Search Results - (( developing consumer machine algorithm ) OR ( java application learning algorithm ))

Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

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Analysis on The Prediction of Homeless using Machine Learning Algorithm by Mohd Burhan, Nur Izza Natilia

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Developing an intelligent system to acquire meeting knowledge in problem-based learning environments by Chiang, A., Baba, M.S.

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Intent-IQ: customer’s reviews intent recognition using random forest algorithm by Mazlan, Nur Farahnisrin, Ibrahim Teo, Noor Hasimah

“…Data annotation using semi-supervised learning which is self-training technique need to be accomplished as the dataset is unlabeled. 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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Energy-efficient algorithms for dynamic virtual machine consolidation in cloud data centers by Khoshkholghi, Mohammad Ali, Derahman, Mohd Noor, Abdullah, Azizol, Subramaniam, Shamala, Othman, Mohamed

“…Based on the proposed algorithms, energy consumption can be reduced…”
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An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

“…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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Waste management using machine learning and deep learning algorithms by Sami, Khan Nasik, Amin, Zian Md Afique, Hassan, Raini

“…For our research we did the comparisons between three Machine Learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Decision Tree, and one Deep Learning algorithm called Convolutional Neural Network (CNN), to find the optimal algorithm that best fits for the waste classification solution. …”
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Customer behavior analysis based on purchasing history and reviews using automated decision-making systems by Allur, Naga Sushma, Deevi, Durga Praveen, Dondapati, Koteswararao, Chetlapalli, Himabindu, Kodadi, Sharadha, Perumal, Thinagaran

“…Techniques: Then, the decision-making process is used to analyze the customer’s data using the regression algorithm. Result: This results in the proposed model, the relationship of the many business models, the formation of the purchases that affect consumer needs, and the strategies that can be developed. …”
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Predictive modelling of nanofluids thermophysical properties using machine learning by Olanrewaju, Alade Ibrahim

“…This thesis aimed to develop machine learning algorithms to estimate the thermophysical properties of commonly used nanofluids. …”
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Machine learning approach for automated optical inspection of electronic components by Lim, Siew Kee

“…The factor that affecting the confidence level of the supervised machine learning algorithm is discussed. …”
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Test case generation from state machine with OCL constraints using search-based techniques / Aneesa Ali Ali Saeed by Aneesa Ali, Ali Saeed

“…Software testing consumes half of the entire software development cost where test case generation is the most cost consuming activity in the whole process. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves by Lia, Kamelia

“…Traditional methods often rely on expert visual assessments, which are labour-intensive, subjective, and time-consuming. The proposed method integrates colour and texture feature-based image analysis with machine learning algorithms for classification. …”
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Development of candy sorting machine by Hazizan, Hairul Ikhwan, Rusli, Ahmad Najmie

“…This project focuses on the development of a candy sorting machine designed to automate the process of sorting candies based on color detection. …”
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Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model by Intan Azmira W.A.R., Ahmad A., Abidin I.Z., Yap K.S., Nasir M.N.M., Upkli W.R.

Kernel and multi-class classifiers for multi-floor wlan localisation by Abd Rahman, Mohd Amiruddin

“…Unlike the classical kNN algorithm which is a regression type algorithm, the proposed localisation algorithms utilise machine learning classification for both linear and kernel types. …”
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Context-Aware Recommender System based on machine learning in tourist mobile application / Nor Liza Saad … [et al.] by Saad, Nor Liza, Khairudin, Nurkhairizan, Azizan, Azilawati, Abd Rahman, Abdullah Sani, Ibrahim, Roslina

“…The objective of this research is to develop a tourist mobile application that can be incorporated with machine learning based recommender system. …”
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Covid-19 Fake News Detection Model On Social Media Data Using Machine Learning Techniques by Liew, Kelvin Kai Xuan

“…Finally, to categorise the covid -19 related dataset, multiple cutting-edge machine learning algorithms were trained. Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT) are the machine learning models presented. …”
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Predicting motorcycle customization preferences using machine learning by Saputra, Ananta, Utoro, Rio Korio, Roedavan, Rickman, Soegiarto, Duddy, Moorthy, Kohbalan, Pratondo, Agus

“…The classification model was developed using the Random Forest algorithm, Support Vector Machine and Logistic Regression with 5-fold Cross validation. …”
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