PREPROCESSING OF HOPKINSON BAR EXPERIMENT DATA: FILTER ANALYSIS by Marcel Adorna, Jan Falta, Tomáš Fíla, Petr Zlámal

“…This work presents a data preprocessing procedure for signal acquired during high strain-rate loading using a custom Split Hopkinson Pressure Bar (SHPB). …”
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A Data-Driven Approach for Urban Heat Island Predictions: Rethinking the Evaluation Metrics and Data Preprocessing by Berk Kıvılcım, Patrick Erik Bradley

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Using preprocessed datasets to construct and interpret multiclass identification models by Cong Wang, Yufeng Fu, Ran Wan, Le Zhao, Hongbo Wang, Junwei Guo, Qiang Liu, Shan Li, Shengtao Ma, Zhicai Wang, Wei Huang, Huimin Liu, Song Yang, Cong Nie

“…Therefore, developing alternative approaches for constructing interpretable and robust models using these data types is crucial.MethodsThis study proposes using preprocessed data—specifically, morphological features extracted from images and chemical component concentrations predicted from NIR spectra—to build multiclass identification models. …”
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Efficient Intrusion Detection System Data Preprocessing Using Deep Sparse Autoencoder with Differential Evolution by Saranya N., Anandakumar Haldorai

“…Efficient data preprocessing will ensure the whole IDS performance with improved detection rate (DR) and reduced false alarm rate (FAR). …”
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Research on the Preprocessing Method of Laser Ranging Data with Complex Patterns Based on a Novel Spline Function by Yanning Zheng, Xue Dong, Zhipeng Liang, Jian Gao, Yang Liu, Qingli Song, Xingwei Han, He Dong

“…How to filter out noise and retain valid information from the high-repetition-rate, high-precision laser ranging data has become a challenge. …”
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Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini Adulteration by Huihui Wang, Kunlun Wang, Biyao Wang, Yan Lv, Xueheng Tao, Xu Zhang, Mingqian Tan

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On the Utilization of Emoji Encoding and Data Preprocessing with a Combined CNN-LSTM Framework for Arabic Sentiment Analysis by Hussam Alawneh, Ahmad Hasasneh, Mohammed Maree

“…Three experiments were conducted with eight-parameter fusion approaches to evaluate the effect of data preprocessing, namely the effect of emoji encoding on their real and emotional meaning. …”
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Preprocessing of Aspect-based English Telugu Code Mixed Sentiment Analysis by Arun Kodirekka, Ayyagari Srinagesh

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Multi group merging algorithm for solving data Shuffle and data skew of securities companies by CAO Yakun, TANG Xiaoyong

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Ridge Regressive Data Preprocessed Quantum Deep Belief Neural Network for Effective Trajectory Planning in Autonomous Vehicles by S. Nirmala Devi, Rajesh Natarajan, Gururaj H. L., Francesco Flammini, Badria Sulaiman Alfurhood, Sujatha Krishna

“…Secondly, Ridge Regressive Data Preprocessing is performed to eliminate noisy data from collected vehicle data. …”
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Prediction and analysis of China’s coastal marine economy: an innovative grey model with the best-matching data-preprocessing techniques by Zerong Wang, Zhijian Cai, Yao Li

“…To address these challenges, this study employs advanced data-preprocessing techniques, accumulating generation operators (AGO) in grey prediction models, to tackle the nonlinear, volatile, and heterogeneous gross ocean product (GOP) data. …”
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Outlier Detection on High-Dimensional Data by wasan shaban, Murtadha Hamad

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Data-driven network intrusion detection using optimized machine learning algorithms by Dauda Adeite Adenusi, Oladosu Oyebisi Oladimeji, Theopilus Adekunle Oyekola, Korede Solomon Olagunju

“…The research investigates the impact of data preprocessing techniques, including data balancing and duplicate removal, on detection performance. …”
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Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 by Morteza Abbasi, Amir Hossein Namadchi, Mehdi Abbasi, Mohsen Abbasi

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Dataset for Traffic Accident Analysis in Poland: Integrating Weather Data and Sociodemographic Factors by Łukasz Faruga, Adam Filapek, Marta Kraszewska, Jerzy Baranowski

“…Road traffic accidents remain a critical public health concern worldwide, with Poland consistently experiencing high fatality rates—52 deaths per million inhabitants in 2023, compared to the EU average of 46. …”
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A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare by Siddhesh Fuladi, D. Ruby, N. Manikandan, Animesh Verma, M. K. Nallakaruppan, Shitharth Selvarajan, Shitharth Selvarajan, Shitharth Selvarajan, Preeti Meena, V. P. Meena, Ibrahim A. Hameed

“…The proposed framework incorporates careful data preprocessing, rational model architecture selection, and optimal parameter tuning to predict smoking with high precision. …”
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Unsupervised Discretization of Continuous Variables in a Chicken Egg Quality Traits Dataset by Zeynel Cebeci, Figen Yıldız

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Optimized machine learning mechanism for big data healthcare system to predict disease risk factor by Venkata Nagaraju Thatha, Silpa Chalichalamala, Udayaraju Pamula, D. Pramodh Krishna, Manjunath Chinthakunta, Srihari Varma Mantena, Shariff Vahiduddin, Ramesh Vatambeti

“…To overcome this, a novel Deep Red Fox belief prediction system (DRFBPS) has been introduced and implemented in Python software. Initially, the data was collected and preprocessed to enhance its quality, and the relevant features were selected using red fox optimization. …”
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Optimizing Machine Learning-Based Ovarian Cancer Prediction Through Normalization Strategies by Roopashri Shetty, Siddhant Gupta, Vansh Mediratta, Shwetha Rai, M. Geetha

“…Ovarian cancer is one of the most challenging cancers to detect early, often leading to poor survival rates. This study explores supervised and unsupervised machine learning and deep learning approaches to improve predictive performance using clinical and biomarker-based data which was scaled through two popular techniques: Min-Max scaling and Z-Score normalization. …”
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Machine-Learning-Driven Identification of Electrical Phases in Low-Sampling-Rate Consumer Data by Dilan C. Hangawatta, Ameen Gargoom, Abbas Z. Kouzani

“…Accurate electrical phase identification (PI) is essential for efficient grid management, yet existing research predominantly focuses on high-frequency smart meter data, not adequately addressing phase identification with low sampling rates using energy consumption data. …”
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