Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning by Jiming Tang, Yao Huang, Dingli Liu, Liuyuan Xiong, Rongwei Bu

“…In the experiment, effective preprocessing measures were taken for problems such as data imbalance, missing values, the encoding of categorical variables, and the standardization of numerical features. …”
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Classical machine learning and artificial neural network (ANN) to predict rejection in weaving industry by Toufique Ahmed

“…Interestingly, traditional machine learning models achieved more than 95% accuracy without any data preprocessing. In contrast, artificial neural networks (ANN) require data preprocessing to achieve high accuracy rates. …”
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Intelligent waste sorting for urban sustainability using deep learning by Gulzar Ahmad, Fizza Muhammad Aleem, Tahir Alyas, Qaiser Abbas, Waqas Nawaz, Taher M. Ghazal, Abdul Aziz, Saira Aleem, Nadia Tabassum, Aidarus Mohamed Ibrahim

“…In this paper, we present an intelligent waste classification system that utilises Convolutional Neural Networks (CNNs) for automatic segregation into twelve categories of waste, employing image data. The model is trained on 15,535 images from a publicly available dataset using preprocessing and data augmentation to increase generalisation and mitigate class imbalance. …”
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Assessing carbon footprint and performance of the Kalman filter track fitter in the CBM experiment by Grigory Kozlov, Grigory Kozlov, Ivan Kisel, Ivan Kisel, Ivan Kisel, Ivan Kisel

“…A key feature of the experiment is the high interaction rate, reaching up to 107 collisions per second, resulting in the production of substantial volumes of experimental data that must be processed and analyzed in real time. …”
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A High-Speed, Multi-Channel Lossless Compression Algorithm for High-Resolution Video on FPGA by Xingkai Du, Longhua Xie, Hongchuan Huang, Huawei Chen, Xiaolong Guo, Tingyu Zhao

“…This approach enhances the transmission frame rate while maintaining image quality. Additionally, a novel “first-in, last-out” data inversion technique is employed to solve the problem of real-time processing of four-channel data, significantly reducing FPGA hardware resource usage. …”
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Deep Reinforcement Learning for Multi-User Massive MIMO With Channel Aging by Zhenyuan Feng, Bruno Clerckx

“…In this paper, considering multiple antennas at all nodes (base station and user terminals), we develop a multi-agent deep reinforcement learning (DRL) framework for massive MIMO under imperfect CSIT, where the transmit and receive beamforming are jointly designed to maximize the average information rate of all users. Leveraging this DRL-based framework, interference management is explored and three DRL-based schemes, namely the distributed-learning-distributed-processing scheme, partial-distributed-learning-distributed-processing, and central-learning-distributed-processing scheme, are proposed and analyzed. …”
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Microsaccades distinguish looking from seeing by Eva Krueger, Andrea Schneider, Ben Sawyer, Alain Chavaillaz, Andreas Sonderegger, Rudolf Groner, Peter Hancock

“…The results support microsaccadic rate reflecting visual attention, and level of visual information processing. …”
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Determination of FFB Raw Material Needs for CPO Production by Forecasting Method at PT. Socfindo Kebun Mata Pao by Mahrani Arfah*, Vivi Anggraini

“…Socfindo Kebun Mata Pao is one of the industrial companies engaged in palm oil processing. The main raw material used in processing Crude Palm Oil (CPO) is Fresh Fruit Bunches (FFB), where the FFB used must be based on good characteristics to get quality CPO. …”
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Investments in Economic Growth and Structural Transformation of Russia by O. S. Sukharev

“…This will allow specifying the tasks of development of the Russian economy, highlighting the directions of structural policy and measures to stimulate economic growth that go beyond the stereotypical orthodox approach, which reduces recommendations to an increase in the accumulation rate and investments. The methodology of the study is the theory of economic growth and structural dynamics, empirical and regression analysis of data, ideas about the investment process and measures to stimulate it, a method for assessing the risk by the standard deviation of gross profit. …”
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Spectral Shaping Method for OFDM Combining Time-Shifted Active Interference Cancellation and Adaptive Symbol Transition by Javier Gimenez, Jose A. Cortes, Eduardo Martos-Naya, Luis Diez

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Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools by Shailendra Pawanr, Kapil Gupta

“…Gaussian Data Augmentation (GDA) was employed to enrich data variability and strengthen model generalization, resulting in the improved predictive performance of the machine learning models. …”
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American Sign Language Recognition Model Using Complex Zernike Moments and Complex-Valued Deep Neural Networks by Selda Bayrak, Vasif Nabiyev, Celal Atalar

“…The model achieves recognition rates of 89.01% on the Sign Language MNIST dataset and 98.67% for holdout and 81.22% for leave-one-subject-out on the Massey University dataset, respectively, without any preprocessing. …”
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Beacon Jointed Packet Reconstruction Scheme for Mobile-Phone Based Visible Light Communications Using Rolling Shutter by Wei-Chung Wang, Chi-Wai Chow, Chia-Wei Chen, Hsiang-Chain Hsieh, Yen-Ting Chen

“…Mobile-phone based visible light communication (VLC) is an attractive method for optical wireless communication. However, the data rate is typically limited by the complementary metal–oxide–semiconductor image sensor frame rate and the processing time gap in the rolling shutter mode operation. …”
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Exploring the restorative benefits of Kuang-Ao features in urban slow-moving greenways based on the healthy city concept by Yi Mei, Jinlu Chen, Jingyue Wu, Zhangyun Fan, Wanyun Zhou

“…ErgoLAB and Excel were utilized for preprocessing data on emotional pleasure, galvanic skin response change rate, perceptual dimension, and average pupil diameter. …”
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Stage-based colorectal cancer prediction on uncertain dataset using rough computing and LSTM models by K. Supriya, A. Anitha

“…However, the disease prediction process depends on the collected data, where the data may contain uncertainty. …”
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Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Usin... by Yan Zhuang, Junyan Zhang, Xiuxing Li, Chao Liu, Yue Yu, Wei Dong, Kunlun He

“…ObjectiveThis study aims to propose a prompt learning real-time framework based on pretrained language models that can automatically label long free-text data with ICD-10 codes for cardiovascular diseases without the need for semiautomatic preprocessing. …”
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ECG Biometrics on Mobile Devices: High-Accuracy Authentication Using i-Vectors and Cepstral Coefficients by F. Saba Kockan, Bulent Bolat

“…The use of a brief 5-second ECG signal minimizes memory and processing power requirements, enabling rapid data processing and real-time authentication. …”
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An improved GRU method for slope stress prediction by Lichun Bai, Ronghui Zhao, Sen Lin, Zishu Chai, Xuan Wang

“…The preliminary preprocessing of open pit mine slope stress data using VMD can provide high decomposition accuracy and can effectively extract localized features in the stress; The method introduces Dung Beetle Optimization (DBO) to determine the number of hidden neuron layers and the optimal learning rate for the GRU. …”
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Machine learning and SHAP value interpretation for predicting cardiovascular disease risk in patients with diabetes using dietary antioxidants by Xinyu Zhang, Sen Lin, Qingling Zeng, Lisheng Peng, Chaoguang Yan

“…Data preprocessing involved collinearity removal, standardization, and class imbalance correction. …”
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CNN-based Gender Prediction in Uncontrolled Environments by Kazım Yıldız, Engin Güneş, Anil Bas

“…As a result of the experiments, 93.71% success rate was achieved on the VGGFace2 data set and 85.52% success rate on the Adience data set. …”
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