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Volume 2

Articles

All Articles

Editorial

Published: 31 Dec 2025

Preface

Volume 2

Moussa Ayyash

Moussa Ayyash
msma@ieee.org

Volume 2 of Computing&AI Connect (CAIC) presents 21 high-quality, peer-reviewed research articles that reflect CAIC’s commitment to advancing the theory, design, and application of computing and artificial intelligence–driven systems. Volume 2 articles cover contemporary challenges in intelligent systems, communication networks, security, data analytics, and human-centered technologies. They also contribute to the development of foundational methodologies and interdisciplinary perspectives. A central focus of this volume is the pervasive role of artificial..

Journal: Computing&AI Connect

Research Article

Published: 30 Dec 2025

Sheaf Cohomology for Fragment-Sequencing in Hierarchical Block Rectangular Matrices with Spectral Gaps in the Presence of Random Effects and White Noise: The Chain

Volume 2

Orchidea Maria Lecian

Orchidea Maria Lecian
omlecian@gmail.com

The sheaf cohomology of topological shift for the block-rectangular matrix-representation of the hierarchical Markov Model incorporates an analytic formulation of white noise and of random effects. New analytical techniques for fragment sequencing are developed. The fragment sequencing is obtained after the topological Markov chain of the adjacency matrix of the corresponding undirected graph; the presence of white noise and that of random effects are included. The paradigm consists in defining..

Journal: Computing&AI Connect

Review Article

Published: 29 Dec 2025

Computing and AI in Genomics-Driven Precision Oncology: Methods, Trials, and Translation

Volume 2

Arbiona Shukulli

Arbiona Shukulli
arbionashukulli@precacollege.org
Rahaf M. Ahmad

Rahaf M. Ahmad
700039113@uaeu.ac.ae

Journal: Computing&AI Connect

Research Article

Published: 20 Dec 2025

The Human Intelligence linguistic Variable Is a Potential Fuzzy Computational Model for the Natural Languish Expression Human Intelligence

Volume 2

Andreea V. Cojocaru

Andreea V. Cojocaru
candreeavalentina@gmail.com
Stefan Balint

Stefan Balint
stefan.balint@e-uvt.ro

This paper presents a fuzzy, computational model for the natural-language term ‘human intelligence’. This mathematical model accounts for multiple interpretations of the term ‘intelligent’ and enables comparison of these interpretations for a given IQ score. The model is a linguistic variable–based mathematical framework specifically designed to represent human intelligence. The model is constructed systematically throughout the paper. First, the term ‘human intelligence’ is represented as a fuzzy set. This step..

Journal: Computing&AI Connect

Research Article

Published: 19 Dec 2025

Enhancing Pest Management in Precision Agriculture: Integration of Improved YOLOv5 and IoT Technology for Efficient Codling Moth Detection

Volume 2

Mohamed Zarboubi

Mohamed Zarboubi
mohamed.zarboubi@edu.uiz.ac.ma
Abdelaaziz Bellout

Abdelaaziz Bellout
abdelaaziz.bellout@edu.uiz.ac.ma
Samira Chabaa

Samira Chabaa
s.chabaa@uiz.ac.ma
Azzedine Dliou

Azzedine Dliou
a.dliou@uiz.ac.ma

Journal: Computing&AI Connect

Research Article

Published: 08 Dec 2025

Security Assurance for 5G Split gNB and AMF Vulnerabilities

Volume 2

Yi-Hsueh Tsai

Yi-Hsueh Tsai
yihsuehtsai@g.ntu.edu.tw
Shiang-Jiun Chen

Shiang-Jiun Chen
annette@ntut.edu.tw

Journal: Computing&AI Connect

Research Article

Published: 25 Nov 2025

6G Communication Systems Utilizing Intelligent Reflective Surfaces for Improved Data Rates Based on AI

Volume 2

Jehan Kadhim Shareef Al-Safi

Jehan Kadhim Shareef Al-Safi
jihan.k.shareef@utq.edu.iq

Intelligent Reflecting Surfaces (IRSs) are essential for energy-efficient 6G networks; however, enhancing their performance to attain both elevated data rates and energy efficiency continues to be a formidable challenge. This paper uses artificial intelligence (AI) to solve this problem by creating a new AI-based optimization algorithm called Dynamic and Static Particle Swarm Optimization (DS-PSO). The suggested AI method intelligently learns the best phase-shift settings for IRS elements, balancing exploration and..