Search Results - (( variable learning mode algorithm ) OR ( java application path algorithm ))

Smart appointment organizer for mobile application / Mohd Syafiq Adam by Adam, Mohd Syafiq

“…In creating this application, NetBeans IDE 6.5and Java Micro Edition (Java ME) are used. …”
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Visdom: Smart guide robot for visually impaired people by Lee, Zhen Ting

“…The system architecture integrates ROS 2 on a Raspberry Pi, with TCP/IP connectivity enabling remote operation. An Android mobile application, developed using Java and the java.net.Socket library, provides an intuitive and accessible user interface for seamless interaction with the robot. …”
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Visdom: Smart guide robot for visually impaired people by Lee, Zhen Ting

“…The system architecture integrates ROS 2 on a Raspberry Pi, with TCP/IP connectivity enabling remote operation. An Android mobile application, developed using Java and the java.net.Socket library, provides an intuitive and accessible user interface for seamless interaction with the robot. …”
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SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA by AHMED IZZELDIN, HUZAIFA TAWFEIG

“…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
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The Effects Of Segmenting And Computational Thinking In Digital Video Courseware On Knowledge Achievement, Self-Efficacy And Motivation Among Students With Different Thinking Style... by Ali, Wan Nor Ashiqin Wan

“…This research used a quasi-experimental design using a 2 x 3 factorial. This study's variables include (i) two treatment modes, "DVC: Learner-paced predefined segment (DVC-LS)" and "DVC: System predefined segment (DVC-SS)"; (ii) knowledge achievement, self-efficacy, and motivation; and (iii) thinking style, which includes legislative, executive, and judicial. …”
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Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition by Ladouali S., Katipo?lu O.M., Bahrami M., Kartal V., Sakaa B., Elshaboury N., Keblouti M., Chaffai H., Ali S., Pande C.B., Elbeltagi A.

“…Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variational Mode Decomposition (VMD) technique and the Extreme Learning Machine (ELM) algorithm as a preprocessing technique for predicting future droughts. …”

Air quality forecasting and mapping in Malaysian urban areas: A hybrid deep learning approach by Nur'atiah Zaini

Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: a systematic review by Ibrahim, Buhari, Suppiah, Subapriya, Ibrahim, Normala, Mohamad, Mazlyfarina, Abu Hassan, Hasyma, Syed Nasser, Nisha, Saripan, M. Iqbal

“…We conducted a systematic review aimed at determining the diagnostic power of rs-fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. …”
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Evaluation of arima and ann stream analytics for air quality monitoring system by Nurmadiha, Osman

“…It is observed that the data in MySQL are successfully exported to the R query table based on the similar number of variables between those two tables. The data stored in the query table act as input to the analytics algorithm, which runs in R-server as well. …”
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Improvement of an integrated global positioning system and inertial navigation system for land navigation application by Hasan, Ahmed Mudheher

“…The integrated GPSIINS system is able to maintain satisfactory accuracy with the maximum error less than 0.82, 0.78, and 0.83 m for position and 0.0414, 0.0273, and 0.0415 m1s for velocity in all directions during maximum GPS outages of 200 second while it requires less than 9 and 5 seconds for learning mode in position and velocity respectively.…”
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