Multi-omics derivation of a core gene signature for predicting therapeutic response and characterizing immune dysregulation in inflammatory bowel disease by Mingming Wang, Liping Liang, Zibo Tang, Jimin Han, Lele Wu, Le Liu, Le Liu, Ye Chen, Ye Chen

“…Immune infiltration (CIBERSORT/ssGSEA), single-cell RNA sequencing, and DSS-colitis models characterized immune dynamics, cellular specificity, and therapeutic response modulation.ResultsWe identified 536 differentially expressed genes significantly enriched in IL-17 signaling, TNF signaling, and cytokine-cytokine receptor interactions. …”
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Analysis of Vertical Ground Reaction Force Data in Predicting Parkinson’s Disease by Varun Jain

“…Additionally, it was found that a Minimum Classification Error Optimized SVM machine learning model using Bayesian statistics was able to classify individuals with Parkinson’s disease using VGRF data at an accuracy of 67.1%, and sensitivity of 80.43%. …”
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Electroencephalogram Based Emotion Recognition Using Hybrid Intelligent Method and Discrete Wavelet Transform by Duy Nguyen, Minh Tuan Nguyen, Kou Yamada

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Going vegan with ChatGPT: Towards designing LLMs for personalized lifestyle changes by Munachiso Okenyi, Grace Ataguba, Kosi Clinton Henry, Sussan Anukem, Rita Orji

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A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model by Wangping Li, Wanchang Zhang, Zhihong Li, Yu Wang, Hao Chen, Huiran Gao, Zhaoye Zhou, Junming Hao, Chuanhua Li, Xiaodong Wu

“…Here we constructed a new method (MEDPSO) by coupling discrete particle swarm optimization algorithm with maximum entropy model (MaxEnt) to extract water bodies using Landsat 8 Operational Land Imager (OLI) images. …”
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Data annotation quality in smart farming industry by Catarina Silva, Dinis Costa, Joana Costa, Bernardete Ribeiro

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Harnessing SVM for Sentiment Analysis: Insights from Gojek's Instagram Engagement by Muhammad Juan Savero, Ali Ibrahim, Yadi Utama, Endang Lestari

“…The results showed that the SVM model achieved 70.82% accuracy in classifying user sentiment. …”
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A Lightweight Kernel Density Estimation and Adaptive Synthetic Sampling Method for Fault Diagnosis of Rotating Machinery with Imbalanced Data by Wenhao Lu, Wei Wang, Xuefei Qin, Zhiqiang Cai

“…However, in real-world settings, intelligent fault diagnosis faces challenges due to imbalanced fault data and the complexity of neural network models. These challenges are particularly pronounced when defining decision boundaries accurately and managing limited computational resources in real-time machine monitoring. …”
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A Framework for 3D Plant Simulation of Meal-Kit-Packaging Robot Automation System by Tae Hyong Kim, Byoung Il Gu, Ki Hyun Kwon, Ah-Na Kim

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BengalDeltaFish: A local dataset for fish detection in Bangladeshi marketsMendeley Data by Sabrina Alim Dipa, Arjun Pal, Md. Shoaib Shahria, Md. Delwar Shahadat Deepu, Raiyan Rahman

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Effect of bending load on electrical conductivity of carbon/epoxy composites filled with nanoparticles using design of experiment and artificial neural networks by Ali Sadollah, Seyed Morteza Razavi, Abobakr Khalil Al-Shamiri

“…We analyze the input factors and employ prediction methods such as the design of experiments (DOE), artificial neural networks (ANNs), and extreme learning machines (ELM) to forecast the response factors. The ANNs and ELM models prove effective in accurately predicting data, and the model generated by DOE is statistically valid with a confidence level exceeding 95 %. …”
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Deep learning-based approach for extracting inflorescence morphology features in cut chrysanthemum by Shanpeng Xu, Jingshan Lu, Yin Wu, Huahao Liu, Fadi Chen, Fei Zhang, Sumei Chen, Weimin Fang, Zhiyong Guan

“…A ShuffleNet V2 model achieved 95.24 % accuracy in flower type classification, with lightweight characteristics (1.26 M parameters, 0.15 GFLOPs), fast inference time (14.78 ms per image), and 67.65 FPS. …”
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Sunrise in the Desert: Leveraging Big Data Analytics for Predictive Solar Energy Production in Saudi Arabia by Da'ad Albahdal, Maha Almousa, Wijdan Aljebreen, Aeshah A. Almutairi

“…Using meteorological variables, including air conditions, wind patterns, relative humidity, and barometric pressure from 44 stations, we evaluated three machine learning models—Linear Regression (LR), Support Vector Regression (SVR), and Decision Tree (DT)—for predicting daily solar radiation. …”
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An Automatic Irrigation System Based on Hourly Cumulative Evapotranspiration for Reducing Agricultural Water Usage by Yongjae Lee, Seung-un Ha, Xin Wang, Seungyong Hahm, Kwangya Lee, Jongseok Park

“…To address this issue, the proposed system utilizes environmental data collected from a field sensor (FS), the Korea meteorological administration (KMA), and a virtual sensor based on a machine learning model (ML) to calculate the hourly ET and automate irrigation. …”
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The Mechanism of Street Spatial Form on Thermal Comfort from Urban Morphology and Human-Centered Perspectives: A Study Based on Multi-Source Data by Fei Guo, Mingxuan Luo, Chenxi Zhang, Jun Cai, Xiang Zhang, Hongchi Zhang, Jing Dong

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Game Interactive Learning: A New Paradigm towards Intelligent Decision-Making by Junliang Xing, Zhe Wu, Zhaoke Yu, Renye Yan, Zhipeng Ji, Pin Tao, Yuanchun Shi

“…It then optimizes the learning objectives from equilibrium analysis using reformed machine learning algorithms to compute and pursue promising decision results for practice. …”
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Interfacial behavior prediction and regulation on inclined superhydrophobic surfaces with transfer learning-assisted method by Yibin Liu, Konghua Yang, Yuchao Luo, Jiawei Xiong, Yunhong Liang, Zhihui Zhang, Chunbao Liu

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Integration of Deep Learning Neural Networks and Feature-Extracted Approach for Estimating Future Regional Precipitation by Shiu-Shin Lin, Kai-Yang Zhu, He-Yang Huang

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Skin Microbiome alterations in heroin users revealed by full-length 16S rRNA sequencing by Ruiyang Tao, Xiang Wang, Xiaoyuan Zhen, Ruocheng Xia, Kaiqin Chen, Han Zhang, Yu Deng, Suhua Zhang

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A New Framework for Classifying University-Industry Collaboration Using Synthetic Minority Oversampling Technique and Stacking Ensemble by Uzapi Hange, Ezenwa Chike Nwanesi, Monhesea Obrey Patrick Bah

“…This approach leverages the collective merits of multiple single models, while mitigating class imbalance, to deliver optimal classification results. …”
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