A Secure Biometric Passkey Pipeline Combining Continuous Thinking Machine Models with Post-Quantum and  Neuro-Symbolic Cryptography

Nahla Abdulnabee Sameer; Bashar M. Nema

doi:https://doi.org/10.54216/JISIoT.170217

Journal of Artificial Intelligence and Metaheuristics | 2025

Quantum-Inspired Machine Learning: Bridging Classical and Quantum Algorithms

Integration of quantum-inspired algorithms in machine learning has opened up new horizons for improving predictive performance, efficiency, and scalability across a broad spectrum of application domains. This paper presents a comparative investigation between traditional machine learning techniques and quantum-inspired models. Experi-ments demonstrate that quantum-inspired approaches exhibit higher accuracy, training effectiveness, and stability on difficult datasets than traditional methods. Results point towards higher convergence rates, shorter runtime, and enhanced generalization capacity in quantum-inspired models, realized in the form of enhanced accuracy, precision, recall, and F1-scores. Receiver operating characteristic (ROC) and precision–recall analyses further confirm the superior discriminative power of quantum-inspired approaches. Results point toward the potential of quantum-inspired machine learning as an interface between conventional algorithms and the new frontier of quantum computing, with a stepping stone to future-proof intelligent systems.

groups

Ahmed Hamid Elias mail -

Dhurgham Abbas Mohsin Albojwaid mail -

Ahmed Younus Abdulkadhim mail -

Raad S. Alhumaima mail -

Laith Farhan mail

link https://doi.org/10.54216/JAIM.100205

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2026

Strategic Priorities for Ensuring Commercial Bank Liquidity: A Business Intelligence and Data Analytics Approach to the Banking System of Uzbekistan

This study identifies strategic priorities for ensuring the liquidity of commercial banks in Uzbekistan through a business data analytics framework that combines regulatory liquidity indicators with a structural analysis of bank funding. Using aggregate data published by the Central Bank of Uzbekistan and the International Monetary Fund for 2020–2025, the paper develops a descriptive, diagnostic, and scenario-based analytics pipeline comprising ratio analysis, a logarithmic decomposition of the loan-to-deposit ratio, a currency-specific funding-gap measure, a deposit run-off sensitivity test, and constant-growth scenarios. The results show that short-term buffers are ample: at the end of 2025 the liquidity coverage ratio stood at 207.5 percent, the net stable funding ratio at 119.9 percent, and high-quality liquid assets at 21 percent of total assets, enough to cover 1.96 times the outflows of a severe run-off scenario. The structural position is weaker. Loans still exceed deposits by 186.7 trillion soums, about 80 percent of this gap is denominated in foreign currency, and the gap is concentrated in state-owned banks. Scenario arithmetic indicates that the loan-to-deposit ratio declines only marginally unless deposit growth exceeds credit growth by a sustained margin. Seven priorities follow, ranging from stable deposit mobilisation and currency-matched funding to the risk-sensitive calibration of liquidity requirements, market and safety-net infrastructure, and bank-level liquidity analytics. The study shows how business intelligence built on publicly reported data can support evidence-based liquidity decisions by bank managers and supervisors.

groups

Azizjon Kholmatov mail

link https://doi.org/10.54216/AJBOR.140207

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

On Convex Combinations of Starlike and Convex Functions Associated with the Epicycloid Domain

This paper introduces the class Mε,4L, defined through a convex combination of starlike and convex functions associated with a four-cusped epicycloid domain, where the parameter satisfies 0 ≤ ε ≤ 1. Unlike earlier studies that focused on circular or conic domains, this work extends the geometric framework to epicycloidal domains. Within this framework, sharp estimates for the first coefficients are obtained, together with the Fekete-Szeg¨o inequality and the second Hankel determinant evaluations. These findings extend several classical results for starlike and convex functions and offer new perspectives on analytic function theory related to epicycloidal domains.

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Nur Athirah Hani Senin mail -

Yuzaimi Yunus mail -

Nur Hazwani Aqilah Abdul Wahid mail -

Rashidah Omar mail

link https://doi.org/10.54216/IJNS.270121

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

Extending One-Way ANOVA to Neutrosophic Sets: A Method for Uncertainty-Based Decision Making

Classical statistical methods assume that data are precise and free from uncertainty, which may not hold in many real-world applications. Neutrosophic statistics provides a flexible framework for handling indeterminacy, vagueness, and inconsistency in data. In this paper, we propose a new formulation of one-way analysis of variance (ANOVA) within the neutrosophic framework. The method treats membership, indeterminacy, and non-membership components separately, with explicit F -tests for each, and employs a maximum-based decision rule to determine significance. We also compare the proposed method with the classical one-way ANOVA. The results demonstrate that the neutrosophic ANOVA is more sensitive in detecting group differences, particularly in cases where the classical approach yields smaller F -values and may fail to reject the null hypothesis. These findings highlight the potential of neutrosophic ANOVA as a more robust alternative to classical ANOVA for analyzing data with inherent uncertainty and indeterminacy.

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Sasiwimon Iwsakul mail -

Ronnason Chinram mail

link https://doi.org/10.54216/IJNS.270122

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2025

Predictability of Stock Price Fluctuations with an Application of Agricultural Companies Data

The research aimed to predict the fluctuations in closing Stock Price of four agricultural companies listed on the Iraq Stock Exchange using daily closing Stock Price data from 11/3/2015 to 15/3/2025. The symmetric and asymmetric ARCH model was applied to the research data. The results of the GARCH models showed that the closing price behavior of the companies (Al-Ahliyah for Agricultural Production, Middle East for Fish, Iraqi for Meat Production and Marketing) achieved a GARCH (1,1) rank, indicating that the effect of past error variance (ARCH) was of rank 1, in addition to the conditional variance element GARCH also being of rank 1. Meanwhile, the results showed that the closing prices for the Iraqi Seed Production Company were of rank GARCH (1,2). The results indicated that the first-order variance parameter was greater than one for all agricultural companies, suggesting that the fluctuations in stock closing prices exhibit a slight upward trend, which aligns with the logic of financial behavior in financial markets.

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Ahmad Hussein Battal mail -

Abdulrazaq Shabeeb mail -

Bha Aldan Abdulsattar Faraj mail -

Wisam Al-Anezi mail -

Faisal Ghazi Faisal mail

link https://doi.org/10.54216/AJBOR.130104

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2025

The Relationship between Foreign Direct Investment and Employment Growth in Southeast Asia

This study examines the relationship between Foreign Direct Investment (FDI) and employment growth in Southeast Asia, focusing on Malaysia, Vietnam, and Indonesia. It uses panel data from 2004 to 2023 and applies frameworks based on Neoclassical and Endogenous Growth theories using Excel and STATA software. The results indicate that job creation is strongly influenced by foreign direct investment, especially in the industrial and service sectors, with Vietnam showing the strongest correlation. These findings suggest that FDI can help countries boost economic development. This research provides valuable guidance for policymakers to attract targeted investments and promote sustainable employment opportunities.

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Farkhod Abdurakhmonov mail -

Shakhnoza Medetbaeva mail

link https://doi.org/10.54216/AJBOR.130105

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

Application of Wireless Body Area Networks and Wearable Sensors for Monitoring Sports People Health

Health reconnaissance frameworks are currently a more significant issue and examination subject. A few applications, like military, home consideration, medical clinic, athletic preparation, and the crisis control framework, have been laid out for wellbeing observation research. Competitors' lives require a lot of activity and exercise for wellness and wellbeing. The capacity to screen the imperative indications of the competitor that mirror the physical and physiological state of the individual, particularly during an apprenticeship, is fundamental both for the competitor and for the mentor to keep away from overtraining, wounds, and sickness or to change the power and time as per the information estimated — wearable checking gadgets associated with remote correspondence advances. In the model, utilizing remote innovations implies that devices utilized by competitors discuss information with other remote hubs progressively and make a small correspondence organization. The utilization of remote sensor correspondence and the need to impart between sensors has prompted the formation of wireless sensor networks (WSN) and wireless body area networks (WBANs). This paper presented a wireless sensor network-based athlete health monitoring (WSN-AHM) method and concentrated on their growth phases. Since it is a remote and versatile wellbeing reconnaissance arrangement, it can give medical care specialist organizations a valuable remote checking device to diminish the expense of their administrations. WSNs and their correspondence advancements and principles can be utilized in these reconnaissance applications, accentuating wearing exercises through the entire and relative show of realities on well-known correspondence conventions.

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May Kamil Al-Azzawi mail -

Saad Hameed Abid mail

link https://doi.org/10.54216/FPA.210222

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

Irrigation Iot Sensor Data Analytics Using Bio-Inspired Data Mining Techniques

Recently, irrigation management has been considered one of the most significant areas of research in smart vertical farming. Hence, it is essential to optimize freshwater usage for smart vertical farming management due to the lack of freshwater sources. It is observed that the soil moisture level and temperature data need to be appropriately examined and analyzed to predict the water irrigation level in a smart farming platform. Hence, in this work, the Internet of Things (IoT) sensors have been utilized to collect and monitor the soil moisture level, ambient temperature level, and humidity level data effectively. Besides, the collected sensor information has been analyzed and predicted to recognize the appropriate utilization of the optimum level of freshwater using Grey Wolf optimizer integrated recurrent network models. Therefore, this approach successfully analyzes the sensors' data and predicts the required level of irrigation based on motor ON and OFF conditions. The generated data from the sensor has been evaluated using the Keras model using the python language, and the performance is assessed based on the accuracy ratio. This model obtained a maximum of (0.995%) accuracy in forecasting the optimum irrigation level. The proposed system will utilize less voltage to minimize the power consumption ratio up to 35% in the irrigation process with 99.5% accuracy in forecasting the optimum irrigation level.

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Maysaa H. Abdulameer mail -

Saif M. Ali mail -

Deshinta Arrova Dewi mail

link https://doi.org/10.54216/FPA.210223

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

An Optimized Artificial Neural Network Model Using JAYA Algorithm for Energy Consumption Forecasting in Oman

The Accurate energy forecasting is vital for strategic planning, particularly in de-veloping economies with rapidly evolving demand patterns. This study pro-poses a hybrid Artificial Neural Network (ANN) model optimized using a modified JAYA algorithm to forecast energy consumption in Oman. The JAYA algorithm’s parameter-free, metaheuristic search improves ANN train-ing by enhancing convergence speed and reducing the risk of local minima. Historical data from 2017–2021—comprising GDP, population, and oil and gas production—were used as inputs. Model performance was benchmarked against an ANN trained with the Artificial Bee Colony (ABC) algorithm using mean square error (MSE), mean absolute error (MAE), relative error (RE), and root mean square error (RMSE) as evaluation metrics. Results show that ANN–JAYA consistently outperformed ANN–ABC, achieving lower error rates and greater robustness. The proposed approach offers a reliable deci-sion-support tool for policymakers and energy authorities, enabling more ef-fective resource allocation and long-term planning. Future research will ex-tend the framework to integrate renewable energy indicators and real-time data for adaptive, sustainable forecasting.

groups

Zainab Hamed AlSidairi mail -

Saraswathy Shamini Gunasekaran mail

link https://doi.org/10.54216/JISIoT.170216

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A Secure Biometric Passkey Pipeline Combining Continuous Thinking Machine Models with Post-Quantum and Neuro-Symbolic Cryptography

The generation of cryptographic keys from biometric traits offers a secure alternative to password-based authentication, but is hindered by challenges related to entropy, reproducibility, and adversarial resistance. This work presents a dual-path framework in which a Continuous Thinking Machine Model (CTMM) extracts multimodal embeddings from iris and fingerprint data. Feature vectors undergo projection through principal component analysis and graph-based distance encoding, followed by chaotic sequence modeling with Lorenz-like dynamics and an error-correcting routine to stabilize bitstreams. A secure mixing function consolidates the outputs, while SHA3-512 ensures deterministic expansion. Final passkeys are generated using the Kyber512 post-quantum key encapsulation mechanism (KEM), with neuro-symbolic reasoning applied as a validation layer to enforce entropy, avalanche properties, and inter-user separation. Evaluation confirmed compliance with NIST statistical tests, including monobit, runs, and longest-run assessments, while the system maintained a near-zero false acceptance rate. The originality of this work lies in combining CTMM-driven multimodal feature extraction with a quantum-safe cryptographic pipeline, augmented by neuro-symbolic validation, to establish a reproducible and secure method for biometric passkey generation in high-assurance authentication contexts.

groups

Nahla Abdulnabee Sameer mail -

Bashar M. Nema mail

link https://doi.org/10.54216/JISIoT.170217

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new