Search Results - (( java segmentation using algorithm ) OR ( waste evaluation learning algorithm ))

Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

“…Finally, the algorithm found, which would solve the image segmentation problem.…”
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Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

“…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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Development of seven segment display recognition using TensorFlow on Raspberry Pi by Gunawan, Teddy Surya, Mohammad Reduwan, Haque, Yaacob, Mashkuri, Mohamed Azmin, Nor Fadhillah, Ismail, Nanang, Ismail, Md Yusof, Kartiwi, Mira, A. Rahman, Azhar

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Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia by Latif S.D., Hazrin N.A.B., Younes M.K., Ahmed A.N., Elshafie A.

Medical Waste Detection and Classification Through YOLO Algorithms by Moktar M.H.B., Hajjaj S.S.H., Mohamed H.

Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works by Moktar M.H., Hajjaj S., Mohamed H., Weng L.Y.

An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification by Wan Azman,, Wan Nur Fadhlina Syamimi, Ku Azir, Ku Nurul Fazira, Mohd Khairuddin, Adam

“…After five days of storage, the freshness of cooked leftovers was evaluated using an electronic nose combined with machine learning algorithms. …”
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ESS-IoT: The Smart Waste Management System for General Household by Wong S.Y., Han H., Cheng K.M., Koo A.C., Yussof S.

A hybrid P-graph and WEKA approach in decision-making: waste conversion technologies selection by Ali, Rabiatul Adawiyah, Nik Ibrahim, Nik Nor Liyana, Wan Abdul Karim Ghani, Wan Azlina, Sani, Nor Samsiah, Hon, Loong Lam

“…Then, using the WEKA software, the data from the feasible structure would be processed and evaluated using the chosen algorithm. The J48 is the best model for equipment selection using an 80:20 ratio train and test learning technique in WEKA. …”
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Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater by Khan M.S.J., Sidek L.M., Kumar P., Alkhadher S.A.A., Basri H., Zawawi M.H., El-Shafie A., Ahmed A.N.

Predicting the rutting parameters of nanosilica/waste denim fiber composite asphalt binders using the response surface methodology and machine learning methods by Al-Sabaeei, Abdulnaser M., Alhussian, Hitham, Abdulkadir, Said Jadid, Giustozzi, Filippo, Mohd Jakarni, Fauzan, Md Yusoff, Nur Izzi

“…It is challenging to predict the mechanical properties of modified asphalt binders because of their complex nonlinear viscoelastic behavior. This study evaluates and compares the feasibility of using the response surface methodology (RSM) and machine learning (ML) methods to predict the shear strain, accumulated shear strain, non-recoverable creep compliance (Jnr), and percentage of recovery (%R) of the base binder, nanosilica (NS)- modified, waste denim fiber (WDF)-modified, and NS/WDF composite asphalt binders. …”
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Prediction of biochemical oxygen demand in Mexican surface waters using machine learning / Maximiliano Guzmán-Fernández ... [et al.] by Maximiliano, Guzmán-Fernández, Misael, Zambrano-de la Torre, Claudia, Sifuentes-Gallardo, Oscar, Cruz-Dominguez, Carlos, Bautista-Capetillo, Juan, Badillo-de Loera, Efrén, González Ramírez, Héctor, Durán-Muñoz

“…Two groups were formed and used as input to four machine learning algorithms. Random forest algorithm obtained the best performance. …”
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Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar by Abd Ghafar, Muhamad Azfar

“…The SLFN is trained by an algorithm named Extreme Learning Machine (ELM). The extracted features will be fed up into SLFN to classify the fault. …”
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Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar by Abd Ghafar, Muhamad Azfar

“…The SLFN is trained by an algorithm named Extreme Learning Machine (ELM). The extracted features will be fed up into SLFN to classify the fault. …”
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Automation of plastic waste sorting through robotic technology by Chong, Yoong Kiat

“…This project presents the design, development, fabrication and evaluation of an automated waste sorting system integrating computer vision, robotic actuation and electronic control. …”
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Diabetic retinopathy pathological signs detection using image enhancement technique and deep learning / Abdul Hafiz Abu Samah …[et al.] by Abu Samah, Abdul Hafiz, Ahmad, Fadzil, Osman, Muhammad Khusairi, Md Tahir, Noritawati, Idris, Mohaiyedin, Abd. Aziz, Nor Azimah

“…Therefore, it is time-wasting and risky for humans to make mistake. In general, this paper introduces an automated machine learning algorithm for detecting diabetic retinopathy (DR) in fundus images. …”
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Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management by almahameed, Bader aldeen, Bisharah, Majdi

“…This study examines the utilization of different Machine Learning algorithms, such as Linear Regression, Decision Trees, Support Vector Machines (SVM), Gradient Boosting, Random Forest, K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN) Regression, and Particle Swarm Optimization (PSO), in the domain of predictive modeling and cost optimization in the field of construction project management. …”
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Evaluation of machine learning classifiers in faulty die prediction to maximize cost scrapping avoidance and assembly test capacity savings in semiconductor integrated circuit (IC)... by Mohd Fazil, Azlan Faizal, Mohd Shaharanee, Izwan Nizal, Mohd Jamil, Jastini

“…In this research work, a new framework is proposed for model training and evaluation for the machine learning application in semiconductor test with objective to screen bad die using machine learning before die attachment to package. …”
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Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm by Fakharudin, Abdul Sahli

“…The EANN used a modified activation function to meet the optimisation requirement. To evaluate the EANN model, 19 samples of experimental data from Zainol on the regression modelling of biogas production from banana stem waste were selected. …”
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Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption by Zamani, Seyed Ali

“…The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
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