5 research outputs found
IDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers
No full textUnderstanding the genetic components of Alzheimer’s disease (AD) via transcriptome analysis often necessitates the use of invasive methods. This work focuses on overcoming the difficulties associated with the invasive process of collecting brain tissue samples in order to measure and investigate the transcriptome behavior of AD. Our approach called IDEEA ( I nformation D iffusion model for integrating gene E xpression and E EG data in identifying A lzheimer’s disease markers) involves systematically linking two different but complementary modalities: transcriptomics and electroencephalogram (EEG) data. We preprocess these two data types by calculating the spectral and transcriptional sample distances, over 11 brain regions encompassing 6 distinct frequency bands. Subsequently, we employ a genetic algorithm approach to integrate the distinct features of the preprocessed data. Our experimental results show that IDEEA converges rapidly to local optima gene subsets, in fewer than 250 iterations. Our algorithm identifies novel genes along with genes that have previously been linked to AD. It is also capable of detecting genes with transcription patterns specific to individual EEG bands as well as those with common patterns among bands. In particular, the alpha2 (10–13 Hz) frequency band yielded 8 AD-associated genes out of the top 100 most frequently selected genes by our algorithm, with a p -value of 0.05. Our method not only identifies AD-related genes but also genes that interact with AD genes in terms of transcription regulation. We evaluated various aspects of our approach, including the genetic algorithm performance, band-pair association and gene interaction topology. Our approach reveals AD-relevant genes with transcription patterns inferred from EEG alone, across various frequency bands, avoiding the risky brain tissue collection process. This is a significant advancement toward the early identification of AD using non-invasive EEG recordings
BLIP-CC: Adapting the BLIP for Change Captioning Task in Remote Sensing BLIP-CC: BLIP in Uzaktan Algilamada De?gi sim zetleme G revine Uyarlanmasi
No full textIn remote sensing change captioning task, the change region can be detected by examining images taken from the same region at different times, and the change can be expressed in natural language. With this motivation, this study aims to adapt the BLIP (Bootstrapping Language-Image Pre-training) model, a general-purpose language-visual model, to the task of change captioning in the field of remote sensing. To perform change captioning with the BLIP model, updates were made to the model, different parameters were tested, and the training process was followed. Different methods were followed to obtain the change features from the images. The models obtained from experiments were tested and the most appropriate methods were selected, and the generation of meaningful sentences expressing the changes between RS images was achieved. In this context, comparable results have been obtained with the RSICCformer study, which is considered a benchmark in the field
Introducing MOSAIC-SEN2-CC: A Multispectral Dataset and Adaptation Framework for Remote Sensing Change Captioning
No full textRemote Sensing Image Change Captioning (RSICC) aims to generate descriptive sentences that effectively characterize the changes between bitemporal images. Although the state-of-the-art methods focus on predicting captions from RGB image pairs, change captioning in multispectral images has not been investigated yet. For this purpose, we created a new MOSAIC-SEN2-CC dataset, which contains 5232 pairs of multispectral (MS) images captured from Sentinel-2 satellites and 26 160 change captions over a 12-month period. Our dataset consists of a total of eight categories, namely Wildfire (WF), Flood (FL), Wetland (WET), Green Field (GF), Glacier (GL), Urban (UR), Agriculture (AG), along with a No-Change (NO) category. In this article, we propose a Multispectral Image Change Captioning framework that consists of BigEarthNet Feature Extractor, Feature Enhancement, and Transformer-Based Decoder modules to effectively benefit from spectral band information. Specifically, the state-of-the-art methods, such as RSICCformer, Chg2Cap, and PSNet, are adapted to work with BigEarthNet models using ten spectral band images. Detailed comparisons that include attention visualizations, RGB versus MS tradeoffs, change captions, and performance metrics further demonstrate its effectiveness and ability to address RSICC challenges
Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework
No full textMotor imagery brain-computer interfaces (MI-BCIs) have gained a lot of attention in recent years thanks to their potential to enhance rehabilitation and control of prosthetic devices for individuals with motor disabilities. However, accurate classification of motor imagery signals remains a challenging task due to the high inter-subject variability and non-stationarity in the electroencephalogram (EEG) data. In the context of MI-BCIs, with limited data availability, the acquisition of EEG data can be difficult. In this study, several data augmentation techniques have been compared with the proposed data augmentation technique adaptive cross-subject segment replacement (ACSSR). This technique, in conjunction with the proposed deep learning framework, allows for a combination of similar subject pairs to take advantage of one another and boost the classification performance of MI-BCIs. The proposed framework features a multi-domain feature extractor based on common spatial patterns with a sliding window and a parallel two-branch convolutional neural network. The performance of the proposed methodology has been evaluated on the multi-class BCI Competition IV Dataset 2a through repeated 10-fold cross-validation. Experimental results indicated that the implementation of the ACSSR method (80.47%) in the proposed framework has led to a considerable improvement in the classification performance compared to the classification without data augmentation (77.63%), and other fundamental data augmentation techniques used in the literature. The study contributes to the advancements for the development of effective MI-BCIs by showcasing the ability of the ACSSR method to address the challenges in motor imagery signal classification tasks
Robust Change Captioning in Remote Sensing: SECOND-CC Dataset and MModalCC Framework
No full textExisting remote sensing image change captioning (RSICC) methods often fail under challenges, such as illumination differences, viewpoint changes, and blur effects, leading to inaccuracies, especially in no-change regions. Moreover, images acquired at different spatial resolutions and with registration errors tend to affect the captions. To address these issues, we introduce SECOND-CC, a novel RSICC dataset featuring high-resolution RGB image pairs, semantic segmentation maps, and diverse real-world scenarios. SECOND-CC contains 6041 pairs of bitemporal remote sensing images and 30 205 sentences describing the differences between the images. In addition, we propose MModalCC, a multimodal framework that integrates semantic and visual data using advanced attention mechanisms, including cross-modal cross attention and multimodal gated cross attention. In addition, we adapt MModalCC to handle noisy semantic inputs by integrating a semantic change detector, improving its robustness for real-world applications. Detailed ablation studies and attention visualizations further demonstrate its effectiveness and ability to address the challenges of RSICC. Comprehensive experiments show that MModalCC outperforms state-of-the-art RSICC methods, including RSICCformer, Chg2Cap, and PSNet with +4.6% improvement on BLEU4 score and +9.6% improvement on CIDEr score in SECOND-CC dataset. MModalCC was further validated on the LEVIR-MCI benchmark, where it achieved an average score of 83.51, significantly outperforming previous state-of-the-art methods
