Search Results - (( model evaluation case algorithm ) OR ( _ classification modeling algorithm ))

Modified word representation vector based scalar weight for contextual text classification by Abbas Saliimi, Lokman

“…For this experiment, the modified word vectors serve as input to train a Machine Learning (ML) model for the text classification process, aiming for the developed ML model to have a significantly smaller parameter count. …”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

“…The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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Evaluation of different peak models of eye blink EEG for signal peak detection using artificial neural network by Adam, A., Ibrahim, Z., Mokhtar, N., Shapiai, M.I., Mubin, M.

“…This study evaluates the performance of eye blink EEG signal peak detection algorithm for four different peak models which are Dumpala's, Acir's, Liu's, and Dingle's peak models. …”
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Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network by Asrul, Adam, Zuwairie, Ibrahim, Mohd Ibrahim, Shapiai, Marizan, Mubin

“…This study evaluates the performance of eye blink EEG signal peak detection algorithm for four different peak models which are Dumpala’s, Acir’s, Liu’s, and Dingle’s peak models. …”
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Classification of imbalanced travel mode choice to work data using adjustable svm model by Qian, Y., Aghaabbasi, M., Ali, M., Alqurashi, M., Salah, B., Zainol, R., Moeinaddini, M., Hussein, E.E.

“…The SVMAK model outperformed these models, and in some cases improved the accuracy of the minority class classification. …”
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Modern fuzzy min max neural networks for pattern classification by Al Sayaydeh, Osama Nayel Ahmad

“…Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh by Kuldeep Kaur , Ragbir Singh

“…To evaluate the efficacy of the models, publicly available financial and credit card data sets are evaluated. …”
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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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Mining The Basic Reproduction Number (R0) Forecast For The Covid Outbreak by Rajogoval, Illayakantthan

“…The COVID-19 Basic Reproduction Number, R0 a predictive model is developed using a linear regression classification algorithm to predict the COVID-19 Basic Reproduction Number, Robased on the actual COVID-19 Basic Reproduction Number, R0. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…It was shown that up to 95.02% of the trained Random Forest Model could be classified, indicating that the established framework is viable for pallet classification. …”
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Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works by Moktar M.H., Hajjaj S., Mohamed H., Weng L.Y.

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Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad, Khyrina Airin Fariza Abu Samah and Nuwairah Aimi Ahmad... by Ahmad, Nurul Atirah, Abu Samah, Khyrina Airin Fariza, Ahmad Kushairi, Nuwairah Aimi

“…This project implements the Naive Bayes algorithm as the classification algorithm. The classification is set to two categories: Eligible or Ineligible. …”
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CNN-LSTM: hybrid deep neural network for network intrusion detection system; a case by Halbouni, Asmaa Hani, Gunawan, Teddy Surya, Habaebi, Mohamed Hadi, Halbouni, Murad, Kartiwi, Mira, Ahmad, Robiah

“…Based on the binary and multiclass classification, the model was trained using three datasets: CIC-IDS 2017, UNSW-NB15, and WSN-DS. …”
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Topic modelling analysis of depression therapy text: a preliminary study / Teh Faradilla Abdul Rahman ... [et al.] by Abdul Rahman, Teh Faradilla, Mohd Said, Raudzatul Fathiyah, Buja, Alya Geogiana, Mat Nayan, Norshita

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Deep learning algorithm and IoT based system for Photovoltaic (PV) defect images classification by Mazlan, Nurul Atikah

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ios Mobile Malware Deep Analysis And Classification For Social Media And Online Banking Exploitation by Muhammad ‘Afif Bin Husainiamer

Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset by Abd Rahman, M. Hafiz Fazren, Wan Salim, Wan Wardatul Amani, Abd-Wahab, Firdaus

“…Besides that, real world data are likely to be complex, incomplete and unorganized making it a challenge to develop models around it. This research aims to develop a robust prediction model for classification of type 2 diabetes mellitus (T2DM), with the interest of a Malaysian population, using several well-known machine learning algorithm such as Decision Tree, Support Vector Machine and Naïve Bayers. …”
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Feature engineering techniques to classify cause of death from forensic autopsy reports / Ghulam Mujtaba by Ghulam , Mujtaba

“…These master feature vectors were fed as input to six machine learning algorithms to construct and evaluate the classification models. …”
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Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications by Shayea G.G., Zabil M.H.M., Albahri A.S., Joudar S.S., Hamid R.A., Albahri O.S., Alamoodi A.H., Zahid I.A., Sharaf I.M.

“…In the context of autism spectrum disorder (ASD) triage, the robustness of machine learning (ML) models is a paramount concern. Ensuring the robustness of ML models faces issues such as model selection, criterion importance, trade-offs, and conflicts in the evaluation and benchmarking of ML models. …”

Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan by Meisam , Gordan

“…In the modeling phase, amongst all DM algorithms, the applicability of machine learning, artificial intelligence and statistical data mining techniques were examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to detect the hidden patterns in vibration data. …”
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