banking transformation in Jordan, promoting ESG objectives, reducing financial access disparities, and strengthening customer loyalty. The findings confirm that fintech significantly contributes to sustainable, inclusive, and customer-centric banking practices. These insights support the notion that fintech adoption not only redefines banking operations but also charts a sustainable and socially responsible future for the Jordanian financial sector. Full article

This study explores the genetic regulators of microRNAs (miRNAs) using a set of machine learning models to predict miRNA expression levels from gene expression data. Employing machine learning, we accurately predicted the expression of 353 human miRNAs (R² > 0.5), revealing robust miRNA–gene regulatory relationships. By analyzing the coefficients of these predictive models, we identified genetic regulators for each miRNA and highlighted the multifactorial nature of miRNA regulation. Further network analysis uncovered that miRNAs with higher predictive accuracy are more densely connected to their top predictive genes, reflecting strong regulatory control within miRNA–gene networks. To refine these insights, we filtered the miRNA–gene interaction networks to identify miRNAs specifically associated with enriched pathways, such as synaptic function and cardiovascular processes. From this pathway-centric analysis, we present a curated list of miRNAs and their genetic regulators, pinpointing their activity within distinct biological contexts. Additionally, our study provides a comprehensive set of metrics and coefficients for the genes most predictive of miRNA expression, along with a filtered subnetwork of miRNAs linked to specific pathways and phenotypes. By integrating miRNA expression predictors with network analysis and pathway enrichment, this work advances our understanding of miRNA regulatory mechanisms and their roles across distinct biological systems. Our approach enables researchers to train custom models using TCGA data and predict miRNA expression from gene expression inputs. Full article

Background: In an increasingly digitized supply chain landscape, small and medium-sized enterprises (SMEs) face mounting challenges in regard to delivering differentiated and responsive customer experiences. This study investigates the role of Industrial Internet of Things-enabled coopetition networks (IIoT-CNs) in enhancing the customer experience and value cocreation among SMEs. Grounded in Service-Dominant Logic, this research explores how interfirm collaboration and real-time data integration influence key performance indicators (KPIs), including perceived product quality, delivery timeliness, packaging standards, and product performance. Methods: An experimental design involving SMEs in Portugal’s ornamental stone sector contrasts traditional operations with digitally integrated coopetition practices. Results: While individual KPI improvements were not statistically significant, regression analysis revealed a significant positive relationship between IIoT-CN participation and the overall customer experience. The reduced variance in the performance metrics further suggests increased consistency and reliability across the network. Conclusions: These findings highlight IIoT-CNs as a promising model for SME digital transformation, contingent on trust, interoperability, and collaborative governance. This study contributes empirical evidence and practical insights for advancing customer-centric innovation in SME-dominated supply chains. Full article

The emergence of InsurTech has significantly transformed the traditional insurance industry, leading to the development of a new ecosystem characterized by digital intermediation, strategic partnerships, and increasing interdependence among actors. This paper investigates the structural configuration of the InsurTech ecosystem, emphasizing its role in reshaping how value is created, delivered, and captured across the industry. Based on a sample of 364 active InsurTech firms from 2020 to 2023, the research employs network analysis to map the interactions and co-occurrences among seven defined archetypes: Enablers, Innovators, Connectors, Integrators, Protectors, Transformers, and Disruptors. The findings reveal a trend toward higher density and functional complementarity among archetypes by providing a framework for understanding the dynamics of the InsurTech ecosystem and the strategic implications. Building on these findings, this paper introduces a novel five-phase framework for understanding the ecosystem’s evolution: (1) digitalization and technologies, (2) customer-centric approach, (3) data and analytics, (4) platform-based business models, and (5) ecosystem partnerships. This research advances the theoretical understanding of InsurTech as a networked system of role-based interdependencies and provides a methodological approach to analyzing this scenario through network theory. Furthermore, it contributes to academic discourse and industry practice, offering practical guidance for insurers, startups, and policymakers by enabling actionable insights into the strategic positioning of InsurTech archetypes within the evolving insurance industry landscape. Full article

In this paper, we propose a methodology designed to deliver actionable insights that help businesses retain customers. While Machine Learning (ML) techniques predict whether a customer is likely to churn, this alone is not enough. Explainable Artificial Intelligence (XAI) methods, such as SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), highlight the features influencing the prediction, but businesses need strategies to prevent churn. Counterfactual (CF) explanations bridge this gap by identifying the minimal changes in the business–customer relationship that could shift an outcome from churn to retention, offering steps to enhance customer loyalty and reduce losses to competitors. These explanations might not fully align with business constraints; however, alternative scenarios can be developed to achieve the same objective. Among the six classifiers used to detect churn cases, the Balanced Random Forest classifier was selected for its superior performance, achieving the highest recall score of 0.72. After classification, Diverse Counterfactual Explanations with ML (DiCEML) through Mixed-Integer Linear Programming (MILP) is applied to obtain the required changes in the features, as well as in the range permitted by the business itself. We further apply DiCEML to uncover potential biases within the model, calculating the disparate impact of some features. Full article