OPH-Guard: An Operationally Interpretable Tree-Ensemble Framework for Phishing URL Screening in Secure Web Access Management

Reem Atassi

doi:https://doi.org/10.54216/JCIM.180104

Financial Technology and Innovation | 2025

Credit-Card Engagement Segmentation for Embedded FinTech Product Strategy: Evidence from Expenditure Microdata

Digital card products create business value only when issued accounts translate into sustained, responsible use. This paper develops an expenditure-based segmentation model for embedded FinTech card strategy using a real credit-card micro-dataset. Average monthly card expenditure is treated as an observable engagement outcome and is examined alongside income, age, and home-ownership status. The empirical design combines descriptive portfolio profiling, robust regression, cross-validated prediction, and product-action mapping. The results show that income is the strongest observed driver of monthly spend, but the relationship is nonlinear and does not fully explain customer heterogeneity. A quartile-based segmentation separates low-use, developing-use, active-use, and premium-use customers, with mean monthly expenditure increasing from 39.92 to 666.35 across the four operating segments. The analysis argues that card engagement should be managed as a portfolio state rather than as a simple activation metric. The study contributes a transparent business-analytics framework that links observed card expenditure to embedded-finance decisions, including activation support, limit calibration, reward design, and repayment-aware engagement monitoring.

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Samandarboy Sulaymanov mail -

Olimjonov Abbosjon Olimjonovich mail

link https://doi.org/10.54216/FinTech-I.050103

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2025

Trustworthy Digital Onboarding Readiness in FinTech Markets: A Business Analytics Model for e-KYC Conversion

Digital onboarding has become a decisive business capability for financial technology firms because customer acquisition, compliance screening, product activation, and trust formation now occur in the same online journey. This paper proposes a Digital Onboarding Readiness model for evaluating whether a market has the conditions required to convert identity verification into sustained FinTech usage. The model combines account access, digital payment use, mobile internet readiness, digital identity support, open-finance policy, regulatory onboarding readiness, and consumer trust into a business-oriented index. A cross-market indicator panel is analysed using descriptive profiling, maturity clustering, readiness decomposition, and predictive interpretation. The results show that strong account ownership alone does not guarantee onboarding maturity. Markets with advanced identity and policy infrastructure may still face low payment-use conversion, while markets with widespread digital payments may be constrained by trust and regulatory readiness gaps. The findings suggest that FinTech firms should treat onboarding as a portfolio capability rather than a front-end compliance step. The paper contributes a transparent measurement framework for market entry, platform partnerships, and responsible e-KYC investment decisions.

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Ilknur Ozturk mail

link https://doi.org/10.54216/FinTech-I.050104

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2025

Beyond the Branch: Consumer Adoption, Satisfaction, and Financial Advisor Acceptance of FinTech Services Across Retail Banking, Challenger, and Wealth Management Segments

The structural transformation of retail financial services by mobile banking platforms, FinTech applications, open banking ecosystems, and AI-powered credit and advisory tools has created both unprecedented opportunities for financial inclusion and a pronounced gap between adoption rates achievable in high-digital-literacy segments and those attainable in mainstream and mass-market contexts. Comparative evidence assessing customer satisfaction and financial advisor acceptance simultaneously across multiple FinTech service domains and institutional segments remains sparse, limiting the evidence base available to practitioners and policymakers designing inclusive FinTech deployment strategies. The present investigation enrolled retail banking customers across four institutional segments—traditional banks, challenger banks, credit unions, and private banking divisions alongside a parallel cohort of relationship managers and financial advisors, to assess adoption rates and satisfaction across four FinTech domains: mobile and digital banking, FinTech financial applications, open banking and personal financial management, and AI-powered credit assessment and advisory services. Significant between-segment variation was documented across all four domains, with private banking customers reporting the highest satisfaction and adoption and credit union customers the lowest. AI-powered credit and advisory services elicited the lowest customer satisfaction across all segments and the largest customer-advisor divergence. Digital financial literacy and prior FinTech experience emerged as the two strongest independent predictors of adoption. The investigation contributes a validated crosssegment measurement instrument, customer-profile-specific adoption profiles, and evidence-based recommendations for financial service providers deploying FinTech capabilities across heterogeneous customer bases.

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Serkan Yilmaz Kandir mail -

Murat Ismet Haseki mail

link https://doi.org/10.54216/FinTech-I.050105

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

Motion Vector–Guided Object Detection and Tracking for Smart Surveillance Systems

Multiple moving object detection and tracking are challenging roles in many computer vision applications such as object navigation and human identification. Object tracking is one of the key challenges for securing against crime, supporting public safety, and enabling effective traffic management systems. In video surveillance applications, detection of multiple moving vehicles from video is the major task for tracking and understanding the behavior of the detected objects. Performance of object detection algorithms is degraded by factors such as fog or haze, occlusion, dynamic background, poor illumination, and low resolution. Fog is one of the major bottlenecks of video surveillance applications. The proposed Dark Channel Prior algorithm using guided filter (GDCP) is adapted for fog removal. The Gaussian Mixture Model (GMM) is proposed for detecting multiple moving objects, and features are extracted from the detected objects using Motion Vector Estimation. The K-Nearest Neighbor algorithm is used for tracking the moving objects (vehicles) using the detected features. Efficiency is improved due to the adoption of the proposed fog-removal algorithm and feature extraction for effective tracking. There are wide varieties of applications in moving object detection and tracking.

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V. Vinothini mail -

N. Devi mail -

R. Roja mail -

G. Mahendran mail

link https://doi.org/10.54216/IJAACI.080101

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

XAI-DermNet: A Dual-Modality Deep Learning and Explainable AI Fusion Framework for Transparent Skin Lesion Diagnosis from Dermoscopic Images

The advancement of trustworthy diagnostic tools in dermatological automation is hindered by the limited transparency of current deep learning systems, which function as opaque models and impede clinical acceptance. This research presents a novel intelligent framework for skin lesion analysis that unites deep learning methodologies with explainable artificial intelligence (XAI) principles to address this interpretability deficit. The proposed approach utilizes a transfer-learned ResNet50 architecture for robust image classification, coupled with Local Interpretable Model-agnostic Explanations (LIME) to furnish clear, visual justifications for the model’s outputs. Performance assessment on the HAM10000 benchmark yielded a classification accuracy of 94.3%, with a validation accuracy of 91.8%. Concurrently, the LIME framework effectively identified and visualized diagnostically critical features in the lesion images, thereby elucidating the model’s reasoning process for medical practitioners. These findings confirm that augmenting high-performance deep learning with post-hoc explanatory techniques yields a credible and understandable diagnostic instrument, thereby promoting clinician trust and facilitating data-informed medical judgments. Subsequent developments will prioritize scalable cloud implementation, interoperability with healthcare information systems, extension to underrepresented lesion categories, and rigorous evaluation in diverse clinical environments.

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Sukkirtha K. mail -

Anbuchelian S. mail -

John A. mail

link https://doi.org/10.54216/IJAACI.080102

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

A Hybrid CNN Bi-LSTM Framework for Multi-Class Plant Disease Detection and Health Value Estimation

Accurate and early identification of plant diseases is essential for ensuring sustainable agriculture and maximizing crop productivity. This paper presents a hybrid deep learning framework integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks for multi-class plant disease detection, classification, and Plant Health Value (PHV) estimation. The proposed framework begins with a comprehensive data preprocessing pipeline involving image resizing, normalization, and augmentation to improve model robustness. The CNN module extracts critical spatial and visual features such as lesion shape, leaf texture, and color intensity, while the BiLSTM model captures temporal and sequential feature correlations to accurately learn disease progression patterns. A Decision Support System (DSS) is incorporated to compute the Plant Health Value (PHV), where PHV ranges from 0% (Healthy) to 100% (Severely Unhealthy), indicating the severity of disease infection. Additionally, the DSS generates actionable recommendations to assist in early intervention and treatment planning. Experimental results on a multi-species plant dataset demonstrate that the proposed CNN–BiLSTM hybrid model significantly improves accuracy, interpretability, and early disease prediction compared to conventional CNN based methods, offering a robust and intelligent framework for automated plant health monitoring.

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Janani J. mail -

Gautham R. mail -

Suguna C. mail -

Ponni K. mail

link https://doi.org/10.54216/IJAACI.080103

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

Green IoT and Sustainable Wireless Sensor Networks: A Deep Reinforcement Learning Approach for Energy Optimization and Qos Enhancement

Due to the increasing adoption of IoT applications, there is a growing necessity for energy-efficient and sustainable WSN. Yet, traditional routing protocols tend to face problems like energy wastage, congestion, unreliable communication, and shorter network life spans under dynamic network conditions. This study presents the development of a DRL-powered Green IoT framework to enhance efficient communication through WSN while optimizing QoS performance. Specifically, the proposed framework employs the Deep Q-Network, Double Deep Q-Learning, adaptive clustering, and multi-objective optimization in order to enhance both routing and QoS performance. The model makes use of residual energy, congestion levels, throughput, delivery rate, and communication delays during its decision-making processes. Experimentation with the model was performed by making use of Python and NS-3. The simulation results showed that the presented model performed better than traditional routing methods like LEACH, PEGASIS, and HEED when evaluated on factors like energy preservation, enhanced throughput, minimized congestion, reduced delays, and increased network life spans. It can be concluded that DRL-powered communication optimization is a viable solution for the future development of Green IoT communication systems.

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S. Phani Praveen mail -

Massila Kamalrudin mail -

Sai Vellela mail -

Deshinta Arrova Dewi mail -

Dedeepya Pulletikurthy mail -

Klodian Dhoska mail

link https://doi.org/10.54216/IJAACI.080104

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Journal of International Economics Research | 2026

Sport tourism as a Driver of Soft Power and Regional Growth

The research shows the currently increasing role of the industry of sport tourism in the Republic of Uzbekistan and how it impacts on the economic and social development of the state from 2018 to 2025. Based on secondary data from international and national organizations and analytical think centres, the paper analyses trends in tourist arrivals, tourism revenue and the impact of government reforms: visa liberalisation and investment in sport facilities. The results of this study shows that sport tourism is a crucial factor in economic diversification, aiding job creation, regional development and the growth of small and medium businesses. The different international competitions such as boxing or judo championships raise the country's status globally and also contribute to strengthening its strength and cultural interaction. Because of different reforms and because more people are now interested in active travel, tourism in Uzbekistan has recovered after the pandemic. However, scientists still have not studied enough how sport affects the economy of the country and small businesses. Overall, the results indicate that sport tourism is very important for the development of Uzbekistan. It can help the country to reach its long term goals and become one of the best places for active and sport tourism in Central Asia.

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Khodjaeva Dildora mail -

Khaydarova Marjona mail

link https://doi.org/10.54216/JIER.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Sustainable Development and Green Technology | 2026

Price-Aware and Explainable Analytics for Urban Electric Vehicle Charging Networks: Forecasting Utilization Regimes for Sustainable Charging Operations

The efficient functioning of the electric-vehicle charging systems that are publicly operated has become focused on reliable short-horizon forecasting. The paper establishes an explainable and price-conscious analytical model to predict short-term charging usage and demonstrate the utility of tariff signals in an urban charging system. The analysis is based on UrbanEV benchmark, a new six months hourly panel of Shenzhen public charging infrastructure, which integrates occupancy, charging time, charging volume, electricity tariffs, service charges, weather and spatial descriptors. The concept of charging occupancy is considered an operation state variable with connection to queue exposure, reliability of service, and tactical intervention. A succinct mathematical formulation is created to use it in one-step-ahead utilization forecasting and in interpreting low-, medium-, and high-utilization regime. The empirical analysis is pegged to benchmark evidence reported to UrbanEV, where transformer-based forecasting had the best node-level performance and TimeXer had the best RMSE values of 0.07 in occupancy, 2.73 in charging duration, and 43.66 in charging volume. Further discussion indicates that occupancy prediction is accurate enough to justify regime based intervention and strongest additional gains are obtained through the joint effect of pricing variables and temperature-price interactions as opposed to single covariates. The results justify the justifiable, price-conscious forecasting as an operational decision tool to alleviate congestion, design tariffs and specific capacity planning in sustainable charging networks.

groups

Heba Moselhy mail -

Noura Metawa mail

link https://doi.org/10.54216/JSDGT.060104

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

OPH-Guard: An Operationally Interpretable Tree-Ensemble Framework for Phishing URL Screening in Secure Web Access Management

Phishing URLs still present a security threat to organizations because they enable credential theft and account takeover together with payment fraud and unauthorized digital service access. The existing research on phishing detection has been studied extensively yet most published papers still show a preference for predictive performance assessment compared to operational system capabilities and tests and governance system implementation. The researchers developed OPH-Guard as an operational security system which uses compact tree ensembles to identify phishing URLs for their secure web access management system. The integrated workflow system enables institutional and small enterprise to implement public data ingestion and feature validation together with tabular model learning and post-hoc explanation and security-action mapping. The empirical evaluation used a public GitHub-hosted phishing URL dataset which contains 11,481 labeled records and 87 predictive features. The researchers conducted a comparison between three tree-based learners according to a stratified 80/20 hold-out protocol which included Decision Tree and Random Forest and Extra Trees. The actual results from Extra Trees produced the highest accuracy score of 0.9856 which included 0.9921 precision and 0.9791 recall and 0.9855 F1-score and 0.9984 ROC-AUC from the held-out test results. The study investigates security relevance for top predictors through google index and page rank and domain age and phish hints which provide evidence that the resulting model enables organizations to manage browsing risk through URL triage together with secure information management controls. The study presents a reproducible framework together with a complete screening algorithm and a summary of existing research from ten studies and a system which connects model results to security operations.

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Reem Atassi mail

link https://doi.org/10.54216/JCIM.180104

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new