COVID-19 appears to be having a devastating influence on world health and wellbeing. Moreover, the COVID-19 confirmed cases have recently increased to over 10 million worldwide. As the number of verified cases increase, it is more important to monitor and classify healthy and infected people in a timely and accurate manner. Many existing detection methods have failed to detect viral patterns. Henceforth, by using COVID-19 thoracic x-rays and the histogram-oriented gradients (HOG) feature extraction methodology; this research work has created an accurate classification method for performing a reliable detection of COVID-19 viral patterns. Further, the proposed classification model provides good results by leveraging accurate classification of COVID-19 disease based on the medical images. Besides, the performance of our proposed CNN classification method for medical imaging has been assessed based on different edge-based neural networks. Whenever there is an increasing number of a class in the training network, the accuracy of tertiary classification with CNN will be decreasing. Moreover, the analysis of 10 fold cross-validation with confusion metrics can also take place in our research work to detect various diseases caused due to lung infection such as Pneumonia corona viruspositive or negative. The proposed CNN model has been trained and tested with a public X-ray dataset, which is recently published for tertiary and normal classification purposes. For the instance transfer learning, the proposed model has achieved 85% accuracy of tertiary classification that includes normal, COVID-19 positive and Pneumonia. The proposed algorithm obtains good classification accuracy during binary classification procedure integrated with the transfer learning method. Journal of ISMAC (2021) Vol.03/ No.02 Pages: 132-148 http://irojournals.com/iroismac/ DOI: https://doi.org/10.36548/jismac.2021.2.006

https://irojournals.com/iroismac/V3/I2/06.pdf

Design of Accurate Classification of COVID-19 Disease in X-Ray Images Using Deep Learning Approach

Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient’s death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.

Coronavirus (COVID-19) is a new virus of viral pneumonia. It can outbreak in the world through person-to-person transmission. Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease. The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems. The wireless body area network, cloud computing, fog computing, and clinical decision support system are integrated to provide a comprehensive and complete model for disease detection and monitoring. By monitoring a person with COVID-19 in real time, physicians can guide patients with the right decisions. The proposed framework has three main layers (i.e., a patient layer, cloud layer, and hospital layer). In the patient layer, the patient is tracked through a set of wearable sensors and a mobile app. In the cloud layer, a fog network architecture is proposed to solve the issues of storage and data transmission. In the hospital layer, we propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient’s X-ray scan images and transfer learning. The proposed model achieved promising results compared to the state-of-the art (i.e., accuracy of 97.95% and specificity of 98.85%). Our framework is a useful application, through which we expect significant effects on COVID-19 proliferation and considerable lowering in healthcare expenses.

https://www.mdpi.com/2079-9292/9/9/1439/pdf

End-To-End Deep Learning Framework for Coronavirus (COVID-19) Detection and Monitoring

Due to the rapid change in technologies, new data forms exist which lead to a huge data size on the internet. As a result, some learning platforms such as e-learning systems must change their methodologies for data processing to be smarter. This paper proposes a framework for smoothly adapt the traditional e-learning systems to be suitable for smart cities applications. Learning Analytics (LA) has turned into a noticeable worldview with regards to instruction of late which embraces the current progressions of innovation, for example, cloud computing, big data processing, and Internet of Things. LA additionally requires a concentrated measure of preparing assets to create applicable investigative outcomes. Be that as it may, the customary methodologies have been wasteful at handling LA difficulties.

A Framework for Big Data Analysis in Smart Cities

The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

/pdf/smart-healthcare-in-the-age-of-ai-recent-advances-challenges-18nz3rifag.pdf

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

The intensive care unit (ICU) admits the most seriously ill patients requiring extensive monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great risk of dying and for providing suitable interventions to save their lives. Accordingly, early prediction of patients at high mortality risk will enable their provision of appropriate and timely medical services. Although various severity scores and machine-learning models have recently been developed for early mortality prediction, such prediction remains challenging. This paper proposes a novel stacking ensemble approach to predict the mortality of ICU patients. Our approach is more accurate and medically intuitive compared to the literature work. Data were prepared and feature selection was processed under the supervision of the ICU domain expert. The data were split into six modalities based on the expert's decisions. For the prediction process, a separate classifier was selected for each modality based on the performance of the classifiers. We utilized the most popular and diverse classifiers in the literature, including linear discriminant analysis, decision tree (DT), multilayer perceptron, k-nearest neighbor, and logistic regression (LR). Then, a stacking ensemble classifier was constructed and optimized based on the fusion of these five classifier decisions. The framework was evaluated using 10,664 patients from the medical information mart for intensive care (MIMIC III) benchmark dataset. To predict patient mortality, extensive experiments were conducted using the patients' time series data of different lengths. For each patient, the first 6, 12, and 24 hours of the first stay were tested. The results indicate that our model outperformed the state-of-the-art approaches in terms of accuracy (94.4%), F1 score (93.7%), precision (96.4%), recall (91.1%), and area under the receiver operator characteristic (ROC) curve (93.3%). These results demonstrate the ability and efficiency of our approach to predict ICU mortality.

/pdf/intensive-care-unit-mortality-prediction-an-improved-patient-30cawxjx10.pdf

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

Sepsis is a life-threatening disease that is associated with organ dysfunction. It occurs due to the body’s dysregulated response to infection. It is difficult to identify sepsis in its early stages, this delay in identification has a dramatic effect on mortality rate. Developing prognostic tools for sepsis prediction has been the focus of various studies over previous decades. However, most of these studies relied on tracking a limited number of features, as such, these approaches may not predict sepsis sufficiently accurately in many cases. Therefore, in this study, we concentrate on building a more accurate and medically relevant predictive model for identifying sepsis. First, both NSGA-II (a multi-objective genetic algorithm optimization approach) and artificial neural networks are used concurrently to extract the optimal feature subset from patient data. In the next stage, a deep learning model is built based on the selected optimal feature set. The proposed model has two layers. The first is a deep learning classification model used to predict sepsis. This is a stacking ensemble of neural network models that predicts which patients will develop sepsis. For patients who were predicted to have sepsis, data from their first six hours after admission to the ICU are retrieved, this data is then used for further model optimization. Optimization based on this small, recent timeframe leads to an increase in the effectiveness of our classification model compared to other models from previous works. In the second layer of our model, a multitask regression deep learning model is used to identify the onset time of sepsis and the blood pressure at that time in patients that were predicted to have sepsis by the first layer. Our study was performed using the medical information from the intensive care MIMIC III real-world dataset. The proposed classification model achieved 0.913, 0.921, 0.832, 0.906 for accuracy, specificity, sensitivity, and AUC, respectively. In addition, the multitask regression model obtained an RMSE of 10.26 and 9.22 for predicting the onset time of sepsis and the blood pressure at that time, respectively. There are no other studies in the literature that can accurately predict the status of sepsis in terms of its onset time and predict medically verifiable quantities like blood pressure to build confidence in the onset time prediction. The proposed model is medically intuitive and achieves superior performance when compared to all other current state-of-the-art approaches.

Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning

This paper proposes a customized Ubiquitous smart Teaching and smart-Learning system that use Internet of Things (IoT), huge information, supercomputing, and profound figuring out how to give improved advancement, administration, and conveyance of instructing and learning in smart society settings. A proof of an idea is that framework has been created in light of the structure. A detailed, gritty outline, execution, and assessment of the smart learning framework, including its five parts, are given utilizing 11 utilized datasets.

Ubiquitous smart learning system for smart cities

The advancement of innovations empowers learners to take in more viable, proficiently, adaptable and serenely. Smart learning, an idea that portrays learning in advanced age, has increased expanded consideration. This paper examines the meaning of Smart learning and shows an applied structure. The smart teaching method structure incorporates class-based separated direction [13], gather based communitarian learning, individual-based customized learning and mass-based generative learning. This paper proposes the Higher Particle Optimization (HPO) clustering as an instrument to trigger learning engagement activities. Utilizing (HPO), learners are clustered utilizing likeness measures construed from watched skill, meta-fitness, and certainty values, notwithstanding viability measures of instructional devices. A reenactment thinks about demonstrates that the (HPO)-based clustering is more ideal than Parallel M-implies grouping.

Samir Abdelrazek

Papers

End-To-End Deep Learning Framework for Coronavirus (COVID-19) Detection and Monitoring

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning

Ubiquitous smart learning system for smart cities

Evaluating Learners’ Progress in Smart Learning Environment