A big shame. The painful part is that the Fulani are all over Sahara Africa where there is huge deforestation and rain drought thats driving them into banana republic.

Fulani are causing same havoc they are causing in here in Ghana, Mali and other Sahara Africa countries. It is strange how our own govt keep making excuses for these so called foreigners.

'Bush meat' caught the hunters . . .

Myself2:


Wrong way

The index used here is generosity index which is an index showing how much you gave out relative to your net worth.

So as far as generosity index is the subject, then Chuck Feeney should be number 1 on the list

How?

sarrki:
Let’s build our system

Our system will build the structure we are wasting funds to build

Thank you. Let us tart with Buhari. The abacha $350m can be used to construct 21,000 units of houses at $16,600 each and house as many as 120,000 people going by 6 people to a house benchmark instead of sharing it to underaged voters as N5,000.

Segzy008:
The system killed you not the weed

Which system ?

I doubt this story. Nobody can plead for you and they will take you back in covenant. I know people who when to WOFBI bible school of winner and repented after expulsion and begged for years. I doubt the part where he was given a second chance.

^^^
Will you get out of here and go cremate those maggi-flavoured carcass of chickens and cows of your you call bbq! nonsense.
What kind of suya (bbq) merchant is online 247 doing FTC when we all know bbq(suya) takes time to prepare ?
Political Suya Merchant A.K.A. BBQ Merchant!

4kings:

If you're dealing with comparing two documents like two news articles.
Then the importance similarities is not really high. Because if tf-idf is based on words and a news articles about "Israel and trump" would have similar words that tf-idf can use to rank similarities.
However for a better result consider applying stemming and stopwords to reduce noise.

But i just realised you were talking about document titles, well if that's the case you can use word embeddings to vectorize words to find meaning, however the approach towards word embeddings would tend to classify only contextual words and not in an antonyms-synoynms structure like "hot" and "cold" would have the same vector point.

Python has NLTK, TextBlob and Spacy which are NLP packages and they can be used easily to connect to wordnet to find words similarity, so i'm sure java would have similar packages or better still write one that connects to wordnet yourself.
This approach might not be effective because it will require making http requests on every word and you might not be able to afford to store all words and synonyms somewhere.

So if you are finding similarities in general plain text then that's difficult, word embeddings is the best choice i can think of right now and you could get already trained model online for your task but i doubt it will perform well for just general plain text unless your task is singular domain related then you can train the word embedding yourself.

The best way to approach this is to analyse the document itself and not just the title, with tf-idf to get a good result.

OK. I made some progress. I was able to obtain the body of the news. This means I now have the title, the excerpt and the body text in full.

So far tf-idf is giving me unpleasant matches. for example the news about the psychic pig prediction super eagles semi final adventure appears along other news related to super eagle like in the same cluster. with the new about "super eagle team song by olamide and phyno" and the other one by simi et al.

My next target is to reduce stop words but I am not using a list of words directly. I am using POSTagger (Parts Of Speech Tagger) to eliminate the injunctions, modifiers and other category stop words generally fall into. With this approach words like for, from, a, to, with, am, when, soon, & am get eliminated because of the category POSTagger is placing them and not because I specified those words directly.


Something tells me you know what I need but I haven't been able to communicate it in the right words to you yet. As I google more I am learning new things. I know what lemmatization, tokenization, clustering is now.


Modified:
What I am looking for is clustering of news covering same issue, event or happening. If given 10 articles from a number of news outlets (e.g vanguard, punch, tribune and cable) and five of those are reporting Buhari's expose of budget padding by senate and HOR , Then I need to be able to cluster those five together and nothing more. I hope this explains it to you.

After some experimentation with simple machine learning I realize there are Machine Learning services out there and I need one to help match two closely related news. It is much like the popular WordPress "Related Posts" feature.
Any one in the house should help.

@Syrine234, Can one get export ready cocoa powder and butter for purchase from Nigeria ?
If possible the going rate for the above are needed also. Thanks in advance.

4kings:

If you're dealing with comparing two documents like two news articles.
Then the importance similarities is not really high. Because if tf-idf is based on words and a news articles about "Israel and trump" would have similar words that tf-idf can use to rank similarities.
However for a better result consider applying stemming and stopwords to reduce noise.

But i just realised you were talking about document titles, well if that's the case you can use word embeddings to vectorize words to find meaning, however the approach towards word embeddings would tend to classify only contextual words and not in an antonyms-synoynms structure like "hot" and "cold" would have the same vector point.

Python has NLTK, TextBlob and Spacy which are NLP packages and they can be used easily to connect to wordnet to find words similarity, so i'm sure java would have similar packages or better still write one that connects to wordnet yourself.
This approach might not be effective because it will require making http requests on every word and you might not be able to afford to store all words and synonyms somewhere.

So if you are finding similarities in general plain text then that's difficult, word embeddings is the best choice i can think of right now and you could get already trained model online for your task but i doubt it will perform well for just general plain text unless your task is singular domain related then you can train the word embedding yourself.

The best way to approach this is to analyse the document itself and not just the title, with tf-idf to get a good result.

I finally implemented the tf-idf thing, it appears to be grossly inadequate for what i am looking to achieve.
My goal was to detect news with same message from various news vendor or media houses. I have tried matching them by titles, excerpt and full body but no luck so far. The stop words in tf-idf are really a headache, like u suggested lemmatization should help but i strongly suspect it may not solve the challenge of reducing stop words to acceptable level.

I am still looking for the solution to it just in case.
I felt since the WordPress similar post feature is so common I should be able to get this done in another language, java, but that's not the story so far.

micodon:
never save blobs on your rdbms. save the fingerprint image somehwere on disk and save the filepath in a column in the user table on the db for reference

Why

blessedemperor:


But what sir?

u haven't sampled enough info yet . . . it has been a long time now, are u still on it ?

AZeD1:

You are missing the point.
codertempler wrote the following on page one


And I responded that the size of the power plant is not a factor in pricing. Pricing is set by NERC so Mr A building a 500MW plant doesn't me his power would be cheaper than Mr B who's plant is 250MW.

That's our argument. It has nothing to do with the technology of the turbine.

Thank God you finally admitted your mistake. The CCGT thing has a massive scale of economy effect. We can learn from each other without arguing.

AZeD1:

If you had constant power you would not need an inverter. Just you would not need a generator.

I don't think the point is constant power but cheap and competitive power. If the CCGT thing is done it can save as high as N20 per KwHr. I implore you to research it and codertempler to keep his antics in check. Let's learn from each other and make this forum a sane place.

AZeD1:

I'm not confusing power generation with consumption. The guy was/is saying build bigger plants to make power cheaper and I'm saying power price is determined by the regulator not by the size of your plant so economy of scale is not a factor.

I think cost of power generation is at the plant is different from and do affect cost by NERC.
I can also assure that scale of economy is as presented by @ so far. Don't know he isn't posting his/her findings here but he has been right to a large extent so far.
Lagos is trying to build competitive plants and I think they should build large ones that can benefit from CCGT arrangement.

AZeD1:

Lol at power system enthusiast.

Have you seen a power company with a warehouse to store excess power not used by consumers?


You are clueless.
So a businessman would build a giant power plant in the hope that consumers would store the excess power he is generation on their phones?

Power generation companies don't store power, anything generated must be evacuated and used otherwise its lost. Before you can get license to build a power plant, you must already have buyers(aka signed a purchase agreement).

Sorry to disturb but do you know what inverters are?
They store electrical energy chemically to battery. Power can be stored on a small To medium scale but not on a large scale. The there are over 6 methods of starring electrical power.

AZeD1:

If I have only one TV in my house, it doesn't matter how much electricity is sold, I would only pay for what my TV uses.
This is unlike airtime, where if a company does promo I can keep on loading my phone and use the airtime later or cement or any other physical item that can be hoarded. With power, if I don't use it now, it's wasted, so no matter how cheap electricity becomes I would not buy another TV because of that.

In developed countries, power is cheaper at night so as to encourage people to use power during off peak periods which then leaves power for those who need it during the day.

I could decide to use my washing machine by 2pm but the power company would prefer I do it by 11pm so as to reduce the load on their systems during on peak periods and also to ensure their machines are not idling during off peak periods.

Another thing to note is power plants are designed to run non stop and it's more profitable that way so power companies tend to build what they can sell. If demand increases, they'll increase capacity.

I think you are confusing power generation with power consumption . When generating power with gas turbines, heat is generated and given off, now if the engine is huge enough and gives off enough heat, This heat can be utilized to drive another set of pistons.
That is what the CCGT thing is all about. it means that when a gas plant is huge enough its excess heat can be recycled immediately to generated more power. When done on the right scale this practise can significantly lower the cost of power generation. This is what CodedTemplar is driving at.
Power consumption is different from power generation.

Plus those other stuff about of peak period and lower chargers are irrelevant facts as far as this thread is concerned.

AZeD1:
Nope you are the ignorant one here.
You are like those people who compared the privatization of PHCN with the telecommunication sector claiming competition would bring down the price.

Now you are claiming economy of scale in power sector forgetting that a tv would consumer 55 watts no matter how much the cost of power is.

It doesn't matter how cheaper you make your power my consumption is limited by my applicances.

Can you explain further sir. How can power usage remain same even at a large scale of generation.?

AZeD1:

Do you know the difference between the infrastructure belonging to TCN and the one that belongs to Distribution companies?

Even the article states that its embedded power.

Sir did anybody dispute that it is embedded power? What the EFF!

sollye:
Okay. How can I get the SIM questions

Try torrent or file sharing sites like 4shared/Dropbox etc.

sollye:
Okay. How can I get the SIM questions

sim is mostly paid or you can try torrent / 4shared.

jerseyboy:
How does a governor justify flying private jets while there are schools without CHAIRS.

people are more focused on pastors with jet.

As long as Buhari is in charge all roads leads to the North.

krissconnect:
Ignorant mofos, just like their senile president. So cos someone was able to withstand the pains of Cain, that means he can protect their daughter? Nonsense. Foolanis

It is a strategy to keep their bloodline pure. They use it aggressively against non-tribe suitors.

The govt is finally waking up and now realize the current structure isn't sustainable. The church unis are opening eyes that mediocrity sustained with crowd mentality won't last forever.
Cc : HigherEd, CodeTemplar

deomellIo:



Ogbeni thank you jare. Don't mind ipob losers

This isn't the original deomello i know oh . . . Did Aregbe withdraw your camera and id card.

In governance, fiscal transformation supercedes infrastructural transformation.
I expect to see how this leads to a transformation of Osun's finances and not structures. Any Tom, Dick and Harry can take loans to build big structures but not everybody can take loan to invest and make profit.

The global economy is shutting down because of Nigeria's Buhari. Kudos.

greatermax77:
Where is Lagos rice farm located?

It is located in Kebbi. The term LAKE in "lake rice" is a shortened form of LA gos-KE bbi.
It features lagos brain/equipment and kebbi hands/land.

Purehuman:


Bros it is not your concern. Face your state and stop fuming. You keep highlighting bad things about the state, what's wrong with you?

Tell us your holy state.

Osun state . . . Lol