## Kids Gaming Website, High Searches Keyword in Internet, Newbie Friendly,No experience Needed

Hello, Thank you for watching my auction. Today I am selling my new beautiful gaming site https://kidsgamesfree.net

Currently there are so many searches online for this keyword. As example the term 'kids games online' has 27.000 searches a month in google, 'kids games online for free' has 22.000 searches.

Domain name are chosen carefully so I only choose the best domain name and high value, this is Premium Domain Name ( Exact Match Keyword Domain, worth thousands) so that…

Kids Gaming Website, High Searches Keyword in Internet, Newbie Friendly,No experience Needed

## Gaming Website, High Searches Keyword in Internet, Newbie Friendly,No experience Needed

Hello, Thank you for watching my auction. Today I am selling my new beautiful gaming site https://kidsgamesfree.net

Currently there are so many searches online for this keyword. As example the term 'kids games online' has 27.000 searches a month in google, 'kids games online for free' has 22.000 searches.

Domain name are chosen carefully so I only choose the best domain name and high value, this is Premium Domain Name ( Exact Match Keyword Domain, worth thousands) so that…

Gaming Website, High Searches Keyword in Internet, Newbie Friendly,No experience Needed

## Time Complexity for Nearest Neighbor Searches in kd-trees

Nearest neighbor searches in kd-trees run in logarithmic time, as shown by Friedman et al. However, I have some difficulty to fully understand the proof.

In order to calculate the average number of buckets examined by the k-d tree searching algorithm described above, it is necessary to calculate the average number of buckets overlapped by the region $$S_m(X_q)$$.

$$S_m(X_q)$$ is the smallest ball centered at $$X_q$$ that exactly contains the $$m$$ points closest to $$X_q$$.

I don’t get why only the regions overlapping $$S_m(X_q)$$ are examined. Consider the following example, where we want to compute the black point that is closest to the orange point $$X_q$$. $$S_m(X_q)$$ is the green circle in this case, so according to the proof the algorithm should only search both lower buckets.

However, the searching algorithm will find as first candidate solution the black point in the lower right region. Then, it will also search regions that intersect the blue circle, in particular the upper right region.

So, isn’t it too restricted to compute only the buckets that intersect the green circle?

## Should a retail web-site always return the same items for identical searches made by different users?

We supply a large number of products for purchase through our web-site. There is a new initiative to apply a third-party AI product to hijack searches to return products based on both the search term and predictions from browsing history and other people’s search history with successfully processed sales. If I now search for a keyword, I will get a different set of products returned than if someone else searches for the same thing. If I pass an URL of my search to a friend to compare the products, we will have different lists, so cannot discuss. The list, I find, changes day-to-day, on my own machine due to the AI’s suggestions.

Is this a design “no-no”?

Should the AI be solely used for recommendations and not for the core search results?

Is there any guideline to cite that makes suggestions on this?

I have also put this on: Software Recommendations

## Shelfmark of the Ephemeral Catalogue

Intelligo ___

You are able to sense which written texts within the target room contain a verbatim phrase. (See table for determining ease factor roll for determining appropriate phrase related to topic of interest)

For as long as you maintain this spell, the text or texts that contain the phrase being searched for are surrounded by small motes of light resembling dust reflecting sunlight. These motes are a purely cosmetic effect.

• Range: Voice
• Duration: Concentration
• Target: Room

## What is the appropriate form for this spell?

I am having difficulty determining the appropriate form for the spell.

• Animal? The pages are vellum.
• Mentem? Detecting the thoughts the authors put down on the pages.
• Herbam? For pages that are papyrus or paper.
• Terram? To detect the soot or iron-gall ink of the writing.

## How to Get Your Desired Keywords into Searches to Boost Sales

Hello Friends,

I run a successful and active group that helps sellers (Amazon, Walmart) get desired keywords out to buyers to utilize algorithms to place your product on the pages you need for better sales. Using your data on valued keywords, you can relay this to me as your agent to facilitate searches, purchases, and reviews. For this service we charge a commission depending on the product and what services you want.

email:

How to Get Your Desired Keywords into Searches to Boost Sales

## Calculating probability of detection given two independent searches

Assume you are searching an area for a missing item. The item is static and does not move.

The probability of the item being in the area is 0.6; the expected search effectiveness is 0.4. Thus, the probability of detecting the item in a given search is 0.6 * 0.4 = 0.24.

Assuming each search is independent of the one before, how do you calculate the combined probability of detection for two searches? Intuitively, it can’t be twice as good as there will be some overlap in searches, nor can it be less than 0.24, if the two searches directly correspond.

## Difference between “Search Volume” & “Average Monthly Searches”

Hi Friends!

I use keyword planner for keyword research. I encounter with 2 words in the search matrix.
I generally use average monthly searches for deciding the keywords still I want to know what does this "Search Volume" signifies.

Please give me insight – "Search Volume" Vs "Average Monthly Searches"

Thank you.

## Developing an automated lawn mower that searches N by N grid

I am developing software for a lawn mower that will mow all grass on an N by N grid. The grid contains boulders at certain grid spots. For example, there may be boulders on grid coordinates [3,2] and [5,1]. The mower cannot go over the boulder.

Currently my mower works for grids that only have 0-1 boulders. Some of the harder maps will contain 10 boulders on a 6 by 6 grid, which my mower seems to fail most of the lawn with.

My current algorithm is flawed in that I am looking at the mower on the start location, scanning its surrounding boxes and then going to a grid piece if it has grass to mow. My problem is that if the current grid my mower is on detects a grass to the east and a grass to the west, it will just go to the east and forget about that grass it detected to the west.

Should I change my algorithm to DFS? Should I use backtracking? Is this a graph problem? Any suggestions on a better algorithm?