Search Results - (( developing systematic means algorithm ) OR ( java implication based algorithm ))

Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review by Diandra Chika Fransisca, Sukono, Diah Chaerani, Nurfadhlina Abdul Halim

Hand gesture recognition in ASD related motion using YOLOv8 algorithm / Muhammad Afiq Mohd Ali by Mohd Ali, Muhammad Afiq

“…The study focuses on designing a Hand Gesture Recognition application by means of the YOLOv8 deep learning algorithm to recognize hand gestures associated with autism spectrum disorder (ASD). …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

“…In cluster labelling process, a cluster labelling algorithm based on calculation of minimum-distance (MD) between cluster mean and class mean was developed to label the clusters. …”
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A systematic review of recurrent neural network adoption in missing data imputation by Nur Aqilah, Fadzil Akbar, Mohd Izham, Mohd Jaya, Mohd Faizal, Ab Razak, Nurul Aqilah, Zamri

“…Performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Area Under the Receiver Operating Characteristic Curve (AU-ROC), Mean Squared Error (MSE), and Mean Relative Error (MRE) are commonly used to evaluate these models. …”
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Collision avoidance mechanisms using artificial potential field for UAVs by Abdul Azis, Fadilah, Tan, Jie Sim, Md Ghazaly, Mariam, Mohamad Hanif, Noor Hazrin Hany

“…The developed simulation framework serves as a reproducible platform for benchmarking and future algorithmic extensions. …”
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A systematic review of recurrent neural network adoption in missing data imputation by Nur Aqilah, Fadzil Akbar, Mohd Izham, Mohd Jaya, Mohd Faizal, Ab Razak, Nurul Aqilah, Zamri

“…Performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Area Under the Receiver Operating Characteristic Curve (AU-ROC), Mean Squared Error (MSE), and Mean Relative Error (MRE) are commonly used to evaluate these models. …”
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Development of multi-objective optimization methods for integrated scheduling of handling equipment (AGVs, QCs, SP-AS/RS) in automated container terminals by Homayouni, Seyed Mahdi

“…Therefore, two meta-heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA) algorithm, were developed to optimize the integrated scheduling of handling equipment. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Lower Form Grade System (LF-Grade Sys) / Norhanisha Yusof ... [et al.] by Yusof, Norhanisha, A. Sanggar, Logineey, Kalidason, Tiivashkkar, Saravanan, Puvithra

“…Therefore, the objective of this study is to develop a Lower Form Grade System (LF-Grade Sys), which is a web-based application grading system that is designed to help teachers systematically record and store pupil grades. …”
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Survey on job scheduling mechanisms in grid environment by S. M., Argungu, Che Mohamed Arif, Ahmad Suki, Omar, Mohd Hasbullah

“…Grid systems provide geographically distributed resources for both computational intensive and data-intensive applications.These applications generate large data sets.However, the high latency imposed by the underlying technologies; upon which the grid system is built (such as the Internet and WWW), induced impediment in the effective access to such huge and widely distributed data.To minimize this impediment, jobs need to be scheduled across grid environments to achieve efficient data access.Scheduling multiple data requests submitted by grid users onto the grid environment is NP-hard.Thus, there is no best scheduling algorithm that cuts across all grids computing environments.Job scheduling is one of the key research area in grid computing.In the recent past many researchers have proposed different mechanisms to help scheduling of user jobs in grid systems.Some characteristic features of the grid components; such as machines types and nature of jobs at hand means that a choice needs to be made for an appropriate scheduling algorithm to march a given grid environment.The aim of scheduling is to achieve maximum possible system throughput and to match the application needs with the available computing resources.This paper is motivated by the need to explore the various job scheduling techniques alongside their area of implementation.The paper will systematically analyze the strengths and weaknesses of some selected approaches in the area of grid jobs scheduling.This helps researchers better understand the concept of scheduling, and can contribute in developing more efficient and practical scheduling algorithms.This will also benefit interested researchers to carry out further work in this dynamic research area.…”
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Tacit knowledge for business intelligence framework using cognitive-based approach by Surbakti, Herison

“…The approach is based on the theory of systematic functional linguistics, developed into interview protocols to be asked to tacit knowledge owners. …”
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Predictive modelling of nanofluids thermophysical properties using machine learning by Olanrewaju, Alade Ibrahim

“…This thesis aimed to develop machine learning algorithms to estimate the thermophysical properties of commonly used nanofluids. …”
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Machine learning-based risk prediction model for medication administration errors in neonatal intensive care units: a prospective direct observational study by Henry Basil, Josephine, Lim, Wern Han, Syed Ahmad, Sharifah M., Menon Premakumar, Chandini, Mohd Tahir, Nurul Ain, Mhd Ali, Adliah, Seman, Zamtira, Ishak, Shareena, Mohamed Shah, Noraida

“…Ten machine learning (ML) algorithms were applied with features derived from systematic reviews, incident reports, and expert consensus. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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Economic analysis of rehabilitation approaches for water distribution networks: comparative study between Egypt and Malaysia by Abdelrahman, M. Farouk, Rahimi, A. Rahman, Noor Suraya, Romali

“…This comparison which is developed depending on the systematic review could be a reference for future studies or surveys which could be done on different countries in the future. …”
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A design of precision linear z-transverse antenna positioner by Lim, Chun Khai

“…Besides, knowledge on software design is also of paramount important because the designed instrument run by own developed control algorithm. It is a positioner powered by a DC motor as an actuator which is equipped by a user friendly graphical user interface (GUI) written uniquely featured with several distinct and systematic operating modes and flexible data display. …”
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An improved diabetes risk prediction framework : An Indonesian case study by Sutanto, Daniel Hartono

“…Pre-processing resolves the issue of missing data and hence normalizes the data.Outlier treatment employs k-mean clustering to validate the class.Suitable components were selected through comparison of classifier algorithms and feature selection.Attribute weighting based feature selection was selected for assigning weightage.Weighted risk factor was used on training dataset in order to improve accuracy and computation time of the prediction. …”
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Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination by Nallakukkala, Sirisha, Tackie-Otoo, Bennet Nii, Aliyu, Ruwaida, Lal, Bhajan, Nallakukkala, Jagadish Ram Deepak, Devi, Gayathri

“…In this context. ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. …”
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Analyzing the cost-effectiveness of enhancement approaches for rehabilitating water distribution network by Abdel Rahman Mohamed Ismail, Farouk Abdel Mageed Rady

“…Additionally, the data from Malaysia suggest two more cost-effective enhancement approaches: zoning network and genetic algorithm. These two techniques might possess great potential for other developing countries, such as Egypt. …”
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