Ghulam Mubashar Hassan

Md Faraz Kabir Khan, Saeed Anwar, Ghulam Mubashar Hassan

The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before. We introduce GenSyn10, a CIFAR-10-aligned synthetic image dataset of 60,000 images (10 classes, 32$\times$32, 50k/10k split) generated using three architecturally diverse state-of-the-art models: FLUX.2-dev (Rectified Flow Transformer), HunyuanImage-3.0 (MoE Transformer), and Qwen-Image-2512 (Multimodal Diffusion Transformer), to advance research in AI-generated image detection. A central challenge in this domain is that detectors perform well on known generators but degrade on unseen ones. GenSyn10 addresses this limitation by curating data from multiple contemporary architectures under a standardized generation protocol, enabling controlled and systematic evaluation of out-of-distribution (OOD) generalization to novel generators. Images are generated using a template-based prompt engine and downsampled to ensure consistency. We evaluate 17 image classification models under a four-stage protocol: real-data baseline, zero-shot transfer, fine-tuning, and retention. Despite a measurable domain gap, CIFAR-10-trained models achieve up to 96.86\% zero-shot accuracy on GenSyn10, increasing to 99.88\% after fine-tuning. In binary real-vs-synthetic classification, fine-tuned models achieve 97-99.9\% accuracy on seen generators but drop to 79-96\% on images from an unseen generator, highlighting persistent limitations in OOD generalization. These results establish GenSyn10 as a controlled benchmark for studying synthetic image detection beyond single-generator settings, supporting research on robustness, domain adaptation, and cross-generator generalization.

Sania Zahan, Zulqarnain Gilani, Ghulam Mubashar Hassan et al.

Autism diagnosis presents a major challenge due to the vast heterogeneity of the condition and the elusive nature of early detection. Atypical gait and gesture patterns are dominant behavioral characteristics of autism and can provide crucial insights for diagnosis. Furthermore, these data can be collected efficiently in a non-intrusive way, facilitating early intervention to optimize positive outcomes. Existing research mainly focuses on associating facial and eye-gaze features with autism. However, very few studies have investigated movement and gesture patterns which can reveal subtle variations and characteristics that are specific to autism. To address this gap, we present an analysis of gesture and gait activity in videos to identify children with autism and quantify the severity of their condition by regressing autism diagnostic observation schedule scores. Our proposed architecture addresses two key factors: (1) an effective feature representation to manifest irregular gesture patterns and (2) a two-stream co-learning framework to enable a comprehensive understanding of its relation to autism from diverse perspectives without explicitly using additional data modality. Experimental results demonstrate the efficacy of utilizing gesture and gait-activity videos for autism analysis.

Sania Zahan, Ghulam Mubashar Hassan, Ajmal Mian

Athlete performance measurement in sports videos requires modeling long sequences since the entire spatio-temporal progression contributes dominantly to the performance. It is crucial to comprehend local discriminative spatial dependencies and global semantics for accurate evaluation. However, existing benchmark datasets mainly incorporate sports where the performance lasts only a few seconds. Consequently, state-ofthe-art sports quality assessment methods specifically focus on spatial structure. Although they achieve high performance in short-term sports, they are unable to model prolonged video sequences and fail to achieve similar performance in long-term sports. To facilitate such analysis, we introduce a new dataset, coined AGF-Olympics, that incorporates artistic gymnastic floor routines. AFG-Olympics provides highly challenging scenarios with extensive background, viewpoint, and scale variations over an extended sample duration of up to 2 minutes. In addition, we propose a discriminative attention module to map the dense feature space into a sparse representation by disentangling complex associations. Extensive experiments indicate that our proposed module provides an effective way to embed long-range spatial and temporal correlation semantics.

Zainy M. Malakan, Ghulam Mubashar Hassan, Ajmal Mian

Visual Story-Telling is the process of forming a multi-sentence story from a set of images. Appropriately including visual variation and contextual information captured inside the input images is one of the most challenging aspects of visual storytelling. Consequently, stories developed from a set of images often lack cohesiveness, relevance, and semantic relationship. In this paper, we propose a novel Vision Transformer Based Model for describing a set of images as a story. The proposed method extracts the distinct features of the input images using a Vision Transformer (ViT). Firstly, input images are divided into 16X16 patches and bundled into a linear projection of flattened patches. The transformation from a single image to multiple image patches captures the visual variety of the input visual patterns. These features are used as input to a Bidirectional-LSTM which is part of the sequence encoder. This captures the past and future image context of all image patches. Then, an attention mechanism is implemented and used to increase the discriminatory capacity of the data fed into the language model, i.e. a Mogrifier-LSTM. The performance of our proposed model is evaluated using the Visual Story-Telling dataset (VIST), and the results show that our model outperforms the current state of the art models.

Hezam Albaqami, Ghulam Mubashar Hassan, Amitava Datta

Seizure type identification is essential for the treatment and management of epileptic patients. However, it is a difficult process known to be time consuming and labor intensive. Automated diagnosis systems, with the advancement of machine learning algorithms, have the potential to accelerate the classification process, alert patients, and support physicians in making quick and accurate decisions. In this paper, we present a novel multi-path seizure-type classification deep learning network (MP-SeizNet), consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory neural network (Bi-LSTM) with an attention mechanism. The objective of this study was to classify specific types of seizures, including complex partial, simple partial, absence, tonic, and tonic-clonic seizures, using only electroencephalogram (EEG) data. The EEG data is fed to our proposed model in two different representations. The CNN was fed with wavelet-based features extracted from the EEG signals, while the Bi-LSTM was fed with raw EEG signals to let our MP-SeizNet jointly learns from different representations of seizure data for more accurate information learning. The proposed MP-SeizNet was evaluated using the largest available EEG epilepsy database, the Temple University Hospital EEG Seizure Corpus, TUSZ v1.5.2. We evaluated our proposed model across different patient data using three-fold cross-validation and across seizure data using five-fold cross-validation, achieving F1 scores of 87.6% and 98.1%, respectively.

Afsah Saleem, Zaid Ilyas, David Suter et al.

Abdominal Aortic Calcification (AAC) is a known marker of asymptomatic Atherosclerotic Cardiovascular Diseases (ASCVDs). AAC can be observed on Vertebral Fracture Assessment (VFA) scans acquired using Dual-Energy X-ray Absorptiometry (DXA) machines. Thus, the automatic quantification of AAC on VFA DXA scans may be used to screen for CVD risks, allowing early interventions. In this research, we formulate the quantification of AAC as an ordinal regression problem. We propose a novel Supervised Contrastive Ordinal Loss (SCOL) by incorporating a label-dependent distance metric with existing supervised contrastive loss to leverage the ordinal information inherent in discrete AAC regression labels. We develop a Dual-encoder Contrastive Ordinal Learning (DCOL) framework that learns the contrastive ordinal representation at global and local levels to improve the feature separability and class diversity in latent space among the AAC-24 genera. We evaluate the performance of the proposed framework using two clinical VFA DXA scan datasets and compare our work with state-of-the-art methods. Furthermore, for predicted AAC scores, we provide a clinical analysis to predict the future risk of a Major Acute Cardiovascular Event (MACE). Our results demonstrate that this learning enhances inter-class separability and strengthens intra-class consistency, which results in predicting the high-risk AAC classes with high sensitivity and high accuracy.

Khanh Nguyen, Dasith de Silva Edirimuni, Ghulam Mubashar Hassan et al.

3D assets have rapidly expanded in quantity and diversity due to the growing popularity of virtual reality and gaming. As a result, text-to-shape retrieval has become essential in facilitating intuitive search within large repositories. However, existing methods require canonical poses and support few object categories, limiting their real-world applicability where objects can belong to diverse classes and appear in random orientations. To address this challenge, we propose RI-Mamba, the first rotation-invariant state-space model for point clouds. RI-Mamba defines global and local reference frames to disentangle pose from geometry and uses Hilbert sorting to construct token sequences with meaningful geometric structure while maintaining rotation invariance. We further introduce a novel strategy to compute orientational embeddings and reintegrate them via feature-wise linear modulation, effectively recovering spatial context and enhancing model expressiveness. Our strategy is inherently compatible with state-space models and operates in linear time. To scale up retrieval, we adopt cross-modal contrastive learning with automated triplet generation, allowing training on diverse datasets without manual annotation. Extensive experiments demonstrate RI-Mamba's superior representational capacity and robustness, achieving state-of-the-art performance on the OmniObject3D benchmark across more than 200 object categories under arbitrary orientations. Our code will be made available at https://github.com/ndkhanh360/RI-Mamba.git.

Khanh Nguyen, Dasith de Silva Edirimuni, Ghulam Mubashar Hassan et al.

Locating and retrieving objects from scene-level point clouds is a challenging problem with broad applications in robotics and augmented reality. This task is commonly formulated as open-vocabulary 3D instance segmentation. Although recent methods demonstrate strong performance, they depend heavily on SAM and CLIP to generate and classify 3D instance masks from images accompanying the point cloud, leading to substantial computational overhead and slow processing that limit their deployment in real-world settings. Open-YOLO 3D alleviates this issue by using a real-time 2D detector to classify class-agnostic masks produced directly from the point cloud by a pretrained 3D segmenter, eliminating the need for SAM and CLIP and significantly reducing inference time. However, Open-YOLO 3D often fails to generalize to object categories that appear infrequently in the 3D training data. In this paper, we propose a method that generates 3D instance masks for novel objects from RGB images guided by a 2D open-vocabulary detector. Our approach inherits the 2D detector's ability to recognize novel objects while maintaining efficient classification, enabling fast and accurate retrieval of rare instances from open-ended text queries. Our code will be made available at https://github.com/ndkhanh360/BoxOVIS.

Rini Anggrainingsih, Ghulam Mubashar Hassan, Amitava Datta

It is challenging to control the quality of online information due to the lack of supervision over all the information posted online. Manual checking is almost impossible given the vast number of posts made on online media and how quickly they spread. Therefore, there is a need for automated rumour detection techniques to limit the adverse effects of spreading misinformation. Previous studies mainly focused on finding and extracting the significant features of text data. However, extracting features is time-consuming and not a highly effective process. This study proposes the BERT- based pre-trained language models to encode text data into vectors and utilise neural network models to classify these vectors to detect misinformation. Furthermore, different language models (LM) ' performance with different trainable parameters was compared. The proposed technique is tested on different short and long text datasets. The result of the proposed technique has been compared with the state-of-the-art techniques on the same datasets. The results show that the proposed technique performs better than the state-of-the-art techniques. We also tested the proposed technique by combining the datasets. The results demonstrated that the large data training and testing size considerably improves the technique's performance.

Haifa Almutairi, Ghulam Mubashar Hassan, Amitava Datta

Classification of sleep stages plays an essential role in diagnosing sleep-related diseases including Sleep Disorder Breathing (SDB) disease. In this study, we propose an end-to-end deep learning architecture, named SSNet, which comprises of two deep learning networks based on Convolutional Neuron Networks (CNN) and Long Short Term Memory (LSTM). Both deep learning networks extract features from the combination of Electrooculogram (EOG), Electroencephalogram (EEG), and Electromyogram (EMG) signals, as each signal has distinct features that help in the classification of sleep stages. The features produced by the two-deep learning networks are concatenated to pass to the fully connected layer for the classification. The performance of our proposed model is evaluated by using two public datasets Sleep-EDF Expanded dataset and ISRUC-Sleep dataset. The accuracy and Kappa coefficient are 96.36% and 93.40% respectively, for classifying three classes of sleep stages using Sleep-EDF Expanded dataset. Whereas, the accuracy and Kappa coefficient are 96.57% and 83.05% respectively for five classes of sleep stages using Sleep-EDF Expanded dataset. Our model achieves the best performance in classifying sleep stages when compared with the state-of-the-art techniques.

Asmaa Shati, Ghulam Mubashar Hassan, Amitava Datta

A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people's daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various respiratory illnesses and lung disorders. The traditional diagnosis of such sounds requires specialized knowledge, which can be costly and reliant on human expertise. Despite this, recent advancements, such as cough audio recordings, have emerged as a means to automate the detection of respiratory conditions. Therefore, this research aims to explore various acoustic features that enhance the performance of machine learning (ML) models in detecting COVID-19 from cough signals. It investigates the efficacy of three feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, when applied to two machine learning algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and therefore proposes an efficient CovCepNet detection system. The proposed system provides a practical solution and demonstrates state-of-the-art classification performance, with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the Virufy dataset for COVID-19 detection from cough audio signals.

Danish Ali, Ajmal Mian, Naveed Akhtar et al.

Accurate brain tumor segmentation using multiparametric MRI is critical for effective treatment planning. However, in clinical settings, complete acquisition of all MRI sequences is not always possible. The absence of certain MRI modalities results in substantial performance degradation in existing segmentation methods, which typically rely on naive feature concatenation or direct fusion strategies. To address this limitation, we propose a novel segmentation model D3Seg which is designed to maintain stable performance under missing-modality settings. D3Seg introduces Multi-hop Modality Graph Fusion (MMGF) to model higher order inter-modality dependencies, a lightweight diffusion-based imputation mechanism to compensate for missing T1ce representations in latent space, and probability-space decision refinement to mitigate dominant class overconfidence and improve delineation of underrepresented tumor subregions. Extensive evaluation on BraTS 2023 dataset demonstrates that our D3Seg model consistently improves segmentation performance under missing modality configurations. The proposed model achieves approximately 1.5-2.0% Dice improvement on enhancing tumor (ET) and around 1.0% on tumor core (TC) across multiple missing modality configurations compared to the current state-of-the-art model, while maintaining computational efficiency.

Khanh Nguyen, Ghulam Mubashar Hassan, Ajmal Mian

Recent open-world representation learning approaches have leveraged CLIP to enable zero-shot 3D object recognition. However, performance on real point clouds with occlusions still falls short due to unrealistic pretraining settings. Additionally, these methods incur high inference costs because they rely on Transformer's attention modules. In this paper, we make two contributions to address these limitations. First, we propose occlusion-aware text-image-point cloud pretraining to reduce the training-testing domain gap. From 52K synthetic 3D objects, our framework generates nearly 630K partial point clouds for pretraining, consistently improving real-world recognition performances of existing popular 3D networks. Second, to reduce computational requirements, we introduce DuoMamba, a two-stream linear state space model tailored for point clouds. By integrating two space-filling curves with 1D convolutions, DuoMamba effectively models spatial dependencies between point tokens, offering a powerful alternative to Transformer. When pretrained with our framework, DuoMamba surpasses current state-of-the-art methods while reducing latency and FLOPs, highlighting the potential of our approach for real-world applications. Our code and data are available at https://ndkhanh360.github.io/project-occtip.

Sania Zahan, Ghulam Mubashar Hassan, Ajmal Mian

Older people are susceptible to fall due to instability in posture and deteriorating health. Immediate access to medical support can greatly reduce repercussions. Hence, there is an increasing interest in automated fall detection, often incorporated into a smart healthcare system to provide better monitoring. Existing systems focus on wearable devices which are inconvenient or video monitoring which has privacy concerns. Moreover, these systems provide a limited perspective of their generalization ability as they are tested on datasets containing few activities that have wide disparity in the action space and are easy to differentiate. Complex daily life scenarios pose much greater challenges with activities that overlap in action spaces due to similar posture or motion. To overcome these limitations, we propose a fall detection model, coined SDFA, based on human skeletons extracted from low-resolution videos. The use of skeleton data ensures privacy and low-resolution videos ensures low hardware and computational cost. Our model captures discriminative structural displacements and motion trends using unified joint and motion features projected onto a shared high dimensional space. Particularly, the use of separable convolution combined with a powerful GCN architecture provides improved performance. Extensive experiments on five large-scale datasets with a wide range of evaluation settings show that our model achieves competitive performance with extremely low computational complexity and runs faster than existing models.

Shahin Hakemi, Naveed Akhtar, Ghulam Mubashar Hassan et al.

Neural network training tends to exploit the simplest features as shortcuts to greedily minimize training loss. However, some of these features might be spuriously correlated with the target labels, leading to incorrect predictions by the model. Several methods have been proposed to address this issue. Focusing on suppressing the spurious correlations with model training, they not only incur additional training cost, but also have limited practical utility as the model misbehavior due to spurious relations is usually discovered after its deployment. It is also often overlooked that spuriousness is a subjective notion. Hence, the precise questions that must be investigated are; to what degree a feature is spurious, and how we can proportionally distract the model's attention from it for reliable prediction. To this end, we propose a method that enables post-hoc neutralization of spurious feature impact, controllable to an arbitrary degree. We conceptualize spurious features as fictitious sub-classes within the original classes, which can be eliminated by a class removal scheme. We then propose a unique precise class removal technique that makes a single-weight modification, which entails negligible performance compromise for the remaining classes. We perform extensive experiments, demonstrating that by editing just a single weight in a post-hoc manner, our method achieves highly competitive, or better performance against the state-of-the-art methods.

Jamie Vo, Naeha Sharif, Ghulam Mubashar Hassan

The early detection of Alzheimer's Disease is imperative to ensure early treatment and improve patient outcomes. There has consequently been extenstive research into detecting AD and its intermediate phase, mild cognitive impairment (MCI). However, there is very small literature in predicting the conversion to AD and MCI from normal cognitive condition. Recently, multiple studies have applied convolutional neural networks (CNN) which integrate Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) to classify MCI and AD. However, in these works, the fusion of MRI and PET features are simply achieved through concatenation, resulting in a lack of cross-modal interactions. In this paper, we propose a novel multimodal neuroimaging attention-based CNN architecture, MNA-net, to predict whether cognitively normal (CN) individuals will develop MCI or AD within a period of 10 years. To address the lack of interactions across neuroimaging modalities seen in previous works, MNA-net utilises attention mechanisms to form shared representations of the MRI and PET images. The proposed MNA-net is tested in OASIS-3 dataset and is able to predict CN individuals who converted to MCI or AD with an accuracy of 83%, true negative rate of 80%, and true positive rate of 86%. The new state of the art results improved by 5% and 10% for accuracy and true negative rate by the use of attention mechanism. These results demonstrate the potential of the proposed model to predict cognitive impairment and attention based mechanisms in the fusion of different neuroimaging modalities to improve the prediction of cognitive decline.

Juwita juwita, Ghulam Mubashar Hassan, Naveed Akhtar et al.

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on the widely known NIH pancreas dataset and MSD pancreas dataset. Our approach demonstrates a considerable performance improvement, achieving an average Dice Similarity Coefficient (DSC) value of up to 89.53% and an Intersection Over Union (IOU) score of up to 81.16 for the NIH pancreas dataset, and 88.60% DSC and 79.90% IOU for the MSD Pancreas dataset.

Hezam Albaqami, Ghulam Mubashar Hassan, Amitava Datta

Epilepsy is one of the most common brain diseases that affect more than 1\% of the world's population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and highly-specialized. Moreover, EEG manual evaluation is a process known to have a low inter-rater agreement among experts. This paper presents a novel automatic technique that involves extraction of specific features from EEG signals using Dual-tree Complex Wavelet Transform (DTCWT) and classifying them. We evaluated the proposed technique on TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with existing state-of-the-art techniques using overall F1-score due to class imbalance seizure types. Our proposed technique achieved the best results of weighted F1-score of 99.1\% and 74.7\% for seizure-wise and patient-wise classification respectively, thereby setting new benchmark results for this dataset.

Jie Wang, Ghulam Mubashar Hassan, Naveed Akhtar

Artificial Intelligence (AI) relies heavily on deep learning - a technology that is becoming increasingly popular in real-life applications of AI, even in the safety-critical and high-risk domains. However, it is recently discovered that deep learning can be manipulated by embedding Trojans inside it. Unfortunately, pragmatic solutions to circumvent the computational requirements of deep learning, e.g. outsourcing model training or data annotation to third parties, further add to model susceptibility to the Trojan attacks. Due to the key importance of the topic in deep learning, recent literature has seen many contributions in this direction. We conduct a comprehensive review of the techniques that devise Trojan attacks for deep learning and explore their defenses. Our informative survey systematically organizes the recent literature and discusses the key concepts of the methods while assuming minimal knowledge of the domain on the readers part. It provides a comprehensible gateway to the broader community to understand the recent developments in Neural Trojans.

Rini Anggrainingsih, Ghulam Mubashar Hassan, Amitava Datta

The role of social media in opinion formation has far-reaching implications in all spheres of society. Though social media provide platforms for expressing news and views, it is hard to control the quality of posts due to the sheer volumes of posts on platforms like Twitter and Facebook. Misinformation and rumours have lasting effects on society, as they tend to influence people's opinions and also may motivate people to act irrationally. It is therefore very important to detect and remove rumours from these platforms. The only way to prevent the spread of rumours is through automatic detection and classification of social media posts. Our focus in this paper is the Twitter social medium, as it is relatively easy to collect data from Twitter. The majority of previous studies used supervised learning approaches to classify rumours on Twitter. These approaches rely on feature extraction to obtain both content and context features from the text of tweets to distinguish rumours and non-rumours. Manually extracting features however is time-consuming considering the volume of tweets. We propose a novel approach to deal with this problem by utilising sentence embedding using BERT to identify rumours on Twitter, rather than the usual feature extraction techniques. We use sentence embedding using BERT to represent each tweet's sentences into a vector according to the contextual meaning of the tweet. We classify those vectors into rumours or non-rumours by using various supervised learning techniques. Our BERT based models improved the accuracy by approximately 10% as compared to previous methods.

Rajvir Kaur, Kenji Bekki, Ghulam Mubashar Hassan et al.

We present a new method by which the total masses of galaxies including dark matter can be estimated from the kinematics of their globular cluster systems (GCSs). In the proposed method, we apply the convolutional neural networks (CNNs) to the two-dimensional (2D) maps of line-of-sight-velocities ($V$) and velocity dispersions ($σ$) of GCSs predicted from numerical simulations of disk and elliptical galaxies. In this method, we first train the CNN using either only a larger number ($\sim 200,000$) of the synthesized 2D maps of $σ$ ("one-channel") or those of both $σ$ and $V$ ("two-channel"). Then we use the CNN to predict the total masses of galaxies (i.e., test the CNN) for the totally unknown dataset that is not used in training the CNN. The principal results show that overall accuracy for one-channel and two-channel data is 97.6\% and 97.8\% respectively, which suggests that the new method is promising. The mean absolute errors (MAEs) for one-channel and two-channel data are 0.288 and 0.275 respectively, and the value of root mean square errors (RMSEs) are 0.539 and 0.51 for one-channel and two-channel respectively. These smaller MAEs and RMSEs for two-channel data (i.e., better performance) suggest that the new method can properly consider the global rotation of GCSs in the mass estimation. We also applied our proposed method to real data collected from observations of NGC 3115 to compare the total mass predicted by our proposed method and other popular methods from the literature.

Hezam Albaqami, Ghulam Mubashar Hassan, Abdulhamit Subasi et al.

Electroencephalography is frequently used for diagnostic evaluation of various brain-related disorders due to its excellent resolution, non-invasive nature and low cost. However, manual analysis of EEG signals could be strenuous and a time-consuming process for experts. It requires long training time for physicians to develop expertise in it and additionally experts have low inter-rater agreement (IRA) among themselves. Therefore, many Computer Aided Diagnostic (CAD) based studies have considered the automation of interpreting EEG signals to alleviate the workload and support the final diagnosis. In this paper, we present an automatic binary classification framework for brain signals in multichannel EEG recordings. We propose to use Wavelet Packet Decomposition (WPD) techniques to decompose the EEG signals into frequency sub-bands and extract a set of statistical features from each of the selected coefficients. Moreover, we propose a novel method to reduce the dimension of the feature space without compromising the quality of the extracted features. The extracted features are classified using different Gradient Boosting Decision Tree (GBDT) based classification frameworks, which are CatBoost, XGBoost and LightGBM. We used Temple University Hospital EEG Abnormal Corpus V2.0.0 to test our proposed technique. We found that CatBoost classifier achieves the binary classification accuracy of 87.68%, and outperforms state-of-the-art techniques on the same dataset by more than 1% in accuracy and more than 3% in sensitivity. The obtained results in this research provide important insights into the usefulness of WPD feature extraction and GBDT classifiers for EEG classification.

Ghulam Mubashar Hassan, Arcady V. Dyskin, Cara K. MacNish

Reconstruction and monitoring of displacement and strain fields is an important problem in engineering. We analyze the remote and non-obtrusive methods of strain measurement based on photogrammetry and Digital Image Correlation (DIC). The method is based on covering the photographed surface with a pattern of speckles and comparing the images taken before and after the deformation. In this study, a comprehensive literature review and comparative analysis of photogrammetric solutions is presented. The analysis is based on a specially developed Digital Image Synthesizer To Reconstruct Strain in Solids (DISTRESS) Simulator to generate synthetic images of displacement and stress fields in order to investigate the intrinsic accuracy of the existing variants of DIC. We investigated the Basic DIC and a commercial software VIC 2D, both based on displacement field reconstruction with post processing strain determination based on numerical differentiation. We also investigated what we call the Extended DIC where the strain field is determined independently of the displacement field. While the Basic DIC and VIC 2D are faster, the Extended DIC delivers the best accuracy of strain reconstruction. The speckle pattern is found to be playing a critical role in achieving high accuracy for DIC. Increase in subset size for DIC does not significantly improves the accuracy, while the smallest subset size depends on the speckle pattern and speckle size. Increase in the overall image size provides more details but does not play significant role in improving the accuracy, while significantly increasing the computation cost.