Showing posts with label Artificial Intelligence. Show all posts
Showing posts with label Artificial Intelligence. Show all posts

Wednesday, 15 December 2021

HOW ARTIFICIAL INTELLIGENCE IS USED IN WEB DATA EXTRACTION?



Artificial Intelligence and Big Data are the most important topic these days. Web scraping and data extraction technologies are the only reason behind witnessing AI and Big Data. In this blog, we will look at how AI is used to extract data from various websites.

What are the variations evolved in Artificial Intelligence and the extraction of big data? Nowadays, web data extraction has become easy and practical due to the increase in processing power. Powerful web scraping solutions and data extraction technologies will assist you in accessing data even if you do not possess technical knowledge. Artificial intelligence is shown to be the best approach for gathering large data sets from the internet with the least amount of human intervention.

Artificial Intelligence vs. Machine Learning

artificial-intelligence-vs-machine-learning

Machine Learning (ML) and Artificial Intelligence (AI) are not the same things. Computer Learning is the process of teaching a machine to do a given task based on a set of rules and some training samples. To acquire a level of success, the machine learning system requires training and regulations.

Artificial Intelligence, on the other hand, teaches itself using a limited set of rules and random training. It can then construct its very own system of regulations based on the information it receives. As a result, AI is a never-ending learning process.

Artificial neural networks are the reason behind continuous learning taking place in AI. In AI, deep learning and artificial neural networks are employed for language and machine vision, segmentation techniques, language modeling, and human motion.

Use of Artificial Intelligence in Web Data Extraction

use-of-artificial-intelligence-in-web-data-extraction

The internet is a huge database with a lot of information. With this much web data, the possibilities are limitless. The task at hand is to go through this mess of data and make data extraction more straightforward. Data extraction is a time-consuming procedure, even when using powerful web scraping methods. Things, though, are going to shift.

The Massachusetts Institute of Technology recently published a paper on an Artificial Intelligence system that can collect data from the internet and teach itself how to retrieve information. The study presents a data extraction technique that can extract relevant structured data from unstructured documents. In simple terms, the AI system can think like a human. When humans are unable to locate a specific piece of information in a document, we turn to other sources to fill the void. This broadens our understanding of the subject.

This is how the AI system works: it scrapes material from the web on similar topics and fills in the gaps in the information structure.

Artificial Intelligence System Works on Rewards and Penalties

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The 'Confidence score' is used to categorize the data in an AI-based website data extraction method. This reliability index is computed from the patterns in the training data and defines the likelihood of the classification being statistically correct. If indeed the confidence score falls short of the threshold, the device will immediately look on the internet for further relevant information.

It will be considered successful once an acceptable confidence rating is attained by extracting fresh data from the internet and combining it with the current content. If the confidence score isn't met, the procedure is repeated until the most relevant web data is extracted.

This form of the learning process is known as 'Reinforcement learning,' and it operates on the principle of reward-based learning. It works similarly to how people learn. Since there can be a lot of doubt when merging data, especially when there is opposing information involved. The prizes are determined by the correctness of the data. The AI learns how and when to optimally combine multiple bits of extracted data along with the training provided so that the responses, we obtain from the system are as accurate as feasible.

Artificial Intelligence’s Web Data Extraction in Action

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Researchers give it a task to examine the working of the Artificial Intelligence system can extract the information from various websites. The method was designed to examine numerous data sources on mass shootings in the United States and extract the shooter's name. The number of people who were hurt, the number of people who died, and the location. The results were amazing since it was able to extract reliable information in the manner required while outperforming normally instructed data extraction processes by more than 10%!

The Insights of Web Scraping and Data Extraction

the-insights-of-web-scraping-and-data-extraction

With the ever-increasing need for information and the difficulties in obtaining it, AI could be the missing piece in the puzzle. The findings are exciting, pointing to a future in which intelligent machines with human vision can scan, explore, and extract data. This was created purely to inform us of the required information.

The Artificial Intelligence system has the potential to revolutionize everything. A sophisticated system like this will not only reduce time but will also allow us to take advantage of the vast amount of information available on the internet. In the great scale of things, this early study is just a first step toward developing a smart web crawler capable of web scraping. This was done to fill in knowledge gaps in a short amount of time.

For getting more information regarding the use of web data extraction, contact X-Byte Enterprise Crawling!!

Wednesday, 8 December 2021

HOW RETAILORS ARE USING ARTIFICIAL INTELLIGENCE AND IMAGE RECOGNITION FOR PRODUCT MONITORING & ANALYSIS?


With the e-commerce boom, today’s entrepreneurs have discovered that conservative methods of sales promotions or visual merchandising won’t withstand profits in the aggressive CPG industry. A lot of retailers have already implemented image recognition and Artificial Intelligence (AI) to deliver the next-level customer experience, starting a new era of the retail industry. As per Gartner, by the year 2020, 85% of the customer interactions in the retail industry would get organized by AI. Product recommendations, Product discovery, as well as trend analysis, are a few areas for the implementation of image recognition and computer vision. This blog discusses how CPG and retail companies implement image recognition.

1. Audit Product Placements

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Customers are taking important buying decisions at stores and companies need to utilize different technologies to stay ahead in the competition. Collecting key consumer data assists companies in understanding their requirements better. Shelf recognition with computer vision digitizes store checking as well as it is important in collecting important consumer data with AI.

Computer vision with a profound neural network finds objects in images of shelves as well as classifies them depending on the brand, category, as well as items. It is useful in streamlining time-consuming, manual, and error-prone stores audits of SKUs to help the manual efforts directed towards the real job of product selling.

2. Trend Detection in Product Placements

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The product placement in the store could either make products sell like hotcakes or stay persistently on a shelf. Companies like Tesco have already started the usage of image recognition software for better product placements in the stores. Image recognition utilizes photographs of hotspots or shelves and immediately gives insights into preferences, consumer behavior, and product placements on display. As AI can interpret enormous amounts of data as well as offer important insights, which is not possible for humans to achieve with accuracy, planners can discover the product movements, how they get consumed or how good the product placement works towards having optimum ROI from placements as well as advertising messages. AI also gives insights into how any brand’s product is getting used across different social media and allows marketers in the efforts.

3. Evaluating Competition and Compliance

evaluating-competition-and-compliance

The majority of companies rely on sales reputation to audit company products’ placements, brand compliance, and brand presence at different outlets. It could be a tedious job. Image recognition could be used for tracking compliance with merchandising standards for every outlet as well as having superior trade program versions. Machine vision can also be used for tracking competitor brands, brand presence, placements, and policies using a competitor’s distribution, shelf strategies, and channels as well as offer important business ideas.

4. Category Analysis

category-analysis

Whenever you visit any supermarket, you are expected to get a product from P&G, if you want something in the pharmacy and beauty category. It is a result of the company staying watchful with customer trends as well as competitor behavior about the product categories. Using APIs, which quickly organize images into categories, recognize the stocks of competitors in these categories, as well as the competitor provides, allows P&G to create new marketing scenarios that result in more shelf share.

Conclusion

To sum up, image recognition technology helps manufacturers and retailers understand their marketplaces and respond in real-time. The drift is moving from online to offline using image recognition in the CPG industry. Characteristics like calculation or compliance of the product’s performance related to its competitors can be presented in a superior way rather than having manual procedures where there are chances of human problems and errors in procedure scalability. Also, image recognition is further developing allowing the clients to be directly involved with the manufacturers using wearable devices. Therefore, it’s time to comprise this new development and automate.

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