arXiv subjects

Foad Ghaderi

Publications and source records attributed to Foad Ghaderi.

5 recordsLinked to original sources

arXiv2026

ProCAVE: A Self-Adaptive, Full-Lifecycle Edge Caching Framework for Video Streaming via Predictive Bandwidth Estimation and Preference-Aware Deep Reinforcement Learning

The growing demand for mobile video streaming requires edge delivery systems that adapt efficiently to rapid network fluctuations and diverse user preferences. Existing approaches such as FlyCache rely on reactive ABR heuristics and loosely coupled cache policies, limiting their responsiveness and coordination under real-world wireless dynamics. We propose ProCAVE (Proactive Caching with Adaptive Video Experience), a self-adaptive DRL-based framework that unifies predictive bandwidth modeling, proactive bitrate selection, and preference-aware cache control. ProCAVE employs: (i) a lightweight Transformer for short-term throughput forecasting; (ii) a PPO-driven ABR agent; and (iii) a DDPG-based continuous cache controller operating on a high-dimensional global state. Experiments using MovieLens preference traces and Ghent 4G bandwidth measurements show that ProCAVE improves byte hit rate, reduces backhaul load, and enhances QoE compared with FlyCache and other baselines. These results highlight the benefits of predictive, DRL-coordinated control for efficient and user-centric edge video delivery.

cs.NI↗

arXiv2025

Heterogeneous networks in drug-target interaction prediction

Drug discovery requires a tremendous amount of time and cost. Computational drug-target interaction prediction, a significant part of this process, can reduce these requirements by narrowing the search space for wet lab experiments. In this survey, we provide comprehensive details of graph machine learning-based methods in predicting drug-target interaction, as they have shown promising results in this field. These details include the overall framework, main contribution, datasets, and their source codes. The selected papers were mainly published from 2020 to 2024. Prior to discussing papers, we briefly introduce the datasets commonly used with these methods and measurements to assess their performance. Finally, future challenges and some crucial areas that need to be explored are discussed.

q-bio.BM↗

arXiv2025

An Empirical Investigation of Reconstruction-Based Models for Seizure Prediction from ECG Signals

Epileptic seizures are transient neurological events characterized by abnormal and excessive neuron activity in the brain, which are often associated with measurable disturbances in the cardiovascular system. Traditionally, electroencephalogram (EEG) signals have served as the primary modality for seizure prediction due to their direct measurement of brain activity and high diagnostic precision. However, their cost, sensitivity to noise, and practical deployment constraints limit their applicability outside controlled clinical environments. To overcome these challenges, recent studies have increasingly investigated electrocardiogram (ECG) signals as a practical and non-invasive alternative for seizure prediction in real-world settings. Evidence suggests that ECG-derived cardiac signatures may precede clinical seizure onset, offering a viable window for early detection. In this paper, we propose a reconstruction-based anomaly detection framework that integrates time-frequency representations with advanced deep learning models to capture deviations in heart rate dynamics associated with seizure onset. Afterward, reconstruction error is smoothed, and an adaptive thresholding strategy is applied to reduce false alarms. The method was evaluated on the Siena database, achieving a specificity of 99.16%, accuracy of 76.05%, and a false positive rate (FPR) of 0.01/h, with an average prediction horizon of 45 minutes prior to seizure onset. These results demonstrate that ECG-based prediction can provide clinically actionable early warnings while improving patient accessibility and comfort. Nevertheless, this performance reflects a trade-off favoring high specificity over sensitivity, resulting in reduced FPR and aligning with clinical requirements for reliable deployment.

eess.SP↗

arXiv2022

SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images

Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing models for building detection use only one image (pre-disaster image) to detect buildings. This is based on the idea that post-disaster images reduce the model's performance because of presence of destroyed buildings. In this paper, we propose a siamese model, called SiamixFormer, which uses pre- and post-disaster images as input. Our model has two encoders and has a hierarchical transformer architecture. The output of each stage in both encoders is given to a temporal transformer for feature fusion in a way that query is generated from pre-disaster images and (key, value) is generated from post-disaster images. To this end, temporal features are also considered in feature fusion. Another advantage of using temporal transformers in feature fusion is that they can better maintain large receptive fields generated by transformer encoders compared with CNNs. Finally, the output of the temporal transformer is given to a simple MLP decoder at each stage. The SiamixFormer model is evaluated on xBD, and WHU datasets, for building detection and on LEVIR-CD and CDD datasets for change detection and could outperform the state-of-the-art.

cs.CV↗

arXiv2019

Disease gene prioritization using network topological analysis from a sequence based human functional linkage network

Sequencing large number of candidate disease genes which cause diseases in order to identify the relationship between them is an expensive and time-consuming task. To handle these challenges, different computational approaches have been developed. Based on the observation that genes associated with similar diseases have a higher likelihood of interaction, a large class of these approaches relay on analyzing the topological properties of biological networks. However, the incomplete and noisy nature of biological networks is known as an important challenge in these approaches. In this paper, we propose a two-step framework for disease gene prioritization: (1) construction of a reliable human FLN using sequence information and machine learning techniques, (2) prioritizing the disease gene relations based on the constructed FLN. On our framework, unlike other FLN based frameworks that using FLNs based on integration of various low quality biological data, the sequence of proteins is used as the comprehensive data to construct a reliable initial network. In addition, the physicochemical properties of amino-acids are employed to describe the functionality of proteins. All in all, the proposed approach is evaluated and the results indicate the high efficiency and validity of the FLN in disease gene prioritization.

q-bio.MN↗