Search Results - (( surface validation using algorithm ) OR ( java implementation tree algorithm ))

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

“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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Validation on 3D surface roughness algorithm for measuring roughness of psoriasis lesion by Ahmad Fadzil, M Hani, Prakasa, Esa, Nugroho, Hermawan, Affandi, Azura Mohd, Hussein, Suraiya Hussein

“…Roughness algorithm is validated for roughness measurement on abrasive papers at flat surface. …”
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Feature identification in a real surface metrology analysis by means of Double Iteration Sobel (DIS) / Ainaa Farhanah Mohd Razali by Mohd Razali, Ainaa Farhanah

“…The system algorithm is verified and validates on two sloped bumps surface topographies that have been simulated using MATLAB software and measured using the Alicona Infinite Focus Measurement (IFM) device, respectively. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

“…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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Pemetaan Pm10 Dan Aot Menggunakan Teknik Penderiaan Jauh Di Semenanjung Malaysia by San, Limhwee

“…Average filtering was applied to the PM10 map to mininise the noise effect. The proposed algorithms were also validated using the multidate data. …”
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Concrete surface inspection by using unmanned aerial vehicle (UAV) and deep learning algorithms YOLOv7 / Saffa Nasuha Rusdinadi by Rusdinadi, Saffa Nasuha

“…These images are then processed using yolov7, a state-of-the-art object detection algorithm, to accurately identify and classify surface cracks. the study involves the collection of a comprehensive dataset of concrete surfaces with varying crack patterns, pre-processed using Roboflow and Opencv tools to enhance crack features. the annotated dataset is utilized to train and validate the yolov7 model, ensuring high precision which is 96.8% and 90.1% recall in crack detection. the performance of the model is evaluated through metrics such as precision, recall, and f1-score, demonstrating its robustness and reliability in detecting both fine and prominent cracks. …”
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Thin Film Roughness Optimization In The Tin Coatings Using Genetic Algorithms by Fauzi, Nur Faiqah, Mohamad Jaya, Abdul Syukor, Mohammad Jarrah, Mu’ath Ibrahim, Akbar, Habibullah

“…In RSM validation phase, the actual surface roughness fell within 90% prediction interval (PI). …”
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Concrete surface inspection by using Unmanned Aerial Vehicle (UAVs) and deep learning algorithms Yolov7 by Rusdinaidi, Saffa Nasuha, Hashim, Khairil Afendy, Ahmad Dahlan, Zaki

“…These images are then processed using Yolov7, a state-of-the-art object detection algorithm, to accurately identify and classify surface cracks. …”
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3D surface roughness measurement for scaliness scoring of psoriasis lesions by Ahmad Fadzil, M.H., Prakasa, E., Asirvadam, V.S., Nugroho, H., Affandi, A.M., Hussein, S.H.

“…A surface roughness algorithm using structured light projection has been applied to 1999 3D psoriasis lesion surfaces. …”
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Impact of evolutionary algorithm on optimization of nonconventional machining process parameters by B V, Raghavendra, R Annigiri, Anandkumar, Srikatamurthy, JS

“…The results of the three algorithms were validated through a benchmark study employing the Genetic Algorithm. …”
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Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.] by V. Chowdary, Boppana, Jahoor,, Riaz, Ali,, Fahraz, Trishel, Gokool

“…Surface roughness is often used as a measure to identify surface integrity of machined parts. …”
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Surface Moisture Content Retrieval from Visible/Thermal Infrared Images and Field Measurements by Abdalla HASSABALLA, Abdalhaleem, MATORI , Abdul Nasir, Mohd SHAFRI, Helmi Zulhaidi

“…Moisture content parameters were calculated, and then the moisture content algorithm was generated accordingly for the two study locations with three different “Split-window” algorithms and finally a spatial validation of satellite θ algorithms was conducted for accuracy assessment. …”
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Key schedule algorithm based on coordinate geometry of a three-dimensional hybrid cube by Mushtaq, Muhammad Faheem

“…The Hybrid Cube Encryption Algorithm (HiSea) has been implemented to validate the encryption keys that are generated from the proposed algorithm. …”
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Comparison of supervised classification technique of landuse map using high resolution image / Muhammad Firdaus Mohammad Harun by Mohammad Harun, Muhammad Firdaus

“…The algorithm were used to perform the supervised classification. …”
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A pond-surface-based Biofloc-farm health-monitoring system for African catfish using deep learning methods by Nizam Nordin

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HAPTIC VISUALIZATION USING VISUAL TEXTURE INFORMATION by Adi, Waskito

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Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology by Mohamad Jaya, Abdul Syukor, Muhamad, Mohd Razali, Abd Rahman, Md Nizam, Mohammad Jarrah, Mu'ath Ibrahim, Hasan Basari, Abd Samad

“…Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Surface and Near-surface Moisture Content Assessment using Multi-Temporal Satellite Images over Perak Tengah and Manjung Regions, Malaysia by A. Hassaballa , Abdalhaleem, Matori , Abdul Nasir, M. Shafri, Helmi Z.

“…Moisture content maps were generated from both satellites for each surface cover type then; generalized moisture maps were produced through correlating the three different surface cover types for each satellite using weightage method. …”
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High Order Polynomial Surface Fitting for Measuring Roughness of Psoriasis Lesion by Ahmad Fadzil, Mohd Hani

“…The surface roughness algorithm has been validated against 328 lesion models of known roughness on a medical mannequin. …”
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