Developing an efficient heartbeat monitoring system has become a focal point in numerous healthcare applications. Specifically, in the last few years, heartbeat classification for arrhythmia detection has gained considerable interest from researchers. This paper presents a novel deep representation learning method for the efficient detection of arrhythmic beats. To mitigate the issues associated with the imbalanced data distribution, a novel re-sampling strategy is introduced. Unlike the existing oversampling methods, the proposed technique transforms majority-class samples into minority-class samples with a novel translation loss function. This approach assists the model in learning a more generalized representation of crucially important minority class samples. Moreover, by exploiting an auxiliary feature, an augmented attention module is designed that focuses on the most relevant and target-specific information. We adopted an inter-patient classification paradigm to evaluate the proposed method. The experimental results of this study on the MIT-BIH arrhythmia database clearly indicate that the proposed model with augmented attention mechanism and over-sampling strategy significantly learns a balanced deep representation and improves the classification performance of vital heartbeats.

Computation offloading enables intensive computational tasks to be separated into multiple computing resources for overcoming hardware limitations. Leveraging cloud computing, edge computing can be enabled to apply not only large-scale and personalized data but also intelligent algorithms based on offloading the intelligent models to high-performance servers for working with huge volumes of data in the cloud. In this paper, we propose a getaway-centric Internet of Things (IoT) system to enable the intelligent and autonomous operation of IoT devices in edge computing. In the proposed edge computing, IoT devices are operated by a decision-making model that selects an optimal control factor from multiple intelligent services and applies it to the device. The intelligent services are provided based on offloading multiple intelligent and optimization approaches to the intelligent service engine in the cloud. Therefore, the decision-making model in the gateway is enabled to select the best solution from the candidates. Also, the proposed IoT system provides monitoring and visualization to users through device management based on resource virtualization using the gateway. Furthermore, the gateway interprets scenario profiles to interact with intelligent services dynamically and apply the optimal control factor to the actual device through the virtual resource. For implementing the improved energy optimization using the proposed IoT system, we propose two intelligent models to learn parameters of a user’s residential environment using deep learning and derive the inference models to deploy in the intelligent service engine. The inference models are used for predicting a heater energy consumption that is applied to the heater. The heater updates the environment parameters to reach the user-desired values. Moreover, based on two energy consumption values, the decision-making model brings a smaller value to operate the heater to enable reducing the energy consumption as well as providing a user-desired environment.

Emotion recognition based on electroencephalography (EEG) signals has many potential applications. However, the development of an emotion recognition model for real-world deployment encounters significant setbacks in learning an invariant representation due to data distribution shift. Some prior studies adopted domain adaptation methods to tackle this issue. However, these studies perform marginally to tackle distribution shift in the source data and neglect to focus on the extraction of domain and target-specific features, which are crucial for domain invariant representation learning. In this study, we propose an assisted domain adaptive representation learning (ADARL) method that learns domain invariant and emotion-specific features for recognizing emotions across different subjects. The proposed architecture introduces novel attention modules to segregate domain-specific and emotion-specific feature maps by exploiting the inter-relationship between different channels and bands. The domain-specific residual feature maps (peripheral features) extracted by the proposed attention mechanism are used to assist the domain discriminator that forces the model to learn a generalized representation of EEG signals. To tackle the inter-subject variability in the source data, we introduce a novel class-aware compactness loss function to improve the domain adaptation transfer efficiency. The experiments conducted on three EEG datasets (SEED, SEED-IV, and DEAP) demonstrate that the ADARL method significantly improves emotion recognition performance and efficiently alleviates the domain shift problem by learning a generalized representation of EEG signals.