Search Results (34)

Accurate weather classification plays a vital role in disaster management and minimizing economic losses. However, satellite-based weather classification remains challenging due to high inter-class similarity; the computational complexity of existing deep learning models, which limits real-time deployment on resource-constrained edge devices; and the limited interpretability of model decisions in practical environments. To address these challenges, this study proposes SatNet-B3, a quantized, lightweight deep learning framework that integrates an EfficientNetB3 backbone with custom classification layers to enable accurate and edge-deployable weather event recognition from satellite imagery. SatNet-B3 is evaluated on the LSCIDMR dataset and demonstrates high-precision performance, achieving 98.20% accuracy and surpassing existing benchmarks. Ten CNN models, including SatNet-B3, were experimented with to classify eight weather conditions, Tropical Cyclone, Extratropical Cyclone, Snow, Low Water Cloud, High Ice Cloud, Vegetation, Desert, and Ocean, with SatNet-B3 yielding the best results. The model addresses class imbalance and inter-class similarity through extensive preprocessing and augmentation, and the pipeline supports the efficient handling of high-resolution geospatial imagery. Post-training quantization reduced the model size by 90.98% while retaining accuracy, and deployment on a Raspberry Pi 4 achieved a 0.3 s inference time. Integrating explainable AI tools such as LIME and CAM enhances interpretability for intelligent climate monitoring. Full article

This paper presents a PRISMA-guided systematic literature review (2015–2025) of 20 empirical studies on digital transformation in retail banking, examining how artificial intelligence (AI) strengthens cybersecurity, enables FinTech collaboration through interoperable APIs and open-banking infrastructures, and embeds data-driven decision-making across core functions. We searched major databases, applied predefined eligibility criteria, appraised study quality, and coded outcomes related to digital adoption, operational resilience, and customer experience. The synthesis indicates that AI-enabled controls and API-mediated partnerships are consistently associated with higher digital-maturity indicators, conditional on robust model-risk governance and prudent third-party/outsourcing management. Benefits span improved customer experience, efficiency, and inclusion; however, legacy systems, regulatory fragmentation, cyber threats, and organizational resistance remain binding constraints. We propose a unified framework linking technology choices, regulatory design, and organizational outcomes, and distill actionable guidance for policymakers (e.g., interoperable standards, proportional AI governance, sector-wide cyber resilience) and bank managers (sequencing AI use cases, risk controls, and partnership models). Future research should assess emerging technologies—including quantum-safe security and central bank digital currencies (CBDCs)—and their implications for digital-banking stability and trust. Full article

Artificial intelligence (AI) has emerged as a transformative force across various sectors, providing innovative solutions and enhancing operational processes. In the e-commerce domain, AI has significantly contributed to customer-centric approaches and strategic decision-making, fostering superior customer experiences. This study investigates the role and impact of AI in the Saudi e-commerce sector, focusing on the perspectives of female customers and retailers. Grounded in sociotechnical theory, the research employs a mixed-methods approach, combining quantitative surveys and semi-structured interviews. The quantitative findings demonstrate that AI-enabled e-commerce positively influences customer experience, customer satisfaction, and operational efficiency. Key AI capabilities, such as task automation, personalized recommendations, and predictive analytics, enhance online retail systems’ performance. The qualitative analysis highlights both the opportunities and challenges associated with AI adoption, emphasizing the need for specialized infrastructure and skilled professionals. Participants recommend addressing the skill gap and adopting phased implementation strategies to optimize AI integration. This study provides actionable insights and strategic recommendations for policymakers and stakeholders in the Saudi e-commerce sector. Full article

Non-player characters (NPCs) play a crucial role in creating engaging and immersive experiences in role playing games (RPGs). Traditional NPC interactions often rely on scripted dialogues, which can limit their ability to adapt dynamically to player input. This study presents a novel framework that enhances NPC interactions by integrating advanced conversational systems. Utilizing Open AI’s natural language processing capabilities, RPG Maker MZ as the game development platform, and JavaScript for customization, the framework introduces context-aware dialogues that respond intelligently to player queries and actions. By bridging the gap between game AI and conversational systems, this approach enables more lifelike and meaningful NPC behavior. Experimental results indicate that the proposed system significantly improves the narrative depth and overall player experience. These findings demonstrate the potential of combining AI-driven chatbots with game development tools to redefine the role of NPCs in modern gaming. Full article

ADHD and misophonia are developmental neurological disorders that are currently increasing in prevalence due to excessive acoustic and visual pollution. ADHD, which is characterized by a lack of attention and excessive impulsive hyperactivity, and misophonia, which is hypersensitivity to sounds accompanied by a severe emotional and psychological reaction, are both affected by the user’s spatial environment to a great extent. Spatial design can contribute to increasing or decreasing these unfavorable sensory triggers that affect individuals with ADHD and/or Misophonia. However, the role of architectural spatial design as a therapeutic approach to alleviate the symptoms of Misophonia and ADHD has never been proposed before in the literature, despite its accumulative and chronic effects on the user’s experience in everyday life in terms of well-being and productivity. Therefore, the current work discusses this problem of neglecting the potential effect of architectural spatial design on alleviating Misophonia and ADHD. Thus, the objective of the current work is to propose customized architectural spatial design as a therapeutic approach to alleviate Misophonia and ADHD through adopting the compatible architectural trends of minimal and metaphysical architecture. The methodology of the current work includes a theoretical proposal of this customized architectural spatial design for alleviating these two special neurological conditions. This includes introducing and analyzing these two neurological conditions and their relation to and interaction with architectural spatial design, analyzing minimal and metaphysical architectural trends employed in the proposed therapeutic architectural design, and then proposing augmented and virtual reality as auxiliary add-ons to the architectural spatial design to boost its therapeutic effect. Minimal architecture achieves the “no emotion” criteria through reduced forms, patterns, and colors and adopts simple geometry and natural materials to reduce sensory stressors or stimuli, in order to alleviate the loss of attention and distraction prevalent in those with ADHD, as well as allowing the employment of acoustic materials to achieve acoustic comfort and noise blockage for Misophonia relief. Metaphysical architecture leads the hierarchy of sensory experience through the symbolistic, dynamic, and enigmatic composition of forms and colors, which enhance the spatial analysis and cognitive capacities of the inhabitants. Meanwhile, the use of customized virtual and augmented reality environments is an effective add-on to minimal and metaphysical architectural spaces thanks to its proven therapeutic effect in alleviating various neurological disorders and injuries. At this level of intervention, VR/AR can be used as an add-on to minimal-architecture design, to simulate varied scenarios, as minimal design offers a clean canvas for simulating these varied virtual environments. The other option is to build these customized VR/AR scenarios around a specific architectural element as an add-on metaphysical architecture design to lead the sensory experience and enable the user to detach from the physical constraints of the space. AI-generated designs were used as a proof of concept for the proposed customized architectural spatial design following minimal and metaphysical architecture, as well as to provide AR and VR scenarios as add-on architecture to enhance the therapeutic effect of these architectural spaces for Misophonia and ADHD patients. Furthermore, the validity of VR/AR as a therapeutic approach, alongside the customized architectural design, was discussed, and it was concluded that this study proves the need for extended clinical studies on its efficiency in the long run, which will be conducted in the future. Full article

Ensuring food quality and safety is a growing challenge in the food industry, where early detection of contamination or spoilage is crucial. Using gas sensors combined with Artificial Intelligence (AI) offers an innovative and effective approach to food identification, improving quality control and minimizing health risks. This study aims to evaluate food identification strategies using supervised learning techniques applied to data collected by the BME Development Kit, equipped with the BME688 sensor. The dataset includes measurements of temperature, pressure, humidity, and, particularly, gas composition, ensuring a comprehensive analysis of food characteristics. The methodology explores two strategies: a neural network model trained using Bosch BME AI-Studio software, and a more flexible, customizable approach that applies multiple predictive algorithms, including DT, LR, kNN, NB, and SVM. The experiments were conducted to analyze the effectiveness of both approaches in classifying different food samples based on gas emissions and environmental conditions. The results demonstrate that combining electronic noses (E-Noses) with machine learning (ML) provides high accuracy in food identification. While the neural network model from Bosch follows a structured and optimized learning approach, the second methodology enables a more adaptable exploration of various algorithms, offering greater interpretability and customization. Both approaches yielded high predictive performance, with strong classification accuracy across multiple food samples. However, performance variations depend on the characteristics of the dataset and the algorithm selection. A critical analysis suggests that optimizing sensor calibration, feature selection, and consideration of environmental parameters can further enhance accuracy. This study confirms the relevance of AI-driven gas analysis as a promising tool for food quality assessment. Full article

Background: Colon capsule endoscopy (CCE) has seen increased adoption since the COVID-19 pandemic, offering a non-invasive alternative for lower gastrointestinal investigations. However, inadequate bowel preparation remains a key limitation, often leading to higher conversion rates to colonoscopy. Manual assessment of bowel cleanliness is inherently subjective and marked by high interobserver variability. Recent advances in artificial intelligence (AI) have enabled automated cleansing scores that not only standardise assessment and reduce variability but also align with the emerging semi-automated AI reading workflow, which highlights only clinically significant frames. As full video review becomes less routine, reliable, and consistent, cleansing evaluation is essential, positioning bowel preparation AI as a critical enabler of diagnostic accuracy and scalable CCE deployment. Objective: This CESCAIL sub-study aimed to (1) evaluate interobserver agreement in CCE bowel cleansing assessment using two established scoring systems, and (2) determine the impact of AI-assisted scoring, specifically a TransUNet-based segmentation model with a custom Patch Loss function, on both interobserver and intraobserver agreement compared to manual assessment. Methods: As part of the CESCAIL study, twenty-five CCE videos were randomly selected from 673 participants. Nine readers with varying CCE experience scored bowel cleanliness using the Leighton–Rex and CC-CLEAR scales. After a minimum 8-week washout, the same readers reassessed the videos using AI-assisted CC-CLEAR scores. Interobserver variability was evaluated using bootstrapped intraclass correlation coefficients (ICC) and Fleiss’ Kappa; intraobserver variability was assessed with weighted Cohen’s Kappa, paired t-tests, and Two One-Sided Tests (TOSTs). Results: Leighton–Rex showed poor to fair agreement (Fleiss = 0.14; ICC = 0.55), while CC-CLEAR demonstrated fair to excellent agreement (Fleiss = 0.27; ICC = 0.90). AI-assisted CC-CLEAR achieved only moderate agreement overall (Fleiss = 0.27; ICC = 0.69), with weaker performance among less experienced readers (Fleiss = 0.15; ICC = 0.56). Intraobserver agreement was excellent (ICC > 0.75) for experienced readers but variable in others (ICC 0.03–0.80). AI-assisted scores were significantly lower than manual reads by 1.46 points (p < 0.001), potentially increasing conversion to colonoscopy. Conclusions: AI-assisted scoring did not improve interobserver agreement and may even reduce consistency amongst less experienced readers. The maintained agreement observed in experienced readers highlights its current value in experienced hands only. Further refinement, including spatial analysis integration, is needed for robust overall AI implementation in CCE. Full article

Artificial intelligence (AI) is reshaping mobile food-ordering apps, yet its impact on customer experience (CX) has not been fully mapped. Following systematic review guidelines (PRISMA 2020), a search of SCOPUS, Web of Science, ScienceDirect, and Google Scholar in March 2025 identified 55 studies published between 2022 and 2025. Since 2022, research has expanded from intention-based studies to include real-time app interactions and live app experiments. This shift has helped to identify five key CX dimensions: (1) instrumental usability: how quickly and smoothly users can order; (2) personalization value: AI-generated menus and meal suggestions; (3) affective engagement: emotional appeal of the app interface; (4) data trust and procedural fairness: users’ confidence in fair pricing and responsible data handling; (5) social co-experience: sharing orders and interacting through live reviews. Studies have shown that personalized recommendations and chatbots enhance relevance and enjoyment, while unclear surge pricing, repetitive menus, and algorithmic anxiety reduce trust and satisfaction. Given the limitations of this study, including its reliance on English-only sources, a cross-sectional design, and limited cultural representation, future research should investigate long-term usage patterns across diverse markets. This approach would help uncover nutritional biases, cultural variations, and sustained effects on customer experience. 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 paper explores the role of artificial intelligence (AI) in personalizing social media marketing strategies and its impact on customer experience, with a focus on consumers within the MENA region. Using data collected from an online questionnaire completed by 893 individuals, the study confirms that AI significantly enhances social media marketing by offering personalized content, optimizing influencer selection, and enabling real-time consumer interaction. These capabilities not only increase customer awareness but also improve user experience and purchase intentions. Key AI tools such as influencer marketing, content optimization, and customization are effective in capturing consumer attention, although further research is necessary to deepen understanding. By examining AI’s ability to analyze vast datasets and support targeted marketing efforts, the study contributes to both academic and practical discourse, offering insights that businesses can use to refine their AI-driven social media strategies. Ultimately, the research aims to guide marketers through the complexities of AI deployment, ensuring its benefits are fully realized for consumers. Full article

The past 15 years of extensive experience teaching VR system development has taught us that haptic feedback must be more sophisticatedly integrated into VR systems, alongside the already realistic high-fidelity visual and audio feedback. The third generation of students is enhancing VR interactive experiences by incorporating haptic feedback through traditional, proven, commercially available gamepad controllers. Insights and discoveries gained through this process contributed to the development of versatile Unity custom editor tool, which is the focus of this article. The developed tool supports a wide range of use cases, enabling the visual, parametric, and descriptive creation of reusable haptic effects. To enhance productivity in commercial development, it supports the creation of haptic and haptic/audio stimulus libraries, which can be further expanded and combined based on object-oriented principles. Additionally, the tool allows for the definition of specific areas within the virtual space where these stimuli can be experienced, depending on the virtual object the avatar holds and the activities they perform. This intuitive platform allows the design of reusable haptic effects through graphical editor, audio conversion, programmatic scripting, and AI-powered guidance. The sophistication and usability of the tool have been demonstrated through several student VR projects across various application areas. Full article

This systematic literature review investigates the integration of artificial intelligence (AI) and the Internet of Things (IoT) in marketing, with a focus on their application in enhancing consumer engagement, personalization, and strategic decision-making. Using the Scopus database and a refined keyword search strategy, the study identified 223,671 initial records, which were narrowed down to 121 peer-reviewed academic articles after applying strict inclusion and exclusion criteria. Thematic analysis revealed that foundational technologies—such as machine learning, big data, and deep learning—dominate the field, while marketing strategy, decision systems, and customer experience emerge as central application areas. Co-citation and keyword network analyses indicate a technocentric and interdisciplinary knowledge structure, but also expose significant gaps in research related to ethics, regulation, consumer trust, and small business contexts. The review highlights opportunities for future research in underexplored areas such as sentiment analysis, sustainability, and human–AI interaction. For practitioners, the findings underscore the strategic importance of AI and IoT in driving personalized, data-driven marketing, while emphasizing the need for ethical transparency and regulatory alignment. Limitations include reliance on a single database, potential exclusion of relevant studies due to keyword constraints, and a focus on peer-reviewed journal articles only. This review addresses key gaps in the literature by offering a focused synthesis of current research and proposing directions for more balanced and responsible innovation in AI-enabled marketing. Full article

Amazon has been flooded with artificial intelligence (AI)-generated product reviews that offer minimal value to customers. These AI reviews merely echo the given product descriptions without providing any authentic information on how buyers feel when using the products. Therefore, an AI review-identifying method was developed to enhance the quality of the review-reading experience in this study. A dataset of 6217 Amazon reviews was compiled including 1116 identified as AI-generated ones. They were classified with a 99.25% F1 score on the test data using the term frequency–inverse document frequency (TF–IDF) and support vector classifier (SVC). The developed method enables the detection of AI-generated reviews on the internet, fostering an authentic and reliable platform. Full article

This review synthesizes current knowledge on the transformative impacts of artificial intelligence (AI)—computational systems capable of performing tasks requiring human-like reasoning—on business innovation. It addresses the potential of AI to reshape strategies, operations, and value creation across various industries. Key themes include AI-driven business model innovation, human–AI collaboration, ethical governance, operational efficiency, customer experience personalization, organizational capability development, and adoption disparities. AI enables scalable product development, personalized service delivery, and data-driven strategic decisions. Successful implementations hinge on overcoming technical, cultural, and ethical barriers, with ethical AI adoption enhancing consumer trust and competitiveness, positioning responsible innovation as a strategic imperative. For practitioners, this review offers evidence-based frameworks for aligning AI with business objectives. For academics, it identifies research frontiers, including longitudinal impacts, context-specific roadmaps for small- and medium-sized enterprises, and sustainable innovation pathways. This review conceptualizes AI as a driver of systemic organizational transformation, requiring continuous learning, ethical foresight, and strategic ability for competitive advantage. Full article

This research tackles an essential gap in understanding how passengers prefer to interact with artificial intelligence (AI) or human agents in airline customer service contexts. Using a mixed-methods approach that combines statistical analysis with fuzzy set theory, we examine these preferences across a range of service scenarios. With data from 163 participants’ Likert scale responses, our qualitative analysis via fuzzy set methods complements the quantitative results from regression analyses, highlighting a preference model contingent on context: passengers prefer AI for straightforward, routine transactions but lean towards human agents for nuanced, emotionally complex issues. Our regression findings indicate that perceived benefits and simplicity of tasks significantly boost satisfaction and trust in AI services. Through fuzzy set analysis, we uncover a gradient of preference rather than a stark dichotomy between AI and human interaction. This insight enables airlines to strategically implement AI for handling routine tasks while employing human agents for more complex interactions, potentially improving passenger retention and service cost-efficiency. This research not only enriches the theoretical discourse on human–computer interaction in service delivery but also guides practical implementation with implications for AI-driven services across industries focused on customer experience. Full article