Search Results - (( model evaluation a algorithm ) OR ( label classification model algorithm ))

Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout by Dzakiyullah, Nur Rachman

“…The study employed two MLC frameworks: Problem Transformation methods (Binary Relevance, Classifier Chains, Label Power Set, and Calibrated Label Ranking) and Algorithm Adaptation. …”
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A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms by Yu, Chiung Chang, A Hamid, Isredza Rahmi, Abdullah, Zubaile, Kipli, Kuryati, Amnur, Hidra

“…Deep learning requires high computation power and a large dataset to operate the classification model. …”
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Modified word representation vector based scalar weight for contextual text classification by Abbas Saliimi, Lokman

“…The evaluation showcases significant improvements, with the test ML model achieving a best accuracy score of 0.571, a 46% increase from the baseline, and a best F1 score of 0.727, a 30% increment from the baseline. …”
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Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer by Tuerxun, Adilijiang

“…Furthermore, the Lazy-IBK algorithm have been validated to produce the best classifier model, with the machine learning algorithm performance of 65.26%, recall of 65.3%, and 65.4% F-measured as compared to other evaluated machine learning classifier algorithms proposed within the WEKA data mining algorithm. …”
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Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad by Ahmad, Nurul Atirah

“…This project implements the Naive Bayes algorithm as the classification algorithm. The collected data from LinkedIn profiles then undergoes data preprocessing. …”
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Semi-supervised learning for sentiment classification with ensemble multi-classifier approach by Aribowo, Agus Sasmito, Basiron, Halizah, Abd Yusof, Noor Fazilla

“…Supervised sentiment analysis ideally uses a fully labeled data set for modeling. However, this ideal condition requires a struggle in the label annotation process. …”
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Breast Cancer Prediction Model Using Machine Learning by Muhammad Amin, Bakri, Inna, Ekawati

“…Evaluation of the classification performance of each algorithm is carried out by analysing its sensitivity, specificity, and accuracy. …”
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On the training sample size and classification performance: An experimental evaluation in seismic facies classification by Babikir, I., Elsaadany, M., Sajid, M., Laudon, C.

“…We trained and evaluated support vector machine (SVM), random forest (RF), and neural network (NN) models using a 10-fold cross-validation (CV) procedure. …”
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A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network by Ridwan M.A., Radzi N.A.M., Azmi K.H.M., Abdullah F., Ahmad W.S.H.M.W.

“…The research is divided into three parts, which are (1) dataset development, (2) algorithm development, and (3) algorithm performance evaluation. …”

Improving multi-resident activity recognition in smart home using multi label classification with adaptive profiling by Mohamed, Raihani

“…When the data are induced with the lower quality model, the performance is also truncated. Furthermore, there is tendency that multi label classifications used instead of traditional single label classification technique. …”
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Customer analysis with machine vision by Tiong, Wei Jie

“…Each selected model is then retrained, evaluated and compared to the existing models. …”
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Dynamic android malware category classification using semi-supervised deep learning by Mahdavifar, Samaneh, Kadir, Andi Fitriah Abdul, Fatemi, Rasool, Alhadidi, Dima, Ghorbani, Ali A

“…Our offered dataset comprises the most complete captured static and dynamic features among publicly available datasets. We evaluate our proposed model on CICMalDroid2020 and conduct a comparison with Label Propagation (LP), a well-known semi-supervised machine learning technique, and other common machine learning algorithms. …”
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The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers by Zhang, Xiaolei, Iqbal bin Saripan, M., Wu, Yanjun, Wang, Zhongxiao, Wen, Dong, Cao, Zhendong, Wang, Bingzhen, Xu, Shiqi, Liu, Yanli, Marhaban, Mohammad Hamiruce, Dong, Xianling

“…The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. …”
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Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification by Ashraf, Arselan, Gunawan, Teddy Surya, Arifin, Fatchul, Kartiwi, Mira, Sophian, Ali, Habaebi, Mohamed Hadi

“…To address these issues, we propose a comprehensive CNN-based model for real-time detection and classification of five primary emotions: anger, happiness, neutrality, sadness, and surprise. …”
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Fake news detection: A machine learning approach by Yeoh, Dennis Guan Lee

“…The final model chosen to be deployed was a model trained using a Multinomial Naïve Bayes algorithm.…”
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A direct ensemble classifier for learning imbalanced multiclass data by Samry @ Mohd Shamrie Sainin

“…Many real-world multiclass classification problems can be represented into a setting where non-crisp label need to be observed. …”
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Context enrichment framework for sentiment analysis in handling word ambiguity resolution by Yusof, Nor Nadiah

“…The evaluation of WAR’s performance includes a comparison with the baseline model and evaluating the accuracy through the summation approach, which is based on aligning word polarity values with the document's assigned class label. …”
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Automated Detection and Classification of Retinal Vein Occlusion Using Ultra-widefield Retinal Fundus Images and Transfer Learning by Ivy Ong Siaw Yin, Ong

“…The approach seeks to utilise knowledge from pretraining to enhance the performance of the segmentation model. The study also evaluates the classification model trained with lesion masks to classify images accurately into the respective categories. …”
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Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques by Hassan Zaidi, Syed Muhammad, Jamil Alsayaydeh, Jamil Abedalrahim, Khan, Abdul Hafeez, Khan, Abdullah Ayub, AlZubi, Ahmad Ali, Ogunshola, Benny, Herawan, Safarudin Gazali

“…However, the automatic classification of chart images remains a significant challenge, particularly in the absence of labeled data. …”
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A coherent knowledge-driven deep learning model for idiomatic - aware sentiment analysis of unstructured text using Bert transformer by Bashar M. A., Tahayna

“…Machine learning and deep neural networks have shown promise in accurately representing and classifying sentiment, but they require large amounts of labeled data to train the models. In this context, the proposed novel strategy aims to eliminate the need for human labeling of the idiomatic lexicon and fine-tuning the classifier to handle the sentiment classification of tweets containing idiomatic expressions. …”
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