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1
Using fuzzy association rule mining in cancer classification
Published 2011“…A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. …”
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2
Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee
Published 2023“…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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3
Optimized feature construction methods for data summarizations of relational data
Published 2014“…The summarized data will then be fed to any classification algorithm to perform the classification task. …”
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Reverse migration prediction model based on machine learning / Azreen Anuar
Published 2024“…Thus, this research aim to develop a reverse migration prediction model based on machine learning. …”
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5
Pemetaan Pm10 Dan Aot Menggunakan Teknik Penderiaan Jauh Di Semenanjung Malaysia
Published 2006“…The two-band model, terma linear and modified algorithms were selected based on the highe.st correlation coefficient (R) value {> 80%) and the lowest root-mean-square (RMS) error value. …”
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6
Drought-events and potential impacts on land use/land cover dynamics based on AVHRR data of 1992-2003 for central Iran
Published 2011“…Then, the methods with the best performances depicted to produce the spatio-temporal SPI drought index layer. At the second phase, a seasonal-based classification algorithm for AVHRR (Advanced Very High Resolution Radiometer) data developed to enhance the classification accuracy. …”
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7
Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm
Published 2023“…Patterns and relationships between input variables and cheating behaviours are analysed through the application of supervised learning techniques, specifically a classification model. …”
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Final Year Project Report / IMRAD -
8
Identification model for hearing loss symptoms using machine learning techniques
Published 2014“…The model is implemented using both unsupervised and supervised machine learning techniques in the form of Frequent Pattern Growth (FP-Growth) algorithm as feature transformation method and multivariate Bernoulli naïve Bayes classification model as the classifier. …”
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9
Investigation of features for classification RFID reading between two RFID reader in various support vector machine kernel function
Published 2022“…The Polynomial-SVM model is capable of delivering a classification accuracy of 84.81 and 20.00% of the error rate in test data by using the function MIN extracted. …”
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10
Deep plant: A deep learning approach for plant classification / Lee Sue Han
Published 2018“…Modelling the relationship between different plant views (or organs) is important as these images captured from a same plant share overlapping characteristics which are useful for species recognition. …”
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11
Classification with degree of importance of attributes for stock market data mining
Published 2004“…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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12
Relationship between psycho-maturity and performance of sepak takraw
Published 2020“…The Louvain clustering algorithm is used to categorise the players to high, medium and low psycho-matured players based on seven fundamental psychological measures, viz. maturity status, selftalk, activation, imagery, emotion control, automaticity and goal setting. …”
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13
A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping
Published 2014“…An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. …”
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Herbal plant image classification using transfer learning and fine-tuning deep learning model
Published 2024“…Transfer learning is an algorithm that learns to recognize image features in one domain and having the capability to generalize the learnt knowledge to a new domain with a smaller dataset. …”
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15
Driver behaviour classification: a research using OBD-II data and machine learning
Published 2024“…Then, the proposed model makes use of the K-Means algorithm to create driving behaviour labels whether belong to safe or aggressive - validated by the safety score criteria. …”
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16
Handgrip strength evaluation using neuro fuzzy approach
Published 2010“…Multilevel Perception neural network utilizes the back-propagation learning algorithm is suitable to discover relationships and patterns in the dataset. …”
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Landslide susceptibility mapping using decision-tree based chi-squared automatic interaction detection (CHAID) and logistic regression (LR) integration
Published 2014“…This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. …”
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Stress mental health symptom assessment mobile application for young adults
Published 2023“…Subsequently, employing the K-Nearest Neighbors (KNN) algorithm, the model shall forecast the likelihood of experiencing a future panic attack. …”
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Herbal plant image classification using transfer learning and fine-tuning deep learning model
Published 2024“…Transfer learning is an algorithm that learns to recognize image features in one domain and having the capability to generalize the learnt knowledge to a new domain with a smaller dataset. …”
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Durian (Durio zibethinus) ripeness detection using thermal imaging with multivariate analysis
Published 2021“…Linear discriminant analysis (LDA), k-nearest neighbour (kNN), and support vector machine (SVM) were applied for the establishment of the optimal classification modelling algorithms. The SVM classifier gave the overall best performance for the discrimination of durian ripeness with a classification accuracy of 97 %. …”
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