Peer-Reviewed Journal of Computer Science (PRJCS)
type: channel
description: 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.
publisher: Research Publications
language: en
publication name: Peer-Reviewed Journal of Computer Science (PRJCS)
items: Confidential Computing and Trusted Execution Environments: A Survey of Technologies
items: Physical AI: World Models and Embodied Intelligence
items: State-Space Models and Efficient Long-Sequence Architectures Beyond the Transformer
items: Multimodal Large Language Models: A Survey of Vision-Language Architectures
items: Ontology-Based Recommender Systems for Online Learning Platforms: A Review
items: Post-Quantum Cryptography Migration: Kyber And Dilithium in TLS 1.3
items: eBPF-Based Observability: Kernel-Level Tracing For Production Systems
items: Sustainable Green Computing and Carbon-Aware Artificial Intelligence
items: Digital Twins and Cyber-Physical Systems for Industry 5.0
items: Artificial Intelligence-Driven 6G Networks and Intelligent Radio Access
items: Small Language Models for On-Device and Private Intelligence
items: Neuromorphic Computing for Energy-Efficient Artificial Intelligence
items: Post-Quantum Cryptography: Standardisation, Algorithms, and Deployment Migration
items: Retrieval-Augmented Generation for Knowledge-Intensive Language Applications
items: Agentic Artificial Intelligence: Autonomous Multi-Agent Workflows and Orchestration
items: Quantum Computing: Current Advances And Near-Term Applications
items: Edge Computing And Internet Of Things Security Frameworks: A Comprehensive Survey Of Architectures, Threats, And Countermeasures
items: Cybersecurity Threats And Defenses In The Generative AI Era
items: Autonomous Vehicle Perception: Deep Learning Challenges and Solutions
items: Blockchain-Based Digital Identity Management And Verification Systems
items: PREFACE
items: Explainable Artificial Intelligence in Critical Decision-Making Systems
items: Federated Learning for Privacy-Preserving Healthcare Data Analytics
items: Large Language Models: Architectures, Capabilities and Ethical Challenges
items: Comparative Study of Routing Protocols in Mobile Ad Hoc Networks
items: Performance Analysis of TCP and UDP In Wireless LANS
items: Quantum Computing: A Beginner's Guide To Future Processing
items: AI Tools For Small Businesses: Ten Practical Applications

Confidential Computing and Trusted Execution Environments: A Survey of Technologies
type: item
description: Conventional security controls protect data at rest through storage encryption and data in transit through transport encryption, yet data in use, meaning plaintext held in memory and registers during computation, has remained exposed to any sufficiently privileged component of the software stack. Confidential computing addresses this gap by performing sensitive computation inside a hardware-based, attested Trusted Execution Environment (TEE) that isolates code and data even from the operating system, hypervisor, and cloud operator. This survey reviews the motivation, threat model, and technology landscape of confidential computing. It examines process enclaves such as Intel SGX alongside confidential virtual machine designs including Intel TDX, AMD SEV and SEV-SNP, Arm TrustZone with the Confidential Compute Architecture, and the RISC-V Keystone framework, comparing them across isolation granularity, memory encryption, attestation, and vendor. It describes remote attestation and hardware roots of trust, surveys applications spanning confidential artificial intelligence, privacy-preserving analytics, blockchain, key management, and multi-party computation, and positions the paradigm as a complement to homomorphic encryption, secure multi-party computation, and differential privacy rather than a replacement. A dedicated security analysis reports side-channel and transient-execution attacks such as Foreshadow, Plundervolt, and SGAxe, together with representative performance overheads, and the review closes with standardization efforts and future directions including confidential graphics processing units.
subject: Articles
  topic: section
subject: Confidential Computing
  topic: keywords
subject: Trusted Execution Environment
  topic: keywords
subject: Intel SGX
  topic: keywords
subject: AMD SEV-SNP
  topic: keywords
subject: Remote Attestation
  topic: keywords
creator: Dr. R. Pugazhenthi
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-07-13
publication date: 2026-07-13
starting page: 33
ending page: 40

Physical AI: World Models and Embodied Intelligence
type: item
description: Physical AI names a shift in artificial intelligence away from purely digital tasks and toward systems that perceive, reason about, and act in the physical world. Between 2024 and 2026 this shift accelerated as robot foundation models, learned simulators, and embodied agents moved from isolated demonstrations to shared datasets, open policies, and reusable world models. This review organizes the area around two technical pillars. The first is world models: neural networks that learn predictive models of environment dynamics and support planning or behavior learning through imagined rollouts, from the early recurrent world model of Ha and Schmidhuber through the Dreamer family and joint-embedding predictive architectures. The second is generalist action policies, especially vision-language-action models such as RT-1, RT-2, Octo, and OpenVLA trained on cross-embodiment corpora like Open X-Embodiment. We connect these pillars to the simulation and data infrastructure that feeds them, including GPU-accelerated physics, domain randomization, and video generation framed as world simulation. We summarize benchmarks and evaluation practice, survey humanoid and manipulation applications, and discuss open challenges: data scarcity, the sim-to-real gap, safety, generalization, long-horizon control, and reproducible evaluation. All quantitative figures in the tables and charts are representative values collated from disparate reports and are intended to illustrate trends rather than to support controlled comparison. We close with directions toward neuro-symbolic integration and foundation world models.
subject: Articles
  topic: section
subject: Physical AI
  topic: keywords
subject: World Models
  topic: keywords
subject: Embodied Intelligence
  topic: keywords
subject: Vision-Language-Action Models
  topic: keywords
subject: Robot Foundation Models
  topic: keywords
subject: Sim-to-Real
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Generative Simulation
  topic: keywords
creator: Rasmi M
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-07-13
publication date: 2026-07-13
starting page: 25
ending page: 32

State-Space Models and Efficient Long-Sequence Architectures Beyond the Transformer
type: item
description: The self-attention mechanism that powers modern Transformers scales quadratically in time and memory with sequence length, making very long context expensive and often impractical. This survey reviews the emerging family of state-space models (SSMs) and related sub-quadratic architectures that aim to preserve sequence-modeling quality while achieving linear or near-linear cost. Beginning with the complexity wall faced by recurrent networks and attention, the article traces the deep SSM line from the HiPPO theory of continuous memory through the Structured State Space sequence model (S4), its diagonal simplifications, and the parallel-scan formulation of S5. It then examines selective SSMs and Mamba, whose input-dependent parameters and hardware-aware parallel scan deliver linear-time training and constant-memory autoregressive inference. Parallel developments in linear attention, RWKV, RetNet, and the exact but memory-efficient FlashAttention kernel are placed in context, along with hybrid designs such as Jamba that interleave attention and SSM blocks, and the state-space duality that unifies both views in Mamba-2. Applications spanning language, audio, genomics, and vision are summarized. The survey closes with an honest account of empirical trade-offs, including the associative-recall gap between recurrent SSMs and attention, and outlines open challenges. Reported numbers are illustrative and labeled as representative rather than definitive.
subject: Articles
  topic: section
subject: State-Space Models
  topic: keywords
subject: Mamba
  topic: keywords
subject: S4
  topic: keywords
subject: Long-Sequence Modeling
  topic: keywords
subject: Linear Attention
  topic: keywords
subject: Efficient Transformers
  topic: keywords
subject: Selective Scan
  topic: keywords
subject: Sequence Architectures
  topic: keywords
creator: V Sakhtipriyanka
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-07-10
publication date: 2026-07-10
starting page: 17
ending page: 24

Multimodal Large Language Models: A Survey of Vision-Language Architectures
type: item
description: Multimodal large language models, often called vision-language models, extend text-only language models so that a single system can read images together with words and produce grounded natural-language responses. This survey gives a structured and honest account of how these systems are built, trained, and measured. It first sets out the background that made them possible: the transformer sequence model, large pretrained language models, the vision transformer that treats an image as a sequence of patches, and contrastive image-text pretraining that aligns pictures and captions in a shared space. It then organises the design space into three parts, namely the vision encoder, the cross-modal connector that maps visual features into the language model, and the language decoder, and it contrasts query-based connectors, linear projectors, and gated cross-attention as well as early and late fusion. Representative systems including CLIP, Flamingo, BLIP-2, LLaVA, GPT-4V, Gemini, Qwen-VL, and PaliGemma are described from public reports, with proprietary details marked as undisclosed. The survey reviews training stages, benchmark families for visual question answering, document and chart reading, expert reasoning, and grounding, and it treats object hallucination and evaluation contamination as first-class problems. Reported scores are presented only as rounded representative illustrations drawn from heterogeneous sources. The article closes with open challenges in spatial reasoning, faithful evaluation, efficiency, and safety, and with directions for any-to-any and video-capable models.
subject: Articles
  topic: section
subject: Vision-Language Models
  topic: keywords
subject: Multimodal Large LanguageModels
  topic: keywords
subject: Contrastive Pretraining
  topic: keywords
subject: Instruction Tuning
  topic: keywords
subject: Visual Question Answering
  topic: keywords
subject: Hallucination
  topic: keywords
subject: Benchmarks
  topic: keywords
subject: Cross-Modal Fusion
  topic: keywords
creator: M. Keerthika
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-07-09
publication date: 2026-07-09
starting page: 9
ending page: 16

Ontology-Based Recommender Systems for Online Learning Platforms: A Review
type: item
description: The rapid growth of massive open online courses, learning management systems, and open educational repositories has produced an abundance of learning resources that overwhelms learners and complicates the selection of suitable content. Recommender systems have become a standard response to this information overload, yet the classical paradigms of collaborative filtering, content-based filtering, knowledge-based filtering, and their hybrids struggle with cold start, data sparsity, and a persistent semantic gap between raw interaction data and pedagogical meaning. This paper reviews how ontologies, formal and shareable specifications of a domain expressed in languages such as RDF and OWL, have been used to address these limitations in educational recommenders. It surveys the background of recommender paradigms and their weaknesses, explains what an ontology is and how domain, learner, and pedagogical ontologies encode knowledge, and analyses the mechanisms through which ontologies support semantic similarity, logical reasoning, learner modelling, prerequisite and zone of proximal development reasoning, and learning path sequencing. A taxonomy of ontology-based approaches is proposed and representative systems are compared, followed by a discussion of evaluation methods and datasets. The review then examines open challenges, including ontology construction cost, scalability, dynamic knowledge, cold start, evaluation validity, and explainability. It closes by outlining future directions that combine large language models with knowledge graphs, hybrid neuro-symbolic reasoning, and federated privacy-preserving personalization. Throughout, the aim is an honest synthesis of what prior work reports rather than any new empirical claim.
subject: Articles
  topic: section
subject: Ontology
  topic: keywords
subject: Recommender Systems
  topic: keywords
subject: E-Learning
  topic: keywords
subject: Semantic Web
  topic: keywords
subject: Knowledge Graph
  topic: keywords
subject: Learner Modelling
  topic: keywords
subject: Personalization
  topic: keywords
subject: Learning Paths
  topic: keywords
creator: Saritha E
creator: Dr. B Kalpana
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-07-09
publication date: 2026-07-09
starting page: 1
ending page: 8

Post-Quantum Cryptography Migration: Kyber And Dilithium in TLS 1.3
type: item
description: A sufficiently capable quantum computer would break the public-key cryptography that secures the modern internet, and data captured today can be stored until that machine arrives. This harvest-now-decrypt-later threat has pushed standards bodies to act well ahead of the hardware. In 2024 NIST published the first finished post-quantum standards: ML-KEM, derived from CRYSTALS-Kyber, for key establishment, and ML-DSA, derived from CRYSTALS-Dilithium, for digital signatures. This paper examines how these algorithms move into the two protocols that carry most secured traffic, TLS 1.3 and IPsec. We describe the hybrid handshake that runs a classical and a post-quantum key-encapsulation mechanism side by side, we measure the cost of the larger keys and signatures these schemes carry, and we discuss the practical friction points such as oversized certificate chains and fragmented handshake records. Benchmarks reported in the literature show that a hybrid X25519 plus Kyber-768 handshake adds only a small latency penalty on a healthy network, while Dilithium signatures inflate the certificate path enough to matter on constrained links. We close with a staged migration plan built around crypto-agility.
subject: Articles
  topic: section
subject: Post-Quantum Cryptography
  topic: keywords
subject: Kyber
  topic: keywords
subject: Dilithium
  topic: keywords
subject: ML-KEM
  topic: keywords
subject: ML-DSA
  topic: keywords
subject: TLS 1.3
  topic: keywords
subject: IPsec
  topic: keywords
subject: Hybrid Key Exchange
  topic: keywords
subject: Crypto-Agility
  topic: keywords
creator: V. Sakhtipriyanka
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-13
publication date: 2026-06-13
starting page: 20
ending page: 23

eBPF-Based Observability: Kernel-Level Tracing For Production Systems
type: item
description: Understanding what a busy server is really doing has always meant a trade-off. Lightweight sampling misses rare events, while heavyweight tracing perturbs the very system it measures. The extended Berkeley Packet Filter, eBPF, breaks that trade-off by letting operators run small, verified programs inside the Linux kernel, attached to function entry points, network paths, and system calls, without patching the kernel or loading a module. This paper explains how eBPF works, why its verifier and just-in-time compiler make in-kernel code safe enough to run in production, and how the technology underpins a new generation of observability and security tools such as bpftrace, Cilium, and Falco. We walk through the attachment points that matter for monitoring, the map and ring-buffer mechanisms that carry data to user space, and the performance profile that lets eBPF instrument hot paths at single-digit overhead. Reported measurements show kernel-level tracing adding under two percent CPU cost where conventional ptrace-based tools impose an order of magnitude more. We also note the limits: kernel-version dependence, verifier constraints, and the portability work still in progress.
subject: Articles
  topic: section
subject: eBPF
  topic: keywords
subject: Observability
  topic: keywords
subject: Linux Kernel
  topic: keywords
subject: Tracing
  topic: keywords
subject: XDP
  topic: keywords
subject: Performance Monitoring
  topic: keywords
subject: Bpftrace
  topic: keywords
subject: Cilium
  topic: keywords
subject: BPF CO-RE
  topic: keywords
creator: Juby George
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-10
publication date: 2026-06-10
starting page: 16
ending page: 19

Sustainable Green Computing and Carbon-Aware Artificial Intelligence
type: item
description: The energy footprint of large-scale computing has become a pressing concern as artificial intelligence workloads expand. Training a single frontier language model can consume thousands of megawatt-hours, and global data-centre electricity demand is projected to rise sharply over the next decade. This paper surveys the field of sustainable computing with a focus on artificial intelligence: efficiency at the algorithm, model, and system levels; carbon-aware scheduling that exploits regional and temporal variation in grid carbon intensity; hardware efficiency improvements; and reporting frameworks. We discuss the policy landscape, including the EU AI Act and U.S. Executive Order on AI, and the role of standardised carbon accounting. Open challenges in measuring and reducing both operational and embodied emissions of AI systems are highlighted, alongside the trade-offs between efficiency, capability, and equity.
subject: Articles
  topic: section
subject: Green Computing
  topic: keywords
subject: Sustainable AI
  topic: keywords
subject: Carbon-Aware Computing
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: MLPerf
  topic: keywords
subject: Data Centres
  topic: keywords
subject: Embodied Carbon
  topic: keywords
creator: Tintu George
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-10
publication date: 2026-06-10
starting page: 11
ending page: 15

Digital Twins and Cyber-Physical Systems for Industry 5.0
type: item
description: Digital twin technology has progressed from a conceptual framework articulated in the early 2000s to a mature engineering discipline underpinning modern manufacturing, energy, healthcare, and smart-city deployments. A digital twin is a synchronised virtual representation of a physical asset, process, or system, continuously updated by sensor data and used for monitoring, simulation, and optimisation. This paper surveys digital twin architectures, the integration of Internet-of-Things sensing with edge and cloud computing, the role of artificial intelligence in twin construction and inference, and representative applications across Industry 5.0 sectors. We discuss interoperability standards, security concerns, and the open research questions that will shape the next decade of cyber-physical systems.
subject: Articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: Cyber-Physical Systems
  topic: keywords
subject: Industry 5.0
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Simulation
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
creator: T. Ramaprabha
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-09
publication date: 2026-06-09
starting page: 6
ending page: 10

Artificial Intelligence-Driven 6G Networks and Intelligent Radio Access
type: item
description: Sixth-generation (6G) wireless networks are entering the standardisation horizon for the early 2030s, with research roadmaps already published by 3GPP, ITU-R, and major industrial alliances. Unlike previous generations, 6G is being designed as an AI-native system in which machine learning is integrated into the air interface, the radio access network, and the core. This paper surveys the architectural vision of 6G, key enabling technologies sub-terahertz spectrum, reconfigurable intelligent surfaces (RIS), cell-free massive MIMO, AI-RAN, network digital twins, and semantic communications and the role of artificial intelligence at each layer. We discuss energy efficiency, spectrum sharing, integrated sensing and communication, and the security implications of pervasive learning components. Open research problems related to standardisation, explainability, and sustainable deployment are highlighted.
subject: Articles
  topic: section
subject: 6G
  topic: keywords
subject: AI-RAN
  topic: keywords
subject: Reconfigurable Intelligent Surfaces
  topic: keywords
subject: Semantic Communications
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Open RAN
  topic: keywords
subject: Integrated Sensing and Communication
  topic: keywords
subject: Network Digital Twin
  topic: keywords
creator: Sandra Charly
rights: Copyright (c) 2026 Sandra Charly (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-09
publication date: 2026-06-09
starting page: 1
ending page: 5

Small Language Models for On-Device and Private Intelligence
type: item
description: 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.
subject: Articles
  topic: section
subject: Small Language Models
  topic: keywords
subject: On-Device AI
  topic: keywords
subject: Knowledge Distillation
  topic: keywords
subject: Quantisation
  topic: keywords
subject: GPTQ
  topic: keywords
subject: AWQ
  topic: keywords
subject: Edge AI
  topic: keywords
subject: Private Inference
  topic: keywords
creator: Mini T V
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-05-13
publication date: 2026-05-13
starting page: 21
ending page: 25

Neuromorphic Computing for Energy-Efficient Artificial Intelligence
type: item
description: 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.
subject: Articles
  topic: section
subject: Neuromorphic computing
  topic: keywords
subject: spiking neural networks
  topic: keywords
subject: Loihi
  topic: keywords
subject: SpiNNaker
  topic: keywords
subject: energy-efficient AI
  topic: keywords
subject: event-driven sensing
  topic: keywords
subject: edge intelligence
  topic: keywords
creator: Kochumol Abraham
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-05-12
publication date: 2026-05-12
starting page: 16
ending page: 20

Post-Quantum Cryptography: Standardisation, Algorithms, and Deployment Migration
type: item
description: 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.
subject: Articles
  topic: section
subject: Post-quantum Cryptography
  topic: keywords
subject: Lattice Cryptography
  topic: keywords
subject: ML-KEM
  topic: keywords
subject: ML-DSA
  topic: keywords
subject: SPHINCS+
  topic: keywords
subject: NIST Standardisation
  topic: keywords
subject: Harvest Now Decrypt Later
  topic: keywords
subject: Hybrid TLS
  topic: keywords
creator: Bini P B
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-05-10
publication date: 2026-05-10
starting page: 11
ending page: 15

Retrieval-Augmented Generation for Knowledge-Intensive Language Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: Retrieval-augmented generation
  topic: keywords
subject: dense retrieval
  topic: keywords
subject: vector databases
  topic: keywords
subject: GraphRAG
  topic: keywords
subject: language models
  topic: keywords
subject: question answering
  topic: keywords
subject: knowledge grounding
  topic: keywords
creator: Meena Jose Komban
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-05-09
publication date: 2026-05-09
starting page: 6
ending page: 10

Agentic Artificial Intelligence: Autonomous Multi-Agent Workflows and Orchestration
type: item
description: 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.
subject: Articles
  topic: section
subject: Agentic AI
  topic: keywords
subject: large language models
  topic: keywords
subject: multi-agent systems
  topic: keywords
subject: tool use
  topic: keywords
subject: planning
  topic: keywords
subject: ReAct
  topic: keywords
subject: AutoGen
  topic: keywords
subject: autonomous workflows
  topic: keywords
creator: Rejina P V
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-05-09
publication date: 2026-05-09
starting page: 1
ending page: 5

Quantum Computing: Current Advances And Near-Term Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: NISQ
  topic: keywords
subject: Quantum Advantage
  topic: keywords
subject: Quantum Algorithms
  topic: keywords
subject: Quantum Computing
  topic: keywords
subject: Quantum Machine Learning
  topic: keywords
subject: Qubit
  topic: keywords
subject: Variational Quantum
  topic: keywords
subject: Eigensolver
  topic: keywords
creator: Win Mathew John
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-04-29
publication date: 2026-04-29
starting page: 27
ending page: 32

Edge Computing And Internet Of Things Security Frameworks: A Comprehensive Survey Of Architectures, Threats, And Countermeasures
type: item
description: 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.
subject: Articles
  topic: section
subject: Edge computing
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: IoT security
  topic: keywords
subject: fog computing
  topic: keywords
subject: intrusion detection
  topic: keywords
subject: federated learning
  topic: keywords
subject: lightweight cryptography
  topic: keywords
subject: MEC
  topic: keywords
creator: T. Ramaprabha
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-04-09
publication date: 2026-04-09
starting page: 21
ending page: 26

Cybersecurity Threats And Defenses In The Generative AI Era
type: item
description: 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.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Adversarial Machine Learning
  topic: keywords
subject: Deepfake Detection
  topic: keywords
subject: AI-Powered Threats
  topic: keywords
subject: Neural Network Defense
  topic: keywords
subject: Phishing Detection
  topic: keywords
creator: Ginne M James
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-04-09
publication date: 2026-04-09
starting page: 7
ending page: 13

Autonomous Vehicle Perception: Deep Learning Challenges and Solutions
type: item
description: 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.
subject: Articles
  topic: section
subject: autonomous vehicles
  topic: keywords
subject: deep learning
  topic: keywords
subject: object detection
  topic: keywords
subject: LiDAR point cloud
  topic: keywords
subject: sensor fusion
  topic: keywords
subject: semantic segmentation
  topic: keywords
subject: perception systems
  topic: keywords
subject: KITTI benchmark
  topic: keywords
creator: Juby George
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-04-09
publication date: 2026-04-09
starting page: 14
ending page: 20

Blockchain-Based Digital Identity Management And Verification Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Blockchain
  topic: keywords
subject: Digital Identity
  topic: keywords
subject: Self-Sovereign Identity
  topic: keywords
subject: Decentralized Identifiers
  topic: keywords
subject: Verifiable Credentials
  topic: keywords
subject: Identity Management
  topic: keywords
subject: Distributed Ledger Technology
  topic: keywords
creator: Tintu George
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-04-09
publication date: 2026-04-09
starting page: 1
ending page: 6

PREFACE
type: item
description: 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.
subject: Articles
  topic: section
creator: Dr. Mini T V
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-09
publication date: 2026-03-09

Explainable Artificial Intelligence in Critical Decision-Making Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: Interpretability
  topic: keywords
subject: SHAP
  topic: keywords
subject: LIME
  topic: keywords
subject: Grad-CAM
  topic: keywords
subject: Healthcare AI
  topic: keywords
subject: Trustworthy AI
  topic: keywords
subject: Algorithmic Transparency
  topic: keywords
subject: Critical Systems
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Bini P B
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-09
publication date: 2026-03-09
starting page: 27
ending page: 33

Federated Learning for Privacy-Preserving Healthcare Data Analytics
type: item
description: 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.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Privacy-Preserving Machine Learning
  topic: keywords
subject: Healthcare Analytics
  topic: keywords
subject: Differential Privacy
  topic: keywords
subject: Electronic Health Records
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: Distributed Machine Learning
  topic: keywords
creator: Manasy Jayasurya
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-09
publication date: 2026-03-09
starting page: 20
ending page: 26

Large Language Models: Architectures, Capabilities and Ethical Challenges
type: item
description: 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.
subject: Articles
  topic: section
subject: Large language models
  topic: keywords
subject: Transformer architecture
  topic: keywords
subject: natural language processing
  topic: keywords
subject: ethical AI
  topic: keywords
subject: bias mitigation
  topic: keywords
subject: GPT-4
  topic: keywords
subject: responsible artificial intelligence
  topic: keywords
creator: Saritha E
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-09
publication date: 2026-03-09
starting page: 13
ending page: 19

Comparative Study of Routing Protocols in Mobile Ad Hoc Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: MANET
  topic: keywords
subject: AODV
  topic: keywords
subject: DSR
  topic: keywords
subject: OLSR
  topic: keywords
subject: DSDV
  topic: keywords
subject: routing protocols
  topic: keywords
subject: mobile ad hoc networks
  topic: keywords
subject: NS-3
  topic: keywords
creator: Raji N
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-09
publication date: 2026-03-09
starting page: 8
ending page: 12

PREFACE
type: item
description: 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.
subject: Articles
  topic: section
creator: Mini T V
rights: Copyright (c) 2026
date: 2026-02-09
publication date: 2026-02-09

Performance Analysis of TCP and UDP In Wireless LANS
type: item
description: 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.
subject: Articles
  topic: section
subject: TCP
  topic: keywords
subject: UDP
  topic: keywords
subject: WLAN
  topic: keywords
subject: IEEE 802.11
  topic: keywords
subject: throughput
  topic: keywords
subject: delay
  topic: keywords
subject: jitter
  topic: keywords
subject: wireless networks
  topic: keywords
subject: NS-3
  topic: keywords
creator: Manasy Jayasurya
rights: Copyright (c) 2026
date: 2026-02-09
publication date: 2026-02-09
starting page: 21
ending page: 25

Quantum Computing: A Beginner's Guide To Future Processing
type: item
description: 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.
subject: Articles
  topic: section
subject: Entanglement
  topic: keywords
subject: Post-quantum cryptography
  topic: keywords
subject: Quantum algorithms
  topic: keywords
subject: Quantum computing
  topic: keywords
subject: Qubits
  topic: keywords
subject: Superposition
  topic: keywords
creator: Rejina P V
rights: Copyright (c) 2026
date: 2026-02-09
publication date: 2026-02-09
starting page: 16
ending page: 20

PREFACE
type: item
description: 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.
subject: Articles
  topic: section
creator: Dr. Mini T V
rights: Copyright (c) 2026
date: 2026-01-09
publication date: 2026-01-09

AI Tools For Small Businesses: Ten Practical Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: small business
  topic: keywords
subject: automation
  topic: keywords
subject: machine learning
  topic: keywords
subject: natural language processing
  topic: keywords
subject: SaaS
  topic: keywords
creator: Sandra Charly
rights: Copyright (c) 2026
date: 2026-02-09
publication date: 2026-02-09
starting page: 11
ending page: 15