Trending November 2023 # Looking For ‘Bad Guys’ – Discovering Networks Of Sites # Suggested December 2023 # Top 11 Popular

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Creating networks of (interlinked) sites is a widespread tactic of PageRank and ranking manipulation. To own a lot of websites is perfectly OK but to own a lot of websites for the sake of “link juice” is not good (per Google at least). The line is not always easy to define algorithmically therefore most often than not Google frowns upon any interlinked network it can spot.

Not so long ago Google was much worse and slower at identifying networks; that accounted for the tactic extreme popularity. Really, that’s an appealing idea of owning a number of established sites ready to pass link juice rather than spending weeks link building and baiting.

Consequently, Google got very aggressive towards networks blacklisting all sites suspected of being involved into a network. Therefore, the ability of spotting a network of websites is very useful for several reasons:

you should always make sure your site is not associated with a network:

avoid linking to a site being part of a network;

avoid buying getting a link from a number of such sites (yes, in this case incoming links may also be harmful);

finding your competitors‘ network is another valuable way to explore their SEO tactics.

Of course, that’s not very easy to spot a network (and besides, some networks are perfectly legit, like b5media for example). Anyway, if you are able to find a network, be sure Google will do that too; so that must be not a very good neighborhood to join. Here are a few ways of identifying a network of sites:

Check for similar/same sidebar/bottom/sitewide outbound links;

Visit each site and search for similar/same templates, contact information, ‘about’ pages, etc;

Check for similar/same IPs (here is a handy tool to do that);

Check for similar/same Whois data;

Check for similar/same backlink patterns.

You're reading Looking For ‘Bad Guys’ – Discovering Networks Of Sites

Security Secrets The Bad Guys Don’t Want You To Know

Artwork: Diego AguirreYou already know the basics of internet security, right?

Remember, however, that security is all about trade-offs. With most of these tips, what you gain in security, you lose in convenience. But hey, it’s your computer. Be as paranoid as you want to be.

Avoid Scripting

JavaScript is very popular, and for good reason. It works in almost all browsers, and it makes the Web a lot more dynamic. But it also enables bad guys to trick your browser more easily into doing something that it shouldn’t. The deception could be something as simple as telling the browser to load an element from another Web page. Or it could involve something more complicated, like a cross-site scripting attack, which gives the attacker a way to impersonate the victim on a legitimate Web page.

JavaScipt attacks are everywhere. If you use Facebook, you may have seen one of the latest. Lately, scammers have set up illegitimate Facebook pages offering things like a free $500 gift card if you cut and paste some code into your browser’s address bar.

But miscreants can add JavaScript to hacked or malicious Web pages, too. To avoid attacks there, you can use a free Firefox plugin called NoScript that lets you control which Websites can and cannot run JavaScript in the browser. NoScript goes a long way toward preventing rogue antivirus programs or online attacks from popping up when you visit a new Website.

NoScript also comes with a cross-site scripting blocker. Cross-site scripting has been around for a while, but these days bad guys are using it more frequently than ever to seize control of online accounts on sites such as Facebook and YouTube.

Unfortunately, neither Internet Explorer nor Safari has a NoScript equivalent, but IE users can adjust their Internet Zones security settings to require prompts before scripting. And IE 8 includes new cross-site scripting protection to ward off some attacks.

Disabling JavaScript in Adobe Reader can help, too. According to Symantec, last year nearly half of all Web-based attacks were associated with malicious PDF files. If victims had adjusted their settings to make it impossible for PDFs to execute JavaScript, they would have thwarted most of those attacks.

The same holds true for Reader, where PDF-based forms may not submit properly if you’ve disabled JavaScript; nevertheless, many people don’t mind simply turning on Reader’s JavaScript whenever they need it.

Back Out of Rogue Antivirus Offers

Far too many people have had this experience recently: You’re surfing the Web on a totally legitimate site when a scary-looking warning message pops up suddenly. It tells you that your computer is infected. You try to get rid of it, but more windows keep popping up, urging you to scan your computer.

If you do this, the scan invariably finds security problems and offers to sell you software that will take care of the problem. This is rogue antivirus software. The only thing the software does is put money into the pockets of criminals.

Here’s what you do:

First off, never buy the software. It simply doesn’t work, and often it will trash your system. Either press Alt-F4 to close your browser directly or press Ctrl-Alt-Delete to open your system’s task manager and shut the browser down from there. Closing the browser generally puts an end to the pop-up problem.

Another way to steer clear of rogue antivirus attacks is to be careful when reading up on a hot news story. The bad guys follow Google Trends and Twitter’s Trending topics, and they can quickly promote one of their malicious Web pages to the top of Google search results.

Next: Use Less-Popular Apps; Verify That Your Programs Are Up-to-Date

Onedrive Stuck On Looking For Changes Screen?

OneDrive stuck on Looking for changes screen? [Full Fix]

If OneDrive is stuck on Looking for changes screen, try applying the solutions below.

When they fail to work or you simply lack the time for troubleshooting, chúng tôi comes in handy.

Keep in mind that there are plenty of other cross-platform cloud backup services to pick from.

Whether you are looking for similar solutions or just general tips, we have you covered with this OneDrive Troubleshooting Hub.

Even though OneDrive is probably the best-suited cloud service for Windows 10, issues like stuck on Looking for changes… or Processing changes can render it completely unusable.

Users reported that they’re unable to sync anything on their OneDrive desktop client due to this inexplicable issue.

Luckily, we prepared a few possible solutions to this problem. If you’re having a hard time with this issue, make sure to check the steps below.

What can I do if OneDrive is stuck on Looking for changes? 1. Unlink account and link it again

First, let’s start with the obvious. Since OneDrive is, as the majority of other cloud-storage services are, a multiplatform application, there’s a chance that something went astray with the account.

Namely, the user account that’s linked to multiple OneDrive applications, can run into a halt occasionally. What you’ll need to do is to simply unlink account and link it again.

This is analog to sign out/sign in troubleshooting, and it should help you resolve this or similar issues. Follow the instructions above in order to do so.

On the other hand, if your OneDrive desktop client is still stuck Looking for changes even after you’ve taken these steps, make sure to continue with the additional solutions.

2. Use a different cloud storage provider

If you are looking for the best OneDrive alternative, there are thankfully plenty of worthwhile storage software with similar features and lower error rates.

You will find up-to-standard cloud storage software for Windows that have great security and plenty of management and sharing features.

We recommend two-factor authentication, and generous storage space. Also, make sure that the software supports the file types you work with.

Many cloud storage services include task management and collaboration tools to help you maintain a good workflow, so switching will not be a hassle even if you use OneDrive for professional purposes.

3. Delete 0-byte files

Open your OneDrive folder on local PC storage.

Press F3 to instantly access the Search bar.

Type the following line in the search bar:

size: 0

If you see any search results that are 0 bytes in size, make sure to delete them.

Look for changes.

Now, some users reported that the problem is in, believe it or not, 0-bytes ghost files. Many applications store files that are empty and of no use.

Now, if there’s no size and the file is empty, OneDrive will have a hard time uploading it to online storage from your PC’s local storage.

This will cause a never-ending loop of the file processing and you’ll be stuck for ages.

So, basically, your next task is to navigate to the OneDrive folder, locate and delete empty, 0-bytes files. Afterward, you can restart your PC and give OneDrive another try.

Furthermore, if you’re having issues with a lot of temporary files, make sure to check this useful article on how to deal with those by using solely Windows resources.

4. Run OneDrive troubleshooter

Download the OneDrive Troubleshooting tool.

Run the tool and choose Next.

Wait until the process is finished and check for the error resolution.

Windows 10 issues can also be addressed with pre-installed or downloadable troubleshooting tools.

Expert tip:

This troubleshooter should scan for possible errors, restart related services, and, hopefully, resolve all issues. If this tool fell short, make sure to check additional steps.

5. Change the sync folder location

Yes, surely, you’ll lose a lot of time by changing the sync folder location. If your bandwidth is slow and you have a lot of files, it can take some time for OneDrive to re-sync them again.

For more information on how you can increase bandwidth on Windows 10, check out this guide.

However, changing the syn folder location is probably the most reliable solution for this peculiar OneDrive problem.

Namely, by changing the sync folder, you should be able to start the sync procedure again. That way, by adding file by file to the upload queue, you can confirm which exact file caused the OneDrive halt and remove it accordingly.

Follow the instructions above to change the sync folder location in OneDrive.

For the majority of users, this proved as the most viable solution. On the contrary, if you’re still unable to get OneDrive to start with updating, there are still other solutions to take into consideration.

6. Reset OneDrive

%localappdata%MicrosoftOneDriveonedrive.exe /reset

Before you move to reinstallation, which is possible after the major Windows 10 updates, you should try resetting. In order to do so, you’ll need to use the elevated Command Prompt line.

Hopefully, the uploading halt will be fixed and you’ll be able to upload your files just like before.

7. Reinstall OneDrive

In the Windows Search bar, type

Control

and choose

Control Panel.

Fundamentals Of Deep Learning – Introduction To Recurrent Neural Networks

Introduction

Let me open this article with a question – “working love learning we on deep”, did this make any sense to you? Not really – read this one – “We love working on deep learning”. Made perfect sense! A little jumble in the words made the sentence incoherent. Well, can we expect a neural network to make sense out of it? Not really! If the human brain was confused on what it meant I am sure a neural network is going to have a tough time deciphering such text.

There are multiple such tasks in everyday life which get completely disrupted when their sequence is disturbed. For instance, language as we saw earlier- the sequence of words define their meaning, a time series data – where time defines the occurrence of events, the data of a genome sequence- where every sequence has a different meaning. There are multiple such cases wherein the sequence of information determines the event itself. If we are trying to use such data for any reasonable output, we need a network which has access to some prior knowledge about the data to completely understand it. Recurrent neural networks thus come into play.

In this article I would assume that you have a basic understanding of neural networks, in case you need a refresher please go through this article before you proceed.

Table of Contents

Need for a Neural Network dealing with Sequences

What are Recurrent Neural Networks (RNNs)?

Understanding a Recurrent Neuron in Detail

Forward Propagation in a Recurrent Neuron in Excel

Back propagation in a RNN (BPTT)

Implementation of RNN in Keras

Vanishing and Exploding Gradient Problem

Other RNN Architectures

Need for a Neural Network dealing with Sequences

Before we deep dive into the details of what a recurrent neural network is, let’s ponder a bit on if we really need a network specially for dealing with sequences in information. Also what are kind of tasks that we can achieve using such networks.

The beauty of recurrent neural networks lies in their diversity of application. When we are dealing with RNNs they have a great ability to deal with various input and output types.

Sentiment Classification – This can be a task of simply classifying tweets into positive and negative sentiment. So here the input would be a tweet of varying lengths, while output is of a fixed type and size.

Image Captioning – Here, let’s say we have an image for which we need a textual description. So we have a single input – the image, and a series or sequence of words as output. Here the image might be of a fixed size, but the output is a description of varying lengths

Language Translation – This basically means that we have some text in a particular language let’s say English, and we wish to translate it in French. Each language has it’s own semantics and would have varying lengths for the same sentence. So here the inputs as well as outputs are of varying lengths.

So RNNs can be used for mapping inputs to outputs of varying types, lengths and are fairly generalized in their application. Looking at their applications, let’s see how the architecture of an RNN looks like.

What are Recurrent Neural Networks?

Let’s say the task is to predict the next word in a sentence. Let’s try accomplishing it using an MLP. So what happens in an MLP. In the simplest form, we have an input layer, a hidden layer and an output layer. The input layer receives the input, the hidden layer activations are applied and then we finally receive the output.

Let’s have a deeper network, where multiple hidden layers are present. So here, the input layer receives the input, the first hidden layer activations are applied and then these activations are sent to the next hidden layer, and successive activations through the layers to produce the output. Each hidden layer is characterized by its own weights and biases.

Since each hidden layer has its own weights and activations, they behave independently. Now the objective is to identify the relationship between successive inputs. Can we supply the inputs to hidden layers? Yes we can!

Here, the weights and bias of these hidden layers are different. And hence each of these layers behave independently and cannot be combined together. To combine these hidden layers together, we shall have the same weights and bias for these hidden layers.

We can now combines these layers together, that the weights and bias of all the hidden layers is the same. All these hidden layers can be rolled in together in a single recurrent layer.

So it’s like supplying the input to the hidden layer. At all the time steps weights of the recurrent neuron would be the same since its a single neuron now. So a recurrent neuron stores the state of a previous input and combines with the current input thereby preserving some relationship of the current input with the previous input.

Understanding a Recurrent Neuron in Detail

Let’s take a simple task at first. Let’s take a character level RNN where we have a word “Hello”. So we provide the first 4 letters i.e. h,e,l,l and ask the network to predict the last letter i.e.’o’. So here the vocabulary of the task is just 4 letters {h,e,l,o}. In real case scenarios involving natural language processing, the vocabularies include the words in entire wikipedia database, or all the words in a language. Here for simplicity we have taken a very small set of vocabulary.

Let’s see how the above structure be used to predict the fifth letter in the word “hello”. In the above structure, the blue RNN block, applies something called as a recurrence formula to the input vector and also its previous state. In this case, the letter “h” has nothing preceding it, let’s take the letter “e”. So at the time the letter “e” is supplied to the network, a recurrence formula is applied to the letter “e” and the previous state which is the letter “h”. These are known as various time steps of the input. So if at time t, the input is “e”, at time t-1, the input was “h”. The recurrence formula is applied to e and h both. and we get a new state.

The formula for the current state can be written as –

Here, Ht is the new state, ht-1 is the previous state while xt is the current input. We now have a state of the previous input instead of the input itself, because the input neuron would have applied the transformations on our previous input. So each successive input is called as a time step.

In this case we have four inputs to be given to the network, during a recurrence formula, the same function and the same weights are applied to the network at each time step.

Taking the simplest form of a recurrent neural network, let’s say that the activation function is tanh, the weight at the recurrent neuron is Whh and the weight at the input neuron is Wxh, we can write the equation for the state at time t as –

The Recurrent neuron in this case is just taking the immediate previous state into consideration. For longer sequences the equation can involve multiple such states. Once the final state is calculated we can go on to produce the output

Now, once the current state is calculated we can calculate the output state as-

Let me summarize the steps in a recurrent neuron for you-

A single time step of the input is supplied to the network i.e. xt is supplied to the network

We then calculate its current state using a combination of the current input and the previous state i.e. we calculate ht

The current ht becomes ht-1 for the next time step

We can go as many time steps as the problem demands and combine the information from all the previous states

Once all the time steps are completed the final current state is used to calculate the output yt

The output is then compared to the actual output and the error is generated

The error is then backpropagated to the network to update the weights(we shall go into the details of backpropagation in further sections) and the network is trained

Let’s take a look of how we can calculate these states in Excel and get the output.

Forward Propagation in a Recurrent Neuron in Excel

Let’s take a look at the inputs first –

The inputs are one hot encoded. Our entire vocabulary is {h,e,l,o} and hence we can easily one hot encode the inputs.

Now the input neuron would transform the input to the hidden state using the weight wxh. We have randomly initialized the weights as a 3*4 matrix –

Step 1:

Now for the letter “h”, for the the hidden state we would need Wxh*Xt. By matrix multiplication, we get it as –

Step 2:

Now moving to the recurrent neuron, we have Whh as the weight which is a 1*1 matrix as and the bias which is also a 1*1 matrix as

For the letter “h”, the previous state is [0,0,0] since there is no letter prior to it.

Step 3:

Now we can get the current state as –

Since for h, there is no previous hidden state we apply the tanh function to this output and get the current state –

Step 4:

Now we go on to the next state. “e” is now supplied to the network. The processed output of ht, now becomes ht-1, while the one hot encoded e, is xt. Let’s now calculate the current state ht.

Whh*ht-1 +bias will be –

Wxh*xt will be –

Step 5:

Now calculating ht for the letter “e”,

Now this would become ht-1 for the next state and the recurrent neuron would use this along with the new character to predict the next one.

Step 6:

At each state, the recurrent neural network would produce the output as well. Let’s calculate yt for the letter e.

Step 7:

The probability for a particular letter from the vocabulary can be calculated by applying the softmax function. so we shall have softmax(yt)

If we convert these probabilities to understand the prediction, we see that the model says that the letter after “e” should be h, since the highest probability is for the letter “h”. Does this mean we have done something wrong? No, so here we have hardly trained the network. We have just shown it two letters. So it pretty much hasn’t learnt anything yet.

Now the next BIG question that faces us is how does Back propagation work in case of a Recurrent Neural Network. How are the weights updated while there is a feedback loop?

Back propagation in a Recurrent Neural Network(BPTT)

To imagine how weights would be updated in case of a recurrent neural network, might be a bit of a challenge. So to understand and visualize the back propagation, let’s unroll the network at all the time steps. In an RNN we may or may not have outputs at each time step.

In case of a forward propagation, the inputs enter and move forward at each time step. In case of a backward propagation in this case, we are figuratively going back in time to change the weights, hence we call it the Back propagation through time(BPTT).

In case of an RNN, if yt is the predicted value ȳt is the actual value, the error is calculated as a cross entropy loss –

Et(ȳt,yt) = – ȳt log(yt)

E(ȳ,y) = – ∑ ȳt log(yt)

We typically treat the full sequence (word) as one training example, so the total error is just the sum of the errors at each time step (character). The weights as we can see are the same at each time step. Let’s summarize the steps for backpropagation

The cross entropy error is first computed using the current output and the actual output

Remember that the network is unrolled for all the time steps

For the unrolled network, the gradient is calculated for each time step with respect to the weight parameter

Now that the weight is the same for all the time steps the gradients can be combined together for all time steps

The weights are then updated for both recurrent neuron and the dense layers

The unrolled network looks much like a regular neural network. And the back propagation algorithm is similar to a regular neural network, just that we combine the gradients of the error for all time steps. Now what do you think might happen, if there are 100s of time steps. This would basically take really long for the network to converge since after unrolling the network becomes really huge.

In case you do not wish to deep dive into the math of backpropagation, all you need to understand is that back propagation through time works similar as it does in a regular neural network once you unroll the recurrent neuron in your network. However, I shall be coming up with a detailed article on Recurrent Neural networks with scratch with would have the detailed mathematics of the backpropagation algorithm in a recurrent neural network.

Implementation of Recurrent Neural Networks in Keras

Let’s use Recurrent Neural networks to predict the sentiment of various tweets. We would like to predict the tweets as positive or negative. You can download the dataset here.

We have around 1600000 tweets to train our network. If you’re not familiar with the basics of NLP, I would strongly urge you to go through this article. We also have another detailed article on word embedding which would also be helpful for you to understand word embeddings in detail.

Let’s now use RNNs to classify various tweets as positive or negative.

# import all libraries import keras from keras.models import Sequential from keras.layers import Dense, Activation, Dropout from keras.layers.convolutional import Conv1D from chúng tôi import Tokenizer from keras.preprocessing.sequence import pad_sequences import pandas as pd import numpy as np import spacy nlp=spacy.load("en") #load the dataset train=pd.read_csv("../datasets/training.1600000.processed.noemoticon.csv" , encoding= "latin-1") Y_train = train[train.columns[0]] X_train = train[train.columns[5]] # split the data into test and train from sklearn.model_selection import train_test_split trainset1x, trainset2x, trainset1y, trainset2y = train_test_split(X_train.values, Y_train.values, test_size=0.02,random_state=42 ) trainset2y=pd.get_dummies(trainset2y) # function to remove stopwords def stopwords(sentence): new=[] sentence=nlp(sentence) for w in sentence: if (w.is_stop == False) & (w.pos_ !="PUNCT"): new.append(w.string.strip()) c=" ".join(str(x) for x in new) return c # function to lemmatize the tweets def lemmatize(sentence): sentence=nlp(sentence) str="" for w in sentence: str+=" "+w.lemma_ return nlp(str) #loading the glove model def loadGloveModel(gloveFile): print("Loading Glove Model") f = open(gloveFile,'r') model = {} for line in f: splitLine = line.split() word = splitLine[0] embedding = [float(val) for val in splitLine[1:]] model[word] = embedding print ("Done."),len(model),(" words loaded!") return model # save the glove model model=loadGloveModel("/mnt/hdd/datasets/glove/glove.twitter.27B.200d.txt") #vectorising the sentences def sent_vectorizer(sent, model): sent_vec = np.zeros(200) numw = 0 for w in sent.split(): try: sent_vec = np.add(sent_vec, model[str(w)]) numw+=1 except: pass return sent_vec #obtain a clean vector cleanvector=[] for i in range(trainset2x.shape[0]): document=trainset2x[i] document=document.lower() document=lemmatize(document) document=str(document) cleanvector.append(sent_vectorizer(document,model)) #Getting the input and output in proper shape cleanvector=np.array(cleanvector) cleanvector =cleanvector.reshape(len(cleanvector),200,1) #tokenizing the sequences tokenizer = Tokenizer(num_words=16000) tokenizer.fit_on_texts(trainset2x) sequences = tokenizer.texts_to_sequences(trainset2x) word_index = tokenizer.word_index print('Found %s unique tokens.' % len(word_index)) data = pad_sequences(sequences, maxlen=15, padding="post") print(data.shape) #reshape the data and preparing to train data=data.reshape(len(cleanvector),15,1) from sklearn.model_selection import train_test_split trainx, validx, trainy, validy = train_test_split(data, trainset2y, test_size=0.3,random_state=42 ) #calculate the number of words nb_words=len(tokenizer.word_index)+1 #obtain theembedding matrix embedding_matrix = np.zeros((nb_words, 200)) for word, i in word_index.items(): embedding_vector = model.get(word) if embedding_vector is not None: embedding_matrix[i] = embedding_vector print('Null word embeddings: %d' % np.sum(np.sum(embedding_matrix, axis=1) == 0)) trainy=np.array(trainy) validy=np.array(validy) #building a simple RNN model def modelbuild(): model = Sequential() model.add(keras.layers.InputLayer(input_shape=(15,1))) keras.layers.embeddings.Embedding(nb_words, 15, weights=[embedding_matrix], input_length=15, trainable=False) model.add(keras.layers.recurrent.SimpleRNN(units = 100, activation='relu', use_bias=True)) model.add(keras.layers.Dense(units=1000, input_dim = 2000, activation='sigmoid')) model.add(keras.layers.Dense(units=500, input_dim=1000, activation='relu')) model.add(keras.layers.Dense(units=2, input_dim=500,activation='softmax')) return model #compiling the model finalmodel = modelbuild() finalmodel.fit(trainx, trainy, epochs=10, batch_size=120,validation_data=(validx,validy))

If you would run this model, it may not provide you with the best results since this is an extremely simple architecture and quite a shallow network. I would strongly urge you to play with the architecture of the network to obtain better results. Also, there are multiple approaches to how to preprocess your data. Preprocessing shall completely depend on the task at hand.

Vanishing and Exploding Gradient Problem

RNNs work upon the fact that the result of an information is dependent on its previous state or previous n time steps. Regular RNNs might have a difficulty in learning long range dependencies. For instance if we have a sentence like “The man who ate my pizza has purple hair”. In this case, the description purple hair is for the man and not the pizza. So this is a long dependency.

If we backpropagate the error in this case, we would need to apply the chain rule. To calculate the error after the third time step with respect to the first one –

∂E/∂W = ∂E/∂y3 *∂y3/∂h3 *∂h3/∂y2 *∂y2/∂h1 .. and there is a long dependency.

Here we apply the chain rule and if any one of the gradients approached 0, all the gradients would rush to zero exponentially fast due to the multiplication. Such states would no longer help the network to learn anything. This is known as the vanishing gradient problem.

Vanishing gradient problem is far more threatening as compared to the exploding gradient problem, where the gradients become very very large due to a single or multiple gradient values becoming very high.

The reason why Vanishing gradient problem is more concerning is that an exploding gradient problem can be easily solved by clipping the gradients at a predefined threshold value. Fortunately there are ways to handle vanishing gradient problem as well. There are architectures like the LSTM(Long Short term memory) and the GRU(Gated Recurrent Units) which can be used to deal with the vanishing gradient problem.

Other RNN architectures

As we saw, RNNs suffer from vanishing gradient problems when we ask them to handle long term dependencies. They also become severely difficult to train as the number of parameters become extremely large. If we unroll the network, it becomes so huge that its convergence is a challenge.

Long Short Term Memory networks – usually called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. They were introduced by Hochreiter & Schmidhuber. They work tremendously well on a large variety of problems, and are now widely used. LSTMs also have this chain like structure, but the repeating module has a slightly different structure. Instead of having a single neural network layer, there are multiple layers, interacting in a very special way. They have an input gate, a forget gate and an output gate. We shall be coming up with detailed article on LSTMs soon.

Another efficient RNN architecture is the Gated Recurrent Units i.e. the GRUs. They are a variant of LSTMs but are simpler in their structure and are easier to train. Their success is primarily due to the gating network signals that control how the present input and previous memory are used, to update the current activation and produce the current state. These gates have their own sets of weights that are adaptively updated in the learning phase. We have just two gates here, the reset an the update gate. Stay tuned for more detailed articles on GRUs.

End Notes

Top 10 Applications Of Artificial Neural Networks In 2023

The Top 10 Applications of Artificial Neural Networks in 2023

Artificial Neural Networks (ANNs) are rapidly emerging as one of the most powerful and versatile technologies of the 21st century. They are a subset of machine learning that is inspired by the structure and function of the human brain and are capable of learning and adapting to complex patterns in data. In recent years, ANNs have found their way into numerous industries and applications, ranging from speech recognition and image processing to financial forecasting and medical diagnosis.

In this article, we will explore the top 10 applications of ANNs in 2023 and what makes them so effective in these domains.

Image Recognition and Computer Vision

Image recognition is one of the most well-known applications of ANNs. In computer vision, ANNs are used to identify objects, people, and scenes in images and videos. ANNs can learn to identify patterns in pictures and make predictions about what is in the image. This technology is already being used in many fields, including surveillance, autonomous vehicles, and medical imaging.

Speech Recognition and Natural Language Processing (NLP)

Speech recognition and NLP are other popular applications of ANNs. In speech recognition, ANNs are used to transcribe spoken words into text, while in NLP, they are used to analyze and understand the meaning of the text. These technologies are being used in virtual assistants, customer service chatbots, and other applications that require the ability to understand and respond to human speech.

Financial Forecasting and Trading

Financial forecasting and trading are areas where ANNs are being used to make predictions about market trends and stock prices. ANNs can analyze large amounts of financial data and identify patterns and relationships that can be used to make informed decisions. This technology is being used by hedge funds, banks, and other financial institutions to improve their investment strategies and minimize risk.

Medical Diagnosis and Treatment Planning

Medical diagnosis and treatment planning are critical applications of ANNs. In medical diagnosis, ANNs are used to analyze medical images and patient data to identify diseases and disorders. In treatment planning, ANNs are used to develop personalized treatment plans based on a patient’s individual characteristics and medical history. These technologies are helping to improve the accuracy and effectiveness of medical diagnoses and treatments, making healthcare more accessible and affordable for everyone.

Autonomous Vehicles

Autonomous vehicles are one of the most exciting applications of ANNs. In autonomous vehicles, ANNs are used to analyze sensor data and make decisions about how the vehicle should respond to its environment. This technology is being used to develop self-driving cars, drones, and other autonomous vehicles that can operate without human intervention.

Recommender Systems

Recommender systems are another application of ANNs that are changing the way we interact with technology. In recommender systems, ANNs are used to analyze user behavior and make recommendations about products, services, and content that are likely to be of interest to the user. This technology is being used by e-commerce websites, streaming services, and other online platforms to improve the user experience and increase engagement.

Natural Language Generation

Natural language generation is a relatively new application of ANNs that is rapidly gaining popularity. In natural language generation, ANNs are used to generate text that mimics human writing. This technology is being used in news articles, reports, and other forms of content that require the ability to write in a natural and engaging style.

Fraud Detection

Fraud detection is an important application of ANNs that is being used to prevent financial losses and protect businesses and consumers. In fraud detection, ANNs are used to analyze financial transactions and identify patterns that indicate fraudulent activity. This technology is being used by banks, credit card companies and other financial institutions to improve their security measures and reduce the risk of fraud.

Supply Chain Optimization

Supply chain optimization is another area where ANNs are being used to improve efficiency and reduce costs. In supply chain optimization, ANNs are used to analyze data from various stages of the supply chain, from raw materials to finished products, to identify bottlenecks and inefficiencies. This technology is helping companies to streamline their supply chains, reduce waste, and improve their overall performance.

Predictive Maintenance

Hardware Today: Looking Ahead, Our Top 10 List For 2004

While that might seem vaguely positive, the outlook for changes to the enterprise hardware landscape amounts to more than a simple yes or no question. So we looked inward and spoke with vendors and analysts to come up with what believe will be the 10 underlying trends for the server hardware landscape in 2004.

1. More Servers in the Rack, Virtually: Dickens aficionados (or those who recently endured a meager holiday meal) may recall Tiny Tim’s optimistic attempts to turn a single pea into a full-course family meal. Virtualization aims for the same magic, dividing CPU power between resource-hungry tasks. In 2004, virtualization will be used to optimize a wider array of applications, according to Mike Mullany, vice president of marketing for VMware. This year, the technique was applied primarily to file, print, DNS, and DHCP servers. With new capabilities available in virtualization products, says Mullany, in 2004, CIOs will deploy virtualization en masse in a wider general IT infrastructure, virtualizing a diverse assortment of products from Exchange servers to business processing and ERP applications.

2. Viruses and Spam in One Convenient Package: Viruses and spam set new records in 2003. They will not abate in 2004 and instead will begin coordinating their efforts. According to Chris Belthoff, senior analyst at Sophos, the recently enacted CAN-SPAM Act will have little luck canning spam, but it may have the unwanted consequence of pushing virus authors and spam senders into closer cahoots. “The convergence of spam and viruses will likely continue in 2004,” says Belthoff, “with more and more attempts to use viruses to set up networks of machines capable of sending out the spammers’ messages.” Belthoff believes spam’s new status of illegality may cause Hotmail and Yahoo! to crack down, forcing increased spammer reliance on viruses (e.g., trojans) that transform increasing numbers of innocent machines into unwitting spam servers, which Belthoff grimly refers to as “spam zombies.”

3. Cost Cutting to Survive “Upturn”: A continued lack of spending enthusiasm may have vendors thinking it’s the CIOs who have become zombies. Signs of economic optimism, like IDC reporting two consecutive quarters of increased server sales, will not dramatically increase 2004 hardware spending. Cost cutting measures like increased virtualization and Linux deployments are on the rise, and a wider outsourcing trend is developing, as evidenced by recent moves like IBM’s $600 million ING outsource contract. And with 2006 now the projected release date for Longhorn, administrators who time upgrades with new Microsoft releases may focus on making more with what they have in 2004.

4. Linux to Continue Its Growth Spurt: Precluding a swell in sales of alpha versions of Longhorn on the black market, the new Microsoft operating’s far-off release date provides another shot in the arm for Linux. Red Hat’s faster release cycle may prove a valuable edge over Microsoft. If the popular Linux vendor can continue to shake off SCO’s legal challenges, Linux will likely score major victories in the operating system turf wars looming for 2004. CIOs are increasingly granting the open source operating system their trust, as evidenced by IDC’s report of 49.8 percent growth in factory revenue and 51.4 percent growth in unit shipments year-over-year for the third quarter. Red Hat’s new 2.6.0 Linux kernel will better serve multiprocessor and 64-bit server environments, which will increase its general appeal, as will Linus Torvalds’ recent official blessing on the 2.6.0 kernel.

6. More Blades Round Out the Arsenal: Commodity visions like Carr’s also ignore the micro-level diversity inherent in the server room. Server blades are a great example of this. Gartner predicts that by year-end 2008, blades will achieve standardization on the major component level (e.g., chip sets, power supplies, fans, and backplanes). However, an overall interoperable standard for blades isn’t yet on the road map. Such standards run contrary to the business needs of major vendors as they allow too-cheap competition into the arena. For 2004, Gartner, anticipates reductions in premium pricing, increased clarity for heat generation and power consumption issues, and blade virtualization capabilities that will push blades out of their niche market status.

8. Appliance Computing to Continue Its Decline: Sometimes even the most confident predictions can prove wrong. Sun’s recently completed demolition of its Cobalt line marks the end of the not-long-ago highly touted appliance computing trend, which demonstrates the industry’s wider acceptance of the low, but not quite that low, end x86 market. Sun’s confident end-of-lifing of the line for which it traded around $2 billion in stock more than hints at Auld Lang Syne for appliance computing.

9. More Modular, Less Mainframe: Mainframes may be moving toward being another “old acquaintance” best forgotten. IDC’s 2003 third-quarter report showed small and midrange servers revenue increasing as high-end revenue declined. Trends toward utility computing and outsourcing amplify this, as the room for error in pay-as-you-go models may be too small for questions like “Will we need to lease another mainframe this quarter?”

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