Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the public. As a result of COVID reaching the pandemic dimension, the development and implementation of rapid detection methods have become important. In this context, our study aims to detect monkeypox disease in case of a possible pandemic through skin lesions with deep-learning methods in a fast and safe way. Deep-learning methods were supported with transfer learning tools and hyperparameter optimization was provided. In the CNN structure, a hybrid function learning model was developed by customizing the transfer learning model together with hyperparameters. Implemented on the custom model MobileNetV3-s, EfficientNetV2, ResNET50, Vgg19, DenseNet121, and Xception models. In our study, AUC, accuracy, recall, loss, and F1-score metrics were used for evaluation and comparison. The optimized hybrid MobileNetV3-s model achieved the best score, with an average F1-score of 0.98, AUC of 0.99, accuracy of 0.96, and recall of 0.97. In this study, convolutional neural networks were used in conjunction with optimization of hyperparameters and a customized hybrid function transfer learning model to achieve striking results when a custom CNN model was developed. The custom CNN model design we have proposed is proof of how successfully and quickly the deep learning methods can achieve results in classification and discrimination.

A massive volume of unstructured data in the form of comments, opinions, and other sorts of data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of the data, building an accurate predictive model for sentiment analysis remains a challenging task. While various DNN architectures have been applied to sentiment analysis with encouraging results, they suffer from high dimensional feature space and consider various features equally. State-of-the-art methods cannot properly leverage semantic and sentiment knowledge to extract meaningful relevant contextual sentiment features.This paper proposes a sentiment and context-aware hybrid DNN model with an attention mechanism that {intelligently learns and highlights salient features of relevant sentiment context in the text. We first use integrated wide coverage sentiment lexicons to identify text sentiment features then leverage bidirectional encoder representation from transformers to produce sentiment-enhanced word embeddings for text semantic extraction. After that, the proposed approach adapts the BiLSTM to capture both word order/contextual text semantic information and the long-dependency relation in the word sequence. Our model also employs an attention mechanism to assign weight to features and give greater significance to salient features in the word sequence. Finally, CNN is utilized to reduce the dimensionality of feature space and extract the local key features for sentiment analysis. The effectiveness of the proposed model is evaluated on real-world benchmark datasets demonstrating that the proposed model significantly improves the accuracy of existing text sentiment classification.

Recurrent Neural Networks (RNNs) and their variants have been demonstrated tremendous successes in modeling sequential data such as audio processing, video processing, time series analysis, and text mining. Inspired by these facts, we propose human activity recognition technique to proceed visual data via utilizing convolution neural network (CNN) and Bidirectional-gated recurrent unit (Bi-GRU). Firstly, we extract deep features from frames sequence of human activities videos using CNN and then select most important features from the deep appearances to improve performance and decrease computational complexity of the model. Secondly, to learn temporal motions of frames sequence, we design Bi-GRU and feed those deep-important features extracted from frames sequence of human activities to Bi-GRU which learn temporal dynamics in forward and backward direction at each time step. We conduct extensive experiments on realistic videos of human activity recognition datasets YouTube11, HMDB51 and UCF101. Lastly, we compare the obtained results with existing methods to show the competence of our proposed technique.

The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.

With an increasing demand for complex and powerful system-on-chips, modern real-time automotive systems face significant challenges in managing on-chip-temperature. We demonstrate, via real experiments, the importance of accounting for dynamic ambient temperature and task-level power dissipation in resource management so as to meet both thermal and timing constraints. To address this problem, we propose RT-TRM, a real-time thermal-aware resource management framework. We first introduce a task-level dynamic power model that can capture different power dissipations with a simple task-level parameter called the activity factor . We then develop two new mechanisms, adaptive parameter assignment and online idle-time scheduling . The former adjusts voltage/frequency levels and task periods according to the varying ambient temperature while preserving feasibility. The latter generates a schedule by allocating idle times efficiently without missing any task/job deadline. By tightly integrating the solutions of these two mechanisms, we can guarantee both thermal and timing constraints in the presence of dynamic ambient temperature variations. We have implemented RT-TRM on an automotive microcontroller to demonstrate its effectiveness, achieving better resource utilization by 18.2% over other runtime approaches while meeting both thermal and timing constraints.

As modern embedded systems like cars need high-power integrated CPUs--GPU SoCs for various real-time applications such as lane or pedestrian detection, they face greater thermal problems than before, which may, in turn, incur higher failure rate and cooling cost. We demonstrate, via experimentation on a representative CPUs--GPU platform, the importance of accounting for two distinct thermal characteristics—the platform’s temperature imbalance and different power dissipations of different tasks —in real-time scheduling to avoid any burst of power dissipations while guaranteeing all timing constraints. To achieve this goal, we propose a new <u>R</u>eal-<u>T</u>ime <u>T</u>hermal-<u>A</u>ware <u>S</u>cheduling (RT-TAS) framework. We first capture different CPU cores’ temperatures caused by different GPU power dissipations (i.e., CPUs--GPU thermal coupling ) with core-specific thermal coupling coefficients. We then develop thermally-balanced task-to-core assignment and CPUs--GPU co-scheduling . The former addresses the platform’s temperature imbalance by efficiently distributing the thermal load across cores while preserving scheduling feasibility. Building on the thermally-balanced task assignment, the latter cooperatively schedules CPU and GPU computations to avoid simultaneous peak power dissipations on both CPUs and GPU, thus mitigating excessive temperature rises while meeting task deadlines. We have implemented and evaluated RT-TAS on an automotive embedded platform to demonstrate its effectiveness in reducing the maximum temperature by 6−12.2° C over existing approaches without violating any task deadline.