The newest internet-based technology that places a strong emphasis on business computing is called cloud computing. Load balancing aids in enhancing the centralized server's efficiency. Using an analytical tool called cloud analyst, numerous algorithms are analyzed in the current work. Load balancing algorithms are also compared. The cloud computing load balancing issue is a significant one, a crucial element for proper system performance, and it has the potential to slow the industry's rapid growth. Recently, there has been a fast increase in the number of customers from all over the world requesting various services.Keywords: VM, cloud computing, task scheduling, priority scheduling, scheduling algorithms, virtual machines.
The main aims, methods, and results of the study on breast cancer detection using various machine learning classifiers. It seems that the study focused on analyzing the performance of different classifiers such as Logistic Regression, KNN, SVM-LC, SBM-RBF, Gaussian Naïve Bayes, Decision Tree, and Random Forest Classifier on the Wisconsin dataset. The study aimed to measure the accuracy of these classifiers in detecting breast cancer at an early stage. The Wisconsin dataset is a well-known dataset frequently used for breast cancer research and contains relevant features for classification. According to the testing accuracy results you provided, each classifier achieved the following accuracy scores: Logistic Regression=0.9440, K Nearest Neighbor=0.9580, Support Vector Machine (Linear Classifier) =0.9650, Support Vector Machine (RBF Classifier) =0.9650, Gaussian Naïve Bayes=0.9230, Decision Tree=0.9510 and Random Forest Classifier=0.9650. Based on these accuracy outcomes, it can be concluded that the proposed machine learning models, particularly Support Vector Machines (both linear and RBF), as well as K Nearest Neighbor and Random Forest Classifier, performed well in classifying breast cancer using the Wisconsin dataset. Logistic Regression, Decision Tree, and Gaussian Naïve Bayes also achieved reasonably good accuracy scores. The study suggests that the proposed models have the potential to assist medical professionals in accurately classifying breast lesions, which can lead to early detection and better management of breast cancer.Keywords: Breast cancer, Breast Cancer Wisconsin (BCW) diagnostic dataset, Foggy and random centroid, Logistic Regression, KNN, SBM, Gaussian Naïve Bayes, Decision Tree, Random Forest.
This paper presents the Variable Frequency Drive (VFD) varies the frequency of the input power to the motor hereby controlling the speed of the motor. AC voltage input is applied to bridge diode rectifiers that produce a dc output which is then given to the inverter section. The inverter section consists of PWM inverters using IGBTs. SVPWM signals is the train of pulses with fixed magnitude and frequency and a varying pulse width. The output of the PWM inverter is given to the 3 Phase Induction Motor. VFD converts a fixed frequency, fixed voltage sine wave power to a variable frequency, variable output voltage used to regulate the speed of the induction motor. PWM controlling scheme is based on Voltage source inverter type space vector pulse width modulation (SVPWM) and the Conventional-PID controller or Fuzzy-PID controller is employed in closed-loop speed control. It provides better control of motor torque with high dynamic performance. The simulated design is tested using various toolboxes in MATLAB. Simulation results of both controllers are presented for comparison.Keywords: Space Vector Pulse Width Modulation (SVPWM), PID Controller, Fuzzy Logic Controller (FLC), Indirect Motor (IM), VFD, PWM inverter, bridge diode rectifiers.