A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supporting classification is developed on these video streams using cloud-based infrastructure. A key focus in this paper is on tuning hyper-parameters associated with the deep learning algorithm used to construct the model. We further propose an automatic video object classification pipeline to validate the system. The mathematical model used to support hyper-parameter tuning improves performance of the proposed pipeline, and outcomes of various parameters on system's performance is compared. Subsequently, the parameters that contribute toward the most optimal performance are selected for the video object classification pipeline. Our experiment-based validation reveals an accuracy and precision of 97% and 96%, respectively. The system proved to be scalable, robust, and customizable for a variety of different applications.

Applying deep learning models to large-scale IoT data is a compute-intensive task and needs significant computational resources. Existing approaches transfer this big data from IoT devices to a central cloud where inference is performed using a machine learning model. However, the network connecting the data capture source and the cloud platform can become a bottleneck. We address this problem by distributing the deep learning pipeline across edge and cloudlet/fog resources. The basic processing stages and trained models are distributed towards the edge of the network and on in-transit and cloud resources. The proposed approach performs initial processing of the data close to the data source at edge and fog nodes, resulting in significant reduction in the data that is transferred and stored in the cloud. Results on an object recognition scenario show 71\% efficiency gain in the throughput of the system by employing a combination of edge, in-transit and cloud resources when compared to a cloud-only approach.

Mental illness prediction through text involves employing natural language processing (NLP) techniques and deep learning algorithms to analyze textual data for the identification of mental disorders. Therefore, machine learning and deep learning algorithms have been utilized in the existing literature for the detection of mental illness. However, current systems exhibit suboptimal performance primarily due to their reliance on traditional embedding techniques and generic language models to generate text embeddings. To address this limitation, there is a requirement for domain-specific pretrained language models that comprehensively understand the context found in posts of mentally ill patients. Posts from individuals with mental illness often contain metaphorical expressions, posing a challenge for existing models in understanding such figurative language. In this study, we propose a hybrid transformer architecture, comprising MentalBERT and MelBERT pretrained language models, cascaded with CNN models to generate and concatenate deep features. MentalBERT is pretrained on an extensive corpus of text data specifically related to the mental health domain, while MelBERT is trained on a large corpus of metaphorical data for improved understanding of metaphorical expressions. The results reveal outstanding performance of the proposed architecture with an overall accuracy of 92% and an F1-score of 92%, surpassing state-of-the-art models in comparison. This study underscores the necessity for further research in this field and illustrates the potential of advanced technologies to address mental health issues in contemporary society.

As AI advances, new applications and services are being developed to enhance current services. With the emergence of digital twin technology (DT), an exact digital replica of any natural system or physical object/state can be created. DT incorporates several machines and deep learning-based approaches to efficiently analyze and process the collected data in order to understand the system pattern better. Consequently, Health 4.0 applications can get advantages from these cutting-edge technologies. In this article, we focus on Intracranial haemorrhage (ICH) which is a life-threatening emergency that needs immediate diagnosis and treatment. ICH is caused by the bleeding inside the skull or brain. It is classified in five main subtypes; a) subdural hemorrhage (SH), b) epidural haemorrhage (EH), c) intraparenchymal haemorrhage (IH), d) subarachnoid haemorrhage (SAH) and e) intraventricular hemorrhage (IVH). Radiologists typically examine computed tomography (CT) scan of the patients to determine the ICH and its subtype. However, manual assessment of the CT scan is a complex and time-consuming task. Pre-trained convolutional neural networks (CNNs) are state-of-the-art for the ICH classification. But they suffer from the curse of dimensionality and use redundant and noisy features which causes memory and computation overhead. The problem of imbalanced data poses the challenge for achieving model generalization. This paper proposes a hybrid attention-based ResNet architecture for ICH detection and classification. Principal component analysis (PCA) is used for dimensionality reduction and redundant feature removal whereas xtreme gradient boosting (XGBoost) is used for ICH classfication. The class imbalance problem is resolved using deep convolutional generative adversarial network (DCGAN). The proposed system is evaluated using the dataset assembled during Radiologist Society of North America (RSNA) ICH detection challenge 2019. The findings show that our proposed model outperforms existing state-of-the-art models in terms of accuracy and F1-score. ICH classification achieved accuracies of 99.3%, 97.1%, 96.7%, and 96.1%, for detecting EH, IH, IVH, SAH, and SH subtypes respectively, while F1-score is 96.1%, which is also best when compared with the benchmark studies.

Recent technological advancements in e-learning platforms have made it easy to store and manage students’ related data, such as personal details, initial grade, intermediate grades, final grades, and many other parameters. These data can be efficiently processed and analyzed by intelligent techniques and algorithms to generate useful insights into the students’ performance, such as to identify the factors impacting the progress of successful students or the performance of the students who are struggling in their courses and are at risk of failing. Such a framework is scarce in the current literature. This study proposes an interpretable framework to generate useful insights from the data produced by e-learning platforms using machine learning algorithms. The proposed framework incorporates predictive models, as well as regression and classification models to analyze multiple factors of student performance. Classification models are used to systematize normal and at-risk students based on their academic performance, with high precision and accuracy. Regression analysis is performed to determine the inherent linear and nonlinear relationships between the academic outcomes of the students acting as the target or independent variables and the performance indicative features acting as dependent variables. For further analysis, a predictive modeling problem is considered, where the performance of the students is anticipated based on their commitment to a specific course, their performance for the whole course, and their final grades. The efficiency of the proposed framework is also optimized by reliably tuning the algorithmic parameters. Furthermore, the performance is accelerated by empowering the system with a GPU-based infrastructure. Results reveal that the proposed interpretable framework is highly accurate and precise and can identify factors that play a vital role in the students’ success or failure.