International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 4, No. 6, June 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000221

Article Type: Original Research Paper

Title: Strategic Resource Optimization in 6G Multi-Tenant Slicing Networks Using Deep Q-Learning

Names of Authors: Chinedu Aliyu¹, Elena Rostova²

Authors’ Affiliations: ¹Department of Telecommunications Engineering, University of Nigeria, Nsukka, Nigeria; ²Network Systems Research Lab, Helsinki Institute of Technology, Helsinki, Finland

Abstract: The transition toward 6G mobile infrastructures requires the simultaneous management of highly distinct network slices tailored for ultra-reliable low-latency systems and massive machine-type deployments. Static network resource allocations result in severe underutilization or localized bandwidth exhaustion during peak periods. This paper presents a dynamic multi-tenant network slicing optimization model utilizing a Deep Q-Network (DQN) framework. The model tracks live performance telemetry parameters including instantaneous bitrates, queue sizes, and hardware utilization over a continuous time frame. It dynamically scales physical radio resources and virtual routing pathways among independent network operators based on localized demand variables. We simulated this network structure on an enterprise network platform with 20 distinct base stations and 1,000 mobile terminal nodes. The experimental results show a 34.7% improvement in total data throughout efficiency over standard static slicing models. Total network handoff latency was maintained below a strict boundary of 4.5 ms under high mobility stress conditions. A standard statistical evaluation confirmed the significance of this throughput improvement across testing runs (p < 0.01).

Keywords: 6G Infrastructure, Network Slicing, Deep Q-Learning, Resource Management, Multi-Tenant Systems, Telemetry

Manuscript Timeline: Received: February 10, 2023; Revised: April 02, 2023; Accepted: May 12, 2023; Published: June 06, 2023

Citation: Aliyu, C., & Rostova, E. (2023). Strategic Resource Optimization in 6G Multi-Tenant Slicing Networks Using Deep Q-Learning. International Journal of Computer Science and Technology, 4(6), 1–8. DOI: 10.46882/2023/IJCST/000221

International Journal of Computer Science and Technology | Vol. 4, No. 5, May 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000220

Article Type: Original Research Paper

Title: Decentralized Provenance Auditing for Supply Chains Using Smart Contracts and Zero-Knowledge Proofs

Names of Authors: Liam O'Donnell¹, Yuki Tanaka²

Authors’ Affiliations: ¹Department of Software Architecture, Dublin Research Institute, Dublin, Ireland; ²Logistics Technology Infrastructure Division, Tokyo Systems Corp, Tokyo, Japan

Abstract: Global enterprise supply chains require verifiable product tracking mechanisms to combat counterfeiting and ensure compliance with regulatory standards. Traditional centralized databases are vulnerable to internal data tampering and lack transparency across independent suppliers. This paper presents a decentralized provenance auditing framework that combines public blockchain smart contracts with zero-knowledge proofs (ZKPs). The smart contracts log item transformations and custody transfers across production lines sequentially. To protect corporate privacy and trade secrets, the framework uses non-interactive zero-knowledge proofs (zk-SNARKs). This layer allows suppliers to verify regulatory compliance and product origins without revealing proprietary recipes, pricing structures, or specific logistics paths. We deployed and tested the cryptographic protocol on an enterprise Ethereum network setup. The validation tests confirm that creating an authenticity verification proof requires an average processing time of 84 ms. The smart contract transaction gas usage remained highly cost-effective for high-volume logistics tracking operations. The framework provides an absolute defense line against counterfeit data entries while maintaining corporate confidentiality.

Keywords: Supply Chain Provenance, Blockchain, Smart Contracts, Zero-Knowledge Proofs, Data Privacy, Auditing Systems

Manuscript Timeline: Received: June 15, 2022; Revised: July 22, 2022; Accepted: August 10, 2022; Published: May 07, 2023

International Journal of Computer Science and Technology | Vol. 4, No. 4, April 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000219

Article Type: Original Research Paper

Title: Enhancing Cross-Lingual Named Entity Recognition via Contextual Word Embedding Alignment

Names of Authors: Priya Nair¹, Hans-Jürgen Integrated²

Authors’ Affiliations: ¹Center for Computational Linguistics, Indian Institute of Technology, Chennai, India; ²Computational Linguistics Division, Munich Language Labs, Munich, Germany

Abstract: Named Entity Recognition (NER) models achieve high accuracy when trained on large, labeled English datasets but perform poorly in low-resource languages. Manually labeling text corpora across multiple languages is expensive and time-consuming. This paper introduces a cross-lingual NER framework that transfers entity knowledge from English to resource-scarce target languages without requiring manual translations. The framework uses a mathematical alignment layer that projects localized target-language text features into a shared semantic vector space alongside pre-trained English models. We incorporated a token-level attention module to handle variations in word order and syntax across languages. The model was validated on Spanish, Hindi, and Turkish text datasets. The experimental results show a mean F1-score improvement of 11.2% over existing unsupervised cross-lingual models. The framework proved highly effective at identifying complex entities like corporate names and geographic locations across different language syntax models. This alignment strategy provides an efficient method to scale multilingual text analytics tools.

Keywords: Named Entity Recognition, Cross-Lingual Transfer, Natural Language Processing, Word Embeddings, Low-Resource Languages, Semantic Alignment

Manuscript Timeline: Received: June 12, 2022; Revised: July 18, 2022; Accepted: August 08, 2022; Published: April 09, 2023

International Journal of Computer Science and Technology | Vol. 4, No. 3, March 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000218

Article Type: Original Research Paper

Title: Optimizing Energy Usage in Smart Grids via Distributed Multi-Agent Reinforcement Learning

Names of Authors: Aleksei Ivanov¹, Chidi Okoro²

Authors’ Affiliations: ¹Power Systems Computing Lab, Saint Petersburg Tech University, Saint Petersburg, Russia; ²Department of Electrical Engineering, University of Ibadan, Ibadan, Nigeria

Abstract: Modern electrical grids struggle to balance dynamic consumer power demands with highly volatile renewable energy sources like wind and solar. Centralized grid management models face communication delays and scalability bottlenecks when coordinating millions of independent endpoints. This paper presents a decentralized energy management framework that uses a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) model. In this setup, independent software agents represent localized residential neighborhoods and regional solar generation grids. These agents learn to coordinate energy storage allocations and balance localized loads by exchanging minimal peer-to-peer state messages. We simulated this multi-agent architecture using a 120-node electrical distribution setup. The empirical results show a 24.3% reduction in peak-hour grid stress along with an 18.5% optimization in local battery utilization. The decentralized architecture maintained operational stability even during sudden 40.0% generation drops caused by shifting weather conditions. This multi-agent coordination approach provides a resilient method to scale green energy integration across municipal utility networks.

Keywords: Smart Grids, Multi-Agent Systems, Reinforcement Learning, Renewable Energy, Load Balancing, Energy Storage

Manuscript Timeline: Received: June 10, 2022; Revised: July 15, 2022; Accepted: August 05, 2022; Published: March 05, 2023

International Journal of Computer Science and Technology | Vol. 4, No. 2, February 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000217

Article Type: Review Paper

Title: Homomorphic Encryption Pipelines for Secure Cloud Analytics: A Performance Review

Names of Authors: Oliver Smith¹, Sophia Rossi²

Authors’ Affiliations: ¹Department of Cryptographic Research, Manchester Advanced Science University, Manchester, UK; ²Data Protection Systems Division, CyberGuard Labs, Milan, Italy

Abstract: Fully Homomorphic Encryption (FHE) offers a robust privacy framework by enabling mathematical operations directly on encrypted data payloads. This capability allows cloud servers to process sensitive data fields without decrypting the underlying source records. This review paper analyzes the evolution of FHE implementation frameworks over the past decade. We contrast lattice-based mathematical structures including BGV, BFV, and CKKS schemes across cloud environments. The analysis tracks key performance metrics including key sizing requirements, processing overhead factors, and noise growth rates. We compiled empirical benchmarks from 32 peer-reviewed implementations across standard business operations. The evaluation indicates that while CKKS optimizes floating-point operations for machine learning workloads, processing demands remain up to 100x slower than unencrypted workflows. The review outlines hardware acceleration strategies using modern FPGA and ASIC chipsets to mitigate these performance bottlenecks. Finally, we provide a structured configuration guide to help developers choose FHE frameworks based on specific operational requirements.

Keywords: Fully Homomorphic Encryption, Cloud Computing, Data Privacy, Lattice-Based Cryptography, Performance Benchmarks, Hardware Acceleration

Manuscript Timeline: Received: June 05, 2022; Revised: July 12, 2022; Accepted: August 03, 2022; Published: February 11, 2023

International Journal of Computer Science and Technology | Vol. 4, No. 1, January 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000216

Article Type: Original Research Paper

Title: Enhancing Low-Light Video Summarization via Spatial-Temporal Attention Network Models

Names of Authors: Min-Ji Kim¹, David Vance²

Authors’ Affiliations: ¹School of Electronics and Informatics, Seoul National University, Seoul, South Korea; ²Computer Vision Research Systems, Lumina Tech Labs, Denver, USA

Abstract: Processing video security streams recorded in low-light environments is challenging due to high sensor noise and low color contrast. These artifacts cause standard deep learning object trackers and video summarization models to drop critical frames. This paper introduces a specialized spatial-temporal attention model designed to summarize low-light security video footage accurately. The architecture features an internal feature-enhancement layer that amplifies low-contrast details without over-processing high-frequency sensor noise. A recurrent neural layer calculates regional attention maps across consecutive frames to highlight moving subjects. We trained and evaluated the neural system using a comprehensive low-light security dataset. The model achieved a F1-score optimization level of 0.89, outperforming conventional summarization networks by 14.2%. The total video file processing volume was condensed by 65.0%, preserving critical action sequences. This framework provides an efficient method to scale large-scale urban video analytics infrastructure running under challenging environmental conditions.

Keywords: Video Summarization, Spatial-Temporal Attention, Low-Light Enhancement, Computer Vision, Deep Learning, Security Analytics

Manuscript Timeline: Received: June 02, 2022; Revised: July 08, 2022; Accepted: August 01, 2022; Published: January 06, 2023