This research introduces a new approach for predicting reproductive health by using the Sperm Whale Optimization algorithm (SWA) with Artificial Neural Networks (ANN-SWA). SWA is an emerging method with a powerful potential in tackling optimization difficulties based on its adaptability in searching mechanisms. ANN-SWA consists of four phases. The first phase is conditioned by the fertility disease which is a complex and multifactorial condition with increasing incidence. The fertility data is trained (90 cases) and the approach is then used to test findings in the test set (10 cases). In the second phase, the multilayer perceptron (MLP) is used to determine the maximum fitness function by getting the global minimum and hence, it revokes the ANN trapped in local. In the third phase, it optimizes and controls the parameters (weights and biases) to ensure rapid convergence with accuracy. In the fourth phase, ANN-SWA is used to predict the fertility quality and determine the accuracy. The results are verified by comparing them with optimization and classification algorithms. The quantitative and qualitative outcomes show that the proposed approach is able to outperform the current algorithms on the fertility dataset in the convergence rate of classification. The results demonstrate that an artificial neural network based on SWA achieved more than 99.96% of the accuracy of the approach.
Currently, COVID-19 is spreading all over the world and profoundly impacting people's lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients' serial blood counts (their numbers of lymphocytes from days 1 to 15 after admission to hospital) are associated with relapse rates and evaluations of COVID-19 infections. Accordingly, the severity of COV-ID-19 is classified in two categories, serious and non-serious. The experimental results indicate that the proposed CQNN's prediction approach outperforms those of other classification algorithms and its high accuracy confirms its effectiveness.
Abstract:The data gathered from IOTs is considered of high business value. The IOTs devices sense the natural conditions using sensor network comprised of sensor nodes. Mining of big sensor data for useful knowledge extraction is a very challenging task. Frequent itemsets is one of the most effective mining techniques that find important itemsets from big sensor data. In this paper, a MapReduce Frequent Nodesets-based Boundary POC tree (MR-FNBP) framework is proposed for mining Frequent Nodesets for big sensor data. The MapReduce framework is used to implement MR-FNBP to enhance its performance in highly distributed environments. Additionally, the proposed Boundary (FNBP) creates a Boundary as an early stage to exclude the infrequent itemsets, and this may reduce the overall memory and time usage. Moreover, a number of experiments were performed to evaluate the performance of MR-FNBP framework. The results show high scalability rate and a less time consuming process for MR-FNBP framework over different recent systems.
Cardiovascular diseases (CVDs) are the most critical heart diseases. Accurate analytics for real-time heart disease is significant. This paper sought to develop a smart healthcare framework (SHDML) by using deep and machine learning techniques based on optimization stochastic gradient descent (SGD) to predict the presence of heart disease. The SHDML framework consists of two stage, the first stage of SHDML is able to monitor the heart beat rate condition of a patient. The SHDML framework to monitor patients in real-time has been developed using an ATmega32 Microcontroller to determine heartbeat rate per minute pulse rate sensors. The developed SHDML framework is able to broadcast the acquired sensor data to a Firebase Cloud database every 20 seconds. The smart application is infectious in regard to displaying the sensor data. The second stage of SHDML has been used in medical decision support systems to predict and diagnose heart diseases. Deep or machine learning techniques were ported to the smart application to analyze user data and predict CVDs in real-time. Two different methods of deep and machine learning techniques were checked for their performances. The deep and machine learning techniques were trained and tested using widely used open-access dataset. The proposed SHDML framework had very good performance with an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87.
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