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Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 1,093 Documents
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Attention-Driven Hybrid Deep Learning for Automated Alzheimer’s Disease Severity Assessment via MRI Neuroimaging Mahimul Islam Nadim; Zahura Zaman; Md Mizanur Rahman; Abdullah Al Taky; Meherunnesa Tania; Ikteder A. Udoy; Khadiza Khanom; Md. Obaidul Islam; Suman Ahmmed; Ohidujjaman
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-015

Abstract

Early, accurate diagnosis of Alzheimer’s Disease (AD) is vital for effective intervention. Properly classifying its progression from cognitively normal to moderate dementia is essential for tailoring treatment and management plans. The proposed research is a hybrid deep learning framework that integrates the EfficientNet-B3 and the ResNet50 with sophisticated attention units in order to classify the MRI scans of Alzheimer's disease in multi-classes. The model proposed combines both a Convolutional Block Attention Module (CBAM) based on the refinement of channels and space features and Multi-Head Self-Attention based on cross-branch feature interaction. The dual-branch architecture yields complementary features, with EfficientNet-B3 being able to pick fine-grained patterns and ResNet50 being able to pick strong hierarchical representations. The characteristics in both branches are mapped to 512 dimensions, operated by multi-head attention classification. The model and extensive preprocessing were implanted on a series of 33,984 augmented Alzheimer's MRI images in four categories (MildDemented, ModerateDemented, NonDemented and VeryMildDemented. This hybrid model had outstanding performance of 98.21% test accuracy, 98.23% precision, 98.21% recall, and 98.21% F1-score, which was far much better than the accuracies of other baseline architectures such as VGG16 (74.11%), ResNet50 (93.68%), EfficientNetB0 (63.52%), DenseNet121 (64.12%), and CustomCNN (68.87%). These findings support the usefulness of hybrid systems consisting of attention mechanisms to diagnose Alzheimer's disease automatically by using neuroimaging information.
Enhancing Strategic Decision Performance Through AI: The Task-Technology Fit and Managerial Behaviour Link Othman Boulitama; Brahim Sabiri; Nissrine Mouchtakir; Driss Rahli; Karim Sabri
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-022

Abstract

This study examines how artificial intelligence improves strategic decision-making by focusing on the alignment between managerial task requirements and AI capabilities through the lens of Task Technology Fit. The objective is to explain whether the performance benefits associated with AI emerge from mere adoption or from a more precise fit between task demands, AI characteristics, and actual patterns of use. Methodologically, the study relies on a quantitative design based on survey data collected from 360 managers working in medium and large enterprises in Morocco. The proposed research model was tested using Partial Least Squares Structural Equation Modeling to assess the direct and indirect relationships among task characteristics, AI characteristics, Task AI Technology Fit, effective AI use, and strategic decision-making performance. The findings show that both task characteristics and AI characteristics positively and significantly influence Task AI Technology Fit. In turn, this fit strongly enhances effective AI use and strategic decision-making benefits, while effective AI use partially mediates the relationship between fit and performance. These results indicate that organizational value does not arise from symbolic or superficial AI adoption but from purposeful integration aligned with strategic requirements. The study’s novelty lies in extending Task Technology Fit theory to AI enabled strategic contexts and in demonstrating that AI should be understood not as a substitute for managerial judgment but as a mechanism for cognitive augmentation and performance enhancement.
Modelling Pre-Service English Teachers' Readiness for AI Integration: A TPACK–TAM Mixed-Methods Study Amal Mohammad Husein Alrishan
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-020

Abstract

Artificial Intelligence (AI), particularly large language models such as ChatGPT, has advanced rapidly recently, revolutionizing English Language Teaching (ELT); nonetheless, its pedagogically meaningful integration remains uneven and contingent on teacher preparation. Emerging research indicates that AI adoption is shaped more by teachers’ professional knowledge and acceptance views than by technological hurdles. However, empirical information on their interaction, particularly in underexplored contexts, remains scarce. Using an integrated Technological Pedagogical Content Knowledge (TPACK) and Technology Acceptance Model (TAM) framework, this study investigates pre-service English teachers' preparedness for AI integration, conceptualizing readiness as competence-informed acceptance, a novel construct that differs from traditional readiness frameworks by emphasizing the cognitive professional interplay between knowledge and beliefs rather than mere willingness or attitude. An explanatory sequential mixed-methods single-case design was utilized, with survey data (n = 78) analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and qualitative responses examined through reflexive thematic analysis. The results demonstrated that pedagogical knowledge was the strongest predictor of reported usefulness (β = 0.607, p < 0.001) and perceived ease of use (β = 0.546, p < 0.001). Prior AI experience directly predicted intention (β = 0.208, p < 0.001) and moderated the usefulness–intention link (β = 0.061, p = .044), although perceived ease of use had a greater impact on planned future use (β = 0.299, p < 0.001) than perceived usefulness (β = 0.192, p = 0.003). The qualitative results identified the importance of pedagogical rationale and context limitations. The research contributes to the theory, as it combines TPACK and TAM and offers context-related evidence in the MENA region, which supports the preparation of AI in ELT with pedagogy as a priority. Qualitative findings highlighted the role of pedagogical reasoning and contextual constraints. The study advances theory by integrating TPACK and TAM, demonstrating that professional knowledge operates indirectly through acceptance beliefs, and provides context-sensitive evidence from the Middle East and North Africa (MENA) region, supporting pedagogy-first AI preparation in ELT.

Page 110 of 110 | Total Record : 1093

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