International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 2, No. 10, October 2021 | pp. 73–80

Research Article

Title: A Scalable Sharding Protocol with Dynamic Cross-Shard Transaction Verification for High-Throughput Blockchains

Names of Authors: Tan Nguyen¹, Hanh Tran², and Minh Le³

Authors’ Affiliations:
¹Faculty of Computer Science, University of Information Technology, Ho Chi Minh City, Vietnam
²Department of Information Technology, Vietnam National University, Hanoi, Vietnam
³School of Computer Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam

Abstract: Blockchain systems encounter severe scalability barriers due to the constraint that every node must validate every network transaction. Sharding protocols resolve this bottleneck by splitting the network ledger into smaller, parallel processing units called shards. However, current sharding architectures generate heavy communication overhead and elevated transaction latency when executing cross-shard transactions, which compromises overall network throughput. This paper introduces an optimized, secure sharding protocol that utilizes a dynamic cross-shard validation pipeline driven by a two-phase Byzantine Fault Tolerance (BFT) consensus mechanism. The proposed framework implements a lock-free state atomic swap engine, allowing separate shards to verify mutual transaction dependencies simultaneously without stalling local ledger validation. To defend against 51% takeover exploits within individual sub-shards, a verifiable random function (VRF) executes continuous, unpredictable node re-allocation across the network topology. Performance evaluations conducted over a simulated setup of 2000 nodes distributed across 16 shards show a sustained transaction processing capacity of 8500 transactions per second (tx/sec). This performance represents a 42.6% throughput improvement over existing static sharding solutions. Furthermore, cross-shard confirmation latency drops by 31.2%, proving the protocol's viability for high-capacity enterprise ledger networks.

Keywords: Blockchain scalability, Network sharding, Consensus protocols, Byzantine fault tolerance, Cross-shard transactions, Verifiable random function

Manuscript Timeline: Received: July 11, 2021; Revised: August 19, 2021; Accepted: September 15, 2021; Published: October 1, 2021

Citation: Nguyen, T., Tran, H., & Le, M. (2021). A scalable sharding protocol with dynamic cross-shard transaction verification for high-throughput blockchains. International Journal of Computer Science and Technology, 2(10), 73–80. DOI: 10.46882/2021/IJCST/000022

International Journal of Computer Science and Technology | Vol. 2, No. 9, September 2021 | pp. 65–72

Research Article

Title: An Optimized Deep Learning Framework for Real-Time Facial Expression Recognition in Neurological Assessment

Names of Authors: Elena Rostova¹, Dmitry Ivanov², and Anna Kuznetsova³

Authors’ Affiliations:
¹Department of Computer Science, National Research University Higher School of Economics, Moscow, Russia
²Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia
³Institute of Artificial Intelligence, Saint Petersburg State University, Saint Petersburg, Russia

Abstract: Automated facial expression recognition (FER) systems have emerged as powerful non-invasive tools for monitoring patient neurological responses and cognitive disorders in clinical settings. However, applying FER models to clinical diagnostic workflows requires ultra-low latency processing and robust adaptation to irregular lighting and head pose variations. Standard deep neural networks often suffer from high computational overhead and drop in classification accuracy when deployed on lightweight clinical monitoring units. This paper presents an optimized, real-time FER framework utilizing a modified MobileNetV3 architecture combined with a coordinate attention mechanism. The proposed system retains spatial context by embedding positional info into channel attention, enhancing fine-grained micro-expression extraction. To combat dataset bias, the architecture undergoes transfer learning using a combined dataset of AffectNet and clinical facial sequences. Quantitative evaluations show that the proposed system achieves a classification accuracy of 89.6% across seven core emotional states while restricting model parameter size to 4.2 megabytes (MB). Hardware profiling on an embedded edge device yields an average inference speed of 38.5 frames per second (fps). The framework delivers an efficient, reliable solution for continuous patient monitoring during neurological evaluations without requiring high-end computational infrastructure.

Keywords: Facial expression recognition, Edge intelligence, Deep learning, MobileNet, Coordinate attention, Neurological monitoring

Manuscript Timeline: Received: June 15, 2021; Revised: July 20, 2021; Accepted: August 14, 2021; Published: September 1, 2021

Citation: Rostova, E., Ivanov, D., & Kuznetsova, A. (2021). An optimized deep learning framework for real-time facial expression recognition in neurological assessment. International Journal of Computer Science and Technology, 2(9), 65–72. DOI: 10.46882/2021/IJCST/000021

International Journal of Computer Science and Technology | Vol. 2, No. 8, August 2021 | pp. 57–64

Research Article

Title: An Automated Smart Contract Vulnerability Detection Framework Using Static Analysis and Graph Neural Networks

Names of Authors: Yuki Nakamura¹, Aiko Tanaka², and Daiki Sato³

Authors’ Affiliations:
¹Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan
²Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
³Department of Information Engineering, Nagoya University, Nagoya, Japan

Abstract: Smart contracts deployed on public blockchain platforms manage billions of dollars in digital assets, making them high-stakes targets for cyber exploits. Structural vulnerabilities such as reentrancy, integer overflows, and timestamp dependencies have historically led to catastrophic capital losses. Traditional vulnerability identification relies on manual auditing or rule-based static symbolic execution, both of which struggle with high false-positive rates and fail to uncover deep semantic bugs in complex contract control flows. This paper proposes a deep-learning-driven smart contract vulnerability detection framework that combines static analysis with Graph Neural Networks (GNNs). The system compiles raw Solidity smart contract source code into abstract syntax trees (ASTs) and control flow graphs (CFGs), which are then merged into a unified Code Property Graph (CPG) representing data dependencies and execution paths. A Bidirectional Gated Graph Neural Network (BGGNN) processes these graphs to learn vector embeddings of the contract's structural features. Experimental evaluations on a curated dataset of 45,000 smart contracts demonstrate that the proposed framework identifies critical vulnerabilities with an overall accuracy of 96.4% and an F1-score of 0.958, outperforming conventional tools like Oyente, Mythril, and standard recurrent neural networks. The system provides an efficient, automated security validation utility for smart contract deployment pipelines.

Keywords: Smart contract security, Blockchain, Graph neural networks, Static analysis, Vulnerability detection, Deep learning

Manuscript Timeline: Received: May 12, 2021; Revised: June 19, 2021; Accepted: July 15, 2021; Published: August 1, 2021

Citation: Nakamura, Y., Tanaka, A., & Sato, D. (2021). An automated smart contract vulnerability detection framework using static analysis and graph neural networks. International Journal of Computer Science and Technology, 2(8), 57–64. DOI: 10.46882/2021/IJCST/000020

International Journal of Computer Science and Technology | Vol. 2, No. 7, July 2021 | pp. 49–56

Research Article

Title: A Software-Defined UAV Swarm Communication Network Utilizing Multi-Agent Reinforcement Learning for Dynamic Routing

Names of Authors: Sanjay Nair¹, Priya Pillai², and Anand Krishnan³

Authors’ Affiliations:
¹Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India
²Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India
³Department of Electronics and Communications, National Institute of Technology, Calicut, India

Abstract: Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in disaster monitoring, environmental surveillance, and search-and-rescue operations due to their mobility and flexible deployment capabilities. However, maintaining reliable ad-hoc communication links within a flying swarm is difficult because high-speed three-dimensional node mobility causes frequent topological disruptions and link disconnects. Traditional routing protocols face scalability issues and slow convergence, leading to severe packet drops and routing loop overheads. This research introduces a software-defined UAV swarm networking architecture that utilizes a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for adaptive routing control. The framework uses a centralized Software-Defined Networking (SDN) controller on a ground base station to maintain global topology states, while individual UAV nodes run localized packet forwarding policies. The multi-agent learning algorithm models routing as an optimization problem where each UAV learns to select the optimal next-hop neighbor based on node positions, residual energy levels, link qualities, and packet queue constraints. Simulation experiments conducted in dynamic 3D flying corridors show that the MADDPG routing protocol reduces end-to-end delay by 39.4% and improves the overall packet delivery ratio by 22.8% over traditional Ad-hoc On-Demand Distance Vector (AODV) and Optimized Link State Routing (OLSR) protocols. The system ensures robust link stability in high-mobility deployment contexts.

Keywords: UAV swarms, Software-defined networking, Multi-agent reinforcement learning, Dynamic routing, Ad-hoc networks, Wireless communication

Manuscript Timeline: Received: April 15, 2021; Revised: May 22, 2021; Accepted: June 16, 2021; Published: July 1, 2021

Citation: Nair, S., Pillai, P., & Krishnan, A. (2021). A software-defined UAV swarm communication network utilizing multi-agent reinforcement learning for dynamic routing. International Journal of Computer Science and Technology, 2(7), 49–56. DOI: 10.46882/2021/IJCST/000019

International Journal of Computer Science and Technology | Vol. 2, No. 6, June 2021 | pp. 41–48

Research Article

Title: Enhancing Named Entity Recognition in Financial Text Mining Using Domain-Specific Bidirectional Encoder Representations

Names of Authors: John Smith¹, Emily Johnson², and Robert Williams³

Authors’ Affiliations:
¹Department of Computer Science, Stanford University, Stanford, California, United States
²Department of Linguistics, University of California, Berkeley, Berkeley, California, United States
³Center for Data Science, New York University, New York, New York, United States

Abstract: Extracting actionable insights from corporate financial statements, earnings call transcripts, and market news reports requires accurate Named Entity Recognition (NER) models. Financial text mining is complicated by highly specialized vocabularies, ambiguous abbreviation patterns, and semantic constructs that differ sharply from general language corpora. General-purpose pre-trained language models like standard BERT often fail to extract domain-specific entities such as asset tickers, corporate acquisitions, fiscal regulatory terms, and quantitative financial values. This paper introduces FinBERT-NER, an enhanced language model tailored for financial named entity recognition tasks. The model adapts the Bidirectional Encoder Representations from Transformers (BERT) architecture by performing domain-specific continuous pre-training on a massive corporate dataset comprising 8.5 billion words sourced from SEC filings and financial news. A specialized tokenization dictionary is integrated to prevent excessive sub-word fragmentation of financial terminology. Experimental evaluations on manually annotated financial benchmark datasets indicate that FinBERT-NER achieves a precision of 94.2%, a recall of 93.6%, and an F1-score of 93.9%, outperforming base BERT and standard LSTM-CRF models by a wide margin. The model improves extraction accuracy for complex multi-word corporate entities, serving as a reliable foundation for automated market sentiment profiling, regulatory compliance tracking, and quantitative investment algorithms.

Keywords: Named entity recognition, Natural language processing, Financial text mining, BERT, Transformer models, Information extraction

Manuscript Timeline: Received: March 20, 2021; Revised: April 26, 2021; Accepted: May 18, 2021; Published: June 1, 2021

Citation: Smith, J., Johnson, E., & Williams, R. (2021). Enhancing named entity recognition in financial text mining using domain-specific bidirectional encoder representations. International Journal of Computer Science and Technology, 2(6), 41–48. DOI: 10.46882/2021/IJCST/000018

International Journal of Computer Science and Technology | Vol. 2, No. 5, May 2021 | pp. 33–40

Research Article

Title: A Decentralized Identity Management Framework Using Smart Contracts and Zero-Knowledge Proofs

Names of Authors: Amir Hassan¹, Laila Mansour², and Tarek Fadel³

Authors’ Affiliations:
¹Department of Computer Engineering, Cairo University, Giza, Egypt
²Department of Information Technology, Alexandria University, Alexandria, Egypt
³Department of Computer Science, American University in Cairo, Cairo, Egypt

Abstract: Traditional centralized identity management systems pose significant risks, including single points of data breach, corporate data harvesting, and a lack of user control over personal information. Self-Sovereign Identity (SSI) frameworks address these issues by empowering individuals to manage their digital credentials independently. However, deploying SSI on public blockchains introduces tracking issues and data leakage vectors due to the transparent nature of distributed ledgers. This study proposes a decentralized, privacy-preserving identity management framework that integrates permissioned smart contracts with zero-knowledge proofs (ZKPs), specifically utilizing zk-SNARKs. The proposed architecture separates identity verification from credential disclosure, allowing users to generate cryptographic proofs verifying attributes (such as age thresholds or financial capacities) without exposing their underlying personally identifiable information (PII). Smart contracts act as a root of trust, validating these cryptographic proofs and verifying issuer public keys on-chain while storing zero raw user data. Prototyping evaluations conducted on a Hyperledger Fabric network demonstrate that proof generation takes 1.2 seconds on standard smartphones, and verification executes in less than 45 ms on the ledger. The framework successfully mitigates identity fraud, guarantees complete user anonymity, and complies fully with data privacy mandates, making it viable for secure e-governance and decentralized finance applications.

Keywords: Self-sovereign identity, Blockchain, Smart contracts, Zero-knowledge proofs, Privacy preservation, Identity management

Manuscript Timeline: Received: February 18, 2021; Revised: March 24, 2021; Accepted: April 22, 2021; Published: May 1, 2021

Citation: Hassan, A., Mansour, L., & Fadel, T. (2021). A decentralized identity management framework using smart contracts and zero-knowledge proofs. International Journal of Computer Science and Technology, 2(5), 33–40. DOI: 10.46882/2021/IJCST/000017