MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management.

In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks' priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.

COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images.

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.

Characterization of Alginate-Gelatin-Cholesteryl Ester Liquid Crystals Bioinks for Extrusion Bioprinting of Tissue Engineering Scaffolds.

Tissue engineering (TE) is an innovative approach to tackling many diseases and body parts that need to be replaced by developing artificial tissues and organs. Bioinks play an important role in the success of various TE applications. A bioink refers to a combination of a living cell, biomaterials, and bioactive molecules deposited in a layer-by-layer form to fabricate tissue-like structures. The research on bioink attempts to offer a 3D complex architecture and control cellular behavior that improve cell physical properties and viability. This research proposed a new multi-material bioink based on alginate (A), gelatin (G), and cholesteryl ester liquid crystals (CELC) biomaterials, namely (AGLC) bioinks. The development of AGLC was initiated with the optimization of different concentrations of A and G gels to obtain a printable formulation of AG gels. Subsequently, the influences of different concentrations of CELC with AG gels were investigated by using a microextrusion-based 3D bioprinting system to obtain a printed structure with high shape fidelity and minimum width. The AGLC bioinks were formulated using AG gel with 10% weight/volume (w/v) of A and 50% w/v G (AG10:50) and 1%, 5%, 10%, 20%, and 40% of CELC, respectively. The AGLC bioinks yield a high printability and resolution blend. The printed filament has a minimum width of 1.3 mm at a 1 mL/min extrusion rate when the A equals 10% w/v, G equals 50% w/v, and CELC equals 40% v/v (AGLC40). Polymerization of the AGLC bioinks with calcium (Ca2+) ions shows well-defined and more stable structures in the post-printing process. The physicochemical and viability properties of the AGLC bioinks were examined by FTIR, DSC, contact angle, FESEM, MTT assay, and cell interaction evaluation methods. The FTIR spectra of the AGLC bioinks exhibit a combination of characteristics vibrations of AG10:50 and CELC. The DSC analysis indicates the high thermal stability of the bioinks. Wettability analysis shows a reduction in the water absorption ability of the AGLC bioinks. FESEM analysis indicates that the surface morphologies of the bioinks exhibit varying microstructures. In vitro cytotoxicity by MTT assay shows the ability of the bioinks to support the biological activity of HeLa cells. The AGLC bioinks show average cell viability of 82.36% compared to the control (90%). Furthermore, cultured cells on the surface of AGLC bioinks showed that bioinks provide favorable interfaces for cell attachment.

Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model.

Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.

Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities.

Waste generation in smart cities is a critical issue, and the interim steps towards its management were not that effective. But at present, the challenge of meeting recycling requirements due to the practical difficulty involved in waste sorting decelerates smart city CE vision. In this paper, a digital model that automatically sorts the generated waste and classifies the type of waste as per the recycling requirements based on an artificial neural network (ANN) and features fusion techniques is proposed. In the proposed model, various features extracted using image processing are combined to develop a sophisticated classifier. Based on the different features, different models are built, and each model produces a single decision. Besides, the kind of class is determined using machine learning. The model is validated by extracting relevant information from the dataset containing 2400 images of possible waste types recycled across three categories. Based on the analysis, it is observed that the proposed model achieved an accuracy of 91.7%, proving its ability to sort and classify the waste as per the recycling requirements automatically. Overall, this analysis suggests that a digital-enabled CE vision could improve the waste sorting services and recycling decisions across the value chain in smart cities.

COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images.

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.

A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application.

Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls.

Latex agglutination using the periplasmic proteins antigen of Brucella melitensis is a successful, rapid, and specific serodiagnostic test for ovine brucellosis.

Brucellosis, especially caused by Brucella melitensis, is considered the most-widespread zoonosis in the world, particularly in developing countries. This study was planned to develop an accurate test for diagnosis of ovine brucellosis using a specific hot saline extracted soluble Brucella melitensis periplasmic proteins (SBPPs). The efficacy of the latex agglutination test (LAT) using SBPPs compared to the Rose Bengal test (RBT), buffered plate agglutination test (BPAT), serum agglutination test (SAT), and an indirect enzyme-linked immunosorbent assay (i-ELISA) was evaluated in the field diagnosis of ovine brucellosis. The test performance was evaluated by estimating sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), disease prevalence (DP), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) using test agreement and bacteriological culture in 1777 samples. The false-positive result was significantly (P ⩽0.05) lower in LAT than RBT, BPAT, SAT, and i-ELISA. With reference to test agreement, the Se, Sp, PPV, and PLR were highest (P ⩽0.05) in LAT 99.33%, 99.88%, 98.68%, and 827.25%, respectively. With reference to bacteriological culture, the LAT and i-ELISA tests showed a significant difference in Se with SAT. However, no significant difference in specificity was detected. The DP was 8.44% in the five tests. In conclusion, LAT using SBPPs of B. melitensis could be a suitable serodiagnostic field test for ovine brucellosis, with high sensitivity and specificity.