Extreme learning machine proposed by\cite{elm,elms} is a feed forward neural network classifier with single hidden layer in which the weights between input and hidden layer are initialized randomly. ELM uses analytical approach to compute weights between hidden and output layer\cite{elm} ,which makes it faster compared to other gradient based classifiers. ELM fails to handle class imbalance problem effectively. Many variants of Extreme Learning Machine like Weighted Extreme Learning Machine(WELM)\cite{WELM}
function Catch_Errors() { alert("Errors "); } var University = function(uni_data) { var self = this; this.name = uni_data.name; this.latitude = uni_data.latitude; this.longitude = uni_data.longitude; this.country=""; this.county=""; this.label=""; this.region=""; this.country_a=""; this.country_gid=""; this.visible = ko.observable(true); // That is te Website where I found this Amazing API var URL = "https://search.mapzen.com/v1/search?text=London&api_key=mapzen-QEEQwwX" /*
have low requirements on the hardware of the organisation. Besides, one disadvantage of using paper-based system is that the manager has to check records manually, which may cause some unnecessary errors. Another disadvantage is that the current system does not provide the manager with special functions to generate statistic efficiently. For the Hardware Considerations: We have decided to design the proposed system by using MySQL software and base on windows 7 and above computer operate system.
Abstract To test this particular law, we use an inclined ramp, a frictionless cart, a weight attached by string to the cart suspended off of the lower end of the ramp, an instrument used to measure time, velocity, and acceleration known as Logger Pro, and a single 250 gram weight placed on the cart. The measurements for the length and height of the ramp were measured in meters, while the time it took the cart to travel down the ramp was measured in seconds. The purpose of using the two separate weights
Use mathematical intuition and algebraic reasoning to make reasonable predictions. Justify predictions with obtained data • Interpret and reflect the results of an experiment, troubleshoot solutions for unanticipated hurdles, determine a margin of error Materials for Day 3 Preparation for Day 3 • Student Handout (given on Day 1) • Student Reflection (given on Day 2) • 1 Barbie + weight • 10-20 rubber bands (size 32) • 3 Yard Sticks • Tape • Graphing program: • TI-83, TI-84, TI-Nspire • Desmos •
The modern world is being automated in all fields in various aspects. Automation is achieved by the software development and this result in a faster work accomplishment and also in an easy, efficient way. Meanwhile, this progress has also got proportionate threat of misusing the software. As the internet has extended its roots providing access to various networks and also may provide access to inappropriate users. So, it is necessary to protect these networks and this purpose is served by network
Medication Barcode Scanning (MBS) has been considered as one of the significant ways of reducing medication error. It begins from when medication is ordered by the doctor, a pharmacist reviews the order prior to supplying the medication to the nurse who then administers the medication to the patient (Department Veterans Affairs, 2003)). Study stated that from about 450,000 drug adverse effect that occur yearly, about 25% would be avoided with the use of certain technologies like medication barcode
Q1.1) Service learning involves a concerted approach to both serve the community and to develop the individual through services. General volunteering involves just serving the community. For example, volunteering at soup kitchen begins and ends with what one does at the soup kitchen. Service learning does not being and end with the service rendered. In addition to serving the community, there is an element of reflection. The reflection involves introspection, in an effort to develop personally, and
opportunities instead of misfortunes and in healthcare is directed towards patient safety and improving patient outcomes. Allowing employees to report errors without being reprimanded promotes trust. Human errors are costly and can lead to death when providing care to patients. Creating an environment that fosters learning in preventing errors boost employees morale. A learning environment allows individual to reflect on the situation and their behavior that caused harm or potential harm to the
encoding and the (1/estimated error) of the data with the actual data is considered as a fitness function Step 2 The least square technique based on linear, exponential, asymptotic, curvilinear and logarithmic equations has been applied on the available data to produce the estimated data. The error analysis has been made to produce estimated error. It has been observed that average error based on least square technique based on linear equation has shown the minimum error (2.25%) as compared to the other