LLM-Enhanced Multi-Teacher Knowledge Distillation for Modality-Incomplete Emotion Recognition in Daily Healthcare.

The critical importance of monitoring and recognizing human emotional states in healthcare has led to a surge in proposals for EEG-based multimodal emotion recognition in recent years. However, practical challenges arise in acquiring EEG signals in daily healthcare settings due to stringent data acquisition conditions, resulting in the issue of incomplete modalities. Existing studies have turned to knowledge distillation as a means to mitigate this problem by transferring knowledge from multimodal networks to unimodal ones. However, these methods are constrained by the use of a single teacher model to transfer integrated feature extraction knowledge, particularly concerning spatial and temporal features in EEG data. To address this limitation, we propose a multi-teacher knowledge distillation framework enhanced with a Large Language Model (LLM), aimed at facilitating effective feature learning in the student network by transferring knowledge of extracting integrated features. Specifically, we employ an LLM as the teacher for extracting temporal features and a graph convolutional neural network for extracting spatial features. To further enhance knowledge distillation, we introduce causal masking and a confidence indicator into the LLM to facilitate the transfer of the most discriminative features. Extensive testing on the DEAP and MAHNOB-HCI datasets demonstrates that our model outperforms existing methods in the modality-incomplete scenario. This study underscores the potential application of large models in this field. The code is publicly available at https://github.com/yuzhezhangEEG/LM-KD.

DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction.

Dengue is a distinctive and fatal infectious disease that spreads through female mosquitoes called Aedes aegypti. It is a notable concern for developing countries due to its low diagnosis rate. Dengue has the most astounding mortality level as compared to other diseases due to tremendous platelet depletion. Hence, it can be categorized as a life-threatening fever as compared to the same class of fevers. Additionally, it has been shown that dengue fever shares many of the same symptoms as other flu-based fevers. On the other hand, the research community is closely monitoring the popular research fields related to IoT, fog, and cloud computing for the diagnosis and prediction of diseases. IoT, fog, and cloud-based technologies are used for constructing a number of health care systems. Accordingly, in this study, a DengueFog monitoring system was created based on fog computing for prediction and detection of dengue sickness. Additionally, the proposed DengueFog system includes a weighted random forest (WRF) classifier to monitor and predict the dengue infection. The proposed system's efficacy was evaluated using data on dengue infection. This dataset was gathered between 2016 and 2018 from several hospitals in the Delhi-NCR region. The accuracy, F-value, recall, precision, error rate, and specificity metrics were used to assess the simulation results of the suggested monitoring system. It was demonstrated that the proposed DengueFog monitoring system with WRF outperforms the traditional classifiers.

Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds.

The recent impact of COVID-19, as a contagious disease, led researchers to focus on designing and fabricating personal healthcare devices and systems. With the help of wearable sensors, sensing and communication technologies, and recommendation modules, personal healthcare systems were designed for ease of use. More specifically, personal healthcare systems were designed to provide recommendations for maintaining a safe distance and avoiding contagious disease spread after the COVID-19 pandemic. The personal recommendations are analyzed based on the wearable sensor signals and their consistency in sensing. This consistency varies with human movements or other activities that hike/cease the sensor values abruptly for a short period. Therefore, a consistency-focused recommendation system (CRS) for personal healthcare (PH) was designed in this research. The hardware sensing intervals for the system are calibrated per the conventional specifications from which abrupt changes can be observed. The changes are analyzed for their saturation and fluctuations observed from neighbors within the threshold distance. The saturation and fluctuation classifications are performed using random forest learning to differentiate the above data from the previously sensed healthy data. In this process, the saturated data and consistency data provide safety recommendations for the moving user. The consistency is verified for a series of intervals for the fluctuating sensed data. This alerts the user if the threshold distance for a contagious disease is violated. The proposed system was validated using a prototype model and experimental analysis through false rates, data analysis rates, and fluctuations.

Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.

Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor classification (BTC) models have shown significant progress, they encounter limitations like restricted depth, vanishing gradient issues, and difficulties in capturing intricate features. To address these challenges, this paper proposes an efficient skip connections-based residual network (ESRNet). leveraging the residual network (ResNet) with skip connections. ESRNet ensures smooth gradient flow during training, mitigating the vanishing gradient problem. Additionally, the ESRNet architecture includes multiple stages with increasing numbers of residual blocks for improved feature learning and pattern recognition. ESRNet utilizes residual blocks from the ResNet architecture, featuring skip connections that enable identity mapping. Through direct addition of the input tensor to the convolutional layer output within each block, skip connections preserve the gradient flow. This mechanism prevents vanishing gradients, ensuring effective information propagation across network layers during training. Furthermore, ESRNet integrates efficient downsampling techniques and stabilizing batch normalization layers, which collectively contribute to its robust and reliable performance. Extensive experimental results reveal that ESRNet significantly outperforms other approaches in terms of accuracy, sensitivity, specificity, F-score, and Kappa statistics, with median values of 99.62%, 99.68%, 99.89%, 99.47%, and 99.42%, respectively. Moreover, the achieved minimum performance metrics, including accuracy (99.34%), sensitivity (99.47%), specificity (99.79%), F-score (99.04%), and Kappa statistics (99.21%), underscore the exceptional effectiveness of ESRNet for BTC. Therefore, the proposed ESRNet showcases exceptional performance and efficiency in BTC, holding the potential to revolutionize clinical diagnosis and treatment planning.

A fault-tolerant aware scheduling method for fog-cloud environments.

Fog computing is a promising technology that leverages the resources to provide services for requests of IoT (Internet of Things) devices at the cloud edge. The high dynamic and heterogeneous nature of devices at the cloud edge causes failures to be a popular event and therefore fault tolerance became indispensable. Most early scheduling and fault-tolerant methods did not highly consider time-sensitive requests. This increases the possibility of latencies for serving these requests which causes unfavorable impacts. This paper proposes a fault-tolerant scheduling method (FTSM) for allocating services' requests to the most sufficient devices in fog-cloud IoT-based environments. The main purpose of the proposed method is to reduce the latency and overheads of services and to increase the reliability and capacity of the cloud. The method depends on categorizing devices that can issue requests into three classes according to the type of service required. These classes are time-sensitive, time-tolerant and core. Each time-sensitive request is directly mapped to one or more edge devices using a pre-prepared executive list of devices. Each time-tolerant request may be assigned to one or more devices at the cloud edge or the cloud core. Core requests are assigned to resources at the cloud core. In order to achieve fault tolerance, the proposed method selects the most suitable fault-tolerant technique from replication, checkpointing and resubmission techniques for each request while most existing methods consider only one technique. The effectiveness of the proposed method is assessed using average service time, throughput, operation costs, success rate and capacity percentage as performance indicators.