Author name cluster

Imad Rida

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

JBHI Journal 2026 Journal Article

FDEPCA: A Novel Adaptive Nonlinear Feature Extraction Method via Fruit Fly Olfactory Neural Network for IoMT Anomaly Detection

Yihan Chen
Zhixia Zeng
Xinhong Lin
Xin Du
Imad Rida
Ruliang Xiao

With the rapid development of 5G communication technology, the data in the Internet of Medical Things (IoMT) application systems exhibits complex characteristics such as large volume, high dimensionality, nonlinearity, and diversity, which significantly affect the efficiency and detection performance of anomaly detection tasks. How to efficiently extract nonlinear features from high-dimensional data in the context of the IoMT while minimizing information distortion in data objects are challenging problems in recent academic research. A novel adaptive nonlinear feature extraction method via fruit fly olfactory neural network (Fly dimension expansion projection and remain main components by PCA, FDEPCA) is proposed, where 1) the data are mean-centered; 2) a binary sparse random projection matrix is used for dimension expansion projection; and 3) PCA is used to extract principal component information. The proposed method overcomes the problems of present nonlinear feature extraction in the face of high-dimensional outliers where the intrinsic geometric structure of the data is severely distorted and computationally expensive. The dataset after nonlinear feature extraction by the FDEPCA algorithm is applied to specific anomaly detection models, using ROC curves and AUC as evaluation metrics for classification performance. Extensive comparison experiments are conducted on eight publicly available datasets, and experimental results show that compared with the popular nonlinear feature extraction algorithms, the FDEPCA algorithm has better classification performance and projection time advantage. When applied to proximity-based, probability-based, and ensemble-based different anomaly detection models respectively, the FDEPCA algorithm exhibits strong applicability in different types of anomaly detection classifiers.

Details DOI

IS Journal 2025 Journal Article

A Generative Random Modality Dropout Framework for Robust Multimodal Emotion Recognition

Yang Zhang
Hui Chen
Imad Rida
Xianxun Zhu

Multimodal sentiment analysis faces significant challenges in real-world applications due to the frequent absence of modalities caused by privacy concerns, device limitations, or security policies. This article introduces a random modality dropout based on generative approach (RMDG), designed to enhance the robustness and performance of multimodal models under various modality absence scenarios. The RMDG method employs a generative approach during the training phase, where random modality dropout is applied to simulate missing modalities. By leveraging the remaining modalities to predict and regenerate the key features of the missing ones, the model effectively adapts to dynamic and unpredictable modality absences. This strategy not only eliminates the need for separate training or adjustments for each modality combination but also significantly improves the efficiency and accuracy of sentiment analysis in incomplete multimodal data scenarios. Extensive experiments demonstrate that RMDG outperforms existing methods, achieving superior performance in both complete and missing modality conditions.

Details DOI

EAAI Journal 2025 Journal Article

Lightweight transformer-driven multi-scale trapezoidal attention network for saliency detection

Muhammad Talha Usman
Habib Khan
Imad Rida
JaKeoung Koo

State-of-the-art (SOTA) methods in salient object detection (SOD) often struggle to balance computational efficiency with performance. They encounter challenges in capturing complex features due to insufficient attention mechanisms and ineffective integration of multi-scale features. While deep convolutional neural networks (CNNs) provide lightweight architectures, they lack an understanding of global contexts. Transformers excel at capturing global context, but they require considerable resources, limiting their use on constrained platforms. To overcome these limitations, we present a novel saliency detection (SD) network that utilizes a pyramid vision transformer backbone to efficiently extract multi-scale features. The intermediate multi-scale features are refined through contextual feature refinement blocks (CFRBs) with dilated convolutions, which capture rich contextual information at each scale and enhance feature representation. Furthermore, these features are passed on to our newly proposed trapezoidal attention module (TAM), which integrates effective attention blocks. The Adaptive Spatial Coordinate Attention (ASCA) blocks are employed to highlight the importance of spatial locations in the initial two feature maps. As the high-level refined features require channel adjustments, we introduce Compact Channel Gate (CCG) blocks that adaptively recalibrate channel-wise feature responses. Additionally, the medium-scale two feature maps are processed through Feature-Aware Multi-Head Attention (FAMHA) blocks to capture long-range dependencies and global context. The extracted features are progressively upsampled and concatenated to create a high resolution saliency map, which is used for the final predictions. Extensive empirical analysis on six benchmark SD datasets demonstrates the effectiveness of our network, surpassing over 26 SOTA SD methods while maintaining a lightweight architecture. Code, qualitative results, and trained models will be available at: https: //github. com/TalhaUsman-ZERO/TRSNet.

Details DOI

TIST Journal 2024 Journal Article

Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation

Ehsan Elahi
Sajid Anwar
Babar Shah
Zahid Halim
Abrar Ullah
Imad Rida
Muhammad Waqas

With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named KGCAN (Knowledge Graph Enhanced Contextualized Attention-Based Network), which explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing knowledge graph based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics.

Details DOI