Identification of Fake Comments in E-Commerce Based on Triplet Convolutional Twin Network and CatBoost Model by Juanjuan Peng

“…At the same time, the accuracy of the four features was 0.8931, 0.9012, 0.8846, and 0.8961, respectively, which verified the excellent predictive recognition performance. Compared with the other two comparison algorithms, the detection time required by the proposed model was the shortest at 115us and consumed the least amount of resources. …”
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Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. by Maggy T Sikulu-Lord, Michael D Edstein, Brendon Goh, Anton R Lord, Jye A Travis, Floyd E Dowell, Geoffrey W Birrell, Marina Chavchich

“…The spectra were analysed using machine learning to develop predictive models for infection.<h4>Findings</h4>Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). …”
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A cotton organ segmentation method with phenotypic measurements from a point cloud using a transformer by Fu-Yong Liu, Hui Geng, Lin-Yuan Shang, Chun-Jing Si, Shi-Quan Shen

“…The correlation coefficients between the measured values of four phenotypic parameters (plant height, leaf area, and boll volume) ranged from 0.95 to 0.97, demonstrating the accurate predictive capability of the model for these key traits. …”
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Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion by Zifeng Zhang, Ning Li, Yuhang Qian, Huilin Cheng

“…Results This study developed 30 predictive models using ten classifiers across two imaging sequences. …”
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A Probabilistic Approach to Surrogate‐Assisted Multi‐Objective Optimization of Complex Groundwater Problems by Reygie Q. Macasieb, Jeremy T. White, Damiano Pasetto, Adam J. Siade

“…Yet, surrogate model predictive uncertainty remains a profound challenge for MOO, as it could mislead surrogate‐assisted optimization, which may result in either little computational savings from excessive retraining, or lead to suboptimal and/or infeasible solutions. …”
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Development and calibration of roundabout safety performance functions using machine learning: a case study from Amman, Jordan by Diana Al-Nabulsi, Aya Hassouneh

“…In contrast, the Random Forest model achieved an R 2 of 0.992, RMSE of 0.089, and MAE of 0.06, reflecting excellent model fit and predictive accuracy. A key innovation of this research lies in demonstrating that robust, data-driven SPF models can be pre-calibrated using high-resolution geometric and traffic inputs without the need for localized long-term crash records corresponding to the same features and region. …”
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Analyzing the Impact of Geoenvironmental Factors on the Spatiotemporal Dynamics of Forest Cover via Random Forest by Hendaf N. Habeeb, Yaseen T. Mustafa

“…The Random Forest model demonstrated high predictive accuracy, achieving an R² value of 0.918 (RMSE of 0.016 and MAE of 0.013) for 2013 and 0.916 (RMSE of 0.018 and MAE of 0.014) for 2023, underscoring the model’s robustness in handling nonlinear ecological processes. …”
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USING ARTIFICIAL INTELLIGENCE IN OBSTETRICS TO DIAGNOSE FETAL MALFORMATIONS AND PREVENT DISEASES by Елена Валерьевна Литвинова, Оксана Владимировна Носкова

“…The effectiveness of using artificial intelligence and various algorithms based on it to improve the analysis of two-dimensional (2D) and three-dimensional (3D) ultrasound images of fetal structures, assessment of organ function and diagnosis of diseases was proved. …”
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Research on auditory and olfactory regulation methods for abnormal driver emotions based on EEG signals by Bangbei Tang, Bangbei Tang, Yan Li, Yingzhang Wu, Yilun Li, Qizong Yue

“…Notably, music regulation was found to be most effective for mild and moderate tension, reducing tension levels by 63.33% and 68.75%, respectively, whereas fragrance was more efficacious in high tension situations, achieving a 43% reduction. For anger, fragrance regulation proved more beneficial for mild and moderate anger (reducing anger by 66.67 and 73.75%, respectively), while music regulation was most effective in mitigating high anger levels, resulting in a 58% reduction. …”
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Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis by Feng Liu, Tingting Zhang, Yongqiang Yang, Kailun Wang, Jinlan Wei, Ji-Hua Shi, Dong Zhang, Xia Sheng, Yi Zhang, Jing Zhou, Faming Zhao

“…Multiple machine learning algorithms were applied to develop tumor purity prediction models based on transcriptomic profile of OS. …”
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Evaluation of four clinical decision rules in children with minor head trauma: NEXUS II, PECARN, CHALICE, and CATCH by Majid Zamani, Farhad Heydari, Farzin Feyzollahi, Mehrdad Esmaillian, Amir Bahador Boroumand

“…Background: Clinical decision rules could potentially help emergency department (ED) trauma triage, allowing clinicians to prioritize treatment for the most severely injured patients.Objectives: This study evaluated and compared the diagnostic accuracy of the National Emergency X-radiography Utilization Study II (NEXUS II), the Pediatric Emergency Care Applied Research Network (PECARN), the Canadian Assessment of Tomography for Childhood Head Injury (CATCH), and the Children’s Head Injury Algorithm for the Prediction of Important Clinical Events (CHALICE) in identifying intracranial injury (ICI) in children with minor head trauma.Methods: This prospective, cross-sectional, descriptive-comparative study was conducted on children with mild head trauma who presented to the ED. …”
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Exploring the role of repetitive negative thinking in the transdiagnostic context of depression and anxiety in children by Kuiliang Li, Lei Ren, Xiao Li, Chang Liu, Xuejiao Tan, Ming Ji, Xi Luo

“…Additionally, four machine learning algorithms (random forest, support vector machine, decision tree, and extreme gradient boosting) were applied to predict the co-occurrence of depression and anxiety symptoms. …”
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Adaptive Control and Market Integration: Optimizing Distributed Power Resources for a Sustainable Grid by Josue N. Otshwe, Bin Li, Songsong Chen, Feixiang Gong, Bing Qi, Ngouokoua J. Chabrol

“…System performance is improved using advanced control strategies together with real-time market-responsive changes and predictive algorithms. The efficacy of the proposed methodology is validated through a detailed simulation of a small island grid using mixed-integer linear programming (MILP) and particle swarm optimization (PSO), which demonstrates significant operational improvements. …”
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Exploring the possibilities of MADDPG for UAV swarm control by simulating in Pac-Man environment by Artem Novikov, Sergiy Yakovlev, Ivan Gushchin

“…Traditional Rule-Based Pursuit and Prediction Algorithms inspired by the behaviors of Blinky and Pinky ghosts from the classic Pac-Man game are included as benchmarks to assess the impact of learning-based methods. …”
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Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods by Wei Guo, Hao Chen, Feng Wang, Yingjiao Chi, Wei Zhang, Shan Wang, Kezhu Chen, Hong Chen

“…BackgroundSeptic shock in children is an infectious disease caused by low immunity, and its mortality is very high. Early prediction of the risk of death in children with septic shock is helpful for clinicians to judge the severity of the disease, take active treatment measures, and improve the adverse outcomes of patients. …”
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Study on the Processing Technology of Calamine Calcination by Near-Infrared Spectroscopy by Xiaodong Zhang, Long Chen, Yu Bai, Keli Chen

“…Then, matching the near-infrared spectroscopy data with the T value and establishing the T value analysis model using the PLS algorithm were performed. Through cross and independent validation and evaluation, it was proved that the two models were very effective and had strong predictive abilities. …”
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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway by Jun-luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-shun Liang, Hong-hao Zhou, Zhao-qian Liu, Wen-xu Hong, Ji-ye Yin

“…Finally, target gene prediction and functional enrichment analysis were performed on these key RNAs. …”
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Unraveling the oxidative stress landscape in diabetic foot ulcers: insights from bulk RNA and single-cell RNA sequencing data by Jialiang Lin, Linjuan Huang, Weiming Li, Haijun Xiao, Mingmang Pan

“…Functional enrichment analysis showed significant involvement in oxidative stress response. Drug prediction highlighted Thymoquinone and Erlotinib as potential therapeutic candidates. …”
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SUDDEN DEATH IN HYPERTROPHIC CARDIOMYOPATHY: SEARCH FOR NEW RISK FACTORS by N. S. Krylova, E. A. Kovalevskaya, N. G. Poteshkina, A. E. Demkina, F. M. Khashieva

“…The issue for prediction of SCD in this pathology does not lose its importance.Aim. …”
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Multiobjective optimization of CO2 injection under geomechanical risk in high water cut oil reservoirs using artificial intelligence approaches by Fankun Meng, Jia Liu, Gang Tong, Hui Zhao, Chengyue Wen, Yuhui Zhou, Vamegh Rasouli, Minou Rabiei

“…The results show that compared with FOR and CSR, due to the serious nonlinearity, the training and prediction of FSD with the proxy model are not very good. …”
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