World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) U.S. ISSN: 3070-6203
type: channel
description: Title: World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) Publisher: Wissira Research Lab 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: +91 75359 16555
publisher: Wissira Research Lab
language: en
publication name: World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) U.S. ISSN: 3070-6203
issn: 3070-6203
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
items: Personalized VR Learning Environments Using Cognitive AI Agents
items: Gesture-Based Control Interfaces for AI-Powered Smart Environments
items: Neural Interfaces for Direct AI-Avatar Collaboration in the Metaverse
items: AI-Powered Governance for Ethics and Compliance Monitoring Systems
items: Secure Execution of AI Pipelines on Confidential Cloud Infrastructure
items: Adversarial Machine Learning Defense in IoT Ecosystems
items: Cyber-Resilient Protocols for Blockchain Nodes in Decentralized Webs
items: From Correlation to Causation: Integrating Causal Reasoning Frameworks into Deep Learning Models
items: Privacy-Aware AI in Financial Fraud Detection Using Federated Data Lakes
items: Autonomous Cyber Incident Response Using Cognitive Security Agents
items: Trust Metrics in AI Models for Secure Governmental IT Systems
items: Digital Twin-Based Cyber Risk Forecasting in Smart Cities
items: AI-Based Deception Techniques in Cyberwarfare Defense Systems
items: Zero Trust Architectures for Edge-Native AI Inference Systems
items: AI-Powered Threat Modeling for API Gateways in Microservices
items: Blockchain-Enabled Anomaly Detection for Secure CI/CD Pipelines

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)
doi: 10.63345/

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)
doi: 10.63345/

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)
doi: 10.63345/

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)
doi: 10.63345/

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)
doi: 10.63345/

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)
doi: 10.63345/

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)
doi: 10.63345/

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)
doi: 10.63345/

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)
doi: 10.63345/

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)

Personalized VR Learning Environments Using Cognitive AI Agents
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Personalized VR Learning, Cognitive AI Agents, Adaptive Learning, Virtual Reality, Educational Technology
  topic: keywords
creator: Er. Shubham Jain
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: (22
ending page: 32)

Gesture-Based Control Interfaces for AI-Powered Smart Environments
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Gesture Recognition, Smart Environments, AI, HMM, CNN, LSTM, Human–Machine Interaction
  topic: keywords
creator: Er. Raghav Agarwal
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: (12
ending page: 21)

Neural Interfaces for Direct AI-Avatar Collaboration in the Metaverse
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Neural Interfaces, Brain–Computer Interface, Metaverse, AI Avatars, Human–AI Collaboration
  topic: keywords
creator: Dr Munish Kumar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-02
publication date: 2026-03-02
starting page: (01
ending page: 11)

AI-Powered Governance for Ethics and Compliance Monitoring Systems
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: AI Governance, Ethics Monitoring, Compliance Systems, Algorithmic Accountability, Regulatory Technology
  topic: keywords
creator: Lekha Menon
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-02
publication date: 2026-02-02
starting page: (36
ending page: 43)

Secure Execution of AI Pipelines on Confidential Cloud Infrastructure
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Confidential Computing, Trusted Execution Environments, Intel SGX, AMD SEV, Secure AI Pipelines, Cloud Security
  topic: keywords
creator: Rafael Costa
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-02
publication date: 2026-02-02
starting page: (25
ending page: 35)

Adversarial Machine Learning Defense in IoT Ecosystems
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Adversarial Machine Learning, IoT Security, Adversarial Training, Randomized Smoothing, Feature Squeezing, Edge Computing
  topic: keywords
creator: Siddharth Verma
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: (16
ending page: 24)

Cyber-Resilient Protocols for Blockchain Nodes in Decentralized Webs
type: item
description: 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 %.
subject: Original Research Articles
  topic: section
subject: Blockchain Resilience, Decentralized Web, Consensus Security, Intrusion Detection, Peer Validation, DDoS Mitigation
  topic: keywords
creator: Priya Nair
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: (7
ending page: 15)
doi: 10.63345/

From Correlation to Causation: Integrating Causal Reasoning Frameworks into Deep Learning Models
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Causal inference, deep learning, correlation vs causation, structural causal models, counterfactuals, explainable AI, robust learning
  topic: keywords
creator: Mr. Rishi Mishra
creator: Dr. Seema Sharma
rights: Copyright (c) 2025 World Journal of Future Technologies in Computer Science and Engineering https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: (1
ending page: 6)
doi: 10.63345/wjftcse.v2.i1.202

Privacy-Aware AI in Financial Fraud Detection Using Federated Data Lakes
type: item
description: 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.
subject: Original Research Articles
  topic: section
subject: Privacy-Aware AI, Federated Data Lakes, Financial Fraud Detection, Federated Learning, Secure Aggregation
  topic: keywords
creator: Maya Raj
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: 2026-01-13
publication date: 2026-01-13
starting page: (31
ending page: 39)
doi: 10.63345/

Autonomous Cyber Incident Response Using Cognitive Security Agents
type: item
description: 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.
subject: Original Research Articles
  topic: section
creator: Lekha Menon
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: 2026-01-08
publication date: 2026-01-08
starting page: (24
ending page: 30)
doi: 10.63345/

Trust Metrics in AI Models for Secure Governmental IT Systems
type: item
description: Ensuring the trustworthiness of artificial intelligence (AI) models deployed within governmental IT infrastructures is an imperative mission-critical concern. As governments worldwide embrace AI-driven automation and decision support for applications ranging from citizen services to national security, they confront unique challenges: adversarial threats designed to deceive or corrupt models, the potential for biased outcomes that undermine fairness, opaque decision processes that erode stakeholder confidence, and the stringent regulatory and ethical obligations inherent in public-sector operations. This enhanced abstract delves into each dimension of trust—technical robustness, predictive accuracy, model explainability, fairness and non‑discrimination, data privacy and security, and governance oversight—highlighting how they interplay to form a holistic trust posture. We outline a composite Trust Score methodology that normalizes and weights individual sub‑metrics drawn from adversarial robustness testing, accuracy benchmarks, explainability indices (e.g., SHAP attributions), fairness audits (e.g., disparate impact ratios), privacy impact assessments, and compliance checklists mapped to governmental regulations. We discuss the methodological framework used to simulate real-world governmental IT deployments—including the generation of synthetic network telemetry, adversarial attack scenarios, data drift episodes, and policy-violation assessments—and present key findings: the Trust Score’s responsiveness to security breaches, its ability to flag fairness anomalies, and its sensitivity to governance lapses.
subject: Original Research Articles
  topic: section
subject: Trust Metrics, AI Models, Government IT Security, Explainability, Robustness
  topic: keywords
creator: Rafael Costa
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: 2026-01-06
publication date: 2026-01-06
starting page: (16
ending page: 23)
doi: 10.63345/

Digital Twin-Based Cyber Risk Forecasting in Smart Cities
type: item
description: Cyberwarfare has evolved into a sophisticated domain where attackers exploit advanced persistent threats, zero-day exploits, and social engineering to compromise critical systems. Traditional reactive defenses—firewalls, signature-based intrusion detection systems, and static honeypots—are increasingly inadequate against adaptive adversaries who reconnoiter, probe, and pivot within target networks. AI-based deception techniques offer a proactive layer of defense by dynamically generating decoys, obfuscating real assets, and engaging adversaries in controlled environments, thereby gathering actionable threat intelligence and disrupting attack chains. This manuscript presents a comprehensive framework for the design, implementation, and evaluation of an AI-driven deception platform tailored for cyberwarfare defense. We begin by delineating the theoretical underpinnings of deception in cyber defense and surveying existing approaches to honeypots, honeytokens, and dynamic decoys. Building on this foundation, we describe our methodological approach: a modular system architecture comprising a Decoy Generator that fabricates realistic service instances; a Behavior Analyzer that employs machine learning models to classify traffic and predict adversary intent; and an Orchestration Engine that adapts deception strategies in real time. We deploy the platform within a simulated enterprise network and conduct three representative attack scenarios—reconnaissance, credential brute-forcing, and lateral movement—under both static and AI-based configurations. Our results demonstrate a significant increase in detection rates (from 72.5% to 94.2%), extended adversary engagement times (by 133%), and richer intelligence collection, all achieved with acceptable computational overhead. We conclude with a critical analysis of operational considerations, including model maintenance, integration with existing security infrastructures, and adversary-aware countermeasures, and outline future re…
subject: Original Research Articles
  topic: section
subject: AI-Based Deception, Cyberwarfare Defense, Honeypots, Adversary Engagement, Adaptive Decoys
  topic: keywords
creator: Priya Nair
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: 2026-01-03
publication date: 2026-01-03
starting page: (8
ending page: 15)
doi: 10.63345/

AI-Based Deception Techniques in Cyberwarfare Defense Systems
type: item
description: Cyberwarfare has evolved into a sophisticated domain where attackers exploit advanced persistent threats, zero-day exploits, and social engineering to compromise critical systems. Traditional reactive defenses—firewalls, signature-based intrusion detection systems, and static honeypots—are increasingly inadequate against adaptive adversaries who reconnoiter, probe, and pivot within target networks. AI-based deception techniques offer a proactive layer of defense by dynamically generating decoys, obfuscating real assets, and engaging adversaries in controlled environments, thereby gathering actionable threat intelligence and disrupting attack chains. This manuscript presents a comprehensive framework for the design, implementation, and evaluation of an AI-driven deception platform tailored for cyberwarfare defense. We begin by delineating the theoretical underpinnings of deception in cyber defense and surveying existing approaches to honeypots, honeytokens, and dynamic decoys. Building on this foundation, we describe our methodological approach: a modular system architecture comprising a Decoy Generator that fabricates realistic service instances; a Behavior Analyzer that employs machine learning models to classify traffic and predict adversary intent; and an Orchestration Engine that adapts deception strategies in real time. We deploy the platform within a simulated enterprise network and conduct three representative attack scenarios—reconnaissance, credential brute-forcing, and lateral movement—under both static and AI-based configurations. Our results demonstrate a significant increase in detection rates (from 72.5% to 94.2%), extended adversary engagement times (by 133%), and richer intelligence collection, all achieved with acceptable computational overhead. We conclude with a critical analysis of operational considerations, including model maintenance, integration with existing security infrastructures, and adversary-aware countermeasures, and outline future re…
subject: Original Research Articles
  topic: section
subject: AI-Based Deception, Cyberwarfare Defense, Honeypots, Adversary Engagement, Adaptive Decoys
  topic: keywords
creator: Siddharth Verma
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: 2026-01-20
publication date: 2026-01-20
starting page: (1
ending page: 7)
doi: 10.63345/

Zero Trust Architectures for Edge-Native AI Inference Systems
type: item
description: Edge‑Native AI Inference Systems (ENAIS) are rapidly proliferating across domains such as autonomous vehicles, smart manufacturing, healthcare diagnostics, and critical infrastructure monitoring. By bringing AI inference closer to data sources, ENAIS dramatically reduce latency, conserve bandwidth, and enable real‑time decision‑making. However, the shift from centralized cloud environments to widely distributed, resource‑constrained edge nodes introduces unique security challenges: traditional perimeter defenses become ineffective, attack surfaces multiply, and heterogeneity complicates policy enforcement. To address these concerns, this manuscript presents a comprehensive Zero Trust Architecture (ZTA) tailored for ENAIS. Building on the principle of “never trust, always verify,” our design integrates three core components: identity‑centric access controls, micro‑segmentation of inference pipelines, and continuous telemetry‑driven policy adaptation. We detail the architectural blueprint, describe its implementation within a simulation framework, and conduct a rigorous evaluation under realistic threat scenarios. Statistical analysis of the simulation data—covering metrics such as unauthorized access prevention, inference latency, and resource overhead—reveals that the proposed ZTA blocks over 98% of unauthorized actions while incurring only a modest 16.6% latency penalty and minimal CPU and network overhead. These results demonstrate that ZTA can substantially elevate the security posture of ENAIS without compromising real‑time performance requirements. We conclude with a discussion of deployment considerations, potential integration with federated learning, and directions for future work in securing next‑generation edge AI.
subject: Original Research Articles
  topic: section
subject: Zero Trust, Edge Computing, AI Inference, Micro Segmentation, Continuous Authentication
  topic: keywords
creator: Siddharth Verma
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-09
publication date: 2025-12-09
starting page: (32
ending page: 40)
doi: 10.63345/

AI-Powered Threat Modeling for API Gateways in Microservices
type: item
description: In the rapidly evolving landscape of microservice architectures, API gateways occupy a pivotal role by acting as the primary ingress point for client requests, performing critical functions such as routing, protocol translation, policy enforcement, authentication, rate limiting, and observability. These gateways, however, also represent a concentrated attack surface and have become prime targets for adversaries seeking to exploit misconfigurations, logical flaws, and emergent vulnerabilities. Traditional threat modeling approaches—centered on manual decomposition of system components, application of taxonomies like STRIDE, and expert-driven mitigation planning—are time‑consuming, labor‑intensive, and struggle to keep pace with the dynamism of large‑scale microservice deployments. To address these challenges, we present an AI‑powered threat modeling framework specifically tailored for API gateways within microservices environments. Our approach integrates a three‑stage pipeline: automated architecture ingestion to construct detailed system graphs; generative AI to propose candidate threats and attack scenarios based on engineered STRIDE prompts; and predictive anomaly detection leveraging machine‑learning classifiers trained on historical gateway logs. We evaluated our framework across fifty real‑world deployments spanning finance, healthcare, and e‑commerce sectors. The results demonstrate a precision of 0.87 and recall of 0.82 against expert‑verified threat models, alongside a 45% reduction in analysis time (from 22 hours down to 12 minutes per model). Notably, the system identified eighteen previously unrecognized tampering and information‑disclosure scenarios, subsequently validated by security engineers.
subject: Original Research Articles
  topic: section
subject: AI Powered Threat Modeling, API Gateway Security, Microservices, Automated Vulnerability Analysis, Generative AI
  topic: keywords
creator: Meera Iyer
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-06
publication date: 2025-12-06
starting page: (25
ending page: 31)
doi: 10.63345/

Blockchain-Enabled Anomaly Detection for Secure CI/CD Pipelines
type: item
description: Blockchain technology has emerged as a transformative force in securing distributed systems, offering tamper‑evident, decentralized ledgers that ensure data integrity and transparency. As software development organizations increasingly adopt Continuous Integration and Continuous Deployment (CI/CD) pipelines to accelerate release cycles, they concurrently expose themselves to sophisticated threats such as supply‑chain attacks, insider tampering, and configuration drifts that traditional security measures struggle to detect in real time. This manuscript presents ChainSec‑CI, a novel framework that marries the immutability guarantees of a permissioned blockchain—specifically Hyperledger Fabric—with AI‑driven anomaly detection to secure CI/CD pipelines comprehensively. Within ChainSec‑CI, every critical pipeline event—ranging from source code commits and build artifacts to test executions and deployment actions—is recorded on‑chain via lightweight smart contracts, creating a verifiable, append‑only audit trail. To address the high‑volume, heterogeneous nature of pipeline metadata, we extract key features (e.g., stage durations, failure frequencies, configuration hash changes, and sequence anomalies) and feed these into an unsupervised isolation forest model, enabling the system to learn “normal” pipeline behavior without requiring labeled attack data. We developed a Jenkins–Hyperledger Fabric integration prototype and evaluated it using 10,000 synthetic pipeline runs containing both benign operations and injected malicious scenarios such as unauthorized config modifications, abnormally prolonged build or test steps, and out‑of‑order stage executions.
subject: Original Research Articles
  topic: section
subject: CI/CD Pipelines, Blockchain, Anomaly Detection, Hyperledger Fabric, Unsupervised Learning
  topic: keywords
creator: Ravi Sharma
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-05
publication date: 2025-12-05
starting page: (17
ending page: 24)
doi: 10.63345/