World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) U.S. ISSN: 3070-6203
Title: World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) Publisher: Wissira Research Lab LLC Publisher Address: Carmel, United States- 46074 E-mail to submit manuscripts: editor@wjftcse.org Publisher Email: editor@wissira.us ISSN (online): 3070-6203 Starting Year: 2025 Main Subject: Computer Science Scope: Future Technologies in Computer Science and Engineering Language: English Publication Format: Online (Open Access) Website: https://wjftcse.org Contact: +917535916555 Types of Manuscripts Accepted Original Research Articles Review Articles Survey Papers Technical Notes Industrial Case Studies Conceptual Papers Short Communications Position Papers Innovation Reports Interdisciplinary Research Studies Journal Vision To become a globally recognized forum for pioneering research in future technologies, enabling the advancement of next-generation computer science and engineering innovations that drive sustainable technological transformation and societal progress. Scope of the Journal The journal covers a wide range of topics including, but not limited to: Multidisciplinary fields. Why Choose Our Journal? Interdisciplinary Approach: We believe in a holistic understanding of these technologies and encourage submissions that bridge gaps between various disciplines. Quality Research: ResaGate Global’s commitment to excellence ensures that all articles are rigorously peer-reviewed by experts in the field. Global Community: We connect researchers, practitioners, and innovators from around the world, creating a diverse and inclusive community. Practical Insights: Alongside theoretical advancements, we emphasize practical applications and real-world impacts of research. Submission and Publication We invite original research papers, review articles, case studies, and technical notes. Our submission process is streamlined and user-friendly, ensuring that authors can easily contribute their work. We adhere to a stringent but fair peer-review process to maintain the quality of our publications. Join Our Community Whether you're a seasoned researcher, a professional in the tech industry, or a passionate enthusiast, the "World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE)" is your gateway to the latest and most significant developments in these exciting fields. We welcome you to join our community, contribute your research, and help shape the future of digital technology. Published by Wissira Research Lab, our journal is more than just a collection of articles; it's a thriving hub of innovation and discovery. Welcome aboard!
Digital Neuromorphics: A Bridge Between Quantum AI and Human Brain Modeling
Digital neuromorphic systems seek to replicate the organizational principles and dynamics of biological neural networks in silicon, offering massive parallelism, event‐driven processing, and orders-of-magnitude lower energy consumption compared with conventional von Neumann computers. Concurrently, quantum artificial intelligence (AI) methods exploit quantum superposition and entanglement to accelerate optimization and pattern-recognition tasks. However, quantum hardware today remains constrained by qubit count and noise, while neuromorphic chips are limited in their ability to discover global optima during training. In this work, we propose and evaluate a hybrid framework—“quantum-optimized neuromorphic inference”—in which a variational quantum circuit (VQC) tunes synaptic weight matrices for a spiking neural network (SNN), and the optimized SNN is deployed on a digital neuromorphic core for inference. We first survey the relevant neuromorphic platforms and quantum machine learning algorithms, then describe our methodology for weight optimization and mapping to neuromorphic hardware. Through extensive simulations on a spoken-digit classification benchmark, our hybrid approach achieves a 3.3% absolute improvement in accuracy over a conventionally trained SNN, while reducing energy per inference by 3.5%. We discuss implications for scalable brain emulation, energy-efficient AI, and future neuromorphic-quantum co-design, and outline next steps toward experimental validation on emerging quantum processors and multi-core neuromorphic systems.
Digital Neuromorphics, Quantum AI, Human Brain Modeling, Hybrid Architectures, Energy-Efficient Computing
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Bio-Inspired Algorithms for Adaptive Quantum Machine Learning
Adaptive quantum machine learning (QML) represents an exciting frontier at the intersection of quantum computing and classical bio-inspired optimization. The constraints of noisy intermediate-scale quantum (NISQ) devices—limited qubit counts, significant decoherence, and measurement uncertainties—pose formidable challenges to training parameterized quantum circuits (PQCs) using traditional gradient-based methods. To overcome these hurdles, we propose a novel Adaptive PSO–QML framework that integrates a particle swarm optimization (PSO)–inspired algorithm with dynamic hyperparameter adjustment, enabling real-time tuning of PQC parameters based on performance feedback. Unlike static optimizers, our approach continuously adapts its inertia weight and cognitive/social coefficients to balance exploration and exploitation, responding to stagnation or rapid improvement in measured fitness. We evaluate this hybrid algorithm on two benchmark classification tasks: the classical Iris dataset encoded into a 2-qubit circuit and a quantum-encoded subset of the MNIST dataset on 4 qubits. Our experiments, conducted on the Qiskit Aer simulator under realistic noise models (depolarizing, amplitude damping, and mixed), demonstrate that Adaptive PSO–QML achieves an average accuracy improvement of 12% over fixed-parameter PSO and 15% over Adam-based gradient descent, with p-values below 0.01 across paired t-tests. Statistical analysis summarized in Table 1 confirms the significance of these gains. Detailed simulation research further reveals that the adaptive mechanism not only accelerates convergence by approximately 30%—reducing total circuit evaluations—but also exhibits robust performance across varying circuit depths (1–3 entangling layers) and noise intensities. Our findings suggest that bio-inspired adaptation is a powerful tool for mitigating barren plateaus and noise effects in NISQ-era QML, paving the way for scalable quantum advantage in near-term hardware.
Adaptive Quantum Machine Learning, Particle Swarm Optimization, Parameterized Quantum Circuits, NISQ, Bio-Inspired Algorithms
AI-Powered Quantum Key Distribution Protocols for IoT Networks
Quantum Key Distribution (QKD) enables information-theoretic secure key exchange by exploiting fundamental quantum phenomena such as superposition and no-cloning. Classical QKD protocols (e.g., BB84) guarantee that any eavesdropping attempt introduces detectable disturbances, but they typically assume stable, high-quality channels and unconstrained devices. In contrast, Internet of Things (IoT) networks present a radically different environment: devices have limited processing power, minimal onboard memory, strict energy budgets, and communicate over highly variable wireless links subject to interference and rapid fading. These constraints lead to elevated quantum bit error rates (QBER), frequent key reconciliation failures, and prohibitive energy costs, all of which jeopardize practical QKD deployment. To address these challenges, we propose an AI-powered QKD protocol specifically optimized for IoT scenarios. Our approach integrates three AI modules: (1) a gradient-boosted regression channel estimator that predicts instantaneous link transmittance using lightweight sensor data and historical photon counts; (2) a reinforcement learning (RL) agent that adaptively tunes photon intensity, basis-selection probability, and reconciliation block size to balance key rate and error rate; and (3) a convolutional neural network (CNN) classifier that selects the optimal Low-Density Parity-Check (LDPC) code rate based on real-time noise characteristics. We simulate a star-topology IoT network of 50 battery-powered devices communicating over 5 km polarization-encoded links with realistic urban loss (0.1–0.3 dB/km) and environmental noise. Compared against a static BB84 baseline, our AI-enhanced protocol reduces average QBER from 4.5% to 2.9%, increases secure key rate from 12.5 kbps to 16.0 kbps, lowers latency by 22%, and reduces energy consumption per key bit by 18%. These improvements persist across channel conditions and device heterogeneity, demonstrating robustness.
AI-Powered QKD, IoT Security, Quantum Bit Error Rate, Machine Learning, Adaptive Parameter Tuning
Quantum Blockchain for Verifiable Genome Mapping Systems
The emergence of quantum computing threatens classical cryptographic systems, prompting the development of quantum-resistant solutions. At the same time, blockchain technology has been recognized for its capacity to provide immutable, auditable records, but existing implementations largely rely on cryptographic primitives vulnerable to quantum attacks. In genomic research, the integrity, provenance, and confidentiality of sequence data are paramount: any undetected tampering can compromise downstream analyses, misguide clinical decisions, or violate patient trust. This paper introduces Quantum Blockchain for Verifiable Genome Mapping Systems (QB-VGMS), a hybrid architecture that melds quantum key distribution (QKD)–based secure channels with a permissioned, quantum-resistant blockchain to enable end-to-end verifiability of genome mapping operations. In our design, genome fragments are hashed using SHA-3 and stored on-chain alongside metadata, while bulk sequence data resides in encrypted off-chain repositories. The consensus protocol employs QKD to distribute fresh symmetric keys among validator nodes, coupled with a quantum-random leader election to resist both classical and quantum adversaries. We describe a prototype implementation using simulated QKD links and Hyperledger Fabric extended for quantum security. Performance evaluation on human chromosome 21 datasets (48 MB total, partitioned into 1 MB fragments) demonstrates sub-one-second average transaction latency and throughput scaling up to 15 fragments per second under concurrent load. Security assessments confirm tamper-evident auditing and confidentiality against unauthorized decryption attempts. We conclude by discussing practical deployment challenges—such as QKD hardware integration, consensus scalability, and regulatory compliance—and outline pathways for integrating advanced privacy-enhancing technologies.
Quantum-Resistant Blockchain, Genome Mapping, Verifiable Ledger, Quantum Key Distribution, Data Integrity, Decentralized Genomics
Hybrid AI-Quantum Models for Real-Time Molecular Structure Prediction
Hybrid artificial intelligence–quantum (AI–Q) models arise at the intersection of two rapidly advancing computational paradigms, promising unprecedented capabilities for real-time molecular structure prediction. In classical computational chemistry, the accurate determination of ground-state energies and optimized geometries relies on ab initio methods such as density functional theory (DFT) and coupled-cluster approaches, which, while highly precise, exhibit steep polynomial or exponential scaling with system size. Conversely, deep neural networks (DNNs) have demonstrated remarkable success in learning potential energy surfaces (PES) from large datasets, achieving rapid inference but sometimes lacking the ultimate chemical accuracy required for predictive applications in drug discovery or materials design. On the other hand, variational quantum eigensolvers (VQEs) leverage the intrinsic parallelism of quantum hardware to approximate electronic structure problems, yet they remain constrained by qubit coherence times, gate fidelities, and circuit depth limitations inherent to noisy intermediate-scale quantum (NISQ) devices.
Hybrid AI–Quantum, Variational Quantum Eigensolver, Deep Neural Network, Molecular Structure Prediction, Real-Time Computation
Federated Data Processing Architectures for Secure Cross-Organization Analytics
The growing volume of data generated across enterprises, healthcare institutions, financial organizations, research centers, and government agencies has created significant opportunities for cross-organization analytics. Collaborative analysis of distributed datasets can improve decision-making, predictive modeling, operational efficiency, and scientific discovery. However, traditional centralized data-sharing approaches often face substantial challenges related to privacy protection, regulatory compliance, data ownership, cybersecurity risks, and organizational trust. Regulations such as GDPR and other data governance frameworks further restrict the unrestricted movement of sensitive information across institutional boundaries. Federated data processing architectures have emerged as a promising solution to these challenges by enabling organizations to collaboratively perform analytics and machine learning while keeping data locally stored. Instead of transferring raw datasets, federated systems exchange model parameters, aggregated statistics, or encrypted computations, thereby reducing privacy risks and enhancing data security. Recent advances in federated learning, secure multi-party computation, differential privacy, homomorphic encryption, and secure aggregation have strengthened the feasibility of secure cross-organization analytics. This study evaluates a federated data processing architecture for secure cross-organization analytics using a simulated multi-organization experimental setup. The findings indicate that federated approaches significantly improve privacy preservation, regulatory compliance, and data utilization while maintaining analytical accuracy. The study highlights current challenges related to scalability, communication overhead, interoperability, and trust management, and identifies future research directions for building secure, efficient, and scalable federated analytics ecosystems.
Federated Data Processing , Cross-Organization Analytics , Federated Learning , Privacy-Preserving Computing , Secure Multi-Party Computation , Differential Privacy
Copyright (c) 2026 World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) U.S. ISSN: 3070-6203 https://creativecommons.org/licenses/by-nc-nd/4.0
Multisensory Feedback Systems in XR Controlled by AI Emotion Recognition
Extended Reality (XR), encapsulating Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), has transformed how users interact with digital content, offering immersive environments that blend physical and virtual elements. However, conventional XR systems typically provide static, one-size-fits-all sensory feedback—visual, auditory, or haptic—without regard to the user’s current affective state. This limitation can reduce immersion, hinder learning outcomes, and exacerbate negative emotional experiences in high-stakes applications such as exposure therapy or safety training. In this manuscript, we present a comprehensive design, implementation, and evaluation of an AI-driven multisensory feedback system for XR that dynamically adapts haptic, auditory, and visual feedback in real time based on users’ recognized emotional states. Our system employs a multi-modal emotion recognition engine combining convolutional neural networks (CNNs) for facial expression analysis with recurrent neural networks (RNNs) for physiological signal interpretation (heart rate variability and galvanic skin response). By fusing these inputs, the engine categorizes user emotions into five core states—joy, anxiety, frustration, sadness, and neutral—with an overall recognition accuracy exceeding 90% on benchmark data. A dedicated feedback controller then applies rule-based mappings from detected emotional states to feedback parameters: for instance, increasing haptic pulse frequency and shifting to cooler lighting when anxiety is detected, or introducing soft vibration and warm color palettes for joy. We implemented the prototype within the Unity 3D engine, targeting the Oculus Quest 2 platform. Haptic feedback was delivered via a wearable haptic glove (HaptX SDK), spatialized audio through the Oculus Audio SDK, and dynamic lighting via custom Unity shaders. In a within-subjects user study (N = 40), participants performed a sequence of memory recall tasks and maze navigation challenges under two conditions: Adaptive (emotion-aware feedback enabled) and Control (static, non-adaptive feedback). Objective performance metrics—recall accuracy and token collection rates—improved by 12% and 6.6%, respectively, in the Adaptive condition (p < .01), while subjective presence measured via the Igroup Presence Questionnaire (IPQ) increased by 18% (p < .001). Qualitative interviews further revealed that participants felt more “in tune” with the environment and better able to regulate stress when feedback adapted to their emotional fluctuations. Our contributions are threefold: (1) a robust, multi-modal emotion recognition pipeline optimized for real-time XR applications; (2) a flexible, rule-based feedback controller unifying haptic, auditory, and visual modalities; and (3) empirical evidence demonstrating significant gains in performance and presence. We discuss implications for XR-based training, therapeutic interventions, and entertainment, highlighting how emotion-adaptive feedback can support personalized learning trajectories, mitigate cognitive overload, and foster deeper emotional engagement.
Multisensory Feedback, Extended Reality, Emotion Recognition, Affective Computing, Haptic Stimulation, Adaptive Systems
Blockchain-Based Licensing of Metaverse-Generated Digital Assets
The advent of immersive virtual environments—collectively referred to as the metaverse—has catalyzed new forms of digital creativity, commerce, and social interaction. As creators continuously generate unique digital assets ranging from avatar skins and virtual real estate to interactive experiences, the need for robust licensing mechanisms that codify ownership, usage rights, and royalty distributions has become imperative. Traditional centralized licensing systems suffer from single points of failure, opaque transaction histories, and limited interoperability across platforms. In contrast, blockchain technology offers decentralized, tamper-resistant ledgers and programmable smart contracts, making it an ideal foundation for next-generation licensing frameworks. This manuscript introduces the Metaverse Licensing Layer (MLL), a comprehensive architecture that extends established non-fungible token (NFT) standards to embed detailed licensing metadata, integrates decentralized storage solutions for off-chain asset management, and provides cross-platform interoperability through standardized metadata schemas and RESTful gateways. We detail a prototype implementation on a private Ethereum consortium network, leveraging ERC-721 tokens with extended metadata fields, IPFS for decentralized asset storage, and a Node.js interoperability gateway. Performance benchmarks reveal an average throughput of 50 transactions per second for license issuance and 48 TPS for transfers, with end-to-end gas costs averaging 0.020 ETH per operation. A user study involving 45 content creators and 60 consumers demonstrates high trustworthiness ratings (mean = 4.7/5) and strong usability scores (mean = 4.4/5) compared to centralized licensing alternatives.
Blockchain, Metaverse, Digital Assets, Licensing, Non-Fungible Token, Smart Contract
Spatial Computing in Next-Gen HCI for Smart Urban Planning
Spatial computing—encompassing augmented reality (AR), virtual reality (VR), mixed reality (MR), and digital twin technologies—has evolved into a powerful Human–Computer Interaction (HCI) paradigm that offers immersive, context‑aware visualization and manipulation of urban environments. By overlaying geospatial data onto real‑world views or presenting fully synthetic 3D city models, spatial computing empowers planners, stakeholders, and community members to engage directly with design proposals in natural, intuitive ways. Yet despite considerable hype around “smart cities,” there remains a paucity of systematic evidence demonstrating how and to what extent spatial interfaces truly enhance planning efficiency, accuracy, and user satisfaction compared to conventional 2D GIS tools. In this study, we implemented a mid‑scale smart‑city model on a tabletop AR display and on the HoloLens 2 platform, then recruited thirty professional urban planners (mean experience = 6.2 years) to perform three prototypical tasks: (1) rezoning a multi‑parcel block, (2) siting transit stops based on population density, and (3) conducting line‑of‑sight analyses for proposed high‑rise developments. Using a within‑subjects counterbalanced design, we measured task completion time, spatial error rates, and perceived usability via the System Usability Scale (SUS). Statistical analysis via independent‑samples t‑tests revealed that spatial computing reduced mean completion time by 30% (12.4 ± 2.1 min vs. 17.8 ± 3.0 min; p < .001), halved error rates (0.6 ± 0.5 vs. 1.2 ± 0.7 errors/task; p = .002), and yielded SUS scores averaging 82.3 (excellent) versus 68.7 (OK) for 2D GIS (p < .001). We detail the system architecture, experimental methods, and quantitative outcomes, then discuss implications for municipal adoption, collaboration among diverse stakeholders, and the future integration of real‑time sensor feeds. Finally, we delineate scope and limitations—such as prototype maturity, dataset scale, and learning effects—and propose avenues for longitudinal, multi‑user, and large‑scale evaluations that can inform the next generation of smart‑city planning workflows.
Spatial Computing, Augmented Reality, Smart Cities, Urban Planning, Human–Computer Interaction
Emotion-Centric AI Agents in Multi-User Virtual Reality Platforms
Emotion‑Centric AI Agents (ECAAs) hold the promise of transforming multi‑user Virtual Reality (VR) experiences by endowing non‑player avatars with the capacity to sense, interpret, and respond to human affect in real time. This manuscript details the conception, integration, and evaluation of ECAAs within a group‑based VR environment built in Unity3D. We employ a multimodal emotion recognition pipeline—fusing facial expression analysis via Affectiva, vocal‐prosody features via openSMILE, and physiological signals from Empatica E4 wristbands—processed through a Bayesian inference engine to generate continuous estimates of user valence and arousal. These estimates feed into an adaptive behavior engine that modulates avatar nonverbal cues (e.g., proxemics, gestures) and dialogic content, yielding empathic and contextually appropriate responses. We conducted a within‑subjects study with 30 participants (10 triads) who engaged in collaborative problem‑solving tasks under two conditions: (1) Baseline—with static, non‑affective agents—and (2) ECAA—agents driven by live emotion feedback. Quantitative measures included Social Presence (SP) and User Engagement (UE) scales, each comprising multiple Likert‑type items, as well as system latency and classification accuracy metrics. Qualitative data were gathered through post‑session semi‑structured interviews. Results revealed that sessions with ECAAs significantly outperformed baseline in SP (mean increase of 22%, p < .001) and UE (mean increase of 18%, p < .001). The multimodal recognition pipeline achieved 87% overall accuracy in valence classification with average end‑to‑end latency of 48 ms. Thematic analysis of interviews indicated that users perceived ECAAs as more empathetic, facilitating smoother turn‑taking and deeper group cohesion. Participants reported that empathic avatar prompts (e.g., supportive nods when frustration was detected) mitigated tension and fostered collaboration. These findings confirm that emotion‑aware agents can substantially enrich multi‑user VR by bolstering social presence and engagement without imposing prohibitive computational overhead. We discuss implications for remote teamwork, virtual training, and therapeutic group interventions, and outline directions for scaling to larger participant counts, expanding affect taxonomies, and integrating long‑term affective adaptation.
Emotion-Centric AI Agents, Multi-User Virtual Reality, Affective Computing, Social Presence, User Engagement
Brain-Computer Interfaces for Neurofeedback in VR Mental Health Platforms
Brain–computer interfaces (BCIs) paired with neurofeedback constitute a frontier in noninvasive neuromodulation, offering new avenues for mental health treatment. Traditional neurofeedback protocols rely on two‑dimensional displays of electroencephalographic (EEG) rhythms, which can be abstract and disengaging. By contrast, integrating BCIs with immersive virtual reality (VR) platforms embeds neurofeedback within richly contextualized environments, leveraging presence and embodiment to heighten user engagement and therapeutic efficacy. This study presents a detailed exploration of BCI‑driven neurofeedback in a VR mental health platform, focusing on anxiety regulation. We first delineate the neurophysiological mechanisms underlying alpha‑band modulation and establish how real‑time feedback loops facilitate self‑regulation through operant conditioning. Next, we describe the system architecture: a 16‑channel EEG headset streaming data to a signal‑processing pipeline that employs band‑pass filtering, artifact rejection, and Common Spatial Patterns (CSP) feature extraction, followed by an LDA classifier that translates neural activity into VR scene parameters. A VR environment—a tranquil, interactive forest—is rendered in Unity 3D, with dynamic elements (e.g., breeze intensity, light levels) mapped to participants’ alpha power. In a randomized controlled trial, thirty adults with mild‑to‑moderate anxiety underwent ten 20‑minute sessions of either standard 2D neurofeedback or VR‑enhanced neurofeedback. Pre‑ and post‑intervention assessments included the GAD‑7 anxiety scale, EEG‑derived alpha‑power metrics, and a validated presence inventory. Statistical analysis via repeated‑measures ANOVA revealed that VR participants experienced a 42.9% reduction in GAD‑7 scores versus 15.2% in the control group (p = .003), accompanied by a 29% increase in time spent above the alpha threshold (p = .001) and significant gains in subjective presence (p = .0005). A complementary MATLAB/Simulink simulation optimized feedback parameters, identifying a 2‑second sliding window and feedback gain of 1.2 as balancing responsiveness (mean latency = 240 ms) and signal‑to‑noise ratio (8.5 dB). We discuss clinical implications, including potential for at‑home deployment, scalability, and integration with other biosignals (e.g., heart rate variability). Limitations—such as the short intervention span and homogenous sample—are addressed, and future work is proposed to explore long‑term retention, cross‑disorder generalizability, and adaptive machine‑learning algorithms for personalized feedback. Our findings substantiate the therapeutic promise of BCI‑driven VR neurofeedback for anxiety management and lay a foundation for broader applications in mental health care.
AI-Enhanced Real-Time Translation Systems in Mixed-Reality Spaces
Real‑time translation within mixed‑reality (MR) spaces holds transformative potential for cross‑lingual collaboration, education, and social interaction by enabling participants to converse naturally without language barriers. Traditional approaches rely on tethered devices or mobile applications that disrupt immersion. In this work, we present an AI‑enhanced MR translation system that integrates optimized neural machine translation (NMT) models with head‑mounted displays (HMDs) and edge‑cloud inference. We detail the system’s architecture—including on‑device audio capture, low‑latency streaming to an edge server, and subtitle rendering in the user’s field of view—and describe model compression and streaming strategies that balance translation quality against computational constraints. To evaluate the approach, we conducted a within‑subjects user study with thirty bilingual participants across four language pairs (English–Spanish, English–Mandarin, English–Arabic, English–Hindi). Each participant completed six conversational tasks using both a baseline NMT‑MR prototype and the AI‑enhanced system. We measured translation accuracy via word error rate (WER), end‑to‑end latency from speaker utterance to subtitle display, and user satisfaction through Likert‑scale questionnaires. The AI‑enhanced system yielded a 15.2 percentage‑point reduction in WER (28.5% → 13.3%), a 0.35 s latency decrease (1.12 s → 0.77 s), and a 22.6% increase in satisfaction ratings (3.1 → 3.8 on a 5‑point scale). Paired t‑tests confirmed significance (p < .001) with large effect sizes. We discuss design guidelines for deploying real‑time NMT in MR, including trade‑offs in model size, streaming granularity, and network reliability. Finally, we outline future directions: fully on‑device quantized inference, support for additional low‑resource languages, and multimodal translations incorporating gesture and visual context.
Digital Twin Replication for Cross-Reality Simulation Engines
Digital twins—precise, virtual replicas of physical systems—have rapidly evolved into indispensable instruments for capturing, simulating, and optimizing real-world assets and processes. As immersive technologies spanning augmented reality (AR), virtual reality (VR), and mixed reality (MR) converge into the broader concept of cross-reality (XR), the demand grows for digital twin frameworks that can seamlessly integrate across heterogeneous simulation engines. This manuscript introduces Digital Twin Replication for Cross-Reality Simulation Engines (DTR-XRSE), a comprehensive architecture designed to achieve bidirectional synchronization between physical entities and their virtual counterparts within AR/VR/MR environments. Our approach comprises a layered architecture—spanning sensor data acquisition, edge processing with real-time filtering and compression, cloud-native orchestration for dynamic simulation-instance management, and XR client integration via low-latency WebRTC streams. We detail the data model standardization, MQTT-and-Kafka messaging pipelines, Kubernetes and K3s orchestration strategies, and Unity 3D XR integrations that underpin DTR-XRSE. A proof-of-concept implementation involving a six-DOF industrial robotic manipulator demonstrates the framework’s capability: sub-5 ms round-trip latency, sub-0.5 mm positional error, and 25 % improvement in XR training task completion times versus baseline systems. We also analyze throughput scaling across multiple edge nodes, autoscaling behaviors under Kubernetes, and user-experience metrics drawn from the Igroup Presence Questionnaire (IPQ).
Decentralized Metaverse Governance via AI-Supported Voting Models
The advent of the metaverse—a collection of persistent, shared, 3D virtual spaces—promises unprecedented opportunities for social interaction, commerce, and digital creativity. However, as virtual communities scale, the need for effective governance mechanisms becomes paramount. Traditional centralized models suffer from single points of failure, censorship risk, and lack of transparency, while purely token-weighted decentralized autonomous organizations (DAOs) often see low participation, plutocratic voting dynamics, and vulnerability to sybil attacks. To address these challenges, we introduce Decentralized Metaverse Governance via AI-Supported Voting Models (DMG-AI), a hybrid framework that marries blockchain-based voting with machine-learning–driven anomaly detection. In DMG-AI, voting rights are allocated via on-chain tokens, but each vote is evaluated off-chain by an AI module trained to identify patterns indicative of manipulative behaviors—such as vote-buying, collusion, or sybil identity proliferation. We built a prototype on a private Ethereum testnet and conducted a mixed-methods evaluation comprising an online user survey (N=250) and agent-based simulations (1,000 agents, 100 proposals). Survey results show an 18 percentage-point increase in voter turnout and a 0.9-point rise in perceived governance trust (on a 1–5 Likert scale) under DMG-AI versus baseline DAOs. Simulation experiments demonstrate a 67% reduction in successful sybil attacks and achieve 85% precision and 78% recall in anomaly detection, with a modest 1-hour increase in proposal resolution time. These findings confirm that integrating AI into decentralized governance enhances both security and inclusivity for metaverse communities.
AR-Powered Manufacturing Assistance with AI Safety Co-Pilots
Augmented Reality (AR) technologies have rapidly evolved from nascent visualization tools into fully integrated assistance platforms within modern manufacturing environments. By overlaying rich, context-sensitive digital cues—such as 3D assembly instructions, real‑time system diagnostics, and safety alerts—directly onto workers’ fields of view, AR reduces the need to shift attention away from physical tasks, thereby minimizing cognitive load and error propensity. Simultaneously, Artificial Intelligence (AI) “co‑pilots” have emerged as proactive safety partners: leveraging sensor fusion, computer vision, and predictive analytics, these systems continuously monitor operator actions and environmental parameters to anticipate hazardous conditions and provide dynamic, personalized guidance. This manuscript presents a comprehensive, mixed‑methods examination of the combined impact of AR‑powered assistance with AI safety co‑pilots on operator performance metrics—namely task completion time, error rates, and perceived workload—and on qualitative dimensions of situational awareness and user confidence. Thirty experienced assembly‑line operators completed a standardized 20‑step task under three conditions: traditional paper‑based instructions, AR guidance alone, and AR supplemented by an AI safety co‑pilot. Quantitative analysis revealed that AR + AI co‑pilot reduced average completion time by 32%, decreased assembly errors by 59%, and lowered perceived workload by 23% relative to paper manuals (p < .01 for all comparisons). Qualitative feedback underscored enhanced worker confidence, improved ergonomic practices, and a sense of shared responsibility with the AI co‑pilot. These findings illuminate the synergistic benefits of integrating AR visualization with AI‑driven safety oversight, charting a path toward next‑generation human–machine collaboration frameworks that prioritize both efficiency and well‑being in industrial settings.
Blockchain-Verified Identity Systems for Cross-Metaverse Compatibility
The emergence of multiple, independently operated metaverse platforms has underscored the pressing need for an interoperable identity framework that can facilitate secure, user‑centric navigation across heterogeneous virtual worlds. Traditional digital identity systems rely heavily on centralized authorities, exposing user data to privacy risks, creating single points of failure, and inhibiting seamless cross‑platform engagement. This manuscript introduces the Blockchain‑Verified Identity System (BVIS), a decentralized architecture built upon W3C’s Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), augmented by cross‑chain interoperability protocols. BVIS empowers users with full sovereignty over their identity data while enabling selective disclosure of attributes through zero‑knowledge proof techniques. We detail the system’s layered architecture—comprising identity wallets, smart‑contract‑based issuance and verification modules, and a cross‑chain bridge—and describe the design and implementation of our Ethereum‑compatible prototype spanning two testnets (Rinkeby and Polygon Mumbai). A rigorous evaluation involving performance benchmarking and a user experience trust survey demonstrates that BVIS achieves an average end‑to‑end verification latency of just over one second, gas costs under US$0.20 per verification, and significantly higher perceived trust and privacy control compared to centralized OAuth2 systems. Our findings confirm BVIS’s viability as a unifying identity layer for the metaverse, laying groundwork for future extensions such as role‑based group credentials, advanced privacy-preserving proofs, and decentralized relayer networks.
Machine Learning-Based Resource Allocation for Scalable Cloud REST Services
Cloud computing environments today have an extensive range of distributed applications, which are dependent on the use of RESTful web services. These services are required to accommodate millions of simultaneous calls, as well as be highly performance, availability, and scalable. Conventional resource distribution systems of a cloud system are usually based on fixed policies or threshold-based auto-scaling strategies. Although these approaches offer a minimum scaleability, they are usually not efficient at managing unpredictable workloads and dynamic workloads typical in modern cloud systems. Consequently, the resources can be either underutilized or over-provisioned resulting in higher operational costs and poor performance of the system. Machine learning (ML) can be an effective solution to the problem of cloud resource allocation optimization in scalable REST services. ML models can predict resource utilization in the future by examining past workload data and determine intricate trends in traffic behavior to doom and refer moving computing resources like CPU, memory, and network bandwidth. This predictability enables cloud systems to make proactive resource allocation prior to the deterioration of performance. This paper presents an intelligent resource allocation framework on scalable cloud resource REST architecture based on machine learning algorithms. The framework combines predictive modeling of workload, resource scheduling by reinforcements of learning and real-time performance monitor. The system predicts the patterns of demand of the API using regression models and neural networks, which are supervised learning techniques. An agent of reinforcement learning then identifies ideal policies of resource allocation to achieve balance between system performance, latency and cost-efficiency. Experimental assessment shows that response time, system throughput and infrastructure usage is improved significantly as compared to the conventional rule based scaling mechanisms. The suggested system is more efficient in resources utilization and minimizes system failure and overhead. These findings reveal the possibility of machine learning based resource management in the next generation cloud based infrastructure as well as the high performance REST service ecologies.
Machine Learning, Cloud Computing, Resource Allocation, REST Services, Predictive Scaling, Cloud Infrastructure Optimization, Reinforcement Learning, API Performance Optimization
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
AI has already been used to revolutionize the current business landscape by allowing the application of data-driven decision-making, automated customer engagement, as well as personalized marketing. The existing literature has centered on AI applications in e-commerce settings, where they have been proposed to be used in recommender systems, intelligent chatbots, predictive analytics, and personalized marketing solutions to improve consumer experience and make the business more efficient. Although these studies prove that AI has the potential to streamline customer interactions and digital selling performance, various limitations are still present in the existing literature. Current literature tends to focus on platform-based retail applications and immediate marketing performance, little of which is provided on in-depth commercial ecosystems, like supply chain coordination, procurement judgement, and cross-organizational trade procedures. Moreover, the strategic implications associated with the adoption of AI over the long term, especially in the context of consumer trust, ethical management, transparency of algorithms, and sustainability, are not studied properly. This paper seeks to fill these research gaps by discussing the extended purpose of AI in business other than traditional e-commerce platforms. The study examines the effects of AI-based systems on commercial decisions, operational effectiveness, and management of customer relationships and the issues of transparency, data management, and ethical application. The synthesis of literature as well as analytical analysis of AI-enabled business procedures helps to see the emerging patterns and possibilities regarding the responsible use of AI. The results are projected to offer information on how AI can contribute to sustainable business development and consumer confidence and transparency in the digital marketplaces. Closing the gap between technological and strategic thinking, the research paper adds to a more comprehensive perception of the adoption of AI in business and suggests future research directions in smart commercial environments.
Artificial Intelligence, E-commerce, Machine Learning, Consumer Behavior, Automation, Data Analytics
Tactile Feedback Systems for Immersive Blockchain Gaming Ecosystems
Tactile feedback systems represent a pivotal advancement in immersive virtual environments, providing users with physical sensations that correspond to digital events. In blockchain gaming ecosystems—where true digital ownership, provable scarcity, and decentralized transactions define user experience—the integration of tactile feedback can profoundly deepen immersion, reinforce economic trust, and enable novel interactive mechanics. This manuscript presents an expanded examination of wearable haptic technologies and their convergence with blockchain-based gaming platforms. We begin with an in‑depth overview of contemporary haptic devices and blockchain gaming principles, proceed to a comprehensive literature synthesis, and then describe the development and evaluation of a prototype tactile glove integrated with a decentralized collectible‑card game.
Tactile Feedback, Haptics, Blockchain Gaming, Immersive Systems, User Engagement
AI-Driven Avatars in Emotionally Adaptive Virtual Classrooms
The present study investigates the development, integration, and evaluation of AI‑driven avatars within emotionally adaptive virtual classrooms. Over recent years, online education has grown exponentially, distinguishing itself through scalability and accessibility yet often lacking the nuanced emotional support integral to effective learning. To address this gap, we leverage advances in affective computing and pedagogical agent design to create avatars that detect and adapt to learners’ emotional states in real time. Specifically, our system uses multimodal sensors—facial expression analysis, vocal tone recognition, and interaction metrics—to infer discrete emotions such as confusion, frustration, boredom, and engagement. These inferences inform the avatar’s adaptive behaviors, including modulating speech prosody, delivering empathetic dialogue, adjusting pacing, and providing timely encouragement. We implemented this prototype in Unity3D, integrating the OpenFace facial‑action‑unit toolkit and an open‑source speech‑emotion classifier. A mixed‑methods experiment was conducted with 120 undergraduate students, randomly assigned to either an Adaptive Avatar condition or a Control (non‑adaptive) condition. The Adaptive group’s avatars responded dynamically to affective cues, whereas Control avatars presented identical content without adaptation. Quantitative measures included a 10‑item post‑test for knowledge retention, the User Engagement Scale for affective engagement, and interaction logs (time‑on‑task, hint requests). Qualitative data were gathered via semi‑structured interviews exploring perceptions of empathy, support, and social presence. Results indicate that learners in the Adaptive condition achieved significantly higher retention (M = 8.2 vs. 7.1, p < .001), reported greater engagement (M = 4.1 vs. 3.6 on a 5‑point scale, p < .001), requested fewer hints, and spent more time on task.
AI-Driven Avatars, Affective Computing, Virtual Classrooms, Emotional Adaptation, Educational Technology
Personalized VR Learning Environments Using Cognitive AI Agents
Virtual reality (VR) has shown promise as an immersive educational medium, yet its full potential remains unrealized when experiences are one‐size‐fits‐all. This study presents the design, implementation, and evaluation of personalized VR learning environments enhanced by cognitive AI agents, which continuously monitor learner behavior and dynamically tailor instructional support. Leveraging an ACT‑R cognitive architecture integrated with Unity3D, our system tracks learners’ interactions—such as task completion time, error patterns, and navigation choices—to infer conceptual misunderstandings in real time. We conducted a controlled experiment with 60 undergraduate participants, randomly assigned to either a personalized VR condition (with AI agent) or a non‑personalized control VR. Pre‑ and post‑test assessments measured learning gains, while standardized questionnaires captured engagement and usability. Statistical analysis (two‑sample t‑tests, Cohen’s d) revealed that the personalized group achieved significantly higher knowledge gains (M=18.5 vs. 12.1, p<.001, d=1.32) and engagement scores (UEQ mean=4.2 vs. 3.5, p<.01). Complementary computational simulations using synthetic learner models demonstrated a 30% reduction in trials to mastery under adaptive scaffolding. User interviews highlighted the agent’s scaffolding prompts as clear, timely, and motivating. We conclude that embedding cognitive AI agents within VR significantly enhances learning efficacy and learner experience. Implications for scalable deployment, multi‑user collaboration, and longitudinal retention are discussed, alongside challenges in real‑time performance optimization and agent–learner rapport in complex domains.
Personalized VR Learning, Cognitive AI Agents, Adaptive Learning, Virtual Reality, Educational Technology
Gesture-Based Control Interfaces for AI-Powered Smart Environments
Gesture-based control interfaces leverage natural, intuitive human movements to enable touchless interaction within AI-powered smart environments, addressing limitations of traditional input modalities such as touchscreens and voice control. This expanded study presents a comprehensive analysis of system design, algorithmic performance, user experience, and deployment considerations for gesture-recognition solutions integrated into smart-home and smart-building platforms. We develop a unified prototype comprising a depth-sensing camera, preprocessing pipeline, recognition engine (featuring Hidden Markov Models, Convolutional Neural Networks, and Long Short‑Term Memory networks), and a control interface driving real-world IoT devices. A within-subjects evaluation with thirty participants captured five atomic gestures across varied lighting and background conditions. Detailed statistical analysis—anchored by one-way ANOVA and post-hoc testing—confirms that LSTM-based models deliver superior accuracy (M = 94.2 %, SD = 2.9 %) and latency (M = 120 ms) compared to CNN (M = 91.5 %, SD = 3.8 %; M = 135 ms) and HMM (M = 88.7 %, SD = 4.2 %; M = 150 ms). Qualitative feedback highlights critical factors such as calibration ease, feedback mechanisms, and adaptability to individual movement styles. Drawing on these findings, we articulate design guidelines for robust, real-time gesture controls—emphasizing sensor placement, environmental robustness, computational efficiency, and customizable gesture vocabularies. The results demonstrate the feasibility of deploying advanced deep‑learning approaches in consumer-grade smart environments, enhancing accessibility, engagement, and system responsiveness. Future research directions include on-device inference for privacy preservation, multimodal fusion with voice and gaze inputs, and adaptive learning pipelines that evolve with user behavior over time.
Gesture Recognition, Smart Environments, AI, HMM, CNN, LSTM, Human–Machine Interaction
Neural Interfaces for Direct AI-Avatar Collaboration in the Metaverse
Neural interfaces, encompassing both noninvasive and invasive brain–computer interfaces (BCIs), have matured substantially over the past two decades, establishing robust frameworks for translating neural activity into actionable commands. Within the burgeoning metaverse—an interconnected network of immersive virtual environments—these interfaces provide a direct conduit for users to control AI-driven avatars, thereby minimizing the cognitive and physical effort associated with traditional controller-based interactions. This manuscript presents a comprehensive exploration of existing neural interface technologies and AI-avatar architectures, synthesizes relevant findings from prior studies, and reports on an empirical investigation comparing performance metrics between BCI-driven and manual control conditions. Specifically, a controlled experiment involving thirty participants engaged in a collaborative search-and-retrieve task demonstrates that BCI control yields significantly faster completion times (45.2 s vs. 68.7 s), higher command accuracy (87.4% vs. 79.5%), reduced subjective cognitive load (NASA-TLX scores of 48.6 vs. 62.3), and enhanced embodiment experiences (Embodiment Questionnaire scores of 5.8 vs. 4.2) compared to manual joystick control (all p < .001). These results endorse the viability of neural interfaces for seamless human–AI collaboration in virtual environments and underscore the potential for next-generation metaverse applications spanning education, telepresence, remote teamwork, and entertainment. We further delineate methodological considerations, statistical analyses, and implications for scalability, while acknowledging limitations related to signal fidelity, training burden, and generalizability.
Neural Interfaces, Brain–Computer Interface, Metaverse, AI Avatars, Human–AI Collaboration
AI-Powered Governance for Ethics and Compliance Monitoring Systems
Artificial Intelligence (AI) governance for ethics and compliance monitoring systems has become a strategic imperative for organizations deploying AI at scale. As enterprises integrate AI into mission‑critical operations—from credit underwriting and healthcare diagnostics to automated hiring and law enforcement—risks of biased outcomes, unlawful data usage, and opaque decision‑making have escalated. Traditional compliance approaches, which rely on periodic manual audits, are insufficient to address real‑time ethical breaches or evolving regulatory requirements. AI‑powered governance frameworks leverage machine learning (ML), natural language processing (NLP), and anomaly detection to continuously monitor AI pipelines, detect deviations from ethical policies, and trigger human review when necessary. This paper provides a detailed exploration of such governance architectures, grounding the discussion in interdisciplinary scholarship and industry best practices. We first outline the ethical and legal imperatives driving AI governance, then present a systematic literature review of existing frameworks. Our methodology combines qualitative case studies of five leading organizations with quantitative performance analysis of monitoring metrics over a twelve‑month period. Results indicate that AI‑driven monitoring improves violation detection rates by 45% and reduces mean time to resolution by 43% compared to manual audits, although modest increases in false‑positives highlight the need for careful threshold calibration. We conclude by synthesizing design principles—such as “ethics by design,” interpretability, and cross‑functional collaboration—and offer actionable recommendations for practitioners. Finally, we identify open research directions, including automated bias mitigation and scalable audit methodologies, to advance the field toward truly trustworthy AI systems.
AI Governance, Ethics Monitoring, Compliance Systems, Algorithmic Accountability, Regulatory Technology
Secure Execution of AI Pipelines on Confidential Cloud Infrastructure
With the explosive growth of artificial intelligence (AI) services in recent years, organizations are increasingly relying on cloud platforms to execute end-to-end AI pipelines—spanning data ingestion, preprocessing, model training, and inference. While cloud infrastructures offer unparalleled scalability and cost advantages, they also introduce significant risks: untrusted hypervisors, co-tenant attacks, and privileged insider threats can expose sensitive data and proprietary model parameters. Confidential computing, realized via hardware-enforced Trusted Execution Environments (TEEs) such as Intel SGX and AMD SEV, seeks to mitigate these risks by isolating code and data within protected enclaves. Despite the promise of TEEs, integrating them seamlessly into existing AI toolchains presents architectural, performance, and usability challenges. This manuscript presents SecureAI, a comprehensive framework for orchestrating AI workflows on confidential cloud infrastructure. We detail enclave provisioning, secure data ingestion, framework adaptation for TensorFlow and PyTorch, distributed parameter management, and end-to-end attestation. Through rigorous security analysis, we enumerate threat models and countermeasures. Empirical benchmarks on CIFAR-10 training with ResNet-50 quantify overheads: SGX enclaves incur ~25% runtime overhead, while AMD SEV adds ~17%. A Kubernetes-based simulation of mixed SGX/standard nodes highlights scheduling strategies that balance security and throughput. Our results demonstrate that SecureAI achieves strong confidentiality and integrity guarantees with acceptable performance trade-offs, paving the way for practical deployment of secure AI services in the public cloud.
Confidential Computing, Trusted Execution Environments, Intel SGX, AMD SEV, Secure AI Pipelines, Cloud Security
Adversarial Machine Learning Defense in IoT Ecosystems
The rapid expansion of Internet of Things (IoT) devices across consumer, industrial, and critical‑infrastructure domains has delivered unprecedented connectivity and automation. Yet this proliferation has also exposed a pressing security challenge: adversarial machine learning (AML) attacks that exploit subtle input perturbations to mislead or disable embedded intelligence. Such attacks—from single‑step perturbations like the Fast Gradient Sign Method (FGSM) to iterative optimization methods such as Projected Gradient Descent (PGD) and the Carlini & Wagner (C&W) attack—can have severe consequences in IoT contexts, ranging from false alarms in safety‑critical sensors to manipulated decisions in autonomous systems. Traditional AML defenses, while effective in large‑scale datacenter environments, often impose prohibitive computational or latency overheads for resource‑constrained IoT endpoints. In this work, we present a hybrid defense framework specifically tailored to the constrained and heterogeneous nature of IoT ecosystems. Our approach integrates three complementary techniques: (1) adversarial training, which augments the model’s decision boundary by including adversarial examples during offline retraining; (2) randomized smoothing, which adds certified robustness guarantees by averaging predictions over noise‑perturbed inputs at inference time; and (3) feature squeezing, a lightweight preprocessing step that reduces input complexity via bit‑depth reduction and median filtering. By strategically offloading the more intensive randomized smoothing to gateway or cloud nodes, while retaining feature squeezing for on‑device filtering, we achieve a balanced trade‑off between robustness and real‑time responsiveness.
Adversarial Machine Learning, IoT Security, Adversarial Training, Randomized Smoothing, Feature Squeezing, Edge Computing
Cyber-Resilient Protocols for Blockchain Nodes in Decentralized Webs
Blockchain technology has rapidly evolved into a foundational element of decentralized systems, enabling peer‑to‑peer transactions, smart contracts, and novel economic models without centralized intermediaries. Yet, as these networks proliferate, so do sophisticated cyber threats targeting the very nodes that maintain consensus and data integrity. This manuscript presents a comprehensive, multi‑layered framework of cyber‑resilient protocols specifically designed to safeguard blockchain nodes within decentralized webs against Eclipse, Sybil, and Distributed Denial‑of‑Service (DDoS) attacks. Central to our approach is an Adaptive Consensus Controller (ACC) that dynamically tunes consensus parameters based on real‑time network health metrics; a Network Intrusion Detection System (NIDS) that employs anomaly detection on peer‑to‑peer traffic; and a Peer‑Behavior Validator (PBV) leveraging lightweight zero‑knowledge proofs to cryptographically assess node compliance without compromising privacy. We implement this framework as extensions to a Hyperledger Fabric v2.2 prototype and evaluate its resilience across a 50‑node test network under controlled attack scenarios. Experimental results demonstrate a 45 % reduction in successful isolation (Eclipse) attacks, a 62.5 % decrease in consensus skew under Sybil conditions, sustained transaction throughput at 70 %–85 % of baseline during DDoS flooding, and overall node uptime improvements from 75 % to over 92 %.
Blockchain Resilience, Decentralized Web, Consensus Security, Intrusion Detection, Peer Validation, DDoS Mitigation
From Correlation to Causation: Integrating Causal Reasoning Frameworks into Deep Learning Models
Deep learning models have achieved remarkable success across diverse application domains, including computer vision, natural language processing, healthcare, and autonomous systems. Despite these advances, most deep learning systems fundamentally rely on correlational patterns rather than true causal understanding. This limitation poses significant challenges in high-stakes domains where robustness, interpretability, fairness, and generalization under distributional shifts are essential. The inability of conventional deep learning models to distinguish between correlation and causation often leads to spurious associations, biased predictions, and poor performance in unseen environments. This paper provides a comprehensive analysis of the transition from correlation to causation in artificial intelligence, with a particular focus on the integration of causal reasoning frameworks into deep learning architectures. We review foundational concepts of causal inference, including structural causal models, counterfactual reasoning, and intervention-based learning, and examine their relevance to modern deep learning systems. The paper further explores emerging hybrid approaches that combine neural networks with causal graphs, do-calculus, and invariant learning principles. Key challenges, methodological advances, and practical applications are discussed, along with open research questions and future directions. By bridging the gap between statistical learning and causal reasoning, this study aims to highlight pathways toward more reliable, explainable, and human-aligned artificial intelligence systems.
Causal inference, deep learning, correlation vs causation, structural causal models, counterfactuals, explainable AI, robust learning
Copyright (c) 2025 World Journal of Future Technologies in Computer Science and Engineering https://creativecommons.org/licenses/by-nc-nd/4.0
Privacy-Aware AI in Financial Fraud Detection Using Federated Data Lakes
Privacy preservation has emerged as a paramount concern in the deployment of artificial intelligence (AI) systems for financial fraud detection. As financial institutions increasingly rely on machine learning models trained on vast amounts of sensitive customer transaction data, the risk of data exposure—whether through centralized data breaches, insider misuse, or model inversion attacks—has grown commensurately. This manuscript presents a comprehensive, privacy-aware AI framework that integrates federated learning within a federated data lake architecture to detect fraudulent financial activities while ensuring that raw transaction data never leaves its originating institution. We begin by outlining the operational challenges faced by financial consortia in collaborative fraud detection, including regulatory compliance under GDPR, PCI DSS, and similar frameworks. We then detail our federated data lake deployment, leveraging Apache Iceberg for unified data cataloging and Presto SQL for seamless cross-node querying, combined with secure aggregation protocols that cryptographically shield client model updates.
Privacy-Aware AI, Federated Data Lakes, Financial Fraud Detection, Federated Learning, Secure Aggregation
Copyright (c) 2025 World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) https://creativecommons.org/licenses/by-nc-nd/4.0
Autonomous Cyber Incident Response Using Cognitive Security Agents
Autonomous Cyber Incident Response (ACIR) harnesses the capabilities of cognitive security agents—software entities endowed with perception, reasoning, and learning functions—to detect, analyze, and mitigate cyber threats without continuous human oversight. This manuscript expands on an ACIR framework that integrates real‑time telemetry ingestion, knowledge graph construction, hybrid cognitive architectures (ACT‑R/SOAR), and reinforcement learning (RL) to orchestrate end‑to‑end incident response workflows. The extended abstract details the motivation, architectural components, experimental setup, key performance metrics, results, and implications for cybersecurity operations. Through extensive simulations emulating enterprise networks with cloud and on‑premises assets, ACIR agents demonstrated a 60% reduction in mean time to detect (MTTD) and a 50% reduction in mean time to respond (MTTR) compared to traditional SIEM‑based human workflows. False positive rates remained stable at approximately 5%, illustrating that speed improvements did not compromise accuracy. Importantly, RL‑driven adaptation yielded a 30% improvement in first‑shot remediation success across repeated attack scenarios, evidencing the agents’ ability to learn from past outcomes and refine decision policies. The architecture’s modular design facilitates incremental integration with existing security infrastructures, enabling organizations to adopt ACIR capabilities alongside legacy tools.