Search Results - (( java application optimisation algorithm ) OR ( using learning drops algorithm ))

Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Machine learning methods for herschel-bulkley fluids in annulus: Pressure drop predictions and algorithm performance evaluation by Kumar, A., Ridha, S., Ganet, T., Vasant, P., Ilyas, S.U.

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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

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Recognising Malaysia traffic sign with rain drop disturbance using deep learning by Pang, Wan Chee

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Machine learning algorithms for early predicting dropout student online learning by Dewi, Meta Amalya, Kurniadi, Felix Indra, Murad, Dina Fitria, Rabiha, Sucianna Ghadati, Awanis, Romli

“…This study uses access log data recorded in the LMS and student statistical information and calculated data and aims to present a suitable predictive algorithm for dropout early prediction systems for online learning students using machine learning. …”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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Dropping down the maximum item set: improving the stylometric authorship attribution algorithm in the text mining for authorship investigation by Mustafa, Tareef Kamil, Mustapha, Norwati, Azmi Murad, Masrah Azrifah, Sulaiman, Md. Nasir

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A comparative study and simulation of object tracking algorithms by Ji, Yuanfa, Yin, Pan, Sun, Xiyan, Kamarul Hawari, Ghazali, Guo, Ning

“…The algorithms using convolution features and multi-features fusion algorithms have more advantages in tracking accuracy than the algorithm using a single feature, but the tracking speed will also drop rapidly. …”
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DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS by AYOUB MOHAMMED, MOHAMMED ABDALLA

“…The ANN model has been developed using resilient back-propagation learning algorithm. …”
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Musical instrument identification using Convolutional Neural Network (CNN) algorithm / Muhammad Nur Azri Irfan Abdul Rahman by Abdul Rahman, Muhammad Nur Azri Irfan

“…In the development phase, Convolutional Neural Network model was designed and trained using sophisticated techniques of data augmentation, dropping out and hyperparameter tuning under the supervised learning methodology to increase the performance of the system. …”
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Development of predictive modeling and deep learning classification of taxi trip tolls by Al-Shoukry, Suhad, M. Jawad, Bushra Jaber, Zalili, Musa, Sabry, Ahmad H.

“…Commercial navigation includes a wealth of trip-related data, including distance, expected journey time, and tolls that may be encountered along the way. Using a classification algorithm, it is possible to extract drop-off and pickup locations from taxi trip data and estimate if the tour would incur tolls. …”
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Experimental implementation controlled SPWM inverter based harmony search algorithm by Najeeb M., Mansor M., Razali R., Daniyal H., Yahaya J.A.F.

Network Traffic Classification Analysis on Differentiated Services Code Point Using Deep Learning Models for Efficient Deep Packet Inspection by Ahmed Khan, Fazeel, Abubakar, Adamu

“…The data was gathered using real-time packet capturing tools which were then processed and moved with model development using different deep learning algorithms such as, LSTM, MLP, RNN and Autoencoders. …”
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Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability by Bouke, Mohamed Aly, Abdullah, Azizol

“…Additionally, we find that some algorithms are more sensitive to data leakage than others, as seen by the drop in model accuracy when built without leakage. …”
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Automated traffic counting data collection and analysis by Low, Anand Hong Ren

“…This project proposed an automated traffic counting data collection and analysis algorithm that is able to use computer vision to count and measure the speed of vehicles, while also able to classify vehicles into different categories using the power of deep learning and AI. …”
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DEVELOPMENT OF PREDICTIVE MODELING AND DEEP LEARNING CLASSIFICATION OF TAXI TRIP TOLLS by Al-Shoukry S., Jawad B.J.M., Musa Z., Sabry A.H.

“…Commercial navigation includes a wealth of trip-related data, including distance, expected journey time, and tolls that may be encountered along the way. Using a classification algorithm, it is possible to extract drop-off and pickup locations from taxi trip data and estimate if the tour would incur tolls. …”

Predictive modelling of student academic performance using machine learning approaches : a case study in universiti islam pahang sultan ahmad shah by Nurul Habibah, Abdul Rahman

“…With a huge number of students drop out, the higher education institution’s reputation might be dropped. …”
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Indoor occupancy detection using machine learning and environmental sensors / Akindele Segun Afolabi ... [et al.] by Afolabi, Akindele Segun, Akinola, Olubunmi Adewale, Odetoye, Oyinlolu Ayomidotun, Adetiba, Emmanuel

“…In this paper, we experimentally determined the Machine Learning (ML) models that are most robust for use in indoor occupancy detection. …”
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Improving robotic grasping system using deep learning approach by Mohannad K. H., Farag

“…This research aimed to develop a Deep Learning grasp detection model and a slip detection algorithm and integrating them into one innovative robotic grasping system. …”
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