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Real-World Applications of Data Science
Published in Pallavi Vijay Chavan, Parikshit N Mahalle, Ramchandra Mangrulkar, Idongesit Williams, Data Science, 2022
Email filtering applies specified criteria for incoming emails to protect your network from viruses and possible attacks and avoid overloading servers from unwanted emails. The spam filters detect unsolicited, virus-infected, unwanted emails and stop them from entering your mail inboxes. Following are some example filters that are applied to the email system to determine whether it is spam or not.
Machine Learning
Published in Anil Kumar, Priyadarshi Upadhyay, A. Senthil Kumar, Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification, 2020
Anil Kumar, Priyadarshi Upadhyay, A. Senthil Kumar
Artificial intelligence (AI) has one of the applications in machine learning (ML), where machines, software, and sensors use cognition. In the present scenario, real working examples of machine learning are: Virtual Personal Assistants – Siri, Alexa, and Google Assistant are examples of virtual personal assistants. These help in getting information when used through voice.Predictions while Commuting – Traffic predictions are examples in which congestions are found on some of the routes through ML using online GPS location of vehicles.Online Transportation Networks – Here booking a cab app estimates the price of the ride, as price surge hours, by predicting the rider demand using ML.Video Surveillance – Single person monitoring of multiple video cameras is a difficult and boring job. That’s why computers are trained through video surveillance systems, powered by AI, to detect crimes while or before they happen.Social Media Services – Social media platforms use machine learning for their own and user benefits such as: “People You May Know” Here machine learning works on a simple concept considering experiences through user interaction. Face Recognition – Users upload a picture with a friend, and this picture is recognized by Facebook using ML.Similar Pins – Machine learning is the core element of computer vision, which is a technique to extract useful information from images and videos. Pinterest uses computer vision to identify the objects (or pins) in the images and recommend similar pins accordingly.Email Spam and Malware Filtering – There are a number of spam filtering approaches to find spam emails. These spam filters are continuously updated and are powered by machine learning.Online Customer Support – Websites nowadays use Chatbot to reply to customer queries 24 hours a day in place of live representatives; this is possible due to machine learning algorithms.Search Engine Result Refining – Machine learning is used in Google and other search engines to improve the search results.Product Recommendations – Once an item is purchased online, the customer continues to receive emails for shopping suggestions, which happens through machine learning.Online Fraud Detection – Cyberspace has been made secure and monetary frauds can be tracked online through machine learning.
Comparative Investigation of Learning Algorithms for Image Classification with Small Dataset
Published in Applied Artificial Intelligence, 2021
Imran Iqbal, Gbenga Abiodun Odesanmi, Jianxiang Wang, Li Liu
Supervised learning can be further divided into classification and regression. Classification is basically a method of processing some input and mapping it to discrete output. Spam filter is the simplest example of classification; e-mails in inbox are process by machine learning spamming algorithm and if some criteria is fulfilled than e-mails are consider as spam. In regression problem, we try to predict numeric dependences of function value from set of input parameters. For example, housing price prediction and many other engineering problems.
Ensemble one-vs-all learning technique with emphatic & rehearsal training for phishing email classification using psychology
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2018
Around 450 suspected collected phishing emails, from spam filter of email users, were categorised and used for testing and examples are shown in Figure 5(a)–(c). Some demonstration of phishing emails were created for survey and the categorical examples in Section 3 are instances of these. These examples were limited and inadequate for training. We used Vocabulary Expansion approach for enhancement as data augmentation to create better feature space.