Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the required task. Feature Selection based on the use of statistical and correlation matrices for multivariate analysis. For Random Search and Grid Search models, extensive comparison findings are produced utilizing retrieval, F1 score, and precision measurements. The models are evaluated using the metrics and kappa statistics that illustrate the three models’ comparability. The study effort focuses on optimizing function selection, tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification. Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.     

A Novel Approach on Reusing Silicon Wafer Kerf Particle as Potential Filler Material in Polymer Composite

Silicon - In solar panel production stage, it is unavoidable to neglect the huge amount of solar grade silicon particles displaced during the slicing of silicon ingot operation. A subject of the...     

3D word spotting using leap motion sensor

Multimedia Tools and Applications - Leap motion sensor provides a new way of interaction with computers or mobile devices. With this sensor, users can write in air by moving palm or finger, thus,...     

Application of ameliorated Harris Hawks optimizer for designing of low-power signed floating-point MAC architecture

Neural Computing and Applications - Recently established Harris Hawks optimization (HHO) has natural behaviour for finding an optimum solution in global search space without getting trapped in...     

Container-based cluster orchestration systems: A taxonomy and future directions

Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine-grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations to deploy their increasingly diverse workloads derived from modern-day applications such as web services, big data, and internet of things in either proprietary clusters or private and public cloud data centers. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large-scale clusters. These systems are capable of running hundreds of thousands of jobs across thousands of machines. To do so efficiently, they must address several important challenges including scalability, fault tolerance and availability, efficient resource utilization, and request throughput maximization among others. This paper studies these management systems and proposes a taxonomy that identifies different mechanisms that can be used to meet the aforementioned challenges. The proposed classification is then applied to various state-of-the-art systems leading to the identification of open research challenges and gaps in the literature intended as future directions for researchers.     

Frame selection for OCR from video stream of book flipping

Multimedia Tools and Applications - Optical Character Recognition (OCR) in video stream of flipping pages is a challenging task because flipping at random speed causes difficulties in identifying...     

Study and Analysis of the Effect of Harmonics on the Hot Spot Temperature of a Distribution Transformer Using Finite-Volume Method

Transformers are critical components in power systems and their failure can cause long interruption of power supply. The condition of a transformer can be monitored by performing thermal analysis. The use of non-linear devices, such as rectifiers and converters, draws harmonic currents that increase losses in transformers, thereby increasing their operating temperature. In this article, a new numerical approach is presented for determining the rise in hot spot temperature in a 5-kVA, 400/400-V dry-type three-phase transformer laboratory prototype. The key novelty is that the additional winding eddy current loss due to non-linear loads is considered in the numerical modeling. The winding eddy current loss corresponding to harmonic distortion is estimated by conducting experiments and calculations. Numerical simulations are carried out for a wide range of non-linear loads using a commercial computational fluid dynamics package, FLUENT 6.3. The proposed numerical methodology is validated by performing experiments on the transformer for possible non-linear loads and comparing the measured hot spot temperature with the simulated values. Correlation equations for rise in hot spot temperature as a function of total harmonic distortion are presented, which can be used for estimating the life of transformers when connected to different types of loads.