Vol 14 Issue 1 January 2026-March 2026

Abstract: Machine learning (ML)–driven frameworks for intelligent decision-making, predictive analytics, and adaptive control in cyber–physical production systems have propelled smart manufacturing. The complexity of automated and robotic production contexts makes ML model transparency and explainability a major research topic. Recently developed deep learning–based defect prediction methods may capture complicated temporal degradation patterns from high-dimensional sensor data, but their interpretability limits industrial use. In light of these findings, this theoretical exploration examines ML-driven frameworks for intelligent and explainable manufacturing systems using predictive maintenance, Natural Language Processing (NLP), and Large Language Models (LLM) approaches. The study aims to conceptually investigate how deep learning–based time-to-fault prediction methods might be used to explainable manufacturing frameworks for human-centric decision-making. The qualitative study synthesizes literature and references deep learning architectures like sequence learning and temporal modelling utilized for time-to-fault prediction in automated manufacturing and humanoid robotics. Theory-mapped to intelligent manufacturing systems, these techniques use explainability mechanisms, NLP-based semantic interpretation, and LLM-assisted knowledge extraction to turn model outputs into human-understandable insights. Theoretical studies imply that ML-driven predictive frameworks can reduce unplanned downtime, improve maintenance planning accuracy, and improve system dependability, while NLP and LLM components help operators make contextual decisions. The study concludes that transparent and trustworthy intelligent manufacturing systems require explainable ML, NLP, and LLM technologies. The consequences include greater operational efficiency, trust in AI-driven production decisions, and a scalable theoretical underpinning for empirical research and Industry 5.0 implementations.

Keywords: Machine Learning–Driven Frameworks; Explainable Manufacturing Systems; Intelligent Manufacturing Systems; Machine Learning; Natural Language Processing; Large Language Models.

Title: A Study of Machine Learning–Driven Frameworks for Intelligent and Explainable Manufacturing Systems: A Theoretical Exploration

Author: Suraj Shrestha

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 128-134

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Website: www.researchpublish.com

Published Date: 09-March-2026

Abstract: This paper presents a real-time object detection system for visualizing and processing streaming media using OpenCV and the YOLOv5 deep learning model. The proposed system captures live video streams and processes each frame to accurately detect and classify multiple objects in real time. OpenCV is used for video acquisition, preprocessing, and visualization, while YOLOv5 performs fast and reliable object detection by generating bounding boxes and class labels. The system effectively detects objects such as persons, mobile phones, and clocks with high accuracy and smooth frame rates. Experimental results demonstrate that the proposed approach achieves a good balance between detection accuracy and real-time performance on standard laptop hardware. The developed system is scalable and can be extended to advanced applications such as surveillance, traffic monitoring, and intelligent video analytics.

Keywords: OpenCV, YOLOv5, Object Detection, Computer Vision, Deep Learning, Real-Time Video Processing, Image Processing.

Title: Visualizing and Processing Streaming Media with Object Identification Using OpenCV

Author: Sujan C R, Veeresh I Hiremath, Vishal K, Deepika D Pai

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 122-127

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Website: www.researchpublish.com

Published Date: 02-March-2026

Abstract: Accurate classification of fish species plays a crucial role in biodiversity conservation, fisheries management, and aquatic ecosystem monitoring. Traditionally, fish classification methods, rely on manual identification, which are time-consuming, error-prone, and often depend heavily on expert knowledge. To overcome these limitations, this study presents an Improved Classification Model for Fish Species Based on Deep Learning Techniques. The proposed model integrates Convolutional Neural Networks (CNNs) with advanced deep learning architectures such as VGG16, ResNet50 and optimized through transfer learning and fine-tuning strategies to enhance feature extraction and classification accuracy. Fish Image Dataset containing multiple species acquired from kaggle.com was utilized. The dataset was preprocessed using image augmentation techniques including rotation, scaling, flipping, and brightness adjustments. Images were normalized and resized to 224×224 pixels to fit the model input requirements. The study adopted a comparative experimental design where three CNN architectures were trained, validated, and tested on the same dataset. Each model was evaluated using accuracy, precision, recall and F1-score metrics to ensure robust performance assessment. Training was conducted using Python and TensorFlow/Keras frameworks. The experimental results revealed that the Improved ResNet50-based model achieved superior classification performance compared to baseline CNN and VGG16 models, attaining an overall accuracy of 96.2%, precision of 97.4%, recall of 96.2%, and an F1-score of 96.4% on the test set. The improved deep learning model provides a robust, scalable, and automated approach for fish species classification, offering substantial potential for applications in marine biology, aquaculture, and ecological research.

Keywords: Fish Image Recognition, Classification, CNN, VGG16, ResNet, Deep Learning.

Title: Improved Classification Model for Fish Species Based on Deep Learning Techniques

Author: Samuel Oluwatayo Ogunlana, Felix Ola Aranuwa

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 110-121

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Website: www.researchpublish.com

Published Date: 21-February-2026

Abstract: The rapid digital transformation of the telecommunications sector has intensified cybersecurity threats, data protection risks, and ethical challenges associated with artificial intelligence (AI). This study evaluates cybersecurity practices, data protection mechanisms, and safe AI training within a private telecommunications entity in Zimbabwe. Using a mixed-methods research approach, the study assesses organizational preparedness, employee awareness, governance frameworks, and compliance with data protection and emerging AI safety standards. Findings indicate that while foundational cybersecurity controls exist, gaps remain in advanced threat detection, employee training consistency, and structured AI governance. Data protection compliance shows moderate alignment with statutory requirements, though enforcement and staff awareness are uneven. The study concludes that effective cybersecurity and safe AI adoption require integrated governance frameworks, continuous training, and leadership commitment. The article contributes to limited empirical literature on cybersecurity and AI governance in Zimbabwe’s private sector and provides practical recommendations for strengthening digital resilience in telecommunications firms.

Keywords: Artificial Intelligence, Board Induction, Data Protection, Private Communications Entity, Zimbabwe.

Title: Evaluating Cybersecurity, Data Protection, and Safe AI Training in a Private Telecommunications Entity in Zimbabwe

Author: Enock Panganayi Mawuye

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 104-109

Research Publish Journals

Website: www.researchpublish.com

Published Date: 09-February-2026

Abstract:  Guided by the Human-Centered AI–Teaching Presence (HC-AI-TP) framework, this study examined whether artificial intelligence (AI) functions as a mixed blessing in higher education by testing its relationships with perceived learning quality and academic effort and by assessing the moderating role of instructor guidance. Using quantitative, nonexperimental correlational design, survey data were collected from 182 undergraduate and graduate students enrolled in higher education courses where AI tools were available. Measures assessed AI use, perceived learning quality, academic effort, and instructor guidance. Data were analyzed using correlation, linear regression, and moderated regression techniques. Results showed that AI use was positively associated with perceived learning quality (β = .21, p = .005, R² = .04) but negatively associated with academic effort (β = −.26, p < .001, R² = .06). Instructor guidance significantly moderated both relationships, strengthening the positive association between AI use and learning quality (β = .19, p = .003, ΔR² = .03) and reducing the negative association between AI use and academic effort (β = .22, p = .001, ΔR² = .04). All four null hypotheses were rejected. These findings demonstrate that AI’s impact on learning is conditional rather than uniform. While AI use may enhance perceived learning quality, it may also reduce academic effort unless guided by clear instructional expectations. The study provides empirical support for HC-AI-TP by showing that teaching presence is the central mechanism through which AI supports meaningful learning while preserving student effort. The results help explain inconsistent findings in prior research and underscore the importance of human-centered instructional guidance in AI-enabled higher education.

Keywords: artificial intelligence, teaching presence, learning quality, academic effort, higher education.

Title: Artificial Intelligence as a “Mixed Blessing” in Online Higher Education: A Human-Centered AI–Teaching Presence (HC-AI-TP) Perspective

Author: Dr. David Augustine Bull

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 88-103

Research Publish Journals

Website: www.researchpublish.com

Published Date: 02-February-2026

Abstract: The use of artificial intelligence (AI) in higher education assessment has been widely promoted as a mechanism for strengthening accreditation compliance and advancing continuous quality improvement (CQI); however, empirical evidence explaining how faculty and administrator perceptions translate into these outcomes remains limited. Guided by an integrated Technology Acceptance Model–Human-Centered AI–Continuous Quality Improvement (TAM–HCAI–CQI) framework, this study examined the relationships among perceived AI-driven assessment analytics, assessment confidence, perceived accreditation readiness, and CQI capacity in healthcare management education. Using a quantitative, cross-sectional perception-based design, survey data were collected from 64 participants (45 faculty members and 19 academic administrators) engaged in assessment and accreditation activities. Descriptive analyses indicated moderately positive perceptions of AI-driven assessment analytics (M = 3.61, SD = 0.67), with no statistically significant differences between faculty and administrators. Regression analyses demonstrated that perceived AI-driven assessment analytics significantly predicted accreditation readiness (β = .52, p < .001, R² = .27) and CQI capacity (β = .57, p < .001, R² = .32). Mediation analyses using bootstrapping revealed that assessment confidence partially mediated the relationship between AI perceptions and accreditation readiness (indirect effect β = .32, 95% CI [.18, .50]) and fully mediated the relationship between AI perceptions and CQI capacity (indirect effect β = .34, 95% CI [.20, .54]). Role-based analyses further indicated convergence between faculty and administrator perceptions across all study variables. Collectively, these findings demonstrate that the contribution of AI-driven assessment analytics to accreditation readiness and continuous improvement operates primarily through faculty and administrator confidence in assessment evidence, rather than through technology alone. The study explains how institutions move from compliance-oriented assessment practices toward sustained continuous quality improvement, highlighting the importance of human-centered implementation strategies that align AI-supported analytics with accreditation standards. Implications are discussed for institutional practice, accreditation policy, and future research on AI-enabled assessment and quality assurance in healthcare management education.

Keywords: artificial intelligence; assessment analytics; accreditation readiness; continuous quality improvement; assessment confidence; faculty perceptions; administrator perceptions; healthcare management education.

Title: Faculty and Administrator Perceptions of AI-Driven Assessment Analytics in Meeting Accreditation Standards in Healthcare Management Education

Author: Dr. David Bull

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 66-87

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Website: www.researchpublish.com

Published Date: 02-February-2026

Abstract: The rapid integration of artificial intelligence (AI) into online higher education has produced conflicting evidence regarding its impact on higher-order learning. Guided by the Community of Inquiry (CoI) framework and Human-Centered AI–Teaching Presence (HC-AI-TP) theory, this study examined whether the effects of AI-supported instruction on cognitive presence and perceived learning are conditional on teaching presence. Using a cross-sectional quantitative design, survey data were collected from 200 students enrolled in fully online courses. Previously validated instruments were employed, including the Human-Centered AI–Teaching Presence (HC-AI-TP) scale for AI-supported instruction, the CoI Teaching Presence and Cognitive Presence scales, and a validated perceived learning measure. Internal consistency reliability for all instruments was re-established in the present sample prior to hypothesis testing. Pearson correlation analyses indicated strong positive associations among teaching presence, cognitive presence, and perceived learning (r = .55–.68, p < .001), while AI-supported instruction demonstrated a modest bivariate association with cognitive presence (r = .29, p < .001). Regression analyses showed that AI-supported instruction did not independently predict cognitive presence (β = .07, p = .170), whereas teaching presence was a strong positive predictor (β = .49, p < .001). Moderation analysis revealed a significant AI × Teaching Presence interaction (β = .21, p < .001), indicating that AI enhanced cognitive presence only under conditions of strong teaching presence. In contexts of low teaching presence, AI use was associated with diminished cognitive engagement. Mediation analysis further demonstrated that cognitive presence fully mediated the relationship between ethical AI use—operationalized as AI-supported instruction governed by high teaching presence—and perceived learning (indirect effect = .34, 95% CI [.25, .45]). Phase-level analyses showed that the moderating effect of teaching presence was strongest for the integration (β = .27, p < .001) and resolution (β = .31, p < .001) phases of cognitive presence. Demographic variables were non-significant, and robustness checks confirmed the stability of the findings. Overall, the results indicate that AI is neither inherently beneficial nor harmful; its educational value depends on instructional governance through teaching presence. The study advances Bull’s (2026) (HC-AI-TP) theory by positioning cognitive presence as an ethical learning outcome and provides empirical guidance for responsible AI integration in online higher education.

Keywords: artificial intelligence in education; teaching presence; cognitive presence; Community of Inquiry; ethical AI; online learning.

Title: Ethical Use of Artificial Intelligence and Cognitive Presence in Online Learning: A Human-Centered AI–Teaching Presence (HC-AI-TP) Perspective

Author: Dr. David Augustine Bull

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 45-65

Research Publish Journals

Website: www.researchpublish.com

Published Date: 24-January-2026

Abstract: The rapid integration of artificial intelligence (AI) into higher education has outpaced the development of coherent theoretical frameworks explaining how AI influences instructional processes. Existing research often adopts technologically deterministic assumptions, attributing instructional effects directly to AI systems while under-theorizing the role of human pedagogy. This study introduces and confirms Human-Centered Artificial Intelligence and Teaching Presence Theory (HC-AI-TP), a middle-range explanatory theory that conceptualizes AI as instructionally neutral until enacted through human teaching presence. Using a theory-confirmation design with a sample of 200 participants, six theory-derived hypotheses were tested to evaluate the internal coherence, directional logic, and boundary conditions of the theory. Ordinary least squares regression analyses demonstrated that Human-Centered AI strongly predicted Teaching Presence (β = .60, R² = .36, p < .001). Teaching Presence, in turn, significantly predicted both Social Presence (β = .55, R² = .30, p < .001) and Cognitive Presence (β = .58, R² = .34, p < .001). Mediation analyses using bootstrapped indirect effects confirmed that Teaching Presence fully mediated the relationships between Human-Centered AI and both Social Presence (indirect β = .33, 95% CI [.24, .43]) and Cognitive Presence (indirect β = .35, 95% CI [.26, .45]). Direct effects from Human-Centered AI to Social and Cognitive Presence were non-significant, confirming the theory’s boundary condition and rejecting technologically deterministic explanations. Collectively, the findings confirm the internal coherence and empirical plausibility of HC-AI-TP, establishing Teaching Presence as the central mechanism through which AI influences instructional environments and providing a theoretically grounded foundation for subsequent application and outcome-focused research.

Keywords: human-centered artificial intelligence, teaching presence, theory confirmation, instructional mediation, higher education.

Title: Human-Centered Artificial Intelligence and Teaching Presence Theory (HC-AI-TP): A theory of instructional amplification in AI-enabled education

Author: Dr. David Augustine Bull

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 19-44

Research Publish Journals

Website: www.researchpublish.com

Published Date: 24-January-2026

Abstract: The increasing use of artificial intelligence (AI) in online higher education has raised questions about its influence on instructional quality, student belonging, and persistence. Grounded in the Community of Inquiry framework and belongingness theory, this study examined the relationships among AI-supported instruction, teaching presence, sense of belonging, and student retention intent. Using a quantitative, cross-sectional design, data were collected from 150 students enrolled in fully online higher education courses. Data were analyzed using SPSS (Version 29), including descriptive statistics, reliability testing, Pearson correlations, multiple regression analyses, and mediation analyses using PROCESS macro. Results indicated that AI-supported instruction significantly predicted teaching presence (β = .46, p < .001), teaching presence significantly predicted sense of belonging (β = .54, p < .001), and sense of belonging significantly predicted retention intent (β = .59, p < .001). Mediation analysis showed that sense of belonging partially mediated the relationship between teaching presence and retention intent, with a significant indirect effect (b = .28, 95% CI [.17, .41]). Sequential mediation analysis further demonstrated that AI-supported instruction influenced retention intent indirectly through teaching presence and sense of belonging (b = .18, 95% CI [.10, .30]), with indirect effects exceeding direct effects. Overall, the findings indicate that AI contributes to student retention primarily through a human-centered pathway in which enhanced teaching presence fosters students’ sense of belonging, thereby strengthening persistence intentions. These results underscore the importance of integrating AI within relational pedagogical practices to support retention in online higher education.

Keywords: artificial intelligence in education; teaching presence; sense of belonging; online higher education; student retention; mediation analysis.

Title: Artificial Intelligence, Teaching Presence, and Sense of Belonging in Online Higher Education: Implications for Student Retention

Author: David Bull

International Journal of Computer Science and Information Technology Research

ISSN 2348-1196 (print), ISSN 2348-120X (online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 1-18

Research Publish Journals

Website: www.researchpublish.com

Published Date: 05-January-2026