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Showing posts from November, 2020

How MATLAB makes the distinction between P-Cores and E-Cores?

  It is known that modern CPUs have both Performance cores (P-cores) and efficiency cores (E-cores), different types of CPU cores that have different purposes and are designed for different tasks. P-cores typically have higher clock speeds and designed for high-performance tasks, while E-cores operate at lower clock speeds and focus on energy-efficient processing. In MATLAB, maxNumCompThreads returns the current maximum number of computational threads. Currently, the maximum number of computational threads is equal to the number of physical cores on your machine. How MATLAB makes the distinction between P-Cores and E-Cores ? NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects...

Where can I get a CDR report sample for a transport engineer?

  CDRReport has a team of experienced engineers and professional writers who assist candidates prepare their CDR on a regular basis and hence, are in a position to offer you any number of Free CDR Samples that have won positive assessment by EA. You can look at the CDR sample Engineers Australia accepted earlier to see how the  EA guidelines  need to be implemented in the report. Do note that the Free Sample CDR Report we offer you might already be in the EA database as it is part of the  CDR writing  projects we have done for our clients earlier. In the sample CDR below, you will find that it is written on the basis of the latest Migration Skills Assessment (MSA) booklet which is published by the EA from time-to-time and lists the competencies Engineers Australia is looking for. It consists of a Sample CPD, a Sample Career Episode, and a Sample Summary Statement. CDR Sample 1:  Civil Engineering Discipline – ANZSCO Code: 233211 CDR Sample 2:  Mechanic...

USING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR DETECTING SPAM AND NON-SPAM EMAILS

    ABSTRACT We know the increasing volume of unwanted volume of emails as spam. As per statistical analysis 40% of all messages are spam which about 15.4 billion email for every day and that cost web clients about $355 million every year. Spammers to use a few dubious techniques to defeat the filtering strategies like utilizing irregular sender addresses or potentially add irregular characters to the start or the finish of the message subject line. A particular calculation is at that point used to take in the order rules from these email messages. Machine learning has been contemplated and there are loads of calculations can be used in email filtering. To classify these mails as spam and non-spam mails implementation of machine learning algorithm  such as KNN, SVM, Bayesian classification  and ANN  to develop better filtering tool.   Contents ABSTRACT 2 1. INTRODUCTION 4 1.1 Objective : 5 2. Literature Review 5 2.1. Existing Machine learning techniqu...