Merve Sinem Karahan^*, Mehmet Karaköse

Journal of Quantum Computing, Vol.8, pp. 101-121, 2026, DOI:10.32604/jqc.2026.078969 - 21 August 2026

Abstract Accurate prediction of carbon emissions is essential for developing sustainable environmental policies and mitigating global warming. Road transportation represents one of the major sources of global CO₂ emissions due to its dependence on fossil fuels. This study presents a comparative framework that evaluates classical machine learning models alongside a hybrid quantum–classical learning architecture for vehicle-based CO₂ emission prediction. A large-scale vehicle emissions dataset containing 7385 samples collected over approximately seven years was obtained from the official open-data platform of the Government of Canada. Key vehicle characteristics, including engine size, fuel consumption, transmission type, and vehicle class,… More >

Animaw Kerie Aseres^1,2,*, Asrat Mulatu Beyene^3,2, Lemlem Kassa Tegegne^1,2

Journal of Cyber Security, Vol.8, pp. 559-576, 2026, DOI:10.32604/jcs.2026.086559 - 21 August 2026

Abstract Conventional multi-factor and one-time authentication approaches, such as passwords and one-time passwords (OTPs), have become increasingly vulnerable to advanced attack methods, motivating the need for continuous authentication (CA) systems that can verify user identity throughout an active session rather than only at login. For such a system to be effective, it must analyze user behavior reliably and in real time. This paper presents a novel approach to implementing CA on mobile devices using tap and swipe behavioral biometrics combined with machine learning (ML) and multimodal fusion. The dataset was collected from 400 volunteer participants using… More >

Bala Thripura Akasam^*

Journal of Cyber Security, Vol.8, pp. 525-539, 2026, DOI:10.32604/jcs.2026.085438 - 21 August 2026

Abstract The widespread adoption of Artificial Intelligence (AI) coding assistants across enterprise software development teams has accelerated delivery velocity while simultaneously introducing a persistent and empirically documented security quality gap in the code these tools produce. Vulnerability classes including insecure output handling, prompt injection constructs, sensitive information disclosure patterns, and cryptographic misuse appear at elevated rates in AI-generated output regardless of model advancement, while organizational governance frameworks have failed to keep pace with the speed of AI tool deployment, creating conditions in which vulnerable code reaches production through informal risk acceptance rather than accountable remediation processes.… More >

Issah Zabsonre Alhassan^1,2,*, Gaddafi Abdul-Salaam¹, Michael Asante¹, Yaw Marfo Missah¹, Alimatu Sadia Shirazu¹

Journal of Cyber Security, Vol.8, pp. 577-607, 2026, DOI:10.32604/jcs.2026.085303 - 21 August 2026

Abstract A hybrid approach to pseudorandom number generation that couples ensemble learning with the Residue Number System (RNS) is presented in this paper. Unlike conventional deterministic generators that depend solely on direct algorithmic transformation, the proposed method first maps a seed-driven integer sequence into its RNS representation under the coprime moduli set {3, 5, 7, 11}, whose dynamic range is M = 1155, thereby introducing modular non-linearity through a static, stateless feature transformation. A soft-voting ensemble of Logistic Regression, Random Forest, and Support Vector Machine then serves as a decision layer that classifies and re-maps the… More >

Praise Emeka Nze^*, Adeniran Kolade Ademuwagun, Muktar Bello, Fortune Daberechi Ifeanyi, Samaila Musa Abdullahi, John Tighil

Journal of Cyber Security, Vol.8, pp. 469-486, 2026, DOI:10.32604/jcs.2026.083943 - 21 August 2026

Abstract The rapid evolution of adversarial cyber threats demands proactive, scalable security testing methodologies capable of producing realistic, organization-specific attack scenarios. Conventional approaches, including manual red-teaming, scripted Breach and Attack Simulation (BAS) platforms, and tabletop exercises, are constrained by high expert dependency, limited scenario variability, and an inability to dynamically adapt to an organization’s unique threat profile. This paper proposes and evaluates a Large Language Model (LLM)-Assisted Threat-Driven Testing System that integrates the MITRE Adversarial Tactics, Techniques, and Common Knowledge (MITRE ATT&CK) framework v14, a structured knowledge base of adversarial tactics, techniques, and procedures (TTPs), with… More >

Peter Kipngeno Langat^*, Michael Kimwele, Dennis Kaburu

Journal of Cyber Security, Vol.8, pp. 541-558, 2026, DOI:10.32604/jcs.2026.083592 - 21 August 2026

Abstract Zero-day attacks pose a significant threat to computer systems and networks as they exploit weaknesses that have not been recognized by security professionals or software creators and for which there are no existing protective measures. This study introduced an innovative method for identifying Zero-day attacks through a Recurrent neural network model. To effectively mitigate these risks, not only is continuous monitoring essential, but also the implementation of machine learning. The model was trained on network traffic data and leveraged on the ability of Recurrent Neural Networks (RNNs) to learn complex patterns and identify anomalies that… More >

Darlington Okeke^*

Journal of Cyber Security, Vol.8, pp. 487-523, 2026, DOI:10.32604/jcs.2026.080111 - 21 August 2026

Abstract Background: Novel cyber threats to organizations have greatly escalated due to the high digitalization rates of organizations, and cybersecurity readiness is a critical capability of organizations and not a technical issue. Although there is increased awareness, most organizations are ill-equipped due to weaknesses in human behavior, governance, leadership commitment, technology infrastructure, and incident response mechanisms. This paper discusses organizational factors that play a major role in cybersecurity readiness. Methods: Primary data from 230 participants were collected using a questionnaire approach and analyzed using IBM SPSS software. The quantitative methods adopted include descriptive statistics, Cronbach’s reliability More >

Wei-Lung Mao^1,*, Yu-Ying Chiu¹, Chao-Ting Chu², Bing-Hong Lin¹, Jian-Jie Hung³

Intelligent Automation & Soft Computing, Vol.41, pp. 47-47, 2026, DOI:10.32604/iasc.2026.089959 - 19 August 2026

Abstract This article has no abstract. More >

Shounak Mandal¹, Subhadip Pati^1,#, Nirmallyadeb Ray^1,#, Bipasha Guha Roy^2,#, Priyanka Saha³, Deepsubhra Guha Roy^2,*

Digital Engineering and Digital Twin, Vol.4, pp. 27-47, 2026, DOI:10.32604/dedt.2026.081859 - 14 August 2026

Abstract HealthyBrain is a scalable, interoperable, and intelligent Remote Patient Monitoring (RPM) platform built on Internet of Things (IoT) technologies and a modular microservices architecture. The system integrates wearable IoT devices, MQTT (Message Queuing Telemetry Transport)-based lightweight messaging, and high-throughput real-time data streaming via Apache Kafka. Edge-side preprocessing enables low-latency analytics, while machine learning-based anomaly detection models facilitate early identification of critical health events. To ensure clinical interoperability, the platform adheres to the HL7 FHIR (Fast Healthcare Interoperability Resources) standard for electronic health record exchange. The system’s novel contribution lies in the unified integration of edge… More >

Hashim Ali^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.087214 - 13 August 2026

Abstract Intensive care unit (ICU) time series are irregular, incomplete, and computationally demanding to model at high temporal resolution. Dense Transformer attention captures long-range dependencies but evaluates all pairwise interactions, including many stable or clinically weak measurements. This study presents Sparse Physio-Attention, a physiology-guided Transformer that retains critical-range violations, patient-relative deviations, informative missingness patterns, and task-relevant variables before sparse attention is computed. Dynamic routing subsequently removes weak attention edges, and a late-fusion adapter incorporates static electronic health record context. The analysis included 25,368 eligible MIMIC-IV ICU stays, of which 2740 were sepsis positive. On the held-out… More >