Prognostic modeling is essential for forecasting future clinical scores and enabling early detection of Alzheimer's disease (AD). While most existing methods focus on predicting the ADAS-Cog global score, they often overlook the predictive value of its$\mathbf{1 3}$sub-scores, which reflect distinct cognitive domains. Some sub-scores may exert greater influence on determining global scores. Assigning higher loss weights to these clinically meaningful sub-scores can guide the model to focus on more relevant cognitive domains, enhancing both predictive accuracy and interpretability. In this study, we propose a weighted Vision Transformer (ViT)-based multi-task learning (MTL) framework to jointly predict the ADAS-Cog global score using baseline MRI scans and its 13 sub-scores at Month 24. Our framework integrates ViT as a feature extractor and systematically investigates the impact of sub-score-specific loss weighting on model performance. Results show that our proposed weighting strategies are group-dependent: strong weighting improves performance for MCI subjects with more heterogeneous MRI patterns, while moderate weighting is more effective for CN subjects with lower variability. Our findings suggest that uniform weighting underutilizes key sub-scores and limits generalization. The proposed framework offers a flexible, interpretable approach to AD prognosis using end-to-end MRI-based learning. (Github repo provided11https://github.com/mirahhamid/A-Weighted-Vision-Transformer-Based-Multi-Task-Learning-Framework-for-Predicting-ADAS-Cog-Scores.git)

Transforming existing electromyography (EMG) measurement system into portable and wearable devices is key to fuel the revolution of digital healthcare and rehabilitation. Conventional EMG measurement systems that rely on invasive needle electrodes and non-invasive wet and dry contact electrodes are impractical to telehealth applications. Existing capacitive electromyography (cEMG) measurement systems presented by various research groups are typically designed with multi-stage analog front-end circuitry and complex data acquisition (DAQ) systems. This paper proposed a simple and versatile multichannel wireless cEMG measurement system. It consists of flexible cEMG biomedical sensors, a low-noise wireless DAQ module, and a moving average of the squared data (MASq) signal processing techniques. The proposed flexible capacitive biomedical sensor can be insulated by porous and non-porous materials. Only two electrodes are needed to acquire raw EMG signals while achieving low common-mode noise. Overall, the system achieves a high mean pulse signal-to-noise ratio (PSNR) of 13.6 (polyimide film) and 8.3 (micropore). It recorded a linear correlation between the mean root-mean-square (RMS) of the MASq data and muscle strength with a step size of 1 kg. The total power consumption of this system is 41 mW with five EMG input channels, averaging 8 mW each. This low-noise and low power consumption characteristic is ideal for battery-based wearable devices.

Knowledge of the underlying anatomy of the left atrium can promote improved diagnostic protocols and clinical interventions; therefore, automatic segmentation of the left atrium on magnetic resonance imaging (MRI) can support diagnosis, treatment and surgery planning of the heart. Due to the small size of the left atrium with respect to the whole MRI volume, most of the current deep learning approaches are based on cropping or cascading networks. Dense V-Network is an encoder-decoder model designed for volumetric images by introducing a specialised dense feature stack to the standard V-Net model. A minor manipulation in parameters of the Dense V-Network can make it suitable for the fast and efficient segmentation of the left atrium. We present a brief review showing the ability of the Dense V-Network to deal with the issue of class imbalance and the unavailability of a large dataset of left atrium segmentation. We conclude that Dense V-Network can be tailored to left atrium MRI segmentation showing the accuracy that can surpass current methods, potentially supporting cardiac diagnosis and surgery.

Recognition of human behavior is critical in video monitoring, human-computer interaction, video comprehension, and virtual reality. The key problem with behaviour recognition in video surveillance is the high degree of variation between and within subjects. Numerous studies have suggested background-insensitive skeleton-based as the proven detection technique. The present state-of-the-art approaches to skeleton-based action recognition rely primarily on Recurrent Neural Networks (RNN) and Convolution Neural Networks (CNN). Both methods take dynamic human skeleton as the input to the network. We chose to handle skeleton data differently, relying solely on its skeleton joint coordinates as the input. The skeleton joints’ positions are defined in (x, y) coordinates. In this paper, we investigated the incorporation of the Neural Oblivious Decision Ensemble (NODE) into our proposed action classifier network. The skeleton is extracted using a pose estimation technique based on the Residual Network (ResNet). It extracts the 2D skeleton of 18 joints for each detected body. The joint coordinates of the skeleton are stored in a table in the form of rows and columns. Each row represents the position of the joints. The structured data are fed into NODE for label prediction. With the proposed network, we obtain 97.5% accuracy on RealWorld (HAR) dataset. Experimental results show that the proposed network outperforms one the state-of-the-art approaches by 1.3%. In conclusion, NODE is a promising deep learning technique for structured data analysis as compared to its machine learning counterparts such as the GBDT packages; Catboost, and XGBoost.

Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease that requires attentive medical evaluation. Therefore, diagnosing of AD accurately is crucial to provide the patients with appropriate treatment to slow down the progression of AD as well to facilitate the treatment interventions. To date, deep learning by means of convolutional neural networks (CNNs) has been widely used in diagnosing of AD. There are several well-established CNNs architectures that have been used in the image classification domain for magnetic resonance imaging (MRI) images analysis such as LeNet-5, Inception-V4, VGG-16 and Residual Network. However, these existing deep learning-based methods have lack of ability to be spatial invariance to the input data, due to overlooking some salient local features of the region of interest (ROI) (i.e., hippocampal). In medical image analysis, local features of MRI images are hard to exploit due to the small pixel size of ROI. On the other hand, CNNs requires large dataset sample to perform well, but we have limited number of MRI images to train, thus, leading to overfitting. Therefore, we propose a novel deep learning-based model without pre-processing techniques by incorporating attention mechanism and global average pooling (GAP) layer to VGG-16 architecture to capture the salient features of the MRI image for subtle discriminating of AD and normal control (NC). Also, we utilize transfer learning to surpass the overfitting issue. Experiment is performed on data collected from Open Access Series of Imaging Studies (OASIS) database. The accuracy performance of binary classification (AD vs NC) using proposed method significantly outperforms the existing methods, 12-layered CNNs (trained from scratch) and Inception-V4 (transfer learning) by increasing 1.93% and 3.43% of the accuracy. In conclusion, Attention-GAP model capable of improving and achieving notable classification accuracy in diagnosing AD.

Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes in the classifier’s training, data augmentation has been employed, generating more samples in minor classes. The proposed deep learning method has been evaluated on a real wafer map defect dataset and its classification results on the test set returned a 97.91% accuracy. The results were compared with another deep learning based auto-encoder model demonstrating the proposed method, a potential approach for silicon wafer defect classification that needs to be investigated further for its robustness.

Assembly sequence planning (ASP) known as a hard combinatorial optimization problem is an important part of assembly process planning. Determining the sequence of assembly arguably is quite challenging in ASP problem. A good assembly sequence can help to reduce the cost and time of the manufacturing process.This paper presents an implementation of binary Gravitational Search Algorithm (BGSA) for solving an assembly sequence planning (ASP) problem. Initially, each agent is represented by a feasible assembly sequence according to a precedence matrix. Next, binary Gravitational Search Algorithm (BGSA) is used for updating the current to new feasible assembly sequence to solving ASP problem. Using a case study of ASP, the results show that the proposed approach based on BGSA is more efficient for solving the ASP problem in compare to other approaches based on simulated annealing (SA), genetic algorithm (GA), and binary particle swarm optimization (BPSO).

This paper introduces a hybrid approach, namely Hybrid Artificial Neural Network-Naive Bayes classifier, for two-class imbalanced datasets classification. An imbalanced dataset in semiconductor manufacturing test process is chosen as a case study. Outputs prediction in semiconductor manufacturing is helpful for engineer to identify good/bad products earlier and to avoid the bad units from being processed. This application shows the significance of solving the problems. The proposed hybrid approach presented in this paper uses the concept that an Artificial Neural Network (ANN) provides a guidance to Naive Bayes classifier in making better decision by providing an additional input to Naive Bayes. Several experiments are conducted as comparison to the individual classifiers, which are ANN and Naive Bayes. As a result, the proposed Hybrid approach performs better than the individual classifiers and finally overcomes the imbalanced dataset problems in semiconductor manufacturing test process.