Abdul Rehman

DynaMER-ADF: Leakage-Safe Modality-Adaptive Temporal Fusion for Multimodal Physiological Emotion Recognition

Abdul Rehman, Sungchul Mun

Metadata-only release. Updates README and CITATION.cff to reference the corrected v0.1.1 Zenodo DOI. No code, method, result, script, or configuration changes beyond citation metadata.

DynaMER-ADF: Leakage-Safe Modality-Adaptive Temporal Fusion for Multimodal Physiological Emotion Recognition

Abdul Rehman, Sungchul Mun

Metadata-only release. Updates README and CITATION.cff to reference the corrected v0.1.1 Zenodo DOI. No code, method, result, script, or configuration changes beyond citation metadata.

DynaMER-ADF: Leakage-Safe Modality-Adaptive Temporal Fusion for Multimodal Physiological Emotion Recognition

Abdul Rehman, Sungchul Mun

# DynaMER-ADF v0.1.0 Initial public software release of DynaMER-ADF. This release includes reproducibility code, configuration files, split-generation scripts, temporal feature preparation workflows, baseline training scripts, DynaMER-family training scripts, ablation workflows, nested LOSO evaluation, physiological separability diagnostics, and result-generation utilities. Raw datasets, generated …

DynaMER-ADF: Leakage-Safe Modality-Adaptive Temporal Fusion for Multimodal Physiological Emotion Recognition

Abdul Rehman, Sungchul Mun

Maintenance release aligning public model, script, and config filenames with the manuscript nomenclature. No methodological or result changes. Backward-compatible aliases are retained for legacy class names.

Vision-Based Artificial Intelligence for Adaptive Peen Forming: Sensing Architectures, Learning Models, and Closed-Loop Smart Manufacturing

Sehar Shahzad Farooq, Abdul Rehman, Fuad Ali Mohammed Al-Yarimi, Sejoon Park et autres

Peen forming is a dieless manufacturing process used to shape large, thin aerospace panels through controlled shot impacts that induce residual stresses and curvature. Despite long-standing industrial use, process monitoring still depends largely on indirect proxies such as Almen intensity and coverage, …

kr, sa (code pays fourni par la source)

PlantCLR: contrastive self-supervised pretraining for generalizable plant disease detection

Syed Shayan Ali Shah, Faisal Saeed, Muhammad Umair Raza, Abdul Rehman et autres

Deep learning has improved automated plant disease detection by increasing recognition accuracy and robustness compared with traditional vision-based methods. Self-supervised learning (SSL) further reduces dependence on manual labels, but its transferability across heterogeneous agricultural datasets remains insufficiently characterized. Here, we evaluate a …

pk, cn, kr, ca (code pays fourni par la source)

Secure and Privacy-Preserving Intrusion Detection in Wireless Sensor Networks via Perturbation Adversarial Training

Faisal Saeed, Dabin Park, Syed Shayan Ali Shah, Junbeom Lee et autres

cn, kr, pk, ca (code pays fourni par la source)

Performance Evaluation of Collaborative Filtering Recommender System on MovieLens Dataset

Muhammad Ahmad Hafeez, Tahir Sher, Abdul Rehman, Imran Ihsan

In today's technological landscape, recommender systems provide essential personalized suggestions by leveraging user preferences. This study evaluates the computational performance of User-Based (UBCF) and Model-Based Collaborative Filtering (MBCF) as intelligent computing systems on the MovieLens 1M dataset, comparing performance on complete data …

pk, kr (code pays fourni par la source)

Denoising Telerik RadCaptcha: A Comparative Evaluation of the Effectiveness of Pre-Processing Techniques and Deep Learning Methods Using a Novel Dataset

Talha Bin Omar, Tahir Sher, Abdul Rehman, M. Haroon Khan

Text-based CAPTCHAs remain a widely deployed mechanism to distinguish humans from automated bots. The Telerik RadCaptcha, a component of the ASP.NET AJAX suite, generates distorted alphanumeric images with character overlap, intersecting lines, and dynamic background noise. This study introduces a novel, real-world …

pk, kr (code pays fourni par la source)

Explainable AI-Enhanced Ensemble Protocol Using Gradient-Boosted Models for Zero-False-Alarm Seizure Detection from EEG

Abdul Rehman, Sungchul Mun

Epilepsy affects over 50 million people worldwide, yet automated seizure detection systems either achieve moderate sensitivity with excessive false alarms or rely on uninterpretable deep networks. This study presents a patient-independent EEG-based seizure detection framework that achieved zero false alarms in 24 …

kr (code pays fourni par la source)

Trust and class aware service discovery with dual control in the social Internet of Things

Abdul Rehman, Malik Urfa Gul, Syed Rizwan Hassan, Korhan Cengiz et autres

Service discovery in the Social Internet of Things (SIoT) must be both efficient and trustworthy. Dense device graphs and heterogeneous link reliability make naïve traversal ineffective and risk-prone. We address this by constructing an Individual's Small-World SIoT (ISWSIoT) search space via a …

kr, sa, hr (code pays fourni par la source)

LEAF: A Lightweight Language-Enhanced Model for Forestry Analysis in Remote Sensing Imagery

Sanjar Karshiev, Faisal Saeed, Jaesin Ahn, Abdul Rehman et autres

Effective monitoring of deforestation requires accurate interpretation of satellite imagery, where domain expertise and computational efficiency are critical. General purpose vision language models (VLMs) perform well on natural images but struggle in remote sensing (RS), where spatial structure and fine-grained anthropogenic cues …

kr, cn, gb, sa (code pays fourni par la source)