Peer-Reviewed Journal of Computer Science (PRJCS)
The Peer-Reviewed Journal of Computer Science (PRJCS) is a premier academic outlet dedicated to advancing knowledge and research in computer science and its rapidly evolving subfields. PRJCS creates a dynamic space where researchers, engineers, technologists, and practitioners can share groundbreaking ideas, theoretical advancements, and practical innovations shaping the digital world.
Small Language Models for On-Device and Private Intelligence
While the public discussion of language models has been dominated by ever-larger frontier systems, a parallel research line is delivering capable models in the one-to-eight billion parameter regime that run on phones, laptops, and embedded devices. Models such as Microsoft Phi, Google Gemma, Meta Llama 3.2, Apple OpenELM, and Alibaba Qwen-1.5B demonstrate that careful data curation, knowledge distillation, and aggressive post-training quantisation can produce on-device models competitive with much larger ones on common reasoning and instruction-following benchmarks. This paper surveys the small-language-model (SLM) landscape, the algorithmic techniques that make it possible distillation, pruning, quantisation, and architectural innovation and the use cases, deployment patterns, and open challenges of running language models privately on user devices.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Neuromorphic Computing for Energy-Efficient Artificial Intelligence
Neuromorphic computing draws inspiration from the structure and dynamics of biological neural systems to deliver radically more energy-efficient artificial intelligence than conventional von-Neumann hardware. By representing information as sparse, asynchronous spikes and co-locating memory with computation, neuromorphic systems achieve orders-of-magnitude reductions in power for specific workloads such as event-based vision, sensor fusion, and always-on inference. This paper reviews the principles of spiking neural networks (SNNs), surveys major hardware platforms TrueNorth, Loihi 1 and 2, SpiNNaker 1 and 2, BrainScaleS, and Akida and analyses learning rules ranging from biologically plausible spike-timing-dependent plasticity to surrogate-gradient back-propagation. Applications, software ecosystems, evaluation benchmarks, and persistent challenges are discussed.
Post-Quantum Cryptography: Standardisation, Algorithms, and Deployment Migration
The prospect of cryptographically relevant quantum computers threatens public-key primitives underpinning modern digital infrastructure, including RSA, Diffie-Hellman, and elliptic-curve cryptography. Post-quantum cryptography (PQC) responds with algorithms believed to be hard for both classical and quantum adversaries. In August 2024, the U.S. National Institute of Standards and Technology (NIST) finalised the first three PQC standards: FIPS 203 (ML-KEM, based on Kyber), FIPS 204 (ML-DSA, based on Dilithium), and FIPS 205 (SLH-DSA, based on SPHINCS+). This paper surveys the algorithmic families behind these standards, the harvest-now-decrypt-later threat model, hybrid migration strategies, performance trade-offs, and the engineering challenges of transitioning the global cryptographic stack. We examine deployment progress in TLS, SSH, X.509, and constrained devices, and outline open research questions on side-channel security, falcon-style signatures, and the long-term replacement schedule for classical asymmetric cryptography.
Retrieval-Augmented Generation for Knowledge-Intensive Language Applications
Retrieval-augmented generation (RAG) couples a parametric language model with a non-parametric retrieval system, allowing factual grounding to be supplied at inference time rather than baked into model weights. Since the original RAG formulation in 2020, the technique has become the dominant production pattern for question answering, enterprise search, and knowledge assistants. This paper provides a structured survey of RAG: its theoretical motivation, core architectures, advances in retrieval (dense, sparse, hybrid), reranking, query rewriting, and graph-augmented variants. We analyse evaluation methodologies, deployment patterns, and open challenges including factual faithfulness, multi-hop reasoning, and long-context interaction. We argue that RAG is best understood as a system design pattern rather than a single algorithm, and that engineering decisions in chunking, indexing, and orchestration often dominate model-side choices.
Agentic Artificial Intelligence: Autonomous Multi-Agent Workflows and Orchestration
Agentic artificial intelligence (AI) has emerged as a defining paradigm of 2024-2026, in which large language models (LLMs) are coupled with planning, memory, and tool-use mechanisms to act autonomously over extended horizons. This paper presents a structured analysis of agentic AI, covering its conceptual foundations, architectural building blocks, multi-agent orchestration strategies, evaluation benchmarks, and unresolved engineering challenges. We examine prominent frameworks including ReAct, Reflexion, Toolformer, AutoGen, MetaGPT, and Voyager, and analyse how reasoning-acting loops, hierarchical planning, and inter-agent communication enable progress on benchmarks such as GAIA, WebArena, and SWE-bench. The paper further discusses safety, alignment, cost, and reliability concerns that arise when LLM-based agents operate against real tools and external environments. We argue that progress in agentic AI is constrained less by raw model capability and more by orchestration design, evaluation rigor, and the engineering discipline applied to long-horizon execution.
Quantum Computing: Current Advances And Near-Term Applications
Quantum computing has transitioned from a theoretical curiosity to an emerging technological paradigm with demonstrable computational advantages in specific problem domains. This paper presents a comprehensive review of current advances in quantum computing hardware, software, and near-term applications within the Noisy Intermediate-Scale Quantum (NISQ) era. We examine the architectural evolution of leading quantum platforms, including superconducting, trapped-ion, and photonic systems, analyzing their qubit counts, gate fidelities, and coherence times. The paper evaluates key quantum algorithms Shor's factoring algorithm [1], Grover's search algorithm [2], the Variational Quantum Eigensolver (VQE) [3], and the Quantum Approximate Optimization Algorithm (QAOA) [4] in the context of their practical applicability to real-world problems. We further discuss the milestone of quantum computational advantage demonstrated by Google's Sycamore processor [5] and IBM's ambitious hardware roadmap toward fault-tolerant quantum computing [6]. Our analysis reveals that while universal fault-tolerant quantum computing remains a long-term objective, NISQ-era devices are already yielding valuable results in quantum chemistry simulation, combinatorial optimization, and quantum machine learning. We conclude by identifying the critical challenges—error correction, scalability, and algorithm design that must be addressed to realize the full potential of quantum computing.
Edge Computing And Internet Of Things Security Frameworks: A Comprehensive Survey Of Architectures, Threats, And Countermeasures
The rapid proliferation of Internet of Things (IoT) devices has fundamentally transformed the computing landscape, generating unprecedented volumes of data at the network periphery. Edge computing has emerged as a critical paradigm to address the latency, bandwidth, and privacy limitations inherent in traditional cloud-centric IoT architectures. However, the convergence of edge computing and IoT introduces a complex and expanded attack surface that demands rigorous security frameworks. This paper presents a comprehensive survey of edge computing architectures for IoT security, encompassing Multi-access Edge Computing (MEC), fog computing, and cloudlet-based paradigms. We systematically analyze the threat landscape across all IoT layers—device, network, edge/fog, and application—and evaluate existing security frameworks addressing authentication, encryption, and intrusion detection at the edge. A comparative analysis of machine learning-based, deep learning-based, federated, and hybrid intrusion detection systems deployed on edge infrastructure is presented, demonstrating that hybrid ensemble approaches achieve superior detection performance with F1-scores exceeding 95%. We identify critical research gaps in lightweight cryptographic protocols, privacy-preserving computation, and standardized security benchmarks for resource-constrained edge environments. The findings provide actionable guidance for researchers and practitioners designing secure edge-IoT ecosystems.
Cybersecurity Threats And Defenses In The Generative AI Era
The rapid proliferation of generative artificial intelligence (GenAI) technologies, including large language models (LLMs) and generative adversarial networks (GANs), has fundamentally transformed the cybersecurity landscape by simultaneously empowering sophisticated attack vectors and enabling advanced defensive mechanisms. This paper presents a comprehensive analysis of emerging cybersecurity threats catalyzed by generative AI, encompassing AI-generated phishing campaigns, deepfake-based social engineering, automated malware generation, and adversarial exploitation techniques. Concurrently, it examines AI-enhanced defense strategies, including transformer-based threat detection, adversarial training for model robustness, and neural network-driven intrusion detection systems. Through a systematic taxonomy of AI-powered threats and a comparative evaluation of traditional versus AI-augmented defense mechanisms, this study quantifies the paradigm shift in attack sophistication and defense efficacy. Findings reveal that AI-enhanced defenses achieve detection rate improvements ranging from 22.4% to 33.4% over traditional methods across phishing, malware, intrusion, and deepfake detection domains. The paper further discusses regulatory frameworks, ethical considerations, and future research directions essential for maintaining cybersecurity resilience in an AI-driven threat environment.
Autonomous Vehicle Perception: Deep Learning Challenges and Solutions
Autonomous vehicles (AVs) depend on robust perception systems to interpret complex driving environments in real time. Deep learning has emerged as the dominant paradigm for AV perception, enabling breakthroughs in object detection, semantic segmentation, and sensor fusion. However, significant challenges persist, including adverse weather degradation, domain shift across geographic regions, computational constraints for real-time inference, and the long-tail distribution of rare but safety-critical scenarios. This article presents a comprehensive survey of deep learning approaches for autonomous vehicle perception, examining architectures for camera-based, LiDAR-based, and multi-modal fusion systems. We systematically analyze benchmark datasets including KITTI, nuScenes, and Waymo Open, evaluate state-of-the-art models across detection and segmentation tasks, and identify persistent gaps between research performance and deployment requirements. Our analysis reveals that while transformer-based architectures and self-supervised pre-training have substantially improved perception accuracy, challenges in real-time multi-sensor fusion, corner-case handling, and certification for safety-critical deployment remain open research problems requiring interdisciplinary solutions.
Blockchain-Based Digital Identity Management And Verification Systems
Digital identity management remains one of the most pressing challenges in the modern information economy. Traditional centralized identity systems suffer from single points of failure, pervasive data breaches, and an inherent lack of user control over personal information. This paper presents a comprehensive survey of blockchain-based digital identity management and verification systems, examining the architectural paradigms that underpin self-sovereign identity (SSI), federated identity, and decentralized identifier (DID) frameworks. We compare leading blockchain platforms—including Ethereum, Hyperledger Fabric, Solana, Bitcoin, and Polkadot—on critical metrics such as transaction throughput, consensus mechanisms, and smart contract capabilities. Through systematic analysis of real-world deployments in government, healthcare, and financial services, we evaluate the practical viability of decentralized identity solutions. Our comparative assessment reveals that self-sovereign models outperform centralized and federated approaches on security, privacy, and user control dimensions, while facing trade-offs in scalability and interoperability. The paper further identifies open challenges including regulatory alignment, key management usability, and cross-chain identity portability, proposing a research agenda for advancing blockchain-based identity toward mainstream adoption.
The Peer-Reviewed Journal of Computer Science (PRJCS) is pleased to present its forthcoming issue, which brings together a diverse collection of scholarly contributions reflecting the rapid evolution of contemporary computing research. This issue highlights significant advancements in artificial intelligence, networking technologies, privacy-preserving systems, and ethical considerations in modern computational applications.
Explainable Artificial Intelligence in Critical Decision-Making Systems
The deployment of artificial intelligence (AI) in critical decision-making domains—including healthcare diagnostics, financial risk assessment, and autonomous vehicle navigation—has intensified the demand for transparency and interpretability in algorithmic reasoning. Explainable Artificial Intelligence (XAI) has emerged as a pivotal research paradigm aimed at rendering complex machine learning models comprehensible to human stakeholders without substantially compromising predictive performance. This paper presents a comprehensive survey of XAI methodologies, categorizing them into model-agnostic approaches (LIME, SHAP, Anchors), gradient-based techniques (Grad-CAM, Integrated Gradients), and inherently interpretable architectures (decision trees, CORELS, Explainable Boosting Machines). We systematically evaluate these methods across three critical application domains, comparing their explanation fidelity, computational overhead, and alignment with regulatory requirements such as the EU AI Act and GDPR's right to explanation. Our analysis reveals that SHAP achieves the highest average fidelity score (0.88) across domains, while inherently interpretable models offer superior transparency at the cost of reduced capacity for modeling complex non-linear relationships. We further identify key research gaps, including the absence of standardized evaluation benchmarks and the challenge of balancing faithfulness with human comprehensibility. The findings inform practical guidelines for selecting XAI techniques appropriate to specific deployment contexts and regulatory constraints.
Federated Learning for Privacy-Preserving Healthcare Data Analytics
The proliferation of electronic health records (EHRs), medical imaging repositories, and wearable sensor data has created unprecedented opportunities for data-driven healthcare analytics. However, stringent privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) fundamentally constrain the centralized aggregation of sensitive patient data across institutional boundaries. Federated learning (FL) has emerged as a transformative paradigm that enables collaborative model training across distributed healthcare institutions without requiring raw data exchange. This paper presents a comprehensive survey of federated learning methodologies applied to privacy-preserving healthcare data analytics. We systematically examine the architectural foundations of FL, including horizontal and vertical federated learning, federated transfer learning, and split learning variants. We provide a detailed taxonomy of privacy-enhancing mechanisms integrated with FL frameworks, encompassing differential privacy, secure multi-party computation, homomorphic encryption, and trusted execution environments. The paper critically evaluates FL applications across diverse healthcare domains, including medical image analysis, electronic health record mining, genomics, drug discovery, and remote patient monitoring. Furthermore, we analyze key challenges related to statistical heterogeneity, communication efficiency, system heterogeneity, and adversarial robustness in healthcare FL deployments. Our analysis demonstrates that federated learning achieves performance within 1.5–3.2% of centralized baselines across multiple healthcare tasks while maintaining rigorous privacy guarantees. We conclude by identifying open research directions and proposing a roadmap for the clinical deployment of federated learning systems.
Large Language Models: Architectures, Capabilities and Ethical Challenges
Large language models (LLMs) have emerged as transformative artifacts in artificial intelligence, demonstrating unprecedented capabilities in natural language understanding, generation, and reasoning. This paper presents a comprehensive survey of LLM architectures, tracing the evolution from the foundational Transformer architecture to contemporary models such as GPT-4, PaLM, LLaMA-2, and Gemini. We systematically examine the scaling laws that govern model performance, the training methodologies including reinforcement learning from human feedback (RLHF), and the emergent capabilities that arise at scale. Furthermore, we critically analyze the ethical challenges associated with LLMs, including issues of bias amplification, hallucination, environmental impact, and potential misuse. We propose a taxonomy of mitigation strategies encompassing technical interventions, governance frameworks, and participatory design approaches. Our analysis reveals that while LLMs offer remarkable potential across domains such as healthcare, education, and scientific research, their responsible deployment requires coordinated efforts across technical, regulatory, and societal dimensions. This paper contributes to the ongoing discourse on responsible AI by synthesizing architectural advances with ethical considerations, providing researchers and practitioners with a unified perspective on the current state and future trajectory of large language models.
Comparative Study of Routing Protocols in Mobile Ad Hoc Networks
Mobile Ad Hoc Networks (MANETs) are self-organizing wireless networks formed by mobile nodes without reliance on fixed infrastructure. Routing in MANETs presents unique challenges due to dynamic topology changes, limited bandwidth, and constrained energy resources. This paper presents a comparative analysis of four widely studied MANET routing protocols: Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Optimized Link State Routing (OLSR), and Destination-Sequenced Distance Vector (DSDV). Using NS-3 simulations with varying network sizes, node mobility patterns, and traffic loads, the study evaluates each protocol across four key metrics: packet delivery ratio, end-to-end delay, routing overhead, and throughput. Results indicate that reactive protocols (AODV and DSR) outperform proactive protocols (OLSR and DSDV) in networks with low to moderate traffic and high mobility, while proactive protocols demonstrate advantages in dense, high-traffic scenarios with lower mobility. The paper provides protocol selection guidelines based on deployment characteristics.
The rapid evolution of computing technologies continues to reshape the ways organizations design software, manage digital infrastructure, and harness emerging innovations. The current issue of the Peer-Reviewed Journal of Computer Science (PRJCS) brings together a set of scholarly contributions that explore contemporary developments across software engineering, artificial intelligence, cybersecurity practices, networking performance, and emerging computational paradigms. Collectively, the articles in this issue highlight the dynamic interplay between theoretical advancements and practical implementations in modern computing environments.
Performance Analysis of TCP and UDP In Wireless LANS
Wireless local area networks (WLANs) based on the IEEE 802.11 family of standards have become the primary access method for enterprise and residential network connectivity. The performance characteristics of transport layer protocols operating over wireless links differ substantially from their behavior in wired environments due to shared medium contention, signal attenuation, multipath fading, and MAC layer retransmissions. This paper presents a comparative performance analysis of the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) in WLAN environments. Through simulation-based experiments using NS-3 across varying node densities, traffic patterns, and WLAN standards from 802.11b through 802.11ax, the study evaluates throughput, end-to-end delay, jitter, and packet loss for both protocols. Results demonstrate that TCP's congestion control mechanisms, while essential for reliability, significantly reduce throughput and increase latency in wireless environments compared to wired networks. UDP maintains higher throughput and lower delay but at the cost of reliability. The paper discusses the implications for application design and protocol selection in wireless deployments.
Quantum Computing: A Beginner's Guide To Future Processing
Quantum computing represents a fundamentally different computational paradigm that harnesses quantum mechanical phenomena to process information in ways that classical computers cannot efficiently replicate. This paper provides an accessible introduction to quantum computing for IT professionals and developers seeking to understand the technology's principles, current state, and practical implications. The paper covers the foundational concepts of qubits, superposition, entanglement, and quantum gates, followed by an examination of prominent quantum algorithms and their applications. A comparative analysis of current quantum hardware approaches, including superconducting circuits, trapped ions, and photonic systems, contextualizes the engineering challenges that remain. The paper also addresses the implications of quantum computing for cryptography and outlines the emerging field of post-quantum cryptography. Market analysis and industry investment patterns suggest that while general-purpose quantum advantage remains years away, specific domain applications in chemistry, optimization, and financial modeling are approaching practical viability.
It is with great enthusiasm that we present the inaugural issue of the Peer-Reviewed Journal of Computer Science (PRJCS). The establishment of this journal reflects the growing need for a scholarly platform that brings together research addressing the rapidly evolving landscape of computing technologies, digital infrastructure, and cybersecurity challenges. PRJCS aims to foster interdisciplinary dialogue, promote applied and theoretical innovation, and disseminate research that bridges academic insight with real-world technological practice.
AI Tools For Small Businesses: Ten Practical Applications
The proliferation of commercially available artificial intelligence tools has made machine learning and natural language processing capabilities accessible to organizations of all sizes. While large enterprises have dedicated data science teams to develop custom solutions, small businesses can now access comparable capabilities through software-as-a-service platforms that require no specialized technical expertise. This paper identifies and evaluates ten practical AI applications for small businesses spanning customer service, marketing, financial management, operations, and human resources. For each application, the paper describes the underlying technology, assesses representative commercial tools, and presents cost-benefit analyses based on published case studies and vendor data. The findings demonstrate that small businesses adopting targeted AI tools achieve measurable efficiency gains and cost reductions, though success depends on careful tool selection, data readiness, and realistic expectations.
Integrating Security Into Development: Devsecops Fundamentals
The traditional approach of treating security as a final gate before software release has proven inadequate in an era of continuous delivery and rapid deployment cycles. DevSecOps integrates security practices directly into the software development lifecycle, treating security as a shared responsibility across development, operations, and security teams. This paper examines the principles, practices, and tooling that constitute a DevSecOps methodology. It maps specific security activities to each phase of the development pipeline, from threat modeling during planning to runtime application self-protection in production. The paper presents empirical evidence demonstrating that organizations adopting DevSecOps achieve faster vulnerability remediation, reduced breach rates, and improved deployment velocity compared to traditional sequential security approaches. A practical implementation roadmap guides IT teams through the cultural, procedural, and technical changes required for successful DevSecOps adoption.
Low-Code Development: When It Works and When To Code
Low-code development platforms have emerged as a significant force in enterprise software delivery, promising accelerated application development through visual interfaces and pre-built components. This paper evaluates the capabilities and limitations of low-code platforms through comparative analysis with traditional software development approaches. The study examines six application categories to determine where low-code platforms deliver genuine productivity gains and where traditional coding remains necessary. Drawing on market data, vendor assessments, and published case studies, the paper identifies internal workflow automation and departmental applications as the strongest use cases for low-code, while complex integrations, high-performance applications, and products requiring fine-grained customization continue to demand traditional development. A decision framework is proposed to help organizations determine the appropriate development approach for each project.
Getting Started With Kubernetes: A Practical Developer Guide
Kubernetes has become the standard platform for container orchestration, fundamentally changing how applications are deployed, scaled, and managed in production environments. This paper provides a practical introduction to Kubernetes aimed at developers transitioning from traditional deployment models to container-based architectures. The paper covers core concepts including pods, services, deployments, and namespaces, followed by hands-on guidance for cluster setup, application deployment, and operational management. A comparative analysis of managed Kubernetes services from major cloud providers is included alongside a discussion of common pitfalls and best practices drawn from production experience. The paper demonstrates that while Kubernetes introduces significant operational complexity, its benefits in scalability, portability, and resource efficiency justify adoption for teams managing distributed applications at scale.
Copyright (c) 2026 Peer-Reviewed Journal of Computer Science (PRJCS)
Cloud Versus On-Premises: Selecting Infrastructure For Business
The decision between cloud-based and on-premises infrastructure remains one of the most consequential choices facing IT leadership. This paper provides a structured comparison of cloud computing, on-premises data centers, and hybrid architectures across six critical dimensions: total cost of ownership, scalability, security, compliance, performance, and operational complexity. Drawing on industry benchmarking data, vendor-neutral analyses, and published case studies, the paper presents a decision framework that maps organizational requirements to the most suitable deployment model. Findings indicate that no single model is universally superior; rather, the optimal choice depends on workload characteristics, regulatory environment, growth trajectory, and existing technical capabilities. The paper concludes with practical guidelines for organizations evaluating migration or modernization initiatives.
Why Employees Click Phishing Links and Training Strategies
Phishing remains the most prevalent initial attack vector in cybersecurity breaches, with employee interaction serving as the critical enabler. This paper examines the psychological, organizational, and technical factors that lead employees to click on phishing links despite awareness efforts. Drawing on behavioral science research and empirical data from simulated phishing campaigns across multiple industries, the study identifies six primary psychological triggers exploited by attackers: urgency, curiosity, authority impersonation, reward anticipation, habitual inattention, and social proof. The paper then evaluates the effectiveness of various security awareness training methodologies, including traditional classroom instruction, simulated phishing exercises, gamified learning platforms, and just-in-time contextual training. Findings indicate that organizations employing monthly simulated phishing exercises combined with immediate feedback achieve click rate reductions exceeding 80% within twelve months. The paper concludes with a practical training framework that IT teams can adapt to their organizational context.
Building a Zero Trust Security Model For IT Teams
The traditional perimeter-based security model has proven insufficient against modern cyber threats that exploit trusted internal connections and lateral movement within enterprise networks. Zero Trust Architecture (ZTA) operates on the principle of 'never trust, always verify,' treating every access request as potentially hostile regardless of its origin. This paper presents a structured, step-by-step methodology for IT teams to design and deploy a Zero Trust security framework. The approach covers identity and access management, micro-segmentation, continuous monitoring, and policy enforcement across hybrid environments. Case analysis from enterprise deployments demonstrates measurable reductions in breach frequency and detection time. The paper also addresses common obstacles including legacy system integration, user resistance, and budget constraints, offering practical mitigation strategies for each.
Real-Time Data Processing: Tools and Techniques for Better Business Decisions
In today's data-driven business landscape, the ability to process and analyze data in real-time has become a critical competitive advantage. This paper provides a comprehensive examination of real-time data processing technologies, architectures, and methodologies that enable organizations to make faster, more informed business decisions. We explore the fundamental concepts of stream processing, examine leading technologies including Apache Kafka, Apache Flink, and Spark Streaming, and analyze their comparative performance characteristics. Through detailed case studies across multiple industries—including fraud detection, IoT monitoring, and customer analytics—we demonstrate how real-time data processing delivers measurable business value. Our analysis reveals that organizations implementing real-time processing achieve average latency reductions of 98% and decision-making speed improvements of 60%. We present architectural patterns, implementation best practices, and performance optimization techniques essential for successful real-time data systems.
meta name="citation_author" content="Saritha E"
meta name="citation_author_institution" content="Research Scholar, Dept. of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India"
meta name="citation_title" content="Real-Time Data Processing: Tools and Techniques for Better Business Decisions"
meta name="citation_pdf_url" content="https://peerreviewjournal.in/index.php/prjcs/article/download/4/3"
Copyright (c) 2026 Peer-Reviewed Journal of Computer Science
A Systematic Review of Hybrid Sarcasm Detection: Fusing Contextual Embeddings with Handcrafted Linguistic Features
Sarcasm detection (SD) in Natural Language Processing (NLP) constitutes a significant challenge, as sarcastic expressions convey the opposite of their literal meaning, often reversing sentiment polarity. This ambiguity is amplified in text by the absence of non-verbal cues like tone and facial expressions. While modern transformer models excel at capturing deep context, they often fail to register the explicit rhetorical structures inherent in irony ; conversely, traditional feature-based models capture linguistic structure but lack deep semantic understanding. To address these limitations, this paper proposes a novel, two-branch Hybrid Contextual- Linguistic Sarcasm Detector (HCL-SD) framework. The HCL-SD framework synergistically integrates deep contextual embeddings, derived from fine-tuned RoBERTa/DistilBERT models, with a meticulously engineered set of 13 handcrafted linguistic features (such as Entropy, Readability Scores, and Part-of-Speech counts). This dual-branch approach allows the model to simultaneously learn implicit semantic incongruity and explicit rhetorical cues. The resulting fused feature space is classified using an optimized Ensemble Model employing a Majority Voting scheme. Comprehensive experimentation on benchmark datasets, including the News Headlines and Mustard datasets, demonstrates the framework's superior performance. The proposed approach achieved a state-of-the-art F1-Score of 0.997 on the News Headlines dataset. Crucially, the integration of contextual metadata was proven essential for generalization, dramatically improving the cross-domain F1-Score on the Reddit validation dataset from 0.70 to 0.92. This research confirms that combining deep contextual comprehension with explicit linguistic feature engineering is indispensable for constructing robust, efficient, and highly accurate sarcasm detection systems.