Enhancing healthcare with AI: Sustainable AI and IoT-Powered ecosystem for patient aid and interpretability analysis using SHAP by Biplov Paneru, Bishwash Paneru, Sanjog Chhetri Sapkota, Ramhari Poudyal

“…The cloud-stored data can be harnessed to ascertain and supervise one's health and predict forthcoming perils. This study unveils an efficacious decision-making model custom-tailored for Internet of Things (IoT) ventures, and the proposed trained algorithm satiates these requirements, offering efficiency and precision, rendering it appropriate for numerous IoT applications. …”
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Integrative omics analysis reveals the genetic basis of fatty acid composition in Brassica napus seeds by Yuting Zhang, Yunhao Liu, Zhanxiang Zong, Liang Guo, Wenhao Shen, Hu Zhao

“…To elucidate the genetic underpinnings governing fatty acid composition, we then employ a combination of GWAS, TWAS, and dynamic transcriptomic analysis during seed development, along with the POCKET algorithm. We predict six candidate genes that are associated with fatty acid composition. …”
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Automatic identification and characteristics analysis of crack tips in rocks with prefabricated defects based on deep learning methods. by Mingtao Gao, Minhui Li, Lu Chen, Zihao Guo, Chengyang Guo, Liping Li, Changsen Bu

“…Especially in the rock specimen analysis of prefabricated multi-angle cracks, image quality and algorithm adaptability have emerged as the critical bottlenecks restricting the identification accuracy. …”
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Robust Adaptive Multiple Backtracking VBKF for In-Motion Alignment of Low-Cost SINS/GNSS by Weiwei Lyu, Yingli Wang, Shuanggen Jin, Haocai Huang, Xiaojuan Tian, Jinling Wang

“…Furthermore, the modified variational Bayesian Kalman filter (VBKF) that accurately adjusts the measurement noise covariance matrix is proposed, and the Expectation–Maximization (EM) algorithm is employed to refine the prior parameter of the predicted error covariance matrix. …”
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Development of a New Correlation Model for Heat Transfer in Solar Air Heater with Corrugated Absorber Plates Using Swarm Optimization by Abdulkadir Kocer, Uğur Ercan, Zeki Yetgin, Filiz Karaomerlioglu

“…Also, for the first time in the literature, the recent swarm optimization algorithm, namely Honey Formation Optimization with Single Component (HFO-1), is used to optimize the solar air heater and a comparison study with a popular non-swarm optimization, namely the Generalized Reduced Gradient (GRG) optimization, is provided. …”
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Modelling the future of cleaner energy: Explainable artificial intelligence model for green hydrogen production rate estimation by Okorie Ekwe Agwu, Saad Alatefi, Ahmad Alkouh

“…The connection weights algorithm applied to the model enhances its explainability by illustrating the relative contributions of each input variable and their impacts on hydrogen yield. …”
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Investigating lightweight and interpretable machine learning models for efficient and explainable stress detection by Debasish Ghose, Ayan Chatterjee, Indika A. M. Balapuwaduge, Yuan Lin, Soumya P. Dash

“…Promisingly, among the developed models, the k-nearest neighbors (k-NN) algorithm has emerged as the best-performing model, achieving an accuracy score of 99.3% using only three selected features. …”
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MICROCIRCULATORY DISORDERS IN PATIENTS WITH ACUTE CORONARY SYNDROME by Sergey S. Krutitsky, Georgiy P. Plotnikov, Irina N. Sizova, Damir M. Galimzyanov, Dmitriy L. Shukevich, Evgeniy V. Grigoriev

“…Conclusions: Microcirculation parameters can be included in the diagnostic algorithm for ACS in patients with low cardiac output in order to predict cardiogenic shock and multiple organ failure. …”
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A Novel Electromagnetic Induction-Based Approach to Identify the State of Shallow Groundwater in the Oasis Group of the Tarim Basin in Xinjiang During 2000–2022 by Fei Wang, Yang Wei, Rongrong Li, Hongjiang Hu, Xiaojing Li

“…Specific results are shown below: Numerous algorithmic predictions show the ensemble learning algorithm with the smallest error explained 71% of the ECa spatial variability. …”
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Crashworthiness and optimization for foam-filled multi-layer composite lattice structures by Jiye Chen, Wangwang He, Hai Fang, Yong Zhuang, Zhixiong Zhang, Yufeng Zhao

“…Additionally, the equivalent compressive modulus and compressive strength of the FMCLSs were predicted using a rule of mixtures.…”
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APPLICATION OF THE SUPPORT VECTOR MACHINE, LIGHT GRADIENT BOOSTING MACHINE, ADAPTIVE BOOSTING, AND HYBRID ADABOOST-SVM MODEL ON CUSTOMERS CHURN DATA by Felice Elena, Robyn Irawan, Benny Yong

“…In order to classify and predict churning customers, machine learning techniques are required to build the classifier model. …”
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Integrative bioinformatics analysis and experimental validation of key biomarkers driving the progression of cirrhotic portal hypertension by Meilin Li, Lilin Jiang, Yunrui Ru, Zhonghua Lu, Peng Gu

“…In addition, protein-protein interaction networks and machine learning algorithms were conducted to screen center of candidate genes. …”
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Intelligent Grinding System for Medium/Thick Plate Welding Seams in Construction Machinery Using 3D Laser Measurement and Deep Learning by Qifeng Liu, Rencheng Zheng, Pengchao Li, Chao Liu, Deyuan Mi, Jian Wang, Wenli Xie

“…Experiments were conducted to verify the feasibility and accuracy of the 3D line laser measurement technology for weld seam inspections, and that the deep learning algorithm can effectively identify the type and location of the weld seam, thus predicting the trimming strategy. …”
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Investigating the Nonlinear Relationship Between the Built Environment and Urban Vitality Based on Multi-Source Data and Interpretable Machine Learning by Wenhao Liu, Zhen Yang, Chen Gui, Gen Li, Hongyi Xu

“…In this study, we investigate the potential non-linear interactions between the built environment and urban vitality by employing an interpretable spatial machine learning framework that integrates the XGBoost model with the SHapley Additive exPlanations (SHAP) algorithm. Additionally, we analyze the determinants of urban vitality across both micro and macro-scales using multi-source data, semantic segmentation models, and street view imagery. …”
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Physicochemical profiling and ranking of parkinson’s disease drugs through QSPR and Fuzzy TOPSIS analysis by YuLan Chen, Abdul Rauf, Aqsa Shafique, Fairouz Tchier, Adnan Aslam, Keneni Abera Tola

“…Molecular structures were encoded as adjacency matrices using MATLAB 2017, and six Sombor index variants—computed via a custom Maple 2020 algorithm—served as topological descriptors for QSPR modeling. …”
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Magnetic resonance imaging (MRI) radiomics in paediatric neuro-oncology: A systematic review of clinical applications, feature interpretation, and biological insights in the ch... by Teesta Mukherjee, Omid Pournik, Theodoros N Arvanitis

“…SVM was the most used classification algorithm ( n  = 9), with an AUC range of 0.858–0.977. …”
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Quantitative structure-properties relationship, molecular dynamic simulations and designs of some novel lubricant additives by Usman Abdulfatai, Adamu Uzairu, Sani Uba, Gideon Adamu Shallangwa

“…Quantitative structure-properties relationship (QSPR) method was used to design some novel antioxidant lubricant additives, while molecular dynamics simulations were used to calculate their dynamic binding energies on steel and to hydrogen-containing DLC (a-C: H) crystal surfaces. 29 synthesized antioxidant lubricant additives were collected from literature and geometrically optimized by Spartan’14 version 1.1.2 software while Genetic Function Algorithm (GFA) method of the material studio version 8.0 software was used to build the predictive QSPR model. …”
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Unveiling the immune microenvironment of complex tissues and tumors in transcriptomics through a deconvolution approach by Shu-Hwa Chen, Bo-Yi Yu, Wen-Yu Kuo, Ya-Bo Lin, Sheng-Yao Su, Wei-Hsuan Chuang, I.-Hsuan Lu, Chung-Yen Lin

“…The output from DOCexpress_fastqc is seamlessly formatted with mySORT, a web application that employs a deconvolution algorithm to determine the immune content across 21 cell subclasses. …”
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Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions by Qingchun Guo, Zhenfang He, Shanshan Li, Xinzhou Li, Jingjing Meng, Zhanfang Hou, Jiazhen Liu, Yongjin Chen

“…Additionally, the API could be accurately predicted based solely on the value recorded 3 days earlier. …”
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Characterizing Wake Behavior of Adaptive Aerodynamic Structures Using Reduced-Order Models by Kyan Sadeghilari, Aditya Atre, John Hall

“…The previous study used a genetic algorithm to obtain nine TAD shapes that maximized aerodynamic efficiency in Region 2. …”
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