Deep Learning

What Deep Learning is and its challenges?

Profound Learning is the cause of the deterioration of human contribution. Profound learning assumes a huge part in stuffing when a trouble or situation requires the human cerebrum to create complex issues.

Profound learning has been astounding in light of the fact that it overlooks human contribution, especially for complex issues. There are difficulties here, for example, ...Read more

It has numerous benefits, for example,

  1. Profound Learning assumes a predominant part in inVoice acknowledgment frameworks, Image preparing and labeling and web search tools.
  2. Utilizing Neural Networks, customer suggestions are proficiently done.
...Read more

Case Study 1: How to automate manual inspection of circuits on assembly chain process by image processing using Deep Learning
Business Problem Statement

Research has facts to say that companies, which do not embrace technology will die down soon. History has time & again proved that companies, which waste the knowledge of the employees by having them participate in extensive manual laborious work will not survive the competition. In the wake of major disruptions in technological space, companies are still struggling with the manual quality audit process of the goods & services they provide. Industrial revolution, which is trying to solve these challenges, is in nascent stages & starting at a steady pace. However, the applications of the same is not well known to many. Research is still going on in the space of computer vision to solve a lot of these problems.

Business Challenges

  • Time-consuming manual effort to inspect each high-end circuit board
  • Lack of synchronization between the speed of manufacturing & manual inspection leading to incorrect sample size selection
  • Lot of errors in manual inspection process because of time constraint
  • Lack of confidence on the manufactured lot even if the samples are not defective
  • Increase in scrapped circuits, which are identified during assembling process, leading to wastage of raw material, time & effort

Business Solutions

  • Defective circuit boards are identified using image processing. This is achieved by capturing & comparing the Images, of each circuit board, against the historical defective boards, by applying deep learning techniques
  • Classification models automatically segregates the defective circuit board from the non-defective
  • Quality inspection team is upfront provided information on probability of circuit board being defective, leading to inspection of those, which have slightly above threshold probability of being defective

Business Impact

  • Reduction in manual inspection effort by 72%
  • Reduction in the sample size without compromising the effectiveness of inspection process
  • Reduction in the number of human errors in wrongly classifying the circuit boards as non-defective despite being defective
  • Reduced wastage of material, time & effort

Case Study 2:How to use image processing to predict cancer based on tumor ../images
Business Problem Statement

Business Problem Statement: Problem Statement: Developing & under developed countries are struggling to provide medical services at reasonable cost for the down trodden, thereby reducing the life expectancy. Lack of availability of advanced techniques has inflated the cost of medical service by taking it away from the reach of even the middle-class society. Old fashioned techniques for diagnosis has reduced the chances of correctly identifying the diseases, thereby making the medical procedure of treating the disease very costly.

Business Challenges

  • Advanced medical tests also are unable to find out the presence of cancer tumours at an early stage
  • Too many medical tests to diagnose cancer is causing pain to the already distressed patients
  • The massive prices of diagnosis makes it impossible for the vulnerable to claim benefits.

Business Solutions

  • Performed clustering of the tumour cell images of various patients based on various parameters including demographic & previous medical history data
  • Performed deep learning techniques in analyzing the images of tumour cells & classify as benign or malignant
  • Image processing performed to match with the other tumour cell images in determining the stage in which cancer exists

Business Impact

  • Early identification of disease led to better chances of survival through early medication
  • Less cost of diagnosis for patients
  • Less turnaround time in diagnosis the disease
  • Increase in the success rate of effectively diagnosing

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