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Journal articles on the topic 'Min-max normalization'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Sinsomboonthong, Saichon. "Performance Comparison of New Adjusted Min-Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification." International Journal of Mathematics and Mathematical Sciences 2022 (April 22, 2022): 1–9. http://dx.doi.org/10.1155/2022/3584406.

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In this research, the normalization performance of the proposed adjusted min-max methods was compared to the normalization performance of statistical column, decimal scaling, adjusted decimal scaling, and min-max methods, in terms of accuracy and mean square error of the final classification outcomes. The evaluation process employed an artificial neural network classification on a large variety of widely used datasets. The best method was min-max normalization, providing 84.0187% average ranking of accuracy and 0.1097 average ranking of mean square error across all six datasets. However, the p

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Firmansyah, Muhammad Raihan. "Stroke Classification Comparison with KNN through Standardization and Normalization Techniques." Advance Sustainable Science, Engineering and Technology 6, no. 1 (2024): 02401012. http://dx.doi.org/10.26877/asset.v6i1.17685.

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This study explores the impact of z-score standardization and min-max normalization on K-Nearest Neighbors (KNN) classification for strokes. Focused on managing diverse scales in health attributes within the stroke dataset, the research aims to improve classification model accuracy and reliability. Preprocessing involves z-score standardization, min-max normalization, and no data scaling. The KNN model is trained and evaluated using various methods. Results reveal comparable performance between z-score standardization and min-max normalization, with slight variations across data split ratios.

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Anusas-Amornkul, Tanapat, and Naphat Bussabong. "Normalization Technique and Weight Adjustment Analysis for Keystroke Vector Dissimilarity Authentication." WSEAS TRANSACTIONS ON SYSTEMS 23 (September 23, 2024): 206–14. http://dx.doi.org/10.37394/23202.2024.23.23.

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A keystroke dynamics authentication uses keystroke rhythm for each user on a keyboard to verify a real user. The idea is that each user has a unique keystroke rhythm such that it can be determined the identity of a user. To verify a user, a keystroke vector dissimilarity technique was proposed to use keystroke features as a vector and calculate a weight using SoftMax+1 to overcome the Euclidean distance problem. However, the weight has yet to be analyzed in detail. Therefore, this paper aims to find a normalization technique and a weight adjustment to enhance the accuracy of the keystroke vect

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Allorerung, Petronilia Palinggik, Angdy Erna, Muhammad Bagussahrir, and Samsu Alam. "Analisis Performa Normalisasi Data untuk Klasifikasi K-Nearest Neighbor pada Dataset Penyakit." JISKA (Jurnal Informatika Sunan Kalijaga) 9, no. 3 (2024): 178–91. http://dx.doi.org/10.14421/jiska.2024.9.3.178-191.

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This study investigates four normalization methods (Min-Max, Z-Score, Decimal Scaling, MaxAbs) across prostate, kidney, and heart disease datasets for K-Nearest Neighbor (K-NN) classification. Imbalanced feature scales can hinder K-NN performance, making normalization crucial. Results show that Decimal Scaling achieves 90.00% accuracy in prostate cancer, while Min-Max and Z-Score yield 97.50% in kidney disease. MaxAbs performs well with 96.25% accuracy in kidney disease. In heart disease, Min-Max and MaxAbs attain accuracies of 82.93% and 81.95%, respectively. These findings suggest Decimal Sc

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Muhammad Ali, Peshawa J. "Investigating the Impact of Min-Max Data Normalization on the Regression Performance of K-Nearest Neighbor with Different Similarity Measurements." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 10, no. 1 (2022): 85–91. http://dx.doi.org/10.14500/aro.10955.

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K-nearest neighbor (KNN) is a lazy supervised learning algorithm, which depends on computing the similarity between the target and the closest neighbor(s). On the other hand, min-max normalization has been reported as a useful method for eliminating the impact of inconsistent ranges among attributes on the efficiency of some machine learning models. The impact of min-max normalization on the performance of KNN models is still not clear, and it needs more investigation. Therefore, this research examines the impacts of the min-max normalization method on the regression performance of KNN models

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Wahyusari, Retno, Sunardi Sunardi, and Abdul Fadlil. "Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption." Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) 7, no. 1 (2025): 11. https://doi.org/10.28989/avitec.v7i1.2722.

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This research investigates how to accurately predict electrical energy consumption to address growing global energy demands. The study employs three Machine Learning (ML) models: k-Nearest Neighbors (KNN), Random Forest (RF), and CatBoost. To enhance prediction accuracy, the researchers included a data pre-processing step using min-max normalization. The analysis utilized a dataset containing 52,416 records of power consumption from Tetouan City. The dataset was divided into training and testing sets using different ratios (90:10, 80:20, 50:50) to evaluate model performance. Root Mean Square E

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Prasetyowati, Sri Arttini Dwi, Munaf Ismail, and Badieah Badieah. "Implementation of Least Mean Square Adaptive Algorithm on Covid-19 Prediction." JUITA: Jurnal Informatika 10, no. 1 (2022): 139. http://dx.doi.org/10.30595/juita.v10i1.11963.

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This study used Corona Virus Disease-19 (Covid-19) data in Indonesia from June to August 2021, consisting of data on people who were infected or positive Covid-19, recovered from Covid-19, and passed away from Covid-19. The data were processed using the adaptive LMS algorithm directly without pre-processing cause calculation errors, because covid-19 data was not balanced. Z-score and min-max normalization were chosen as pre-processing methods. After that, the prediction process can be carried out using the LMS adaptive method. The analysis was done by observing the error prediction that occurr

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Ambarwari, Agus, Qadhli Jafar Adrian, and Yeni Herdiyeni. "Analysis of the Effect of Data Scaling on the Performance of the Machine Learning Algorithm for Plant Identification." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 1 (2020): 117–22. http://dx.doi.org/10.29207/resti.v4i1.1517.

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Data scaling has an important role in preprocessing data that has an impact on the performance of machine learning algorithms. This study aims to analyze the effect of min-max normalization techniques and standardization (zero-mean normalization) on the performance of machine learning algorithms. The stages carried out in this study included data normalization on the data of leaf venation features. The results of the normalized dataset, then tested to four machine learning algorithms include KNN, Naïve Bayesian, ANN, SVM with RBF kernels and linear kernels. The analysis was carried out on the

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Ahmed, Haval A., Peshawa J. Muhammad Ali, Abdulbasit K. Faeq, and Saman M. Abdullah. "An Investigation on Disparity Responds of Machine Learning Algorithms to Data Normalization Method." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 10, no. 2 (2022): 29–37. http://dx.doi.org/10.14500/aro.10970.

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Data normalization can be useful in eliminating the effect of inconsistent ranges in some machine learning (ML) techniques and in speeding up the optimization process in others. Many studies apply different methods of data normalization with an aim to reduce or eliminate the impact of data variance on the accuracy rate of ML-based models. However, the significance of this impact aligning with the mathematical concept of the ML algorithms still needs more investigation and tests. To identify that, this work proposes an investigation methodology involving three different ML algorithms, which are

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Gde Agung Brahmana Suryanegara, Adiwijaya, and Mahendra Dwifebri Purbolaksono. "Peningkatan Hasil Klasifikasi pada Algoritma Random Forest untuk Deteksi Pasien Penderita Diabetes Menggunakan Metode Normalisasi." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (2021): 114–22. http://dx.doi.org/10.29207/resti.v5i1.2880.

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Diabetes is a disease caused by high blood sugar in the body or beyond normal limits. Diabetics in Indonesia have experienced a significant increase, Basic Health Research states that diabetics in Indonesia were 6.9% to 8.5% increased from 2013 to 2018 with an estimated number of sufferers more than 16 million people. Therefore, it is necessary to have a technology that can detect diabetes with good performance, accurate level of analysis, so that diabetes can be treated early to reduce the number of sufferers, disabilities, and deaths. The different scale values for each attribute in Gula Kar

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Wibawa, Aji Prasetya, Ade Kurnia Ganesh Akbari, Akhmad Fanny Fadhilla, et al. "Forecasting Hourly Energy Fluctuations Using Recurrent Neural Network (RNN)." Frontier Energy System and Power Engineering 5, no. 2 (2024): 50. http://dx.doi.org/10.17977/um049v5i2p50-57.

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Energy forecasting is currently essential due to its various benefits. Energy data analysis for forecasting requires a functional method due to the complexity of the observed data. This forecasting study used the Recurrent Neural Networks (RNN) method. Parameters used include batch size, epoch, hidden layers, loss function, and optimizer obtained from hyperparameter tuning grid search. A comparison of different normalization methods, namely min-max, and z-score, was conducted. Using min-max normalization yielded the best performance with MAPE of 3.9398%, RMSE of 0.0630, and R2 of 0.8996. In te

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Pranolo, Andri, Faradini Usha Setyaputri, Andien Khansa’a Iffat Paramarta, et al. "Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques." ILKOM Jurnal Ilmiah 16, no. 2 (2024): 210–20. https://doi.org/10.33096/ilkom.v16i2.2333.210-220.

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The primary objective of this study is to analyze multivariate time series data by employing the Long Short-Term Memory (LSTM) model. Deep learning models often face issues when dealing with multivariate time series data, which is defined by several variables that have diverse value ranges. These challenges arise owing to the potential biases present in the data. In order to tackle this issue, it is crucial to employ normalization techniques such as min-max and z-score to guarantee that the qualities are standardized and can be compared effectively. This study assesses the effectiveness of the

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Rodríguez, Carlos Gervasio, María Isabel Lamas, Juan de Dios Rodríguez, and Claudio Caccia. "ANALYSIS OF THE PRE-INJECTION CONFIGURATION IN A MARINE ENGINE THROUGH SEVERAL MCDM TECHNIQUES." Brodogradnja 72, no. 4 (2021): 1–17. http://dx.doi.org/10.21278/brod72401.

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The present manuscript describes a computational model employed to characterize the performance and emissions of a commercial marine diesel engine. This model analyzes several pre-injection parameters, such as starting instant, quantity, and duration. The goal is to reduce nitrogen oxides (NOx), as well as its effect on emissions and consumption. Since some of the parameters considered have opposite effects on the results, the present work proposes a MCDM (Multiple-Criteria Decision Making) methodology to determine the most adequate pre-injection configuration. An important issue in MCDM model

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Wibawa, Aji Prasetya, Alfiansyah Putra Pertama Triono, Andien Khansa’a Iffat Paramarta, et al. "Gated Recurrent Unit (GRU) for Forecasting Hourly Energy Fluctuations." Frontier Energy System and Power Engineering 5, no. 1 (2024): 16. http://dx.doi.org/10.17977/um049v5i1p16-25.

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In the current digital era, energy use undeniably supports economic growth, increases social welfare, and encourages technological progress. Energy-related information is often presented in complex time series data, such as energy consumption data per hour or in seasonal patterns. Deep learning models are used to analyze the data. The right choice of normalization method has great potential to improve the performance of deep learning models significantly. Deep learning models generally use several normalization methods, including min-max and z-score. In this research, the deep learning model c

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Dwianti, Westari, and Abdul Halim Dr. "Performa Comparison of the K-Means Method for Classification in Diabetes Patients Using Two Normalization Methods." International Journal of Multidisciplinary Research and Analysis 04, no. 01 (2021): 18–23. https://doi.org/10.47191/ijmra/v4-i1-03.

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The diabetes classification system is very useful in the health sector. This paper discusses the classification system for diabetes using the K-Means algorithm. The Pima Indian Diabetes (PID) dataset is used to train and evaluate this algorithm. The unbalanced value range in the attributes affects the quality of the classification result, so it is necessary to preprocess the data which is expected to improve the accuracy of the PID dataset classification result. Two types of preprocessing methods are used that are min-max normalization and z-score normalization. These two normalization methods

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Jin, Jian, Ming Li, and Long Jin. "Data Normalization to Accelerate Training for Linear Neural Net to Predict Tropical Cyclone Tracks." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/931629.

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When pure linear neural network (PLNN) is used to predict tropical cyclone tracks (TCTs) in South China Sea, whether the data is normalized or not greatly affects the training process. In this paper, min.-max. method and normal distribution method, instead of standard normal distribution, are applied to TCT data before modeling. We propose the experimental schemes in which, with min.-max. method, the min.-max. value pair of each variable is mapped to (−1, 1) and (0, 1); with normal distribution method, each variable’s mean and standard deviation pair is set to (0, 1) and (100, 1). We present t

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OTA, Y., and B. M. WILAMOWSKI. "CMOS IMPLEMENTATION OF A VOLTAGE-MODE FUZZY MIN-MAX CONTROLLER." Journal of Circuits, Systems and Computers 06, no. 02 (1996): 171–84. http://dx.doi.org/10.1142/s0218126696000145.

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In this paper, a general-purpose fuzzy min-max network using a Gaussian-type membership function fuzzifier is proposed. Particularly, CMOS implementations of the Gaussian-type membership function fuzzifier circuits, min-max operators, and the defuzzifier circuit are analyzed. Programmability of the proposed Gaussian-type function fuzzifier can be achieved by changing the gate voltages and the sizes of transistors in the differential pairs. A closed-loop control scheme is used between the fuzzifier and defuzzifier blocks to compensate the global normalization of the denominator in the division

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Shantal, Mohammed, Zalinda Othman, and Azuraliza Abu Bakar. "A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min–Max Normalization." Symmetry 15, no. 12 (2023): 2185. http://dx.doi.org/10.3390/sym15122185.

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In the realm of data analysis and machine learning, achieving an optimal balance of feature importance, known as feature weighting, plays a pivotal role, especially when considering the nuanced interplay between the symmetry of data distribution and the need to assign differential weights to individual features. Also, avoiding the dominance of large-scale traits is essential in data preparation. This step makes choosing an effective normalization approach one of the most challenging aspects of machine learning. In addition to normalization, feature weighting is another strategy to deal with th

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Phyo, Ei Ei, and Ei Ei Myat. "Efficient K-Means Clustering Algorithm Using Feature Weight and Min-Max Normalization." International Journal of Science and Engineering Applications 7, no. 12 (2018): 479–82. http://dx.doi.org/10.7753/ijsea0712.1001.

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Pranolo, Andri, Xiaofeng Zhou, Yingchi Mao, et al. "Exploring LSTM-based Attention Mechanisms with PSO and Grid Search under Different Normalization Techniques for Energy demands Time Series Forecasting." Knowledge Engineering and Data Science 7, no. 1 (2024): 1. http://dx.doi.org/10.17977/um018v7i12024p1-12.

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Advanced analytical approaches are required to accurately forecast the energy sector's rising complexity and volume of time series data. This research aims to forecast the energy demand utilizing sophisticated Long Short-Term Memory (LSTM) configurations with Attention mechanisms (Att), Grid search, and Particle Swarm Optimization (PSO). In addition, the study also examines the influence of Min-Max and Z-Score normalization approaches in the preprocessing stage on the accuracy performances of the baselines and the proposed models. PSO and Grid Search techniques are used to select the best hype

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Mhlanga, Sandile Thamie, and Manoj Lall. "Influence of Normalization Techniques on Multi-criteria Decision-making Methods." Journal of Physics: Conference Series 2224, no. 1 (2022): 012076. http://dx.doi.org/10.1088/1742-6596/2224/1/012076.

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Abstract One of the most significant steps in solving multi criteria decision-making (MCDM) problems is the normalization of the decision matrix. The consideration for the normalization of the data in a judgment matrix is an essential step as it can influence the ranking list. This study investigates the effects of normalization on an AHP-VIKOR hybrid method in the selection of Web services. The Web services considered in this research offer similar functionalities but with different Quality of Services (QoS). For the purpose of this study, ten web services were selected. Each of these service

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Nasution, Darnisa Azzahra, Hidayah Husnul Khotimah, and Nurul Chamidah. "Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN." Computer Engineering, Science and System Journal 4, no. 1 (2019): 78. http://dx.doi.org/10.24114/cess.v4i1.11458.

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Abstrak— Rentang nilai yang tidak seimbang pada setiap atribut dapat mempengaruhi kualitas hasil data mining. Untuk itu diperlukan adanya praproses data. Praproses ini diharapkan dapat meningkatkatkan keakuratan hasil dari pengklasifikasian dataset wine. Metode praproses yang digunakan adalah transformasi data dengan normalisasi. Ada tiga cara yang dilakukan dalam transformasi data dengan normalisasi, yaitu min-max normalization, z-score normalization, dan decimal scaling. Data yang telah diproses dari setiap metode normalisasi akan dibandingan untuk melihat hasil akurasi terbaik klasifikasi d

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Masitha, Alya, Nurul Huda, Deden Istiawan, and Lucky Nur Rohman Firdaus. "Evaluation and Comparison of K-Nearest Neighbors Algorithm Models for Heart Failure Prediction." Building of Informatics, Technology and Science (BITS) 6, no. 3 (2024): 1332–40. https://doi.org/10.47065/bits.v6i3.5925.

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Heart failure is a disease that is one of the most crucial in the world. Researchers have used several machine learning techniques to assist health professionals in the diagnosis of heart failure. K-NN is a technique of supervised learning algorithm that has been successfully used in terms of classification. However, using the K-NN algorithm has stages in terms of data analysis. The data used must also be processed in such a way that it becomes data that is easier to analyse and that the results obtained are also more accurate. Data pre-processing involves transforming raw data into a format t

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Kim, Hyun-Jin, Ji-Won Baek, and Kyungyong Chung. "Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents." IEEE Access 9 (2021): 74802–16. http://dx.doi.org/10.1109/access.2021.3080180.

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Munkhdalai, Lkhagvadorj, Tsendsuren Munkhdalai, Kwang Ho Park, Heon Gyu Lee, Meijing Li, and Keun Ho Ryu. "Mixture of Activation Functions With Extended Min-Max Normalization for Forex Market Prediction." IEEE Access 7 (2019): 183680–91. http://dx.doi.org/10.1109/access.2019.2959789.

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Vafaei, Nazanin, Rita A. Ribeiro, and Luis M. Camarinha-Matos. "Comparison of Normalization Techniques on Data Sets With Outliers." International Journal of Decision Support System Technology 14, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/ijdsst.286184.

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With the fast growing of data-rich systems, dealing with complex decision problems with skewed input data sets and respective outliers is unavoidable. Generally, data skewness refers to a non-uniform distribution in a dataset, i.e. a dataset which contains asymmetries and/or outliers. Normalization is the first step of most multi-criteria decision making (MCDM) problems to obtain dimensionless data, from heterogeneous input data sets, that enable aggregation of criteria and thereby ranking of alternatives. Therefore, when in presence of outliers in criteria datasets, finding a suitable normali

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Calbet, J. A. L., R. Boushel, G. Rådegran, H. Søndergaard, P. D. Wagner, and B. Saltin. "Why is V˙o 2 max after altitude acclimatization still reduced despite normalization of arterial O2 content?" American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 284, no. 2 (2003): R304—R316. http://dx.doi.org/10.1152/ajpregu.00156.2002.

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Acute hypoxia (AH) reduces maximal O2 consumption (V˙o 2 max), but after acclimatization, and despite increases in both hemoglobin concentration and arterial O2 saturation that can normalize arterial O2 concentration ([O2]),V˙o 2 max remains low. To determine why, seven lowlanders were studied at V˙o 2 max(cycle ergometry) at sea level (SL), after 9–10 wk at 5,260 m [chronic hypoxia (CH)], and 6 mo later at SL in AH (Fi O2 = 0.105) equivalent to 5,260 m. Pulmonary and leg indexes of O2 transport were measured in each condition. Both cardiac output and leg blood flow were reduced by ∼15% in bot

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Mandailina, Vera, Abdillah Abdillah, and Syaharuddin Syaharuddin. "Analisis Tingkat Akurasi Variasi Algoritma Min-Max Backpropagation sebagai Pre-Processing Data Time Series." Techno.Com 22, no. 2 (2023): 290–300. http://dx.doi.org/10.33633/tc.v22i2.7995.

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Forecasting data does not have to be static, there are also data with high fluctuations with up and down trends. Therefore, data normalization techniques are very important before training and testing data. This paper aims to test eight types of Min-Max backpropagation algorithms with several types of data, namely static data, seational data, monotonically fluctuating data up and down. A backpropagation network architecture with three hidden layers is used to test these data. The test results show that the 6th Min-Max algorithm has a high level of accuracy. Furthermore, the results of the 6th

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Zhang, L., M. Xu, M. Huang, and G. Yu. "Reducing impacts of systematic errors in the observation data on inversing ecosystem model parameters using different normalization methods." Biogeosciences Discussions 6, no. 6 (2009): 10447–77. http://dx.doi.org/10.5194/bgd-6-10447-2009.

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Abstract. Modeling ecosystem carbon cycle on the regional and global scales is crucial to the prediction of future global atmospheric CO2 concentration and thus global temperature which features large uncertainties due mainly to the limitations in our knowledge and in the climate and ecosystem models. There is a growing body of research on parameter estimation against available carbon measurements to reduce model prediction uncertainty at regional and global scales. However, the systematic errors with the observation data have rarely been investigated in the optimization procedures in previous

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Le, Vu-anh, and Mehmet Dik. "Topology-Preserving Scaling in Data Augmentation." Maltepe Journal of Mathematics 7, no. 1 (2025): 9–26. https://doi.org/10.47087/mjm.1615296.

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We propose an algorithmic framework for dataset normalization in data augmentation pipelines that preserves topological stability under non-uniform scaling transformations. Given a finite metric space \( X \subset \mathbb{R}^n \) with Euclidean distance \( d_X \), we consider scaling transformations defined by scaling factors \( s_1, s_2, \ldots, s_n > 0 \). Specifically, we define a scaling function \( S \) that maps each point \( x = (x_1, x_2, \ldots, x_n) \in X \) to \[ S(x) = (s_1 x_1, s_2 x_2, \ldots, s_n x_n). \] Our main result establishes that the bottleneck distance \( d_B(D, D_S)

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Hameed, S. M. A. Shahul, M. Shahul Hameed, and V. Kamal Nasir. "Using Fuzzy Ranking Technique to Find the Best Traits and Peak Age Group of Electors in an Election." Indian Journal Of Science And Technology 17, no. 23 (2024): 2363–69. http://dx.doi.org/10.17485/ijst/v17i23.2460.

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Objective: To predict the peak-age of age group of voters who turn the table of election results and also to rank the attribute which people look from their candidates. Methods: Improved CETD matrix has been used in the sample size of nearly 150 through unsupervised method. Findings: This study provides: 1. Ordering or ranking of age groups; 2. Identifying the best attribute for the leaders to possess to win the election. Novelty: To get accurate result, the researchers have structured the normalization through max-max (min-min) for improved CETD and rank identification through TOPSIS method.

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Izkhakov, Neriy, Heranmaye Prasad, Nicholas John Vernetti, and Samer Nakhle. "Successful Parathyroidectomy May Not Resolve Hypercalciuria in Patients With Primary Hyperparathyroidism." Journal of the Endocrine Society 5, Supplement_1 (2021): A226. http://dx.doi.org/10.1210/jendso/bvab048.459.

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Abstract Background: Hypercalciuria, with twenty-four-hour urinary calcium of &gt;400 mg/day, is one of the indications for parathyroidectomy in patients with primary hyperparathyroidism. We report five cases where hypercalciuria is not resolved following a successful parathyroidectomy (normalization of serum calcium) in such patients. Here resolution of hypercalciuria is defined as twenty-four-hour urinary calcium of less than 200 mg/day. Clinical Case: This is a case series of five patients who remained hypercalciuric at 6 to 19 months after successful parathyroidectomy. Pre-parathyroide

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Azis, Azminuddin I. S., Irma Surya Kumala Idris, Budy Santoso, and Yasin Aril Mustofa. "Pendekatan Machine Learning yang Efisien untuk Prediksi Kanker Payudara." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 3, no. 3 (2019): 458–69. http://dx.doi.org/10.29207/resti.v3i3.1347.

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Breast Cancer is the most common cancer found in women and the death rate is still in second place among other cancers. The high accuracy of the machine learning approach that has been proposed by related studies is often achieved. However, without efficient pre-processing, the model of Breast Cancer prediction that was proposed is still in question. Therefore, this research objective to improve the accuracy of machine learning methods through pre-processing: Missing Value Replacement, Data Transformation, Smoothing Noisy Data, Feature Selection / Attribute Weighting, Data Validation, and Unba

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Nayak, Dillip Ranjan, Neelamadhab Padhy, Pradeep Kumar Mallick, Mikhail Zymbler, and Sachin Kumar. "Brain Tumor Classification Using Dense Efficient-Net." Axioms 11, no. 1 (2022): 34. http://dx.doi.org/10.3390/axioms11010034.

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Brain tumors are most common in children and the elderly. It is a serious form of cancer caused by uncontrollable brain cell growth inside the skull. Tumor cells are notoriously difficult to classify due to their heterogeneity. Convolutional neural networks (CNNs) are the most widely used machine learning algorithm for visual learning and brain tumor recognition. This study proposed a CNN-based dense EfficientNet using min-max normalization to classify 3260 T1-weighted contrast-enhanced brain magnetic resonance images into four categories (glioma, meningioma, pituitary, and no tumor). The deve

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Thanh, Ngoc Tran, Minh Lam Binh, Tuan Nguyen Anh, and Binh Le Quang. "Load forecasting with support vector regression: influence of data normalization on grid search algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 3410–20. https://doi.org/10.11591/ijece.v12i4.pp3410-3420.

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In recent years, support vector regression (SVR) models have been widely applied in short-term electricity load forecasting. A critical challenge when applying the SVR model is to determine the model for optimal hyperparameters, which can be solved using several optimization methods as the grid search algorithm. Another challenge that affects the response time and the precision of the SVR model is the normalization process of input data. In this paper, the grid search algorithm will be suggested based on data normalization methods including Z-score, min-max,

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Adnan, Arisman, Anne Mudya Yolanda, Gustriza Erda, Noor Ell Goldameir, and Zul Indra. "The Comparison of Accuracy on Classification Climate Change Data with Logistic Regression." Sinkron 8, no. 1 (2023): 56–61. http://dx.doi.org/10.33395/sinkron.v8i1.11914.

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Machine learning methods can be used to generate climate change models. The goal of this study is to use logistic regression machine learning algorithms to classify data on greenhouse gas emissions. The data used is climate change data of several countries obtained from The World Bank, with total greenhouse gas emissions as the response variable and 61 other attributes as explanatory variables. This data is preprocessed using min-max normalization to handle unbalanced ranges, and then the data is split into 70% training data and 30% testing data. Based on the logistic regression modeling, it w

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S, M. A. Shahul Hameed, Shahul Hameed M, and Kamal Nasir V. "Using Fuzzy Ranking Technique to Find the Best Traits and Peak Age Group of Electors in an Election." Indian Journal of Science and Technology 17, no. 23 (2024): 2363–69. https://doi.org/10.17485/IJST/v17i23.2460.

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Abstract <strong>Objective:</strong> To predict the peak-age of age group of voters who turn the table of election results and also to rank the attribute which people look from their candidates. <strong>Methods:</strong> Improved CETD matrix has been used in the sample size of nearly 150 through unsupervised method. <strong>Findings:</strong> This study provides: 1. Ordering or ranking of age groups; 2. Identifying the best attribute for the leaders to possess to win the election.<strong> Novelty:</strong> To get accurate result, the researchers have structur

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Thinh, Hoang Xuan, and Tran Van Dua. "Research on expanding the scope of application of the MARA method." EUREKA: Physics and Engineering, no. 3 (May 27, 2024): 90–99. http://dx.doi.org/10.21303/2461-4262.2024.003169.

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Data normalization is a crucial step in multi-criteria decision-making (MCDM) processes. The choice of data normalization method significantly influences the ranking of alternatives. The available data normalization methods in the MARA (Magnitude of the Area for the Ranking of Alternatives) method may not be applicable in certain cases. This study aims to broaden the application scope of the MARA method. Therefore, an investigation into the compatibility of data normalization methods when combined with the MARA method has been conducted. Ten data normalization methods were utilized, including

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Eesa, Adel S., and Wahab Kh Arabo. "A Normalization Methods for Backpropagation: A Comparative Study." Science Journal of University of Zakho 5, no. 4 (2017): 319. http://dx.doi.org/10.25271/2017.5.4.381.

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Neural Networks (NN) have been used by many researchers to solve problems in several domains including classification and pattern recognition, and Backpropagation (BP) which is one of the most well-known artificial neural network models. Constructing effective NN applications relies on some characteristics such as the network topology, learning parameter, and normalization approaches for the input and the output vectors. The Input and the output vectors for BP need to be normalized properly in order to achieve the best performance of the network. This paper applies several normalization method

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Mrs. K. R. Prabha, Dr. B. Srinivasan. "TNDRM: Data Normalization Using Uncovering Complex Data Structures With Topological Nonlinear Dimensionality Reduction Using Manifold Learning." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 4422–33. http://dx.doi.org/10.52783/tjjpt.v44.i3.2387.

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With the increasing complexity of Distributed Denial of Service (DDOS) attacks in Wireless Sensor Networks (WSNs), the accurate detection of these threats has become imperative. This research presents a robust preprocessing technique for DDOS attack detection, focusing on data normalization through the integration of topological nonlinear dimensionality reduction via manifold learning (TNDRM). Our methodology revolves around transforming the intricate high-dimensional feature space of WSN data into a lower-dimensional representation, all while preserving the intrinsic topology and geometry of

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Suarez-Alvarez, Maria M., Duc-Truong Pham, Mikhail Y. Prostov, and Yuriy I. Prostov. "Statistical approach to normalization of feature vectors and clustering of mixed datasets." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 468, no. 2145 (2012): 2630–51. http://dx.doi.org/10.1098/rspa.2011.0704.

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Normalization of feature vectors of datasets is widely used in a number of fields of data mining, in particular in cluster analysis, where it is used to prevent features with large numerical values from dominating in distance-based objective functions. In this study, a unified statistical approach to normalization of all attributes of mixed databases, when different metrics are used for numerical and categorical data, is proposed. After the proposed normalization, the contributions of both numerical and categorical attributes to a specified objective function are statistically the same. Formul

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Wang, Jing. "Heart Failure Prediction with Machine Learning: A Comparative Study." Journal of Physics: Conference Series 2031, no. 1 (2021): 012068. http://dx.doi.org/10.1088/1742-6596/2031/1/012068.

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Abstract Heart failure is a worldwide healthy problem affecting more than 550,000 people every year. A better prediction for this disease is one of the key approaches of decreasing its impact. Both linear and machine learning models are used to predict heart failure based on various data as inputs, e.g., clinical features. In this paper, we give a comparative study of 18 popular machine learning models for heart failure prediction, with z-score or min-max normalization methods and Synthetic Minority Oversampling Technique (SMOTE) for the imbalance class problem which is often seen in this prob

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Himsar, Himsar. "Payment System Liquidity Index." Talenta Conference Series: Energy and Engineering (EE) 1, no. 2 (2018): 196–210. http://dx.doi.org/10.32734/ee.v1i2.250.

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ISSP is an index that demonstrates payment system’s stability figuring its liquidity (ISLSP) and its operational capability (IOSP). It was formed using two methods, which are statistical normalization and conversion using empirical normalization Min-Max. Basically, this paper intends to evaluate towards variables used in forming ISLSP and basically as a tool to ensure data sensitivity to important events stated. To get ISLSP that is sensitive to RTGS liquidity condition, we use coefficient from each weighted variable through simultaneous regression. We get parameters simbolized , and that are

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HAFS, Toufik, Hatem ZEHIR, and Ali HAFS. "Enhancing Recognition in Multimodal Biometric Systems: Score Normalization and Fusion of Online Signatures and Fingerprints." Romanian Journal of Information Science and Technology 2024, no. 1 (2024): 37–49. http://dx.doi.org/10.59277/romjist.2024.1.03.

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Multimodal biometrics employs multiple modalities within a single system to address the limitations of unimodal systems, such as incomplete data acquisition or deliberate fraud, while enhancing recognition accuracy. This study explores score normalization and its impact on system performance. To fuse scores effectively, prior normalization is necessary, followed by a weighted sum fusion technique that aligns impostor and genuine scores within a common range. Experiments conducted on three biometric databases demonstrate the promising efficacy of the proposed approach, particularly when combine

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Singh, Law Kumar, Munish Khanna, Shankar Thawkar, and Jagadeesh Gopal. "Robustness for Authentication of the Human Using Face, Ear, and Gait Multimodal Biometric System." International Journal of Information System Modeling and Design 12, no. 1 (2021): 39–72. http://dx.doi.org/10.4018/ijismd.2021010103.

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Biometrics is the science that deals with personal human physiological and behavioral characteristics such as fingerprints, handprints, iris, voice, face recognition, signature recognition, ear recognition, and gait recognition. Recognition using a single trait has several problems and multimodal biometrics system is one of the solutions. In this work, the novel and imperative biometric feature gait is fused with face and ear biometric features for authentication and to overcome problems of the unimodal biometric recognition system. The authors have also applied various normalization methods t

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Budiman, Edy, and Ummul Hairah. "COMPARISON OF LINEAR AND VECTOR DATA NORMALIZATION TECHNIQUES IN DECISION MAKING FOR LEARNING QUOTA ASSISTANCE." Journal of Information Technology and Its Utilization 4, no. 1 (2021): 22. http://dx.doi.org/10.30818/jitu.4.1.3897.

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Data normalization is essential for all kinds of decision-making problems, and a lot of effort has been spent on the development of normalization models in multi-criteria decision making (MCDM), but despite all this, there is no definite answer to the question: Which is the most appropriate technique?. This paper compares the popular normalization techniques: Linear Normalization (LN) and Vector Normalization (VN) using VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) Method. The beneficiaries dataset of learning quota was collected of 399 students sample through observation (drive-test measu

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Villegas-Camacho, Octavio, Iván Francisco-Valencia, Roberto Alejo-Eleuterio, Everardo Efrén Granda-Gutiérrez, Sonia Martínez-Gallegos, and Daniel Villanueva-Vásquez. "FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques." Recycling 10, no. 2 (2025): 46. https://doi.org/10.3390/recycling10020046.

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This study examines the potential of machine learning (ML) and deep learning (DL) techniques for classifying microplastics using Fourier-transform infrared (FTIR) spectroscopy. Six commonly used industrial plastics (PET, HDPE, PVC, LDPE, PP, and PS) were analyzed. A significant contribution of this research is the use of broader and more varied spectral ranges than those typically reported in the state of the art. Furthermore, the impact of different normalization techniques (Min-Max, Max-Abs, Sum of Squares, and Z-Score) on classification accuracy was evaluated. The study assessed the perform

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Prasetyowati, Sri Arttini Dwi, Munaf Ismail, Eka Nuryanto Budisusila, De Rosal Ignatius Moses Setiadi, and Mauridhi Hery Purnomo. "Dataset Feasibility Analysis Method based on Enhanced Adaptive LMS method with Min-max Normalization and Fuzzy Intuitive Sets." International Journal on Electrical Engineering and Informatics 14, no. 1 (2022): 55–75. http://dx.doi.org/10.15676/ijeei.2022.14.1.4.

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Huqqani, Ilyas Ahmad, Tay Lea Tien, and Junita Mohamad-Saleh. "Landslide Hazard Analysis Using a Multilayered Approach Based on Various Input Data Configurations." Geosfera Indonesia 6, no. 1 (2021): 20. http://dx.doi.org/10.19184/geosi.v6i1.23347.

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Landslide is a natural disaster that occurs mostly in hill areas. Landslide hazard mapping is used to classify the prone areas to mitigate the risk of landslide hazards. This paper aims to compare spatial landslide prediction performance using an artificial neural network (ANN) model based on different data input configurations, different numbers of hidden neurons, and two types of normalization techniques on the data set of Penang Island, Malaysia. The data set involves twelve landslide influencing factors in which five factors are in continuous values, while the remaining seven are in catego

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Fois, Antioco, Antoine Chatrenet, Emanuela Cataldo, et al. "Moderate Protein Restriction in Advanced CKD: A Feasible Option in An Elderly, High-Comorbidity Population. A Stepwise Multiple-Choice System Approach." Nutrients 11, no. 1 (2018): 36. http://dx.doi.org/10.3390/nu11010036.

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Background: Protein restriction may retard the need for renal replacement therapy; compliance is considered a barrier, especially in elderly patients. Methods: A feasibility study was conducted in a newly organized unit for advanced kidney disease; three diet options were offered: normalization of protein intake (0.8 g/kg/day of protein); moderate protein restriction (0.6 g/kg/day of protein) with a “traditional” mixed protein diet or with a “plant-based” diet supplemented with ketoacids. Patients with protein energy wasting (PEW), short life expectancy or who refused were excluded. Compliance

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