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Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms by Ahmad Pouradabi, Amir Rastegarnia, Azam Khalili, Ali Farzamnia

“…Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. …”
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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

“…One of the important assumptions of the linear model is that the error terms are normally distributed. Unfortunately, many researchers are not aware that the performance of the OLS can be very poor when the data set that one often makes a normal assumption, has a heavy-tailed distribution which may arise as a result of the presence of outliers. …”
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Data Analysis and Machine Learning Algorithms Evaluation for Bioliq AI-based Predictive Tool by Samuel Simbine, Augusto

“…This final year project identified relevant parameters through literature research, analysis and expert interview, and evaluated different machine learning algorithms and identified linear regression as the most applicable and efficient with its R-square of 0.8015, qualifying it to be used for the development of a hybrid model for the AI-based tool for predictive process optimization for chemical plants.…”
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An alternative approach to normal parameter reduction algorithms for decision making using a soft set theory / Sani Danjuma by Sani , Danjuma

“…Many normal parameter reduction algorithms exist to handle parameter reduction and maintain consistency of decision choices. …”
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Statistical approach on grading: mixture modeling by Md. Desa, Zairul Nor Deana

“…In the conditional Bayesian model, we assume the data to follow the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture distribution. …”
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Entropy in portfolio optimization / Yasaman Izadparast Shirazi by Yasaman Izadparast, Shirazi

“…More specifically, we use multi-objective models that are the mean-entropy-entropy (MEE). The purpose of this new model is to overcome the limitations as observed in a traditional model; that is, having performance close to Markowitz’s mean-variance (MV) model when data comes from a normal distribution, but exhibit better performance when data comes from a non-normal distribution. …”
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Confidence intervals (CI) for concentration parameter in von Mises distribution and analysis of missing values for circular data / Siti Fatimah binti Hassan by Hassan, Siti Fatimah

“…Directional statistics is a branch of statistics which deal with the data in angle form in which the method of analysis is different from linear data. For example, the distribution analogues to the normal distribution in linear data is known as circular normal distribution. …”
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TRMM Satellite Algorithm Estimates to Represent the Spatial Distribution of Rainstorms by Marina, Patrick, Mah, Yau Seng, Putuhena, Frederik Josep, Wang, Yin Chai, Onni Suhaiza, Selaman

“…These findings suggest that satellite algorithm estimations from TRMM are suitable to represent the spatial distribution of extreme rainfall.…”
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Statistical approach on grading the student achievement via normal mixture modeling by Md. Desa, Zairul Nor Deana, Mohamad, Ismail, Mohd. Khalid, Zarina, Md. Zin, Hanafiah

“…In the conditional Bayesian model, we assume the data to follow the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture distribution. …”
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Optimisation of multi-stage production-inspection stations using genetic algorithm by Hassan, Azmi, Pham, Duc Trung

“…The optimisation tool to be considered is Genetic Algorithm (GA). …”
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Fire severity and post-fire generation using : Landsat [NDVI], [NBR] and [SAVI] / Nur Izzaty Dali by Dali, Nur Izzaty

“…Pre-fire and post-fire of Landsat 7 ETM+ images were obtained to identify the fire severity using Normalized Burn Ratio algorithms. The objectives of this study are (1) to produce Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Soil Adjusted Vegetation Index (SAVI) and (2) to determine the changes of forest distribution based on NDVI, NBR and SAVI changes. …”
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Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems by Nor Sakinah, Abdul Shukor

“…Hence, a memory-based optimization tool has good potential to retain the optimal design parameter during the PID tuning process. …”
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Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems by Nor Sakinah, Abdul Shukor

“…Hence, a memory-based optimization tool has good potential to retain the optimal design parameter during the PID tuning process. …”
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Impact of evolutionary algorithm on optimization of nonconventional machining process parameters by B V, Raghavendra, R Annigiri, Anandkumar, Srikatamurthy, JS

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VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS. by FAROOQ, HUMERA

“…A tool termed as VIGA-20 (Visualization of Genetic Algorithm using 2-0 Graph) is implemented to accomplish this goal. …”
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A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru by Rahmad Ramadhan, Laska, Anne Mudya, Yolanda

“…However, Z-Score Normalization, sometimes referred to as Standardization, standardizes the data by dividing by the standard deviation and subtracting the mean, maintaining the shape of the distribution and making it resistant to outliers. …”
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Statistical approach on grading the student achievement via mixture modelling by Md. Desa, Zairul Nor Deana, Mohamad, lsmail

“…In the conditional Bayesian model, we assume the Normal Mixture distribution where the grades are distinctively separated means and proportions of the Normal Mixture distribution. …”
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Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer by Iqbal Basheer, Muhammmad Yunus

“…These algorithms are data-driven and do not require thresholds or predefined assumptions. …”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

“…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”

Developing A Prediction Tool To Improve The Shading Efficiency Of The Pedestrian Zones by Khudhayer, Wael A.

“…The development of the prediction tool was conducted base on integrating three sequenced algorithms, which are sun position algorithm, shadow length and position algorithm, and expansion limit algorithm. …”
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