2026. 1^st Issue

Volume XVIII, Number 1

Table of contents 

Full issue  

 

PAPERS FROM OPEN CALL

Rajakani M., Beaulah Jeyavathana R., and Kavitha R. J.
Relay Pursuit-Vathana: A Novel Optimization Approach for Feature Selection in Software Defect Prediction 
Software defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Feature selection, the process of identifying the most relevant features from a large set of potential features which is essential for building effective defect prediction models. In this paper, we propose a novel feature selection model based on the RelayPursuit- Vathana (RP-Vathana) optimization algorithm, inspired by relay races and pursuit dynamics in biological systems. The proposed model aims to identify an optimal subset of features for software defect prediction, maximizing the predictive performance of the resulting classification model. The RP-Vathana algorithm was integrated with a Naïve Bayes classifier and benchmarked on three datasets (PC5, JM1, KC2) to validate its effectiveness in feature selection for defect prediction. The results show that RP-Vathana significantly outperforms existing wrapper-based methods, obtaining mean accuracies of 94.28%, 93.69%, and 96.35% on PC5, JM1, and KC2, respectively, compared to the 83−90% range of rival techniques. While the parameter-free design improves usability, the algorithm's performance on highly noisy or very small datasets warrants future investigation into hybrid extensions for enhanced robustness.

DOI: 10.36244/ICJ.2026.1.1
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Oussama Lagnfdi, Marouane Myyara, and Anouar Darif
A New Deep Learning-Based Approach for IoT Task Offloading in Multi-access Edge Computing 
The exponential growth of Internet of Things (IoT) devices and the growing demand for resource-intensive applications have introduced significant challenges in computation, storage, and network efficiency. Although cloud computing provides partial relief, its centralized nature leads to unacceptable latency for delay-sensitive applications. Multi-access Edge Computing (MEC), especially with the advent of 5G, has emerged as a compelling solution by relocating computation closer to data sources, thereby reducing latency and improving responsiveness in applications such as smart agriculture, autonomous vehicles, augmented reality, and telemedicine. However, efficient workload offloading in MEC environments remains complex due to system heterogeneity, varying application requirements, and limited edge resources. This paper proposes a novel neural network-based approach to computation offloading in MEC, integrating workload allocation and resource management while accounting for application delay sensitivity, processing capacity, and communication constraints. The proposed model enables driving offloading decisions, adapting to fluctuating system states without relying on complex mathematical formulations. Simulation results demonstrate that the approach significantly reduces service time and enhances resource utilization, ensuring responsiveness for modern IoT applications. This research underscores MEC’s potential to meet the rising computational and latency demands of next-generation IoT infrastructure.

DOI: 10.36244/ICJ.2026.1.2
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Hussein Tuama, and Sándor Imre
Enhancing Quantum State Transmission Fidelity through Quantum Orthogonal Frequency Division Multiple Access 
In this paper, we propose quantum orthogonal frequency division multiple access (Q-OFDMA), a novel quantum communication scheme designed to overcome the fidelity limitations imposed by noise in multi-user quantum networks. Inspired by its classical counterpart, Q-OFDMA employs the quantum Fourier transform (QFT) and its inverse (IQFT) to encode and decode information across quantum channels. We evaluate our model under both a depolarization channel and a generalized noise model that interpolates between depolarizing and phase-damping noises. The simulation results conducted on Qiskit platform demonstrate that Q-OFDMA outperforms the reference model, achieving superior average fidelity across varying qubit counts and noise levels.

DOI: 10.36244/ICJ.2026.1.3
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Márton Pál Lipcsey-Magyar, Attila Ármin Madarász, and Adrian Pekar
Beyond JA4+: Flow Statistics vs. TLS Fingerprinting for Encrypted Malware Detection 
The deployment of Encrypted Client Hello (ECH) challenges TLS fingerprinting, a widely used approach for encrypted malware detection, by encrypting the handshake fields these methods rely on. This paper presents a systematic evaluation of flow-based statistical features as a handshakeindependent alternative to fingerprinting. Through validation against the official JA4+ implementation, we establish limitations in fingerprinting approaches for this corpus: only 64.9% of malware families possess unique signatures, placing an inherent ceiling on achievable recall in our evaluation. We evaluate flow-level features—packet counts, timing patterns, and size distributions—across 27 experimental configurations on a dataset of 16,542 flows spanning 101 families (59 malware and 42 benign applications). Random Forest classifiers using combined flow statistics and sequential packet length features achieve 98.11% F1-score for binary malware detection with 97.22% recall, substantially exceeding fingerprinting’s theoretical recall bound of 64.9%. For fine-grained family identification, we obtain 54.81% macro F1 across 101 classes and 48.71% macro F1 for malwareonly attribution, demonstrating that flow-based methods retain meaningful discriminative power where fingerprinting abstains. Across all tasks, Random Forest consistently outperforms neural networks and k-NN, with performance gaps widening in complex multiclass scenarios. These findings highlight flow-based classification as a practical and reproducible approach that can help maintain network security visibility as ECH deployment progresses, showing that behavioral traffic patterns are expected to provide durable signals for detection even as handshake fields become encrypted.

DOI: 10.36244/ICJ.2026.1.4
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Rajakani M., Beaulah Jeyavathana R., and Kavitha R. J.
Relay Pursuit-Vathana: A Novel Optimization Approach for Feature Selection in Software Defect Prediction 
Software defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Feature selection, the process of identifying the most relevant features from a large set of potential features which is essential for building effective defect prediction models. In this paper, we propose a novel feature selection model based on the RelayPursuit- Vathana (RP-Vathana) optimization algorithm, inspired by relay races and pursuit dynamics in biological systems. The proposed model aims to identify an optimal subset of features for software defect prediction, maximizing the predictive performance of the resulting classification model. The RP-Vathana algorithm was integrated with a Naïve Bayes classifier and benchmarked on three datasets (PC5, JM1, KC2) to validate its effectiveness in feature selection for defect prediction. The results show that RP-Vathana significantly outperforms existing wrapper-based methods, obtaining mean accuracies of 94.28%, 93.69%, and 96.35% on PC5, JM1, and KC2, respectively, compared to the 83−90% range of rival techniques. While the parameter-free design improves usability, the algorithm's performance on highly noisy or very small datasets warrants future investigation into hybrid extensions for enhanced robustness.

DOI: 10.36244/ICJ.2026.1.5
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Rajakani M., Beaulah Jeyavathana R., and Kavitha R. J.
Relay Pursuit-Vathana: A Novel Optimization Approach for Feature Selection in Software Defect Prediction 
Software defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Feature selection, the process of identifying the most relevant features from a large set of potential features which is essential for building effective defect prediction models. In this paper, we propose a novel feature selection model based on the RelayPursuit- Vathana (RP-Vathana) optimization algorithm, inspired by relay races and pursuit dynamics in biological systems. The proposed model aims to identify an optimal subset of features for software defect prediction, maximizing the predictive performance of the resulting classification model. The RP-Vathana algorithm was integrated with a Naïve Bayes classifier and benchmarked on three datasets (PC5, JM1, KC2) to validate its effectiveness in feature selection for defect prediction. The results show that RP-Vathana significantly outperforms existing wrapper-based methods, obtaining mean accuracies of 94.28%, 93.69%, and 96.35% on PC5, JM1, and KC2, respectively, compared to the 83−90% range of rival techniques. While the parameter-free design improves usability, the algorithm's performance on highly noisy or very small datasets warrants future investigation into hybrid extensions for enhanced robustness.

DOI: 10.36244/ICJ.2026.1.6
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Rajakani M., Beaulah Jeyavathana R., and Kavitha R. J.
Relay Pursuit-Vathana: A Novel Optimization Approach for Feature Selection in Software Defect Prediction 
Software defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Feature selection, the process of identifying the most relevant features from a large set of potential features which is essential for building effective defect prediction models. In this paper, we propose a novel feature selection model based on the RelayPursuit- Vathana (RP-Vathana) optimization algorithm, inspired by relay races and pursuit dynamics in biological systems. The proposed model aims to identify an optimal subset of features for software defect prediction, maximizing the predictive performance of the resulting classification model. The RP-Vathana algorithm was integrated with a Naïve Bayes classifier and benchmarked on three datasets (PC5, JM1, KC2) to validate its effectiveness in feature selection for defect prediction. The results show that RP-Vathana significantly outperforms existing wrapper-based methods, obtaining mean accuracies of 94.28%, 93.69%, and 96.35% on PC5, JM1, and KC2, respectively, compared to the 83−90% range of rival techniques. While the parameter-free design improves usability, the algorithm's performance on highly noisy or very small datasets warrants future investigation into hybrid extensions for enhanced robustness.

DOI: 10.36244/ICJ.2026.1.7
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Rajakani M., Beaulah Jeyavathana R., and Kavitha R. J.
Relay Pursuit-Vathana: A Novel Optimization Approach for Feature Selection in Software Defect Prediction 
Software defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Feature selection, the process of identifying the most relevant features from a large set of potential features which is essential for building effective defect prediction models. In this paper, we propose a novel feature selection model based on the RelayPursuit- Vathana (RP-Vathana) optimization algorithm, inspired by relay races and pursuit dynamics in biological systems. The proposed model aims to identify an optimal subset of features for software defect prediction, maximizing the predictive performance of the resulting classification model. The RP-Vathana algorithm was integrated with a Naïve Bayes classifier and benchmarked on three datasets (PC5, JM1, KC2) to validate its effectiveness in feature selection for defect prediction. The results show that RP-Vathana significantly outperforms existing wrapper-based methods, obtaining mean accuracies of 94.28%, 93.69%, and 96.35% on PC5, JM1, and KC2, respectively, compared to the 83−90% range of rival techniques. While the parameter-free design improves usability, the algorithm's performance on highly noisy or very small datasets warrants future investigation into hybrid extensions for enhanced robustness.

DOI: 10.36244/ICJ.2026.1.8
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Rajakani M., Beaulah Jeyavathana R., and Kavitha R. J.
Relay Pursuit-Vathana: A Novel Optimization Approach for Feature Selection in Software Defect Prediction 
Software defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Feature selection, the process of identifying the most relevant features from a large set of potential features which is essential for building effective defect prediction models. In this paper, we propose a novel feature selection model based on the RelayPursuit- Vathana (RP-Vathana) optimization algorithm, inspired by relay races and pursuit dynamics in biological systems. The proposed model aims to identify an optimal subset of features for software defect prediction, maximizing the predictive performance of the resulting classification model. The RP-Vathana algorithm was integrated with a Naïve Bayes classifier and benchmarked on three datasets (PC5, JM1, KC2) to validate its effectiveness in feature selection for defect prediction. The results show that RP-Vathana significantly outperforms existing wrapper-based methods, obtaining mean accuracies of 94.28%, 93.69%, and 96.35% on PC5, JM1, and KC2, respectively, compared to the 83−90% range of rival techniques. While the parameter-free design improves usability, the algorithm's performance on highly noisy or very small datasets warrants future investigation into hybrid extensions for enhanced robustness.

DOI: 10.36244/ICJ.2026.1.9
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Rajakani M., Beaulah Jeyavathana R., and Kavitha R. J.
Relay Pursuit-Vathana: A Novel Optimization Approach for Feature Selection in Software Defect Prediction 
Software defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Feature selection, the process of identifying the most relevant features from a large set of potential features which is essential for building effective defect prediction models. In this paper, we propose a novel feature selection model based on the RelayPursuit- Vathana (RP-Vathana) optimization algorithm, inspired by relay races and pursuit dynamics in biological systems. The proposed model aims to identify an optimal subset of features for software defect prediction, maximizing the predictive performance of the resulting classification model. The RP-Vathana algorithm was integrated with a Naïve Bayes classifier and benchmarked on three datasets (PC5, JM1, KC2) to validate its effectiveness in feature selection for defect prediction. The results show that RP-Vathana significantly outperforms existing wrapper-based methods, obtaining mean accuracies of 94.28%, 93.69%, and 96.35% on PC5, JM1, and KC2, respectively, compared to the 83−90% range of rival techniques. While the parameter-free design improves usability, the algorithm's performance on highly noisy or very small datasets warrants future investigation into hybrid extensions for enhanced robustness.

DOI: 10.36244/ICJ.2026.1.10
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Rajakani M., Beaulah Jeyavathana R., and Kavitha R. J.
Relay Pursuit-Vathana: A Novel Optimization Approach for Feature Selection in Software Defect Prediction 
Software defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Feature selection, the process of identifying the most relevant features from a large set of potential features which is essential for building effective defect prediction models. In this paper, we propose a novel feature selection model based on the RelayPursuit- Vathana (RP-Vathana) optimization algorithm, inspired by relay races and pursuit dynamics in biological systems. The proposed model aims to identify an optimal subset of features for software defect prediction, maximizing the predictive performance of the resulting classification model. The RP-Vathana algorithm was integrated with a Naïve Bayes classifier and benchmarked on three datasets (PC5, JM1, KC2) to validate its effectiveness in feature selection for defect prediction. The results show that RP-Vathana significantly outperforms existing wrapper-based methods, obtaining mean accuracies of 94.28%, 93.69%, and 96.35% on PC5, JM1, and KC2, respectively, compared to the 83−90% range of rival techniques. While the parameter-free design improves usability, the algorithm's performance on highly noisy or very small datasets warrants future investigation into hybrid extensions for enhanced robustness.

DOI: 10.36244/ICJ.2026.1.11
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