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All Journal Emerging Science Journal
Ohidujjaman
Department of Computer Science and Engineering, United International University, Dhaka 1212
Environmental Science

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Detecting Genuine Versus Fake Emotions: A Dual-Task Deep Learning Approach Using Facial Expression Analysis Sarah Tasnim Diya; Most. Jannatul Ferdos; Md. Mizanur Rahman; Yadab Sutradhar; Zahura Zaman; Suman Ahmmed; Ohidujjaman
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-018

Abstract

Facial expression recognition (FER) is a relevant field of study with applications in human-computer interaction, healthcare, and security. Although recent approaches demonstrate excellent outcomes on the recognition of basic emotions, the authenticity of expressions (genuine versus fake) remains unexplored. In this work, we propose a dual-task deep learning framework based on EfficientNet-B0, enhanced with a lightweight squeeze-and-excitation (SE) attention mechanism, to collaboratively work on multiclass emotion recognition (seven categories: angry, disgust, fear, happy, neutral, sad and surprise) and authenticity classification (genuine vs fake). The architecture leverages a shared backbone for representing feature, followed by task-dedicated branches trained using categorical cross-entropy and focal loss, respectively. To overcome the lack of publicly available benchmarks incorporating authenticity labels, we designed a curated dataset annotated with both emotional categories and authenticity information. Experimental evaluation demonstrates that the proposed dual-task model with the SE attention mechanism achieves 98.5% accuracy for emotion recognition and 92.2% accuracy for authenticity prediction, emphasizing both the effectiveness of the framework and the inherent challenges of authenticity detection. Moreover, we present a deployable real-time system demonstrating the feasibility of integrating authenticity-aware FER into practical applications such as e-learning analytics, security surveillance, and affective computing.
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.
Co-Authors Abdullah Al Taky Ikteder A. Udoy Khadiza Khanom Mahimul Islam Nadim Md Mizanur Rahman Md. Mizanur Rahman Md. Obaidul Islam Meherunnesa Tania Most. Jannatul Ferdos Sarah Tasnim Diya Suman Ahmmed Yadab Sutradhar Zahura Zaman Zahura Zaman