World Journal of Future Technologies in Computer Science and Engineering
type: channel
description: Title: World Journal of Future Technologies in Computer Science and Engineering Title variant: WJFTCSE ISSN (online): 3070-6203 Publisher: Wissira Research Lab LLC Publisher Address: Carmel, United States- 46074 E-mail to submit manuscripts: editor@wjftcse.org Publisher Email: editor@wissira.us 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 pee…
publisher: Wissira Research Lab LLC
language: en
publication name: World Journal of Future Technologies in Computer Science and Engineering
issn: 3070-6203
items: Cyberbullying and Academic Performance Correlation
items: Challenges and Solutions in Implementing Machine Learning for Fraud Detection in High Volume Transactions
items: Barriers to Digital Access for Girls in Rural Schools
items: Digital Literacy and Social Capital in Rural Women
items: AI-Driven Digital Product Passports for Sustainable Textile Supply Chains
items: Quantum-Inspired Edge AI Optimization for Neural Prosthetics
items: Ethical Challenges of AI and Quantum Synergy in Cognitive Augmentation
items: Intelligent Resource Orchestration in Multi-Cloud Environments
items: Quantum Threat Mitigation in Blockchain AI Architectures
items: Genetic AI Algorithms for Multi-Objective Optimization in Quantum Systems
items: Digital Neuromorphics: A Bridge Between Quantum AI and Human Brain Modeling
items: Bio-Inspired Algorithms for Adaptive Quantum Machine Learning
items: AI-Powered Quantum Key Distribution Protocols for IoT Networks
items: Quantum Blockchain for Verifiable Genome Mapping Systems
items: Hybrid AI-Quantum Models for Real-Time Molecular Structure Prediction
items: Federated Data Processing Architectures for Secure Cross-Organization Analytics
items: Multisensory Feedback Systems in XR Controlled by AI Emotion Recognition
items: Blockchain-Based Licensing of Metaverse-Generated Digital Assets
items: Spatial Computing in Next-Gen HCI for Smart Urban Planning
items: Emotion-Centric AI Agents in Multi-User Virtual Reality Platforms
items: Brain-Computer Interfaces for Neurofeedback in VR Mental Health Platforms
items: AI-Enhanced Real-Time Translation Systems in Mixed-Reality Spaces
items: Digital Twin Replication for Cross-Reality Simulation Engines
items: Decentralized Metaverse Governance via AI-Supported Voting Models
items: AR-Powered Manufacturing Assistance with AI Safety Co-Pilots
items: Blockchain-Verified Identity Systems for Cross-Metaverse Compatibility
items: Machine Learning-Based Resource Allocation for Scalable Cloud REST Services
items: Use of AI in Commerce
items: Tactile Feedback Systems for Immersive Blockchain Gaming Ecosystems
items: AI-Driven Avatars in Emotionally Adaptive Virtual Classrooms

Cyberbullying and Academic Performance Correlation
type: item
description: Cyberbullying has emerged as a pervasive challenge in educational settings, with increasing evidence suggesting significant negative correlations with academic performance. This manuscript examines the multifaceted relationship between cyberbullying victimization and academic outcomes among school-aged populations. Through systematic review of peer-reviewed literature, we explore mechanisms linking cyberbullying to reduced academic achievement, including psychological distress, cognitive disruption, school avoidance, and diminished motivation. Findings indicate that cyberbullying victimization correlates with lower grades, reduced engagement, increased absenteeism, and higher dropout rates. Moderating factors including social support, self-esteem, coping mechanisms, and school climate substantially influence the severity of academic impact. Gender and demographic differences in cyberbullying prevalence and academic consequences are examined. The manuscript concludes that comprehensive school-based interventions combining cyberbullying prevention with academic support, coupled with systemic changes to school climate and digital citizenship education, represent essential components of evidence-based responses. Future research must address longitudinal causality, intersectionality of victimization, and effectiveness of emerging intervention technologies.
subject: Original Research Articles
  topic: section
subject: cyberbullying
  topic: keywords
subject: academic performance
  topic: keywords
subject: victimization
  topic: keywords
subject: school engagement
  topic: keywords
subject: digital harassment
  topic: keywords
creator: Narender Panghal
rights: Copyright (c) 2026 World Journal of Future Technologies in Computer Science and Engineering https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-08-01
publication date: 2026-08-01
starting page: (19
ending page: 27)

Challenges and Solutions in Implementing Machine Learning for Fraud Detection in High Volume Transactions
type: item
description: The growth of digital payments has produced transaction volumes that make manual and rule-based fraud detection increasingly untenable, driving widespread adoption of machine learning (ML) methods capable of learning complex patterns from historical data. This paper reviews the principal technical and operational challenges associated with implementing machine learning for fraud detection in high-volume transaction environments and synthesizes the solutions proposed in the academic literature. Using a qualitative, literature-based methodology, the paper draws on foundational and recent peer-reviewed research to examine four interrelated challenges: severe class imbalance between fraudulent and legitimate transactions, concept drift arising from evolving fraudster behavior and customer habits, the requirement for real-time or near-real-time inference at scale, and the limited interpretability of high-performing models in a regulated financial context. For each challenge, the paper reviews established and emerging solutions, including resampling and cost-sensitive learning techniques, adaptive and streaming learning strategies, ensemble and graph-based architectures, and interpretability frameworks. The paper concludes that no single algorithmic solution resolves the fraud detection problem in isolation; rather, robust high-volume fraud detection systems require the integrated combination of imbalance-aware learning, drift-adaptive retraining, scalable real-time architecture, and human-interpretable decision support, situated within a broader operational pipeline involving investigator feedback and delayed ground-truth labeling.
subject: Original Research Articles
  topic: section
subject: machine learning
  topic: keywords
subject: fraud detection
  topic: keywords
subject: class imbalance
  topic: keywords
subject: concept drift
  topic: keywords
subject: real-time systems
  topic: keywords
subject: high-volume transactions
  topic: keywords
creator: Shweta Shorey
rights: Copyright (c) 2026 World Journal of Future Technologies in Computer Science and Engineering https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-08-01
publication date: 2026-08-01
starting page: (13
ending page: 18)

Barriers to Digital Access for Girls in Rural Schools
type: item
description: Digital technologies are increasingly central to teaching, learning, and civic participation, yet access to these tools remains deeply unequal across gender and geography. This paper examines the barriers that restrict digital access for girls attending rural schools, situating the discussion within the broader literature on the gender digital divide. Drawing on institutional reports from UNESCO, UNICEF, the International Telecommunication Union (ITU), the GSMA, and the World Bank, together with peer-reviewed studies published between 2014 and 2025, the paper synthesises evidence on the infrastructural, socio-cultural, economic, safety-related, and institutional obstacles that disproportionately limit rural girls' engagement with digital learning. The study adopts a qualitative, literature-based design, reviewing secondary data from global surveys, government publications, and academic journals. The findings indicate that unreliable electricity and connectivity infrastructure, patriarchal social norms, device and data unaffordability, safety and privacy concerns, low digital literacy among teachers, parents, and students, and uneven policy implementation combine to leave rural girls further behind their male peers and urban counterparts in digital competence. The paper concludes with recommendations addressing infrastructure expansion, gender-responsive teacher training, community sensitisation, and targeted subsidy schemes, arguing that closing the rural gender digital divide is essential to achieving inclusive and equitable education systems.
subject: Original Research Articles
  topic: section
subject: gender digital divide
  topic: keywords
subject: rural education
  topic: keywords
subject: girls' education
  topic: keywords
subject: ICT access
  topic: keywords
subject: digital literacy
  topic: keywords
subject: educational inequality
  topic: keywords
creator: Rakesh Uniyal
rights: Copyright (c) 2026 World Journal of Future Technologies in Computer Science and Engineering https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-08-01
publication date: 2026-08-01
starting page: (7
ending page: 12)

Digital Literacy and Social Capital in Rural Women
type: item
description: The expansion of mobile internet and digital government services into rural areas has created new opportunities for women to build and mobilize social capital, yet persistent gaps in digital literacy continue to constrain how fully rural women can participate in this transformation. This paper examines the relationship between digital literacy and social capital among rural women, drawing on development studies, gender and technology research, and social capital theory to interpret how digital skills shape rural women's access to information, economic opportunity, and community networks. Using a qualitative, literature-based approach, the paper synthesizes classical social capital theory with contemporary empirical research on the gender digital divide, digital literacy interventions, and rural women's technology use in South Asia, China, and other developing-country contexts. The paper argues that digital literacy functions as a critical precondition for the conversion of digital access into meaningful social capital, enabling rural women to extend bonding ties within family and community while also building new bridging ties to markets, institutions, and information networks beyond the village. At the same time, the literature indicates that entrenched sociocultural norms, unequal household bargaining power, and inadequate contextualized training continue to limit the social capital returns to digital access for many rural women. The paper concludes with implications for digital literacy programming and policy design aimed at converting digital inclusion into durable social capital gains for rural women.
subject: Original Research Articles
  topic: section
subject: digital literacy
  topic: keywords
subject: social capital
  topic: keywords
subject: rural women
  topic: keywords
subject: gender digital divide
  topic: keywords
subject: digital inclusion
  topic: keywords
subject: women's empowerment
  topic: keywords
creator: Dr Dhanashri Rajshri Ravindra Jadhav
rights: Copyright (c) 2026 World Journal of Future Technologies in Computer Science and Engineering https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-08-01
publication date: 2026-08-01
starting page: (1
ending page: 6)

AI-Driven Digital Product Passports for Sustainable Textile Supply Chains
type: item
description: The textile industry faces increasing pressure to improve supply chain transparency, environmental sustainability, and compliance with emerging circular economy regulations. Although Digital Product Passports (DPPs) have been recognized as a promising mechanism for capturing product lifecycle information, existing implementations primarily focus on static traceability and regulatory documentation, offering limited capabilities for intelligent sustainability assessment, predictive analytics, and automated decision support. Furthermore, current studies often investigate blockchain, Internet of Things (IoT), or artificial intelligence (AI) as isolated technologies, leaving a significant research gap in developing an integrated framework that combines secure product traceability with AI-driven sustainability intelligence for textile supply chains. To address this limitation, this paper proposes an Artificial Intelligence-Driven Digital Product Passport (AI-DPP) framework that integrates machine learning, blockchain, IoT-enabled data acquisition, and lifecycle sustainability analytics into a unified architecture. The proposed framework generates comprehensive digital passports containing verified information on material composition, manufacturing processes, environmental indicators, transportation history, certifications, and recycling attributes while employing AI models to predict overall sustainability performance, supplier risk, carbon efficiency, water sustainability, and recyclability. Blockchain technology ensures secure, immutable, and transparent management of passport data across all supply chain participants. The framework is evaluated using a simulated dataset comprising approximately 50,000 textile product records developed from publicly available sustainability datasets. Comparative analysis of multiple machine learning algorithms demonstrates that XGBoost achieves the highest predictive performance with an R² score of 0.947, while the blockchain layer main…
subject: Original Research Articles
  topic: section
subject: Digital Product Passport
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Sustainable Textile Supply Chain
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Circular Economy
  topic: keywords
creator: Dr. Sohrab Bharucha
rights: Copyright (c) 2025 World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: (41
ending page: 50)
doi: 10.63345/wjftcse.v1.i4.301

Quantum-Inspired Edge AI Optimization for Neural Prosthetics
type: item
description: Quantum-inspired optimization algorithms have emerged as a compelling alternative to fully quantum approaches, leveraging classical hardware to simulate key quantum features—such as superposition, entanglement analogs, and parameterized variational circuits—to solve high-dimensional, nonconvex optimization problems more efficiently than traditional heuristics. This manuscript presents Quantum-Inspired Edge AI Optimization for Neural Prosthetics (QEAI-O-NP), a comprehensive framework that integrates a Variational Quantum-Inspired Optimizer (VQIO) with a Spiking Neural Network (SNN) decoder, both tailored for on-device execution on resource-constrained edge platforms. Combining a hybrid quantum-classical metaheuristic surrogate of the Quantum Approximate Optimization Algorithm (QAOA) with low-power neuromorphic inference, QEAI-O-NP addresses challenges of adaptive weight tuning, latency, and energy efficiency inherent to real-time electromyographic (EMG) signal decoding for prosthetic control. The framework preprocesses multi-channel EMG via bandpass filtering and windowing, encodes features into spike trains, and performs classification through a three-layer LIF-based SNN. Periodically, the VQIO formulates the current loss landscape as a cost Hamiltonian and executes two-layer QAOA-inspired tensor-network simulations to extract gradient estimates for synaptic weight updates. Deployed on an NVIDIA Jetson Xavier NX, QEAI-O-NP achieves a 15% absolute gain in decoding accuracy (from 80.2% to 95.2%), reduces inference latency by 30% (from 45.8 ms to 32.1 ms), and cuts energy consumption per inference by 25% (from 193 mJ to 145 mJ) compared to state-of-the-art edge AI baselines. An ablation study explores the trade-offs of QAOA depth and update frequency, guiding practical parameter selection.
subject: Original Research Articles
  topic: section
subject: Quantum-Inspired Optimization
  topic: keywords
subject: Edge AI
  topic: keywords
subject: Neural Prosthetics
  topic: keywords
subject: Spiking Neural Networks
  topic: keywords
subject: Quantum Approximate Optimization Algorithm
  topic: keywords
creator: Dr.S.S. Aravinth
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-07-08
publication date: 2026-07-08
starting page: (33
ending page: 41)

Ethical Challenges of AI and Quantum Synergy in Cognitive Augmentation
type: item
description: The convergence of artificial intelligence (AI) and quantum computing within cognitive augmentation paradigms promises revolutionary enhancements in human–machine collaboration, problem solving, and decision making. Yet, alongside these opportunities arise profound ethical challenges spanning data privacy, individual autonomy, societal equity, and the very definition of personhood. This paper presents a comprehensive, mixed-methods investigation into these challenges. First, a systematic literature review of publications from 2010–2025 across AI ethics, quantum computing, and cognitive neuroscience identifies foundational principles and emergent concerns. Second, semi-structured interviews with twelve domain experts (ethicists, AI researchers, quantum engineers) elicit practitioner perspectives on risks and mitigation strategies. Third, we apply three normative ethical frameworks—principlism, Rawlsian justice, and Sen’s capability approach—to synthesize guidelines for responsible innovation. Key findings include: (1) quantum-cracking of classical encryption threatens confidentiality of sensitive cognitive data; (2) barriers to access risk creating a “cognitive inequality” gap; (3) neuroadaptive interfaces risk covert manipulation absent rigorous informed consent; and (4) the blending of machine computation with neural processes challenges notions of personal agency and identity.
subject: Original Research Articles
  topic: section
subject: Ethics
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Quantum Computing
  topic: keywords
subject: Cognitive Augmentation
  topic: keywords
subject: Privacy
  topic: keywords
subject: Autonomy
  topic: keywords
creator: Aditya Malhotra
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-07-08
publication date: 2026-07-08
starting page: (25
ending page: 32)

Intelligent Resource Orchestration in Multi-Cloud Environments
type: item
description: ntelligent resource orchestration in multi-cloud environments represents a paradigm shift from static, rule-based management toward adaptive, AI-driven coordination of computing assets across heterogeneous infrastructures. With organizations increasingly deploying workloads simultaneously on AWS, Azure, Google Cloud, and private clouds to leverage cost advantages, regional compliance, and specialized services, the complexity of achieving optimal performance, reliability, and cost-effectiveness has grown dramatically. Traditional orchestration tools—while automating provisioning, scaling, and failover—typically rely on preconfigured thresholds or manually tuned policies that cannot respond in real time to rapid workload fluctuations or unforeseen events such as network congestion, hardware failures, or shifts in demand patterns. In contrast, intelligent orchestration frameworks employ machine learning techniques—such as reinforcement learning, predictive analytics, and anomaly detection—to continuously learn from operational telemetry (CPU/memory utilization, network latency, cost metrics) and to adjust resource allocations proactively. This dynamic approach not only improves average response times and system throughput but also enhances resilience by rerouting tasks away from degraded or overloaded nodes. Moreover, by forecasting demand trends, intelligent orchestrators can pre-scale resources to minimize “cold-start” penalties, and by integrating cost signals, they can shift non-critical workloads to lower-price instances or regions during off-peak hours, achieving significant savings. This manuscript first surveys the state of the art in multi-cloud orchestration, highlighting key limitations of static methods and summarizing recent advances in AI-driven solutions. Next, we describe a simulation-based evaluation using CloudSim 5.0, modeling three major public cloud providers with 500 virtual machines each and synthetic workloads derived from Google Cluster Data. T…
subject: Original Research Articles
  topic: section
subject: Multi-Cloud
  topic: keywords
subject: Resource Orchestration
  topic: keywords
subject: AI-Driven
  topic: keywords
subject: Workload Distribution
  topic: keywords
subject: Performance Optimization
  topic: keywords
creator: Madhavi K
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-07-09
publication date: 2026-07-09
starting page: (15
ending page: 24)

Quantum Threat Mitigation in Blockchain AI Architectures
type: item
description: Blockchain AI architectures stand at the nexus of distributed consensus and machine-driven intelligence, offering transformative applications across finance, supply chain, healthcare, and beyond. However, the emergence of scalable quantum computers threatens foundational cryptographic primitives—most notably, elliptic-curve and RSA-based schemes—that secure transaction integrity, key exchange, and smart-contract execution. This manuscript presents a comprehensive evaluation of quantum threat mitigation strategies tailored for blockchain AI systems. We examine three distinct approaches: (1) adoption of post-quantum cryptographic algorithms (lattice-based signatures and hash-based KEMs) that resist Shor’s algorithm; (2) integration of hybrid quantum-classical key distribution leveraging quantum key distribution (QKD) combined with classical post-quantum algorithms; and (3) deployment of trusted execution environments (TEEs) to isolate critical key operations from both classical and quantum side-channel attacks. Our mixed-methods methodology includes simulation on a 50-node blockchain AI testbed, processing 240,000 transactions over 24 hours, and statistical analysis of throughput, latency, and key-exchange performance. The results demonstrate that lattice-based schemes maintain 128-bit quantum security with a moderate performance overhead (~27 % throughput reduction, ~20 % increased latency), while hybrid QKD delivers information-theoretic security but incurs significant latency penalties (115 % higher than baseline). TEEs effectively mitigate side-channels with negligible additional overhead. We conclude by offering practical guidelines for phased deployment—beginning with immediate migration to lattice-based cryptography, followed by pilot QKD integration for high-security use cases—and outline future research directions.
subject: Original Research Articles
  topic: section
subject: Quantum Threat Mitigation
  topic: keywords
subject: Post-Quantum Cryptography
  topic: keywords
subject: Blockchain AI
  topic: keywords
subject: Hybrid QKD
  topic: keywords
subject: Lattice-Based Signatures
  topic: keywords
creator: Shweta Shorey
rights: Copyright (c) 2026 World Journal of Future Technologies in Computer Science and Engineering https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-07-04
publication date: 2026-07-04
starting page: (8
ending page: 14)

Genetic AI Algorithms for Multi-Objective Optimization in Quantum Systems
type: item
description: Multi‐objective optimization in quantum systems poses formidable challenges stemming from the high dimensionality, complex constraints, and conflicting performance metrics inherent to quantum control tasks and circuit synthesis. This study presents a comprehensive exploration of genetic AI algorithms—evolutionary strategies inspired by natural selection—tailored specifically for multi‐objective optimization in quantum computing contexts. We propose a hybrid framework that synergistically combines the elitist, Pareto‐based selection mechanics of NSGA-II with quantum‐inspired crossover and mutation operators designed to preserve entanglement structures and exploit hardware‐specific gate sets. In this framework, candidate solutions encoding circuit topologies or control‐pulse parameters undergo iterative evolution, guided by fitness vectors that balance gate fidelity, circuit depth, and resource overhead. To benchmark performance, we apply the algorithm to canonical quantum computing problems: Fourier transform circuits, QAOA ansätze for MaxCut instances, and random Clifford+T circuits. Through extensive simulation on noise-modelled quantum devices, we evaluate Pareto front coverage, convergence speed, diversity maintenance, and robustness under decoherence. Our results demonstrate that the proposed genetic AI algorithm outperforms classical single‐objective genetic methods and established multi‐objective evolutionary algorithms (e.g., SPEA2) by achieving broader Pareto fronts and faster convergence rates, while maintaining solution diversity. Furthermore, noise-resilience experiments show graceful degradation, indicating practical applicability on near-term quantum hardware. These findings substantiate the potential of evolutionary computation paradigms—augmented with quantum‐inspired operators—to address the intricate trade-off surfaces that define NISQ-era optimization problems, paving the way toward automated quantum circuit design and control parameter tuning for …
subject: Original Research Articles
  topic: section
subject: Quantum Optimization
  topic: keywords
subject: Multi‐Objective Genetic Algorithms
  topic: keywords
subject: NSGA‐II
  topic: keywords
subject: Quantum Circuit Synthesis
  topic: keywords
subject: Pareto Optimization
  topic: keywords
creator: Dr Chethan H
rights: Copyright (c) 2026 World Journal of Future Technologies in Computer Science and Engineering https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-07-03
publication date: 2026-07-03
starting page: (1
ending page: 7)

Digital Neuromorphics: A Bridge Between Quantum AI and Human Brain Modeling
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Digital Neuromorphics, Quantum AI, Human Brain Modeling, Hybrid Architectures, Energy-Efficient Computing
  topic: keywords
creator: Er. Shubham Jain
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-12
publication date: 2026-06-12
starting page: (52
ending page: 63)

Bio-Inspired Algorithms for Adaptive Quantum Machine Learning
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Adaptive Quantum Machine Learning, Particle Swarm Optimization, Parameterized Quantum Circuits, NISQ, Bio-Inspired Algorithms
  topic: keywords
creator: Er. Raghav Agarwal
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-12
publication date: 2026-06-12
starting page: (40
ending page: 51)

AI-Powered Quantum Key Distribution Protocols for IoT Networks
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: AI-Powered QKD, IoT Security, Quantum Bit Error Rate, Machine Learning, Adaptive Parameter Tuning
  topic: keywords
creator: Dr Munish Kumar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-09
publication date: 2026-06-09
starting page: (27
ending page: 39)

Quantum Blockchain for Verifiable Genome Mapping Systems
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Quantum-Resistant Blockchain, Genome Mapping, Verifiable Ledger, Quantum Key Distribution, Data Integrity, Decentralized Genomics
  topic: keywords
creator: Dr. A.H Khan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-09
publication date: 2026-06-09
starting page: (14
ending page: 26)

Hybrid AI-Quantum Models for Real-Time Molecular Structure Prediction
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Hybrid AI–Quantum, Variational Quantum Eigensolver, Deep Neural Network, Molecular Structure Prediction, Real-Time Computation
  topic: keywords
creator: Prof. (Dr) MSR Prasad
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-09
publication date: 2026-06-09
starting page: (1
ending page: 13)

Federated Data Processing Architectures for Secure Cross-Organization Analytics
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Federated Data Processing , Cross-Organization Analytics , Federated Learning , Privacy-Preserving Computing , Secure Multi-Party Computation , Differential Privacy
  topic: keywords
creator: Sarvesh Kumar Gupta
rights: 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
date: 2026-05-12
publication date: 2026-05-12
starting page: (60
ending page: 68)
doi: 10.63345//wjftcse.v2.i2.201

Multisensory Feedback Systems in XR Controlled by AI Emotion Recognition
type: item
description: 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 chall…
subject: Original Research Articles
  topic: section
subject: Multisensory Feedback, Extended Reality, Emotion Recognition, Affective Computing, Haptic Stimulation, Adaptive Systems
  topic: keywords
creator: Er. Raghav Agarwal
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-05
publication date: 2026-05-05
starting page: (48
ending page: 59)

Blockchain-Based Licensing of Metaverse-Generated Digital Assets
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Blockchain, Metaverse, Digital Assets, Licensing, Non-Fungible Token, Smart Contract
  topic: keywords
creator: Dr Munish Kumar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-05
publication date: 2026-05-05
starting page: (37
ending page: 47)

Spatial Computing in Next-Gen HCI for Smart Urban Planning
type: item
description: 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, a…
subject: Original Research Articles
  topic: section
subject: Spatial Computing, Augmented Reality, Smart Cities, Urban Planning, Human–Computer Interaction
  topic: keywords
creator: Dr T. Aswini
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-02
publication date: 2026-05-02
starting page: (24
ending page: 35)

Emotion-Centric AI Agents in Multi-User Virtual Reality Platforms
type: item
description: 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‑aw…
subject: Original Research Articles
  topic: section
subject: Emotion-Centric AI Agents, Multi-User Virtual Reality, Affective Computing, Social Presence, User Engagement
  topic: keywords
creator: Er. Akshit Kohli
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-02
publication date: 2026-05-02
starting page: (13
ending page: 23)

Brain-Computer Interfaces for Neurofeedback in VR Mental Health Platforms
type: item
description: 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/Sim…
subject: Original Research Articles
  topic: section
subject: BCI, Neurofeedback ,VR, Mental Health
  topic: keywords
creator: Drishti Chaudhary
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-02
publication date: 2026-05-02
starting page: (1
ending page: 12)

AI-Enhanced Real-Time Translation Systems in Mixed-Reality Spaces
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Mixed Reality Translation
  topic: keywords
subject: Neural Machine Translation
  topic: keywords
subject: Real Time AI
  topic: keywords
subject: User Study
  topic: keywords
subject: Latency
  topic: keywords
subject: Word Error Rate
  topic: keywords
creator: Dr. Tomás Alvarez
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-03
publication date: 2026-04-03
starting page: (40
ending page: 50)
doi: 10.63345/

Digital Twin Replication for Cross-Reality Simulation Engines
type: item
description: 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).
subject: Original Research Articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: Cross-Reality
  topic: keywords
subject: Simulation Engines
  topic: keywords
subject: Synchronization
  topic: keywords
subject: Cloud-Edge Orchestration
  topic: keywords
creator: Ji-Hoon Park
rights: 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
date: 2026-04-06
publication date: 2026-04-06
starting page: (30
ending page: 39)

Decentralized Metaverse Governance via AI-Supported Voting Models
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Decentralized Governance
  topic: keywords
subject: Metaverse
  topic: keywords
subject: Blockchain
  topic: keywords
subject: AI-Supported Voting
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: User Participation
  topic: keywords
creator: Sneha Iyer
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-04
publication date: 2026-04-04
starting page: (21
ending page: 29)

AR-Powered Manufacturing Assistance with AI Safety Co-Pilots
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Augmented Reality
  topic: keywords
subject: AI Co Pilot
  topic: keywords
subject: Manufacturing Assistance
  topic: keywords
subject: Workplace Safety
  topic: keywords
subject: Human–Machine Collaboration
  topic: keywords
creator: Dr. Peter Novak
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-03
publication date: 2026-04-03
starting page: (11
ending page: 20)

Blockchain-Verified Identity Systems for Cross-Metaverse Compatibility
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Blockchain Verified Identity
  topic: keywords
subject: Cross Metaverse Interoperability
  topic: keywords
subject: Decentralized Identifiers
  topic: keywords
subject: Verifiable Credentials
  topic: keywords
subject: Self Sovereign Identity
  topic: keywords
creator: Shilpa Rani
rights: 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
date: 2026-04-02
publication date: 2026-04-02
starting page: (1
ending page: 10)

Machine Learning-Based Resource Allocation for Scalable Cloud REST Services
type: item
description: 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 m…
subject: Original Research Articles
  topic: section
subject: Machine Learning, Cloud Computing, Resource Allocation, REST Services, Predictive Scaling, Cloud Infrastructure Optimization, Reinforcement Learning, API Performance Optimization
  topic: keywords
creator: Ishu Anand Jaiswal
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: (43
ending page: 49)
doi: 10.63345/wjftcse.v1.i3.101

Use of AI in Commerce
type: item
description: 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 mo…
subject: Original Research Articles
  topic: section
subject: Artificial Intelligence, E-commerce, Machine Learning, Consumer Behavior, Automation, Data Analytics
  topic: keywords
creator: Dr. Ankur Aggarwal
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-07
publication date: 2026-03-07
starting page: (52
ending page: 61)
doi: 10.63345/wjftcse.v2.i1.1

Tactile Feedback Systems for Immersive Blockchain Gaming Ecosystems
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Tactile Feedback, Haptics, Blockchain Gaming, Immersive Systems, User Engagement
  topic: keywords
creator: Dr. Sandeep Kumar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-04
publication date: 2026-03-04
starting page: (42
ending page: 51)

AI-Driven Avatars in Emotionally Adaptive Virtual Classrooms
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: AI-Driven Avatars, Affective Computing, Virtual Classrooms, Emotional Adaptation, Educational Technology
  topic: keywords
creator: Dr. Lalit Kumar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-04
publication date: 2026-03-04
starting page: (33
ending page: 41)