Search Results - (( mobile evaluation a algorithm ) OR ( _ classification model algorithm ))

Text spam messages classification using Artificial Immune System (AIS) algorithms by Nurul Fadhilah Sulaiman

ChoCD : Usable and secure graphical password authentication scheme by Radhi Rafiee Bin Afandi

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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The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

“…It was demonstrated from the simulation investigation that the CWT model could yield a better signal transformation amongst the preprocessing algorithms. …”
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Convolutional neural network based mobile application for poisonous mushroom detection by Amirul, Shazlin Nizam, Mohd Sabri, Norlina, Gloria, Jennis Tan, Redwan, Nurul Ainina, Zhiping, Zhang

“…CNN is one of the deep learning algorithms that is well known for its good performance in image recognition and classification. …”
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Image classification of Aedes mosquitoes using transfer learning / Zetty Ilham Abdullah by Abdullah, Zetty Ilham

“…In all combinations of the hyperparameters employed in the experiment, the use of the pretrained model MobileNetV2 for transfer learning surpasses the use of the pretrained model VGG16. …”
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Comparison of different deep learning object detection algorithms on fruit drying characterization by Umair, Mohammad Yamin, Norazlianie, Sazali, Kettner, Maurice, Mohd Azraai, Mohd Razman, Weiβ, Robert

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

The prediction of stock management for Farmasi Chendering by Gamal, Nurul Fatihah

“…This project focuses on Farmasi Chendering, aiming to develop a predictive stock management system using ABC-VEN analysis and the J48 decision tree algorithm. …”
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Reverse migration prediction model based on machine learning / Azreen Anuar by Anuar, Azreen

“…In addition, the results from the three (3) algorithms that were tested showed that Random Forest outperforms other algorithms by acquiring an accuracy and classification error to predict reverse migration. …”
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Teleworking monitoring system using NILM and K-NN algorithms: a strategy for sustainable smart cities by Yang, Chuan Choong, Noh, Adriana, Ibrahim, Siti Noorjannah, Asnawi, Ani Liza, Mohamed Azmin, Nor Fadhillah

“…This strategy uses non-intrusive load monitoring (NILM) approach to recognize four electrical appliances’ switching events used during teleworking measured from a single power point. Together with an event classification method known as K-Nearest Neighbor (k-NN) algorithm, the teleworking event and duration can be identified.The results were presented using classification metrics that consist of confusion matrix andaccuracy score. …”
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Automated Vehicle Classification (AVC) using machine learning implementation in Malaysia's toll system by Hassan, Raini, Mohd Ridzal, Aisyah Afiqah, Fadzleey, Nur Zulfah Insyirah

“…Thus, this project aims to develop the best model detector for an automated vehicle classification system using computer vision and machine learning algorithms to enhance toll collection efficiency. …”
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Malaysian license plate recognition system using Convolutional Neural Network (CNN) on web application / Nur Farahana Mahmud by Mahmud, Nur Farahana

“…Nowadays, there are numerous license plate recognition systems that have been developed and analysed effectively by previous researchers using different machine learning algorithms. However, according to a recent study, ANN algorithms require a huge amount of training data while BPFFNN algorithms only have an average success rate of 70% in recognizing all the characters. …”
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Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring by Abualsaud, Khalid, Mahmuddin, Massudi, Hussein, Ramy, Mohamed, Amr

“…DCT is combined with the best basis function neural networks for EEG signals classification.Extensive experimental work is conducted, utilizing four classification models.The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value.The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the application’s accuracy.…”
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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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Development of a new robust hybrid automata algorithm based on surface electromyography (SEMG) signal for instrumented wheelchair control by Mohd Hanafi, Muhammad Sidik

“…Based on algorithm evaluation, it shows that one control method couldn’t fit to all persons as per proven in method selection experiment. …”
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Evaluation tool of land suitability for medicinal plants by Rahim, Supli Effendi, Supli, Ahmad Affandi, Damiri, Nurhayati, Zaman, Chairil, Husin, Husin

“…A framework to evaluate the land suitability is initiated by FAO. …”
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AI chatbot system for educational institutions by Tan, Hui Hui

“…This project presents the design and development of an advanced chatbot system powered by deep learning algorithms for intent classification. The chatbot's primary goal is to facilitate effective communication and support for users, particularly students inquiring about admission processes. …”
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Ios Mobile Malware Analysis: A State-of-the-art by Madihah Mohd Saudi, Azuan Ahmad, Muhammad Afif Husainiamer, Mohd Yamani Idna Idris

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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