Sentiment Analysis of User Reviews of the KitaLulus Application on Google Play Store using the Support Vector Machine (SVM) Algorithm by Ahmad Syaifudin Agil Rafsanjani, Diana Laily Fithri, Supriyono Supriyono

“…This study aims to analyze user sentiment toward the KitaLulus application using the Support Vector Machine (SVM) algorithm, combined with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in sentiment data. The research process includes collecting 1,000 user reviews through web scraping, text preprocessing, rating-based labeling, data transformation using TF-IDF, splitting the dataset into 80% training and 20% testing, applying SMOTE, training the SVM model, and evaluating its performance. …”
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Penerapan Face Recognition Pada Sistem Presensi by Andri Nugraha Ramdhon, Fadly Febriya

“…The student attendance system at STT Bandung is basically good but it is still not optimal. The process of collecting student attendance data is still quite time-consuming and still allows human errors to occur in the data input process. …”
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Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders by HU Tianzhu, SHEN Yulong, REN Baoquan, HE Ji, LIU Chengliang, LI Hongjun

“…The message queue based on the operating system was used for data packet caching to achieve persistent processing of high-speed data streams, which effectively improved the stability and reliability of the model. …”
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An Alliance Blockchain Modification Method Based on Chameleon Hashing and Verifiable Secret Sharing by Andi Cui, Su Li, Junlu Wang, Junxiang Ding, Haolin Zhang, Baoyan Song

“…The existing alliance chain system will leave traces throughout the entire process after data is uploaded, which cannot be processed when sensitive information or malicious data appears or the blockchain forks or interrupts after processing. …”
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Accurate detection of selective forwarding attack in wireless sensor networks by Qiong Zhang, Wenzheng Zhang

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A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) by Muhammad Ricky Perdana Putra, Ema Utami

“…The dataset from KDD Cup 2015 contains clickstream from XuetangX website. The pre-processing stage includes selecting the course with the most participants, normalization, SMOTE, feature selection, and breaking it into three: ensemble, blender, and test data. …”
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A fact based analysis of decision trees for improving reliability in cloud computing. by Muhammad Asim Shahid, Muhammad Mansoor Alam, Mazliham Mohd Su'ud

“…The results from secondary data analysis indicate that the Central Processing Unit CPU-Mem Multi classifier has the highest accuracy percentage and the least amount of fault prediction. …”
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A Mixed Method Survey of Characteristics of HIV Care Facilities: Medical Monitoring Project Facility Survey Project by Dustin Williams, John Weiser, Timothy McManus, Hanna B Demeke, Darryl Creel, Jason Craw, Milton Cahoon, Linda Beer

“…ObjectiveWe aimed to describe the survey methods designed to achieve a high response rate from the 1022 facilities providing care to people with HIV as part of the Medical Monitoring Project—including frame development, survey instrument development, facility recruitment, and postsurvey data processing. …”
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Designing an Intelligent Scoring System for Crediting Manufacturers and Importers of Goods in Industry 4.0 by Mohsin Ali, Abdul Razaque, Joon Yoo, Uskenbayeva Raissa Kabievna, Aiman Moldagulova, Satybaldiyeva Ryskhan, Kalpeyeva Zhuldyz, Aizhan Kassymova

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Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…In summary, our approach involving data preprocessing, model tuning, feature selection, and explainability achieved state-of-the-art performance in predicting 30-day mortality rates following hip fractures surgery using a limited set of features, making it highly applicable in clinical settings.…”
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The Use of 3D Convolutional Autoencoder in Fault and Fracture Network Characterization by Feng Xu, Zhiyong Li, Bo Wen, Youhui Huang, Yaojun Wang

“…The validity of the method is verified by testing the seismic simulation data and applying real seismic data. The 3D convolution can directly process the seismic data and maximize the prestack texture attributes and spatial structure information provided by 3D seismic data without dimensionality reduction and other preprocessing operations. …”
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Advancing radiation-induced mutant screening through high-throughput technology: a preliminary evaluation of mutant screening in Arabidopsis thaliana by Zhe Li, Jinhu Mu, Yan Du, Xiao Liu, Lixia Yu, Jianing Ding, Jing Long, Jingmin Chen, Libin Zhou

“…However, challenges remain in the post-processing of large-scale image data and its practical application and evaluation in breeding. …”
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Tracking Upper Limb Motion via Wearable Solutions: Systematic Review of Research From 2011 to 2023 by Eirini Karoulla, Maria Matsangidou, Fotos Frangoudes, Panayiotis Paspalides, Kleanthis Neokleous, Constantinos S Pattichis

“…Moreover, a variety of signal processing techniques, such as Kalman and Butterworth filters, were applied to ensure data accuracy. …”
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Full-waveform Small-footprint LiDAR Multi-target Echo Waveform Lightweight Detection by Spatio-temporal Coupling Models by Zhen XIAO, Yanfeng GU, Yanze JIANG, Xian LI

“…Small-footprint full-waveform Light Detection And Ranging (LiDAR) exhibits significant application potential owing to its high penetration capability and ability to capture complete echo data. However, the efficient and accurate processing of massive echo signals remains a crucial challenge for practical use, particularly in advancing waveform decomposition technology. …”
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Modeling of Tensile Tests Flow Curves Using an Explicit Piecewise Inverse Approach by Aditya Vuppala, Holger Brüggemann, David Bailly, Emad Scharifi

“…During tensile testing at the beginning of the deformation, the stress state is uniaxial; however, as the deformation proceeds, the state changes to triaxial, making the post-processing of experimental data challenging using analytical methods. …”
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Effects of particle size distribution on flotation kinetics of bituminous coal by A. Bahrami, Y. Ghorbani, E. Gulcan, F. Kazemi, H. Kakaei, S. Farajzadeh

“…It is found that the first - order model with rectangular distribution of floatability, provides the best fit to the experimental data obtained from the flotation processes among the tested models.…”
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Contactless Breathing Monitoring at Home and in the Hospital: Protocol for a Low-Cost Frequency-Modulated Continuous-Wave Radar-Based Device by Arnav Hari, Ravishankar Kumar, Brijesh Kumbhani, Sam Darshi, Satyam Agarwal, Jyotindra Singh Sahambi, Suksham Jain, Deepak Chawla

“…Signal-processing capability can be added to this device to process breathing data and analyze the apnea condition arising due to irregular breathing patterns. …”
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Accurate earthquake and mining tremor identification via a CEEMDAN-LSTM framework by Chao Wang, Xiufeng Zhang, Weiming Gao, Fangfang Wang, Jianqi Lu, Jianqi Lu, Zhaoyang Yan, Zhaoyang Yan

“…After completing basic preprocessing steps for both mining tremors and earthquakes, CEEMDAN is used to decompose the data into different intrinsic mode functions (IMFs). …”
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Monitoring Land Surface Temperature on Urban Expansion using Normalised Difference Vegetation Index and Google Earth Engine by Nur Suhaili Mansor, Hapini Awang, Chong Kar Min, Anna Bluszcz, Ade Candra, Shakiroh Khamis

“…First, satellite imagery from Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) was collected using Google Earth Engine (GEE). Second, data preprocessing was performed to derive NDVI, Vegetation Cover Proportion (Pv), and surface Emissivity (Em). …”
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Application and Analysis of Random Forest and Support Vector Classification in Risk Prediction of Childhood Obesity and Hyperuricemia by Wang Y, Shi S, Wei X, Wu Y, Shi Y, Cai J

“…Machine learning (ML) offers an effective framework for constructing efficient risk prediction models in pediatric populations.Objective: This study aimed to develop and evaluate two ML models—Random Forest (RF) and Support Vector Classification (SVC)—to predict the risk of childhood obesity and hyperuricemia by integrating clinical and biochemical variables.Methods: A total of 101 children were enrolled, including 60 with obesity and 41 with obesity plus hyperuricemia. Data preprocessing involved recursive feature elimination (RFE), ROSE-based oversampling, and feature standardization. …”
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