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Relevant bibliographies by topics / Min-max normalization

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Academic literature on the topic 'Min-max normalization'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Min-max normalization"

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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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Book chapters on the topic "Min-max normalization"

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Kiran, Ajmeera, and D. Vasumathi. "Data Mining: Min–Max Normalization Based Data Perturbation Technique for Privacy Preservation." In Proceedings of the Third International Conference on Computational Intelligence and Informatics. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1480-7_66.

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Ramalakshmi, K., S. P. Santhoshkumar, L. Krishna Kumari, et al. "Improved Brain Tumor Segmentation Using Min-Max Normalization in a U-Net Architecture." In Integrative Machine Learning and Optimization Algorithms for Disease Prediction. IGI Global Scientific Publishing, 2025. https://doi.org/10.4018/979-8-3373-1087-9.ch010.

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The procedure of manually identifying brain tumors from MRI scans is difficult and time-consuming. It takes a lot of time and work, which increases the possibility of mistakes. Brain tumor segmentation needs to be accurate and consistent in order to be used for cancer identification, treatment scheduling, and outcome estimation. Effective brain tumor segmentation is vital for early brain tumor detection is necessaryfor improving patient prognosis and treatment alternatives. Even with the increasing usage of for brain imaging and the development of AI detection techniques, creating a reliable a

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Lopez, Kyra Mikaela M., and Ma Sheila A. Magboo. "A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers." In Machine Learning and Artificial Intelligence. IOS Press, 2020. http://dx.doi.org/10.3233/faia200765.

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This study aims to describe a model that will apply image processing and traditional machine learning techniques specifically Support Vector Machines, Naïve-Bayes, and k-Nearest Neighbors to identify whether or not a given breast histopathological image has Invasive Ductal Carcinoma (IDC). The dataset consisted of 54,811 breast cancer image patches of size 50px x 50px, consisting of 39,148 IDC negative and 15,663 IDC positive. Feature extraction was accomplished using Oriented FAST and Rotated BRIEF (ORB) descriptors. Feature scaling was performed using Min-Max Normalization while K-Means Clus

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Kaushik, Rekha, Pritam Goyal, and Atharv Pandey. "NeuroVoice: Leveraging Neural Networks for Precise Gender Classification in Audio." In Computational Intelligence and Machine Learning. Soft Computing Research Society, 2024. https://doi.org/10.56155/978-81-975670-5-6-5.

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In this paper, the model for gender recognition is developed with voice samples using various machine learning algorithms and acoustic parameters. It is divided at the beginning into the training and test data of the dataset. There then follows a number of key steps and techniques as part of the process that improves the performance of this model. The paper focuses on a holistic approach toward gender classification from audio data through various techniques of data preprocessing, augmentation, feature scaling, model development, and their performance evaluation. First, it encodes the class la

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Conference papers on the topic "Min-max normalization"

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Waema, Damaris, Waweru Mwangi, and Petronilla Muriithi. "A Min-Max Based Data Normalization and Maximum Pooling Approach for Improved Maize Leaf Disease Detection." In 2025 IST-Africa Conference (IST-Africa). IEEE, 2025. https://doi.org/10.23919/ist-africa67297.2025.11060470.

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Patel, Chetan, Aarsh Pandey, Rajesh Wadhvani, and Deepali Patil. "Forecasting Nonstationary Wind Data Using Adaptive Min-Max Normalization." In 2022 1st International Conference on Sustainable Technology for Power and Energy Systems (STPES). IEEE, 2022. http://dx.doi.org/10.1109/stpes54845.2022.10006473.

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Gajera, Vatsal, Shubham, Rishabh Gupta, and Prasanta K. Jana. "An effective Multi-Objective task scheduling algorithm using Min-Max normalization in cloud computing." In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 2016. http://dx.doi.org/10.1109/icatcct.2016.7912111.

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Herwanto, Heru Wahyu, Anik Nur Handayani, Aji Prasetya Wibawa, Katya Lindi Chandrika, and Kohei Arai. "Comparison of Min-Max, Z-Score and Decimal Scaling Normalization for Zoning Feature Extraction on Javanese Character Recognition." In 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE). IEEE, 2021. http://dx.doi.org/10.1109/iceeie52663.2021.9616665.

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Sholeh, Muhammad, and Erna Kumalasari Nurnawati. "Comparison of Z-score, min-max, and no normalization methods using support vector machine algorithm to predict student’s timely graduation." In THE 3RD INTERNATIONAL CONFERENCE ON NATURAL SCIENCES, MATHEMATICS, APPLICATIONS, RESEARCH, AND TECHNOLOGY (ICON-SMART2022): Mathematical Physics and Biotechnology for Education, Energy Efficiency, and Marine Industries. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0202505.

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Assiri, Mohammed. "Enhancing Android Security Through Artificial Intelligence: A Hyperparameter-Tuned Deep Learning Approach for Robust Software Vulnerability Detection." In 13th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications. AHFE International, 2025. https://doi.org/10.54941/ahfe1005919.

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Detecting software vulnerabilities is essential for cybersecurity, particularly in Android systems, which are widely used and vulnerable due to their open-source nature. Conventional signature-based malware detection methods are inadequate against sophisticated and evolving threats. This paper introduces a Hyperparameter-Tuned Deep Learning Approach for Robust Software Vulnerability Detection (HPTDLA-RSVD) aimed at enhancing Android security through an optimized deep learning model. The HPTDLA-RSVD methodology encompasses min-max data normalization, feature selection using the Ant Lion Optimiz

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Ramanayake, R. M. D. T., and Chethika Abeynayake. "A COMPARATIVE STUDY OF CRITICAL SUCCESS CRITERIA ON SUSTAINABLE HOUSING; A CASE OF - LOW INCOME HOUSING, SRI LANKA." In Beyond sustainability reflections across spaces. Faculty of Architecture Research Unit, 2021. http://dx.doi.org/10.31705/faru.2021.1.

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Sustainable housing is a popular topic with regard to the SDG, sustainable communities and Sustainable cities. Although different researches have come up with regard to different CSC of specific contexts there are very limited studies on CSC on Sustainable low-income housing. This research aims to compare the CSC on Sustainable low-income Housing in designing stage in Sri Lankan Context. 18 CSC were derived from comprehensive literature review and re-examined through the 27 professionals and ranked from community on three locations. Relative Importance Index- RII, Min Max Normalization and Gap

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Kapuge, A. B. A. K. V. S., J. M. S. J. Bandara, and N. Jayasooriya. "Criteria for Assessing the Effectiveness of a Non Real Time Coordinated Cluster of Signalized Intersections." In 5th International Conference on Advances in Highway Engineering & Transportation Systems 2024, edited by H. R. Pasindu. Transport Engineering Division, Department of Civil Engineering, University of Moratuwa, 2024. https://doi.org/10.31705/icahets.2024.2.

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Given the limited resources available for installing advanced signal controllers, many researchers and professionals believe that well-designed coordinated fixed-time signal control, combined with well-defined corridor coordination, is a cost-effective option. In this study, two closely spaced intersections were considered for analysis. It was identified that a subsystem consists of spatial elements (the link road that connects two signalized intersections) and temporal elements (the relative offset between the signals of the two intersections). Throughput, travel time, delay, queue length, an

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Reports on the topic "Min-max normalization"

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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and f

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