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Conference Site: IEEE i-COSTE 2026

PolypSegNet: An Attention-Guided Multi-Scale Feature Fusion Network for Accurate Colorectal Polyp Segmentation

Colorectal cancer (CRC) is a leading cause of
cancer-related mortality worldwide, making early colorectal
polyp detection essential for improving patient outcomes. However, accurate polyp segmentation remains challenging due to
variations in polyp size, shape, and texture, along with low
contrast, blurred boundaries, and imaging artifacts in endoscopic
images. Although recent CNN-, Transformer-, and foundation
model-based approaches have improved segmentation accuracy,
many remain limited by high computational cost and architectural complexity. To address these issues, this study proposes
PolypSegNet, an efficient encoder–decoder architecture integrating a pretrained ResNet-50 encoder, Multi-Scale Feature Fusion
(MSFF), and Convolutional Block Attention Modules (CBAM).
The framework enhances multi-scale contextual learning and
attention-guided feature refinement while maintaining computational efficiency. Experiments on the Kvasir-SEG dataset achieved
a Dice score of 0.9164 and an IoU score of 0.8655, demonstrating
competitive performance for colorectal polyp segmentation.

Institutional Readiness for AI-Driven Sustainable Business Education: Evidence from Bangladesh

This study makes five significant contributions to the literature on AI-enabled sustainable education. First, it develops a novel institutional readiness framework grounded in Dynamic Capabilities Theory to explain graduate preparedness in AI-driven business education. Second, it integrates quantitative PLS-SEM and qualitative thematic analysis to provide robust empirical evidence on the institutional and technological determinants of sustainable graduate preparedness in Bangladesh. Third, the findings demonstrate that institutional and policy support, including digital infrastructure and AI-enabled learning systems, is a stronger predictor of graduate preparedness than curriculum reform alone. Fourth, the study proposes an AI-Enabled Smart Education Implementation Framework that links AI infrastructure, smart campuses, AI-enabled learning management systems, learning analytics, curriculum personalization, and graduate preparedness. Finally, the research offers practical, technology-oriented recommendations for higher education institutions and policymakers to accelerate AI-driven digital transformation and sustainable human capital development in developing economies.

Intelligent Web Attack Classification Using Ensemble Machine Learning Models

—Secure injection and traversal based attacks on web
Attacks on applications. is a growing issue, as attackers are
becoming more and more familiar with the attacks. Armed with
increasingly sophisticated attacks, payloads that can execute even
more dangerous tasks. Bypass traditional rules-based defence.
In this paper we present a multi-class HTTP request classifier
built on The features include character-level TF-IDF features and
LightGBM ensemble. approach of extended with the SHAP-based
post-hoc explainabil- ity to support. Analysis of Model Decisions
at Analyst level. Following experiments were carried out using the
datasets “CSIC 2010” and ”ECML/PKDD 2007”, which cover:
The accuracy of LightGBM In 11,347 labeled samples in five
categories, is shown. 94.32higher in value than the lowest value. A
controlled ablation is carried To test the accuracy of the character
n-grams over the In contrast, range-based tokenization is 6.12
points better than A word-based tokenization with a vocabulary
of [2,4] and 10,000 words. at the “knee” of the accuracy-memory
curve. The trained pipeline can be fitted in the memory of 80
MB and can run on a normal A CPU chipset that doesn’t
support GPUs, for use with CPU- only hardware. ModSecurity
compatible WAF deployment. In In the SHAP attribution maps,
the SQL metacharacters are shown as well as script-injection.
The following are the key factors: For their class, they were given
tokens, and sequences that lead to path traversal. An operator
of a WAF removes audit trail for every blocked request.

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