Search Results - (( evolution optimization mining algorithm ) OR ( _ implication tree algorithm ))

Y-type Random 2-satisfiability In Discrete Hopfield Neural Network by Guo, Yueling

“…During the retrieval phase, a new activation function and Swarm Mutation were proposed to ensure the diversity of the neuron states. The proposed algorithm and mutation mechanism showed optimal performances as compared to the existing algorithms. …”
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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

“…A new self-adaptive hybrid algorithm (CSCMAES) is introduced for optimization. …”
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Seed disperser ant algorithm for optimization / Chang Wen Liang by Chang , Wen Liang

“…The Seed Disperser Ant Algorithm (SDAA) is developed based on the evolution or expansion process of Seed Disperser Ant (Aphaenogaster senilis) colony. …”
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Laptop price prediction using decision tree algorithm / Nurnazifah Abd Mokti by Abd Mokti, Nurnazifah

“…The project involves data collection, data preparation, and the implementation of the decision tree algorithm for price prediction. The decision tree's effectiveness and accuracy in predicting laptop prices are evaluated through rigorous testing and validation. …”
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Diagnosis and recommender system for diabetes patient using decision tree / Nurul Aida Mohd Zamary by Mohd Zamary, Nurul Aida

“…The phase of this project is divided into data preprocessing, implementation of the decision tree algorithm, and evaluation of the algorithm and prototype. …”
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Classification with degree of importance of attributes for stock market data mining by Khokhar, Rashid Hafeez, Md. Sap, Mohd. Noor

“…The experimental results show that predictive FDT algorithm can generate a relatively optimal tree without much computation effort (comprehensibility), and WFPRs have a better predictive accuracy of stock market time series data. …”
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Implication of image processing algorithm in remote sensing and GIS applications by Pathan, Mubeena, Jusoff, Kamaruzaman, Mohd Sood, Alias, Razali, Yaakob, Qureshi, Barkatullah

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Electromagnetic fields policy implications for risk assessment by Farag A., Hussain H., Said I., Ahmad A.S.

Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…Therefore, feature selection using Relief-f with self-adaptive Differential Evolution (rsaDE) algorithm is proposed to select the most significant features. …”
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A comparison of decision tree, logistic regression, artificial neural network and random forest algorithms to predict suicidal ideation among young adults in Malaysia by Chan, Sin Yin, Ch’ng, Chee Keong

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Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

“…Practical implications In terms of managerial implications, the findings in this research help to frame the adoption of a more advanced analytical approach to forecasting, using a Machine Learning algorithm, in solving a newsvendor problem. …”
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Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids by Chiaverini, Luca, Macdonald, David W., Hearn, Andrew J., Kaszta, Zaneta, Ash, Eric, Bothwell, Helen M., Can, Ozgun Emre, Channa, Phan, Clements, Gopalasamy Reuben *, Haidir, Iding Achmad, Kyaw, Pyae Phyoe, Moore, Jonathan H., Rasphone, Akchousanh, Tan, Cedric Kai Wei, Cushman, Samuel A.

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Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Ahmad Salihin, Samsudin, Amir Izzani, Mohamed, Mohd Mawardi, Saari

“…Three distinct metaheuristic algorithms were investigated: Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA), each integrated with CatBoost to optimize critical parameters including learning rate, tree depth, regularization, and bagging temperature. …”
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Prediction of diabetic retinopathy among diabetic neuropathy in T2DM patients using data mining algorithm / Nur Balqis Oon ... [et al.] by Oon, Nur Balqis, Khairudin, Zuraida, Abd Rahman, Hezlin Aryani, Kamarudin, Norbaizura, Abu Bakar, Nur Syamimi, Abd Aziz, Nor Azimah

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Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar by Ja'afar, Nur Shahirah

“…To overcome these limitations, this research has proposed five machine learning algorithms namely Linear Regression, Lasso, Ridge, Random Forest and Decision Tree. …”
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XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection by Bouke, Mohamed Aly, Alramli, Omar Imhemed, Abdullah, Azizol

“…Traditional machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM) have been employed to mitigate this challenge. …”
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A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science by Balogun, A.-L., Tella, A., Baloo, L., Adebisi, N.

“…The study also revealed that machine learning algorithms such as random forest, gradient boosting machine, and classification and regression trees (CART) accurately predict air pollution hazard when integrated with spatial models. …”
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The future of social entrepreneurship: modelling and predicting social impact by Nur Azreen Zulkefly, Norjihan Abdul Ghani, Chin, Pei Yee, Suraya Hamid, Nor Aniza Abdullah

“…Design/methodology/approach: This study implemented an experimental method using three different algorithms: naive Bayes, k-nearest neighbor and J48 decision tree algorithms to develop and test the social impact prediction model. …”
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The predictive machine learning model of a hydrated inverse vulcanized copolymer for effective mercury sequestration from wastewater by Ghumman, A.S.M., Shamsuddin, R., Abbasi, A., Ahmad, M., Yoshida, Y., Sami, A., Almohamadi, H.

“…A predictive machine learning model was also developed to predict the amount of mercury removed () using GPR, ANN, Decision Tree, and SVM algorithms. Hyperparameter and loss function optimization was also carried out to reduce the prediction error. …”
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A semi-automated requirements prioritisation technique for scalable requirements with stakeholder quantification and prioritisation by Hujainah, Fadhl Mohammed Omar

“…Furthermore, the proposed SRPTackle is based on the combination of the proposed StakeQP technique, the constructed requirement priority value formulation function and the employing of classifying algorithm (K-means and K-means++) and binary search tree. …”
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