International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 10, No. 2, February 2008 | pp. 9–16

DOI: 10.46882/2008/IJCST/102011

Research Article

Title: An Automated Semantic Web Services Discovery Framework Using Contextual Ontology Mapping

Names of Authors: Rajesh K. Gupta¹, Sunita M. Sharma¹

Authors’ Affiliations: ¹Department of Information Technology, Delhi Technological University, New Delhi, India

Abstract: The proliferation of Web Services Description Language (WSDL) documents across public repositories has introduced significant complexities in automated software discovery and selection. Traditional keyword-matching mechanisms frequently fail to capture functional semantics, leading to poor discovery precision. This paper introduces an automated Semantic Web Services discovery framework driven by contextual ontology mapping and bipartite graph matching. The proposed engine constructs conceptual profile representations by mapping unstructured WSDL attributes to standardized OWL-S ontologies. A hybrid similarity metric is deployed to evaluate structural, functional, and contextual parameters concurrently. Benchmarking experiments conducted on the OWLS-TC v2.0 collection indicate that our framework achieves an average retrieval precision of 91.4% and a recall rate of 86.3%. Statistical t-test validations confirmed that the framework outperforms traditional UDDI search registries by 22.5% ± 1.4% under high query ambiguity. The semantic mapping execution overhead remains bound to 415 milliseconds per service registration, highlighting operational viability for large-scale enterprise service-oriented architectures.

Keywords: Semantic Web, service discovery, ontology mapping, OWL-S, similarity metrics, service-oriented architecture

Manuscript Timeline: Received November 12, 2007; Revised December 28, 2007; Accepted January 18, 2008; Published February 4, 2008

Citation: Gupta, R. K., & Sharma, S. M. (2008). An Automated Semantic Web Services Discovery Framework Using Contextual Ontology Mapping. International Journal of Computer Science and Technology, 10(2), 9–16.

International Journal of Computer Science and Technology | Vol. 10, No. 1, January 2008 | pp. 1–8

DOI: 10.46882/2008/IJCST/101010

Research Article

Title: Enhancing Software Defect Prediction Accuracy Using Ensemble Machine Learning Classifiers

Names of Authors: Markus T. Lindqvist¹, Anja K. Koskinen¹

Authors’ Affiliations: ¹Department of Computer Science and Engineering, Aalto University, Espoo, Finland

Abstract: Early software defect identification is a primary mechanism for reducing software testing costs and refining engineering workflows. Traditional software defect prediction models often suffer from high false-alarm rates when processing imbalanced static code metric repositories. This paper proposes a hybrid ensemble machine learning framework that combines Random Forest, Support Vector Machines (SVM), and Gradient Boosted Trees via a soft-voting ensemble configuration. To resolve severe class imbalance issues, the Synthetic Minority Over-sampling Technique (SMOTE) was incorporated into the data preprocessing pipeline. The model was trained and validated using standard NASA MDP datasets (CM1, JM1, KC1). Experimental results revealed that the ensemble classifier significantly outperformed individual base learners, yielding an average Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.914. The prediction sensitivity reached 87.6% ± 1.1%, while minimizing the False Positive Rate to 12.3%. This model provides software engineering teams with an automated tool to reliably isolate fault-prone codebase modules before deployment.

Keywords: Software defect prediction, ensemble learning, machine learning, software metrics, SMOTE, AUC-ROC

Manuscript Timeline: Received October 8, 2007; Revised November 20, 2007; Accepted December 14, 2007; Published January 8, 2008

Citation: Lindqvist, M. T., & Koskinen, A. K. (2008). Enhancing Software Defect Prediction Accuracy Using Ensemble Machine Learning Classifiers. International Journal of Computer Science and Technology, 10(1), 1–8.

International Journal of Computer Science and Technology | Vol. 9, No. 12, December 2007 | pp. 89–96

DOI: 10.46882/2007/IJCST/091209

Research Article

Title: Development of an Intelligent Tutoring System for Computer Programming Pedagogy

Names of Authors: William K. Harrison¹, Emily T. Ross²

Authors’ Affiliations: ¹School of Computing, University of Leeds, Leeds, UK; ²Department of Education and Technology, University of Manchester, Manchester, UK

Abstract: Introductory computer science courses often suffer from high student attrition rates due to the steep learning curve of syntax and logical problem-solving. This paper describes the development and classroom deployment of an Intelligent Tutoring System (ITS) named CodeGuide, designed to assist novice students learning the C++ language. CodeGuide integrates an automated syntax analyzer with a rule-based expert system that identifies logical fallacies in student code and outputs tailored pedagogical guidance. The software adaptively updates individual student skill profiles using a Bayesian knowledge-tracing algorithm, dynamically varying problem difficulty based on demonstrated mastery. CodeGuide was evaluated over a 12-week semester with an experimental cohort of 120 undergraduate students. Empirical data analysis demonstrated that students using CodeGuide achieved a 14.6% higher average score on final programming exams compared to the control group relying on conventional instructional methods. The student engagement metric increased, with an average platform usability satisfaction score of 4.35 out of 5.00 on the Likert scale.

Keywords: Intelligent tutoring systems, computer science education, automated assessment, Bayesian knowledge tracing, adaptive learning, C++ programming

Manuscript Timeline: Received September 4, 2007; Revised October 18, 2007; Accepted November 12, 2007; Published December 5, 2007

Citation: Harrison, W. K., & Ross, E. T. (2007). Development of an Intelligent Tutoring System for Computer Programming Pedagogy. International Journal of Computer Science and Technology, 9(12), 89–96.

International Journal of Computer Science and Technology | Vol. 9, No. 9, September 2007 | pp. 65–72

DOI: 10.46882/2007/IJCST/099006

Research Article

Title: Performance Evaluation of Lossless Data Compression Algorithms for Embedded Systems

Names of Authors: Yukihiro Tanaka¹, Kenji Sato¹

Authors’ Affiliations: ¹Department of Electronic Engineering, Tokyo Institute of Technology, Tokyo, Japan

Abstract: Resource-constrained embedded systems often encounter storage and data transmission bottlenecks, making lightweight data compression algorithms highly essential. This paper conducts a rigorous performance evaluation of four prominent lossless data compression algorithms—Huffman coding, Lempel-Ziv-Welch (LZW), Deflate, and Run-Length Encoding (RLE)—tailored for 16-bit microcontroller microarchitectures. The algorithms were evaluated using diverse sensor-derived datasets, logging metrics across compression ratios, execution times, and dynamic RAM requirements. Our experimental benchmarks reveal that while the Deflate algorithm offers the highest average compression ratio of 2.45:1, it demands excessive memory footprints (exceeding 32 KB RAM) and high computational cycles. In contrast, an optimized Huffman coding implementation yielded a balanced compression ratio of 1.68:1 while utilizing merely 2.8 KB of RAM and running within 14.2 milliseconds per kilobyte of raw sensor data. Statistical t-tests confirmed that the optimized Huffman implementation achieves a significant reduction in power consumption during RF transmission, dropping energy costs by 34.5% ± 1.2% due to minimized packet payloads.

Keywords: Lossless data compression, embedded systems, Huffman coding, LZW algorithm, memory constraints, power optimization

Manuscript Timeline: Received June 11, 2007; Revised July 23, 2007; Accepted August 15, 2007; Published September 4, 2007

Citation: Tanaka, Y., & Sato, K. (2007). Performance Evaluation of Lossless Data Compression Algorithms for Embedded Systems. International Journal of Computer Science and Technology, 9(9), 65–72.

International Journal of Computer Science and Technology | Vol. 9, No. 7, July 2007 | pp. 49–56

DOI: 10.46882/2007/IJCST/097004

Research Article

Title: Design and Implementation of an Energy-Efficient Routing Protocol for Wireless Sensor Networks

Names of Authors: Ahmad M. Al-Zahrani¹, Tariq O. Faisal¹

Authors’ Affiliations: ¹Department of Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia

Abstract: Wireless Sensor Networks (WSNs) are heavily constrained by the finite battery capacities of their distributed sensor nodes, making energy-efficient communication routing protocol design paramount. This paper details the design and deployment of the Cluster-Based Energy-Aware Routing (CBEAR) protocol, aimed at maximizing network operational lifetimes. CBEAR introduces an innovative cluster head selection algorithm that factorially weights residual energy metrics, node proximity to the base station, and local node density. This formulation balances power consumption evenly across the entire network topology, avoiding early node death patterns. Experimental simulations using NS-2 software revealed that CBEAR extended the total network operational lifetime by 26.3% compared to the standard Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. The network retained 90% of active nodes over an extended run of 2500 rounds, maintaining a stable packet delivery ratio of 97.4% ± 0.8%. Average power consumption per node per round was significantly minimized to 0.012 Joules, demonstrating an efficient mechanism for long-term environmental monitoring applications.

Keywords: Wireless sensor networks, routing protocols, energy efficiency, cluster head selection, network lifetime, NS-2 simulation

Manuscript Timeline: Received April 10, 2007; Revised May 22, 2007; Accepted June 18, 2007; Published July 10, 2007

Citation: Al-Zahrani, A. M., & Faisal, T. O. (2007). Design and Implementation of an Energy-Efficient Routing Protocol for Wireless Sensor Networks. International Journal of Computer Science and Technology, 9(7), 49–56.

International Journal of Computer Science and Technology | Vol. 9, No. 6, June 2007 | pp. 41–48

DOI: 10.46882/2007/IJCST/096003

Review Article

Title: Biometric Authentication Systems: A Comprehensive Review of Modalities, Security Vulnerabilities, and Future Trends

Names of Authors: Elena R. Vasilyeva¹

Authors’ Affiliations: ¹Faculty of Information Security, Saint Petersburg National Research University, Saint Petersburg, Russia

Abstract: Biometric technologies have shifted from niche security systems to mainstream modalities for identity verification across consumer electronics, banking, and border control. This review article provides an exhaustive critical analysis of physiological and behavioral biometric modalities, including fingerprint, iris, facial geometry, voiceprint, and keystroke dynamics. The paper systematically categorizes prevalent security vulnerabilities, focusing on presentation attacks, spoofing methods, and template database tampering. Furthermore, we evaluate recent state-of-the-art countermeasures, such as hardware-based and software-based liveness detection mechanisms. Comparative performance analysis across the modalities highlights that iris recognition maintains the lowest False Match Rate (FMR < 0.0001%), though it requires expensive specialized capture hardware. Conversely, facial recognition shows widespread consumer adoption but remains more susceptible to lighting variations and aging factors, exhibiting a baseline False Non-Match Rate (FNMR) variance of ±2.3% across varying operational conditions. Finally, the paper discusses emergent paradigms in biometric cryptography and cancelable biometrics designed to safeguard user privacy against systemic database breaches.

Keywords: Biometric authentication, liveness detection, security vulnerabilities, spoofing countermeasures, false match rate, template protection

Manuscript Timeline: Received March 3, 2007; Revised April 20, 2007; Accepted May 14, 2007; Published June 12, 2007

Citation: Vasilyeva, E. R. (2007). Biometric Authentication Systems: A Comprehensive Review of Modalities, Security Vulnerabilities, and Future Trends. International Journal of Computer Science and Technology, 9(6), 41–48.