Top 7 Big Data Trends to Dominate 2021

 

Big data is a term that describes the large volume of data – both structured and unstructured – that updates a business on a day-to-day basis. But it's not the amount of data that's important. It's what organizations do with the data that matters.

Here are seven top big data trends organizations will need to watch to better reinforce and secure disrupted businesses. Have a look at the summary of those trends

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Quote : "No matter how carefully a project is planned, something may still go wrong with it " - Robert Burns.

Top AI, Data Science, Analytics Appointments In India

 

According to the State of Artificial Intelligence in India report, the hiring activities from the mid-junior to the mid-senior experience levels are on the rise, indicating companies’ interest in developing capabilities in computer vision, robotics, and natural language processing, etc. 

Here, also we have listed the major leadership appointments across data science, machine learning, and artificial intelligence roles of late. 

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VOICE SPECIFICATION USING MACHINE LEARNING AND NEURAL NETWORKS

 

Imagine a conference room where a meeting is taking place. There are several people, they all take turns to speak. We would like to transcribe what they are saying. There are tools that can transcribe voice to text (most of us have that feature on our phones and do not even know it), but can we train a model to learn voices and accurately predict who is speaking just by listening to their voice? Can we predict their gender?

We will tackle these problems by using Machine Learning with Neural Networks.

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THE FORCES BEHIND SOCIAL MEDIA : ALGORITHMS, ARTIFICIAL INTELLIGENCE, AND INTENSITY

Who are the main players in this power shift? It’s not just governments and corporations. Right now, algorithms and artificial intelligence (AI) — and the people behind them — have far more influence than many realize.

Changing minds and influencing behavior has always been the central purpose of media. Headlines are intended to grab our interest and spark emotions that will impact how we think and act. There is nothing necessarily morally wrong about this. It has always been how perceptions are shaped. Even the most unbiased article retains a trace of the author’s opinion. What is different now is that “machines” — in the form of AI and algorithms — have been introduced into the equation and operate at an unprecedented scale.

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Artificial Intelligence Translates Mental Handwriting into Text

 An artificial intelligence software paired with a brain-computer interface decodes neural signals to convert mental handwriting to text, enabling communication for patients with spinal-cord injuries, strokes, and other conditions. More details

Image J

Image Processing and Analysis in Java

ImageJ is written in Java, which allows it to run on Linux, Mac OS X and Windows, in both 32-bit and 64-bit modes.Automate tasks and create custom tools using macros. Generate macro code using thecommand recorder and debug it using the macro debugger. More than 300 macros are available on the ImageJ Web site. Extend ImageJ by developing plugins using ImageJ's built in text editor and Java compiler. More than 500 plugins are available. Use ImageJ as a image processing toolkit (class library) to develop applets, servlets or applications.

website:  https://imagej.nih.gov/ij/index.html

Fuzzy Logic Image Processing

An edge is a boundary between two uniform regions. You can detect an edge by comparing the intensity of neighboring pixels. small intensity differences between two neighboring pixels do not always represent an edge. Instead, the intensity difference might represent a shading effect. The fuzzy logic approach for image processing allows you to use membership functions to define the degree to which a pixel belongs to an edge or a uniform region.

Import RGB Image and Convert to Grayscale

I = imread('peppers.png');
I' = 0.2989*I(:,:,1)+0.5870*I(:,:,2)+0.1140*I(:,:,3); figure; image(I','DataMapping','scaled'); colormap('gray'); title('Input Image in Grayscale') 

Convert Image to Double-Precision Data

I = double(I');
Type = class(I'); scaling = double(intmax(Type)); I = I/scaling; 

Obtain Image Gradient

Gx = [-1 1]; Gy = Gx'; Ix = conv2(I,Gx,'same'); Iy = conv2(I,Gy,'same'); figure; image(Ix,'DataMapping','scaled'); colormap('gray'); title('Ix'); figure; image(Iy,'DataMapping','scaled'); colormap('gray'); title('Iy');

Define Fuzzy Inference System (FIS) for Edge Detection

edgeFIS = newfis('edgeDetection');
edgeFIS = addvar(edgeFIS,'input','Ix',[-1 1]); edgeFIS = addvar(edgeFIS,'input','Iy',[-1 1]);
sx = 0.1; sy = 0.1; edgeFIS = addmf(edgeFIS,'input',1,'zero','gaussmf',[sx 0]); edgeFIS = addmf(edgeFIS,'input',2,'zero','gaussmf',[sy 0]);
edgeFIS = addvar(edgeFIS,'output','Iout',[0 1]);
wa = 0.1; wb = 1; wc = 1; ba = 0; bb = 0; bc = .7; edgeFIS = addmf(edgeFIS,'output',1,'white','trimf',[wa wb wc]); edgeFIS = addmf(edgeFIS,'output',1,'black','trimf',[ba bb bc]);
figure subplot(2,2,1); plotmf(edgeFIS,'input',1); title('Ix'); subplot(2,2,2); plotmf(edgeFIS,'input',2); title('Iy'); subplot(2,2,[3 4]); plotmf(edgeFIS,'output',1); title('Iout')

Specify FIS Rules

r1 = 'If Ix is zero and Iy is zero then Iout is white'; r2 = 'If Ix is not zero or Iy is not zero then Iout is black'; r = char(r1,r2); edgeFIS = parsrule(edgeFIS,r); showrule(edgeFIS)

Evaluate FIS

Ieval = zeros(size(I));% Preallocate the output matrix for ii = 1:size(I,1) Ieval(ii,:) = evalfis([(Ix(ii,:));(Iy(ii,:));]',edgeFIS); end

Plot Results

figure; image(I,'CDataMapping','scaled'); colormap('gray'); title('Original Grayscale Image') figure; image(Ieval,'CDataMapping','scaled'); colormap('gray'); title('Edge Detection Using Fuzzy Logic')