Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications by Md Ashraful Haque, Kamal Hossain Nahin, Jamal Hossain Nirob, Md. Kawsar Ahmed, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Abeer D. Algarni, Mohammed ElAffendi, Ahmed A. Abd El-Latif, Abdelhamied A. Ateya

“…By designing a similar RLC circuit in ADS and simulating it, we validated the results obtained from CST. Both CST and ADS simulators produced comparable reflection coefficients. …”
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Correlation Between Depression-Associated Genes and Cancer Types: Predicting Cancer Based on Mutation Frequencies by Fernando Patricio Carranco-Avila, Clayanela Zambrano-Caicedo, Jonathan Javier Loor-Duque, Ariana Deyaneira Jimenez-Narvaez, Ivan Galo Reyes-Chacon, Paulina Vizcaino, Isidro Rafael Amaro Martin, Manuel Eugenio Morocho-Cayamcela

“…The analysis employed advanced methodologies, including HJ biplot K-means and DBSCAN clustering algorithms for pattern grouping in 2D. This process generated a dataset, enabling the training and testing of machine learning and deep learning classification models. …”
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Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy by Jing Wang, Jingjing Dai, Na Li, Chulong Zhang, Jiankai Zhang, Zuledesi Silayi, Haodi Wu, Yaoqing Xie, Xiaokun Liang, Huailing Zhang

“…We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. …”
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Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) by Hyun-Woo Jo, Myoungsoo Won, Florian Kraxner, Seong Woo Jeon, Yowhan Son, Andrey Krasovskiy, Woo-Kyun Lee

“…To leverage the strengths of both models, this study aimed to integrate human domain knowledge into a machine learning framework. …”
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A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti, Hafsa Binte Kibria, Zareen Tasnim Pear, Md Nahiduzzaman, Md. Faysal Ahamed, Khandaker Reajul Islam, Jaya Kumar, Muhammad E. H. Chowdhury

“…After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. …”
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Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical data by Maomao Guo, Sudong Liang, Zhenghui Guan, Jingcheng Mao, Zhibin Xu, Wenchao Zhao, Hao Bian, Jianfeng Zhu, Jiangping Wang, Xin Jin, Yuan Xia

“…Abstract The incidence of both prostate cancer (PCa) and metabolic syndrome (MS) has been steadily increasing due to changes in population structure and lifestyle. …”
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Integrative analysis identifies IL-6/JUN/MMP-9 pathway destroyed blood-brain-barrier in autism mice via machine learning and bioinformatic analysis by Cong Hu, Heli Li, Jinru Cui, Yunjie Li, Feiyan Zhang, Hao Li, Xiaoping Luo, Yan Hao

“…JUN demonstrated strong associations with both BBB integrity and microglial activation in ASD pathogenesis. …”
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Revolutions at the frontline of multiple myeloma treatment: lessons and challenges to finding a cure by Jeries Kort, Jeries Kort, Andrea Rivera, Andrea Rivera, Sindhuja Senigarapu, James J. Driscoll, James J. Driscoll, James J. Driscoll

“…Bispecific antibodies (BsAbs) are engineered to simultaneously bind to both cancer and immune cells and trigger a direct tumor-specific cytotoxic response. …”
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Apple Yield Estimation Method Based on CBAM-ECA-Deeplabv3+ Image Segmentation and Multi-Source Feature Fusion by Wenhao Cui, Yubin Lan, Jingqian Li, Lei Yang, Qi Zhou, Guotao Han, Xiao Xiao, Jing Zhao, Yongliang Qiao

“…Yield estimation models were constructed using k-nearest neighbors (KNN), partial least squares (PLS), random forest (RF), and support vector machine (SVM) algorithms under both single feature sets and combined feature sets (including vegetation indices, structural feature ratios, SPAD, vegetation indices + SPAD, vegetation indices + structural feature ratios, structural feature ratios + SPAD, and the combination of all three). …”
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The Impact of the Natural Grass-Growing Model on the Development of Korla Fragrant Pear Fruit, as Well as Its Influence on Post-Harvest Sugar Metabolism and the Expression of Key E... by Mingyang Yu, Lanfei Wang, Yan Chen, Weifan Fan, Hao Wang, Kailu Guo, Shutian Tao, Xin Gong, Jianping Bao

“…A classification model was constructed using machine learning algorithms (RF, KNN, SVM), and particle swarm optimization (PSO) was employed to identify key factors. …”
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Comprehensive profiling of chemokine and NETosis-associated genes in sarcopenia: construction of a machine learning-based diagnostic nomogram by Yingwei Wang, Le Wang, Yan Zhang, Minghui Wang, Huaying Zhao, Cheng Huang, Huaiyang Cai, Shuangyang Mo

“…Two machine learning algorithms and univariate analysis were integrated to screen signature genes, which were subsequently used to construct diagnostic nomogram models for sarcopenia. …”
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Toward Digital Twin of Off-Road Vehicles Using Robot Simulation Frameworks by Arianna Rana, Antonio Petitti, Angelo Ugenti, Rocco Galati, Giulio Reina, Annalisa Milella

“…Both models are developed for a tracked robot that uses an innovative articulated passive suspension system on either side that allows each ground wheel to move independently with respect to the vehicle body, providing remarkable adaptability to irregular terrain. …”
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Potassium Fulvate Alleviates Salinity and Boosts Oat Productivity by Modifying Soil Properties and Rhizosphere Microbial Communities in the Saline–Alkali Soils of the Qaidam Basin... by Jie Wang, Xin Jin, Xinyue Liu, Yunjie Fu, Kui Bao, Zhixiu Quan, Chengti Xu, Wei Wang, Guangxin Lu, Haijuan Zhang

“…We integrated three machine learning algorithms—least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—to minimize the bias inherent in any single method. …”
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Shared pathogenesis in polycystic ovaries and rheumatoid arthritis: an analysis of key genes and pathways by Yingying Ji, Pengcheng Xia, Yan Wang, Yang Liu, Feng Wang, Fenggang Sun, Qiang Feng, Qingbin Ni, Yi Li

“…Protein interaction analysis, machine learning algorithms, and validation analyses were employed to identify core genes with potential diagnostic value. …”
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Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learn... by Ting Ge, Guixin He, Qian Cui, Shuangcui Wang, Zekun Wang, Yingying Xie, Yuanyuan Tian, Juyue Zhou, Jianchun Yu, Jinmin Hu, Wentao Li

“…Subsequently, we developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 113 combinations to construct a LUAD CS-related signature (LUAD-CSRS), which were assessed in both training and validation cohorts. …”
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Quality improvement tools to manage deceased organ donation processes: a scoping review by Sonny Dhanani, Ken Lotherington, Lindsay Wilson, Vanessa Silva e Silva, Laura Hornby, Andrea Rochon, Marian Luctkar-Flude, Amina Silva, Samantha Arora, Luciana P. Giorno, Elayne Jackson, Marzieh Latifi, Stefany Petry

“…The tools identified included checklists, algorithms, flow charts, charts, pathways, decision tree maps and mobile apps. …”
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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…In recent years, near-infrared (NIR) spectroscopy combined with algorithmic models has emerged as a popular method for identifying the tobacco leaf position. …”
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The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques by Rafał J. Doniec, Natalia Piaseczna, Konrad Duraj, Szymon Sieciński, Muhammad Tausif Irshad, Ilona Karpiel, Mirella Urzeniczok, Xinyu Huang, Artur Piet, Muhammad Adeel Nisar, Marcin Grzegorzek

“…We used machine learning algorithms (decision trees, support vector machines, nearest-neighbor classifiers, boosted trees, bagged trees, subspace discriminant classifier, subspace k nearest-neighbor classifier, and RUSBoosted Trees) to analyze the data. …”
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Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database by De Su, Jie Zheng, Yue-kai Shao, Jun-ya Liu, Xin-xin Liu, Kun Yu, Bang-hai Feng, Hong Mei, Song Qin

“…LASSO regression was employed for feature selection, and various machine learning algorithms were utilized to train models, including logistic regression (LR), random forest (RF), and gradient boosting (GB), among others. …”
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Trust, Trustworthiness, and the Future of Medical AI: Outcomes of an Interdisciplinary Expert Workshop by Melanie Goisauf, Mónica Cano Abadía, Kaya Akyüz, Maciej Bobowicz, Alena Buyx, Ilaria Colussi, Marie-Christine Fritzsche, Karim Lekadir, Pekka Marttinen, Michaela Th Mayrhofer, Janos Meszaros

“…These include not only algorithmic bias but also human, institutional, social, and societal factors, which are critical to foster AI systems that are both ethically sound and socially responsible. …”
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