Search Results - (( _ evaluation between algorithm ) OR ( a classification learning algorithm ))*

Detection and classification of conflict flows in SDN using machine learning algorithms by Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah

“…Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. …”
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Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

“…Phase 1 is mainly to evaluate the performance of clustering algorithm (K-Means and FCM). …”
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Brain Tumour Classification using Deep Learning with Residual Attention Network: A Comparative Study by Abdulrazak Yahya, Saleh, Sashwini A/P S, Thiagaraju

“…The standard CNN algorithm does not perform well, with a very inconsistent accuracy of between 57% and 71%. …”
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Brain Tumour Classification using Deep Learning with Residual Attention Network : A Comparative Study by Abdulrazak Yahya, Saleh, Sashwini, S. Thiagaraju

“…The standard CNN algorithm does not perform well, with a very inconsistent accuracy of between 57% and 71%. …”
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Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer by Obaid, Omar Ibrahim, Mohammed, Mazin Abed, Abd Ghani, Mohd Khanapi, A. Mostafa, Salama, AL-Dhief, Fahad Taha

“…The experiments of this study have been carried out and managed in Matlab which has a special toolbox for machine learning algorithms.…”
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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

“…This proposed classifier achieved 97.9% classification accuracy on the ISIC dataset. In the third classification algorithm, hybrid features are extracted using AlexNet and VGG-16 through a transfer learning approach where parameter manipulation is implemented to simplify the network. …”
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Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms by Rachmad, Iqbal, Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Tata, Sutabri

“…It involves identifying and isolating relevant information from the images that classification algorithms can use to distinguish between different fruit categories. …”
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Maldroid- attribute selection analysis for malware classification by Rahiwan Nazar, Romli, Mohamad Fadli, Zolkipli, Mohd Zamri, Osman

“…Hence, the objective of this paper is to find the most effective and efficient attribute selection and classification algorithm in malware detection. Moreover, in order to get the best combination between attribute selection and classification algorithm, eight attributes selection and seven categories machine learning algorithm are applied in this study. …”
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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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Improved cuckoo search based neural network learning algorithms for data classification by Abdullah, Abdullah

“…It can also be used to improve the balance between exploration and exploitation of CS algorithm, and to increase the chances of the egg’s survival. …”
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Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms by Al-Rawashdeh, Mohammad, Al Nawaiseh, Moh’d, Yousef, Isam, Bisharah, Majdi, Alkhadrawi, Sajeda, Al-Bdour, Hamza

“…This study compares Bayesian Optimization-based machine learning systems that anticipate earthquake-damaged buildings and to evaluates building damage classification models. Using metrics, this study evaluates Random Forest, ElasticNet, and Decision Tree algorithms. …”
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The formulation of a transfer learning pipeline for the classification of the wafer defects by Lim, Shi Xuen

“…Thus far, there are still limited studies that investigate the classification of wafer defects using TL combined with a classical Machine learning (ML) pipeline. …”
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An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM) by Shamim, Akhtar

“…The scope of this study is tri folded, First, an exhaustive and parametric comparative study on a wide variety of machine learning algorithms is presented to evaluate the performance of machine learning algorithms in energy load prediction. …”
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification by Talpur, N., Abdulkadir, S.J., Hasan, M.H., Alhussian, H., Alwadain, A.

“…Machine learning (ML) practices such as classification have played a very important role in classifying diseases in medical science. …”
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Single classifer vs. ensemble machine learning approaches for mental health prediction by Jetli Chung, Jason Teo

“…One of the promising approaches to achieving fully automated computer-based approaches for predicting mental health problems is via machine learning. As such, this study aims to empirically evaluate several popular machine learning algorithms in classifying and predicting mental health problems based on a given data set, both from a single classifier approach as well as an ensemble machine learning approach. …”
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Multilevel learning in Kohonen SOM network for classification problems by Mohd. Yusof, Norfadzila

“…Self-organizing map (SOM) is a feed-forward neural network approach that uses an unsupervised learning algorithm has shown a particular ability for solving the problem of classification in pattern recognition. …”
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Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches by Amar Lokman, Wan Zakiah Wan Ismail, Nor Azlina Ab Aziz

Cross-project software defect prediction by Bala, Yahaya Zakariyau, Abdul Samat, Pathiah, Sharif, Khaironi Yatim, Manshor, Noridayu

“…In this work, five research questions covering the classification algorithms, dataset, independent variables, performance evaluation metrics used in CPDP studies, and as well as the performance of individual machine learning classification algorithms in predicting software defects across different software projects were addressed accordingly. …”
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Comparison of Gradient Boosting Decision Tree Algorithms for CPU Performance by Haithm Haithm, ALSHARI, Abdulrazak Yahya, Saleh, Alper, ODABAŞ

“…Gradient Boosting Decision Trees (GBDT) algorithms have been proven to be among the best algorithms in machine learning. …”
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Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning by Ismail, Amelia Ritahani, Azhary, Muhammad Zulhazmi Rafiqi, Hitam, Nor Azizah

“…Choosing a suitable optimization algorithm in deep learning is essential for effective model development as it significantly influences convergence speed, model performance, and the success of the train- ing process. …”
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