Search Results - (( java application optimisation algorithm ) OR ( learning application customization algorithm ))

Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk by Elaf Adel Abbas, Nisreen Abbas Hussein

“…The study examines how customer attributes affect virtual experiences. Despite advances in machine learning models for credit assessment, unbalanced datasets and some algorithms’ failure to explain forecasts remain major issues. …”
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Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning by Lim, Wai Ping, Goh, Ching Pang

“…This research explores the application of unsupervised learning, a subset of Artificial Intelligence (AI), to analyze customer behavior in accepting personal loans within the banking sector. …”
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Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set by Yap, Chau Tean

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Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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Intent-IQ: customer’s reviews intent recognition using random forest algorithm by Mazlan, Nur Farahnisrin, Ibrahim Teo, Noor Hasimah

“…Two machine learning model is chosen to build the classification models which are Random Forest (RF) algorithm and Multinomial Naïve Bayes (MNB) algorithm. …”
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Stock indicator scanner customization tool using deep reinforcement learning by Cheong, Desmond YongHong

“…Other than that, many current stock indicator scanners only allow user to specify some simple conditions to scan the stocks and do not harness the advancement of machine learning. This project will deliver a web application with dynamic stock prediction model based on deep reinforcement learning or more particularly, Deep Q-Network (DQN) algorithm which enable input customization. …”
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Stock indicator scanner customization tool by Cheah, Shing Dhee

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Artificial intelligent integrated into sun-tracking system to enhance the accuracy, reliability and long-term performance in solar energy harnessing by Tan, Jun You

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Machine learning based route optimization for the travelling salesman problem with pickup and delivery by Ong, Zhi Ying

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Predicting Customer Behaviour on Buying Life Insurance using Machine Learning by Fabu Ayub A Ghafar, Norhana Abd Rahim, Yumn Suhaylah Yusoff

EEG-based emotion recognition using machine learning algorithms by Lam, Yee Wei

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Predicting open space parking vacancies using machine learning by Lee, Wei Jun

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A customized non-exclusive clustering algorithm for news recommendation systems by Ibrahim, Hamidah, Sidi, Fatimah, Mustapha, Aida, Darvishy, Asghar

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Customer sentiment analysis through social media feedback by Siti Nur Syamimi, Mat Zain

“…In addition, the performance of the customer sentiment analysis model can be enhanced by using deep learning methods. …”
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Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms by Mohd Romlay, Muhammad Rabani, Rashid, Muhammad Mahbubur, Toha @ Tohara, Siti Fauziah, Mohd Ibrahim, Azhar

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Sentiment analysis for online fashion clothing review using Support Vector Machine (SVM) algorithm / Muhammad Hakam Musawi Mohd Hamdi by Mohd Hamdi, Muhammad Hakam Musawi

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Ump Intelligent Chatbot Using Dialogflow by Joachim, Agostain

“…To create such a chatbot, a machine learning algorithm is used to learn the human language that is mainly used during such conversations. …”
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Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms by Mohd Romlay, Muhammad Rabani, Rashid, Muhammad Mahbubur, Toha @ Tohara, Siti Fauziah, Mohd Ibrahim, Azhar

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Customer sentiment analysis through social media feedback: A case study on telecommunication company by Mat Zain, Siti Nur Syamimi, Ramli, Nor Azuana, Adnan, Rose Adzreen

“…Based on the analysis, it was found that there was no negative sentiment from the customers. The data were then split into training and testing to be tested on the three different supervised learning algorithms used in this study which are Support Vector Machine, Random Forest, and Naïve Bayes. …”
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