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The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model)using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID-19 patients. During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models’ loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.

A novel series of anticipated biologically active heterocyclic compounds, such as pyrazole, thiazole, pyridine, acrylamide, thiophene, triazolo­[1,5-a]­pyrimidine, imidazolidine, aminopyrazole, pyrazolo­[5,1-c]­[1,2,4]­triazine, triazolo­[4,3-a]­pyrimidine, benzo­[4,5]­imidazo­[1,2-a]­pyrimidine, pyrido­[2′,3′:3,4]­pyrazolo­[5,1-c]­[1,2,4]­triazine, isoxazole, benzo­[4,5]­imidazo­[2,1-c]­[1,2,4]­triazine, pyrimidine, pyrido­[2′,3′:3,4]­pyrazolo­[1,5-a]­pyrimidine, pyrano­[2,3-d]­pyrimidine, and chromene derivatives, incorporating a sulfonamide-bearing thiazole moiety suitable to utilize as insecticidal agents were synthesized via a versatile, readily accessible cyanoacetanilide, 2-cyano-N-(4-(N-(thiazol-2-yl)­sulfamoyl)­phenyl)­acetamide (1).The structures of the newly synthesized compounds were elucidated by IR, MS, 1H NMR, 13C NMR, distortionless enhancement by polarization transfer (DEPT), 1H–1H correlation spectroscopy (COSY), heteronuclear multiple bond correlation (HMBC), and heteronuclear single quantum coherence (HSQC) spectral analysis. Toxicological and biochemical parameters and biological aspects of the demonstrated compounds of the synthesized products against the cotton leafworm, Spodoptera littoralis, under laboratory conditions were also investigated. Regarding the determined LC50 and LC90 values, sulfonamides bearing a thiazole moiety, 16a, 8, 28, and 31b, showed the most potent toxic effects with LC50 values of 49.04, 62.66, 78.62, and 94.90 ppm, respectively, and toxicity index of 100%, 78.26%, 62.38%, and 51.68%, respectively.

A new 2-cyano-N-(5-ethyl-1,3,4-thiadiazol-2-yl)acetamide was utilized as a versatile precursor for the synthesis of various heterocycles.

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Cloud computing is a modern technology for dealing with large-scale data. The Cloud has been used to process the selection and placement of replications on a large scale. Most previous studies concerning replication used mathematical models, and few studies focused on artificial intelligence (AI). The Artificial Bee Colony (ABC) is a member of the family of swarm intelligence based algorithms. It simulates bee direction to the final route and has been proven to be effective for optimization. In this paper, we present the different costs and shortest route sides in the Cloud with regard to replication and its placement between data centers (DCs) through Multi-Objective Optimization (MOO) and evaluate the cost distance by using the knapsack problem. ABC has been used to solve shortest route and lower cost problems to identify the best selection for replication placement, according to the distance or shortest routes and lower costs that the knapsack approach has used to solve these problems. Multi-objective optimization with the artificial bee colony (MOABC) algorithm can be used to achieve highest efficiency and lowest costs in the proposed system. MOABC can find an optimal solution for the best placement of data replicas according to the minimum distance and the number of data transmissions, affording low cost with the knapsack approach and availability of data replication.Low cost and fast access are characteristics that guide the shortest route in the CloudSim implementation as well. The experimental results show that the proposed MOABC is more efficient and effective for the best placement of replications than compared algorithms. INDEX TERMS Cloud computing, multi-objective optimization, artificial bee colony, replication, cloudsim, and knapsack problem.

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