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Showing 3 results for Machine Learning

Vankamamidi S Naresh, O Sri Nagesh, S Sivaranjanireddi,
Volume 31, Issue 2 (6-2020)
Abstract

Cognitive based (Chatbot) blood bank provides the communication platform among the stakeholders of blood bank. In the past the blood recipient will have to contact the blood bank and the blood donors individually, which is a time consuming process.  To address this issue in this paper we propose a Secure Dynamic Interactive Blood Bank based on Cognitive Computing which can fulfill the blood request of the needy with less hardship. Hence the proposed work aims to overcome this problem by requesting the recipient to simply send a message to a chatbot.  The motivated individuals who are willing to donate blood can register their name by interacting with the chatbot. If the requested blood group is available at the blood bank / registered donor then the recipient will get contact details of the blood bank / registered donors available at that instant. Donor data will be maintained in Cloud database. The proposed system is a cognitive chatbot, which acts as a communication platform among the stakeholders such as blood bank, blood donor and the needy. This system is built using cognitive technology of Google; it makes conversations using chatbots very similar to human conversations, thereby making the proposed system more efficient compared to the existing ones.
K.v.k Sasikanth, K. Samatha, N. Deshai, B. V. D. S. Sekhar, S. Venkatramana,
Volume 31, Issue 3 (9-2020)
Abstract

The Today’s interconnected world generates huge digital data, while millions of users share their opinions, feelings on various topics through popular applications such as social media, different micro blogging sites, and various review sites on every day. Nowadays Sentiment Analysis on Twitter Data which is considered as a very important problem particularly for various organizations or companies who want to know the customers feelings and opinions about their products and services. Because of the data nature, variety and enormous size, it is very practical for several applications, range from choice and decision creation to product assessment. Tweets are being used to convey the sentiment of a tweeter on a specific topic. Those companies keeping survey millions of tweets on some kind of subjects to evaluate actual opinion and to know the customer feelings. This paper major goal would be to significantly collect, recognize, filter, reduce and analyze all such relevant opinions, emotions, and feelings of people on different product or service could be categorized into positive, negative or neutral because such categorization improves sales growth about a company's products or films, etc. We initiate that the Naïve Bayes classifier be the mainly utilized machine learning method for mining feelings from large data like twitter and popular social network because of its more accuracy rates. In this paper, we scrutinize sentiment polarity analysis on Twitter data in a distributed environment, known as Apache Spark.
Sofia Kassami, Abdelah Zamma, Souad Ben Souda,
Volume 33, Issue 3 (9-2022)
Abstract

Modeling supply chain planning problems is considered one of the most critical planning issues in Supply Chain Management (SCM). Nowadays, decisions making must be sufficiently sustainable to operate appropriately in a complex and uncertain environment of the market for many years to beyond the next decade. Therefore, making these decisions in the presence of uncertainty is a critical issue,as highlighted in a large number of relevant publications over the past two decades.The purpose of this investigation is to model a multilevel supply chain problem and determine the constraints that prevent the flow from performing properly, subject to various sources and types of uncertainty that characterize the flow. Therefore, it attempts to establish a generic model that relies on the stochastic approach.  Several studies have been conducted on uncertainty in order to propose an optimal solution to this type of problem. Thus, in this study, we will use the method of "Mixed integer optimization program" which is the basis of the algorithm that will be employed. This inaccuracy of the supply chain is handled by the fuzzy sets. In this paper, we intend to provide a new model for determining optimal planning of tactical and strategical decision-making levels, by building a conceptual model. Therefore, it enables us to model the mathematical programming problem. We investigate in this attempt, attention to solving the mathematical model. So in the resolution we are going through the algorithm in machine learning, therefore providing as in the end an optimal solution for the planning of production.

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