A comparison of tools used for tuberculosis diagnosis in resource-limited settings: a case study at Mubende referral hospital, Uganda. by Adrian Muwonge, Sydney Malama, Barend M de C Bronsvoort, Demelash Biffa, Willy Ssengooba, Eystein Skjerve

“…Clinical variables from a questionnaire and DZM were used to predict TB status in multivariable logistic and Cox proportional hazard models, while optimization and visualization was done with receiver operating characteristics curve and algorithm-charts in Stata, R and Lucid-Charts respectively.…”
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Validation of eight endotypes of lupus based on whole-blood RNA profiles by Peter E Lipsky, Prathyusha Bachali, Amrie C Grammer, Erika Hubbard

“…Objective We previously described a classification system of persons with SLE based on whole blood RNA profiles and a random forest (RF) algorithm to predict individual patient endotypes. …”
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Application of Remote Sensing and GIS in Monitoring Forest Cover Changes in Vietnam Based on Natural Zoning by An Nguyen, Vasily Kovyazin, Cong Pham

“…The study’s reliability was confirmed by a Kappa coefficient of 0.81–0.89. To predict forest cover changes, two methods—the CA-Markov model and the MOLUSCE module—were compared. …”
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Assessing the Impact of Land Use and Land Cover Change on Environmental Parameters in Khyber Pakhtunkhwa, Pakistan: A Comprehensive Study and Future Projections by Mehjabeen Khan, Ruishan Chen

“…Projections for 2100 predict LST rising to 55.3 °C, with NDVI, MNDWI, and NDMI dropping to 0.36, 0.17, and 0.21, respectively. …”
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Draft genome dataset of Streptomyces griseoincarnatus strain R-35 isolated from tidal pool sedimentsMendeley DataNCBI by Danielle Dana Mitchell, Jo-Marie Vreulink, Alaric Prins, Marilize Le Roes-Hill

“…The phylogenomic positioning of S. griseoincarnatus strain R-35 was determined using the Type Strain Genome Server (TYGS) and was found to be related to S. griseoincarnatus JCM 4381T, with a digital DNA-DNA hybridisation (dDDH) value of 84.1%, and an OrthoANIu value of 98.22%. The CARD RGI algorithm on Proksee predicted the presence of 6,107 antimicrobial resistance (AMR) features, 27 biosynthetic gene clusters (BGCs) were predicted using antiSMASH, while 189 carbohydrate-active enzymes (CAZymes) were predicted using dbCAN3. …”
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In vivo and in silico dynamics of the development of Metabolic Syndrome. by Yvonne J W Rozendaal, Yanan Wang, Yared Paalvast, Lauren L Tambyrajah, Zhuang Li, Ko Willems van Dijk, Patrick C N Rensen, Jan A Kuivenhoven, Albert K Groen, Peter A J Hilbers, Natal A W van Riel

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A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources by M. Wajahat Hassan, Thamer Alquthami, Ahmad H. Milyani, Ashfaq Ahmad, Muhammad Babar Rasheed

“…First, the load demand is predicted through a convolutional neural network (CNN) by taking the ISO-NECA hourly real-time data. …”
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Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens by Cheng Han, Shiying Lu, Pan Hu, Jiang Chang, Deying Zou, Feng Li, Yansong Li, Qiang Lu, Honglin Ren

“…Integrating these genes into an ML model based on the support vector machine (SVM) algorithm allows us to predict the zoonotic potential of various Brucella strains with high accuracy. …”
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Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review by Palpasa Shrestha, Bibek Shrestha, Jati Shrestha, Jun Chen

“…The machine learning algorithm first computes the image characteristics deemed significant for making predictions or diagnoses about unseen images. …”
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Forced Response Vibration Analysis of the Turbine Blade with Coupling between the Normal and Tangential Direction by Aram Mahmoodi, Hamid Ahmadian

“…It is shown that the contact model with consideration with coupling effect between tangential and normal direction can predict experimental results (amplitude and frequency of resonance) most of the other contact models used in the turbine field. …”
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Target Detection and Image Enhancement for Underwater Environment: Research on Improving YOLOv7 by Yang Luo, Wen Feng

“…In addition, by introducing the Focal-EIoU loss function, combined with a more accurate penalty mechanism, the matching between the predicted frame and the actual labelled frame is made more accurate, which effectively improves the accuracy and reliability of detection. …”
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Cooperative spectrum sensing scheme based on crowd trust and decision-making mechanism by Xiao-mao WANG, Chuan-he HUANG, Yi-long LV, Bin WANG, Xi-ying FAN, Hao ZHOU

“…A distributed consensus-based scheme by simulating the crowd trust and decision-making mechanism was proposed.This scheme firstly predicts the dynamic trust value among sensing users by the previous cooperative process,and then generates the user's relative trust value,and makes the data interaction among the users by using the combination of relative trust value and decision-making mechanism.All users' state can reach a consensus as the credible and iterative data interaction.All users get the final results by the determinant algorithm.This new spectrum sensing scheme utilizes the imbalance of each users' sensing ability in the real environment.Each secondary user can maintain cooperation with others only through the local information exchange with the neighbors.It is quite different from traditional spectrum sensing scheme,such as OR-rule,1-out-of-N rule and ordinary iterative method.Three SSDF attacks were analysed,on the basis of the corresponding anti-attack policy was proposed.Theoretical analysis and simulation results show that the new scheme is better than the existing cooperative spectrum sensing algorithm in accuracy and security.New scheme not only can improve the accuracy of spectrum sensing but also has the strong anti-attack capability.…”
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Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique by Camille Franklin Mbey, Jacques Bikai, Felix Ghislain Yem Souhe, Vinny Junior Foba Kakeu, Alexandre Teplaira Boum

“…Therefore, we proposed a hybrid artificial intelligence (AI) technique considering sudden changes of consumption in order to accurately predict fraudulent consumers in the smart network. …”
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Making sense of transformer success by Nicola Angius, Pietro Perconti, Alessio Plebe, Alessandro Acciai

“…In particular, available experimental studies turned to test the theory of mind, discourse entity tracking, and property induction in NLMs are examined under the light of the functional analysis in the philosophy of cognitive science; the so-called copying algorithm and the induction head phenomenon of a Transformer are shown to provide a mechanist explanation of in-context learning; finally, current pioneering attempts to use NLMs to predict brain activation patterns when processing language are here shown to involve what we call a co-simulation, in which a NLM and the brain are used to simulate and understand each other.…”
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Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks by Lei Cheng, Zhi Yu, Xinxi Liu

“…The construction of an elastoplastic constitutive model for energy dissipation and crack evolution in rocks is crucial for accurately predicting their failure processes. This study first constructs a theoretical elastoplastic constitutive model by analyzing the mechanical properties of rocks, energy dissipation, and crack evolution under conventional triaxial compression. …”
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Semi-supervised multi-task learning based framework for power system security assessment by Muhy Eddin Za’ter, Amir Sajadi, Bri-Mathias Hodge

“…Additionally, this framework incorporates a confidence measure for its predictions, enhancing its reliability and interpretability. …”
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Are Suggested Hiking Times Accurate? A Validation of Hiking Time Estimations for Preventive Measures in Mountains by Marco Vecchiato, Nicola Borasio, Emiliano Scettri, Vanessa Franzoi, Federica Duregon, Sandro Savino, Andrea Ermolao, Daniel Neunhaeuserer

“…MOVE demonstrated superior accuracy, offering personalized hiking time predictions based on user-specific data and trail characteristics. …”
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Time series forecasting of infant mortality rate in India using Bayesian ARIMA models by Anuj Singh, Tripti Tripathi, Rakesh Ranjan, Abhay K. Tiwari

“…Forecasts based on this model predict a steady decline in IMR from 2024 to 2033. …”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The approach assists in predicting key values of the dependent variable associated with defect occurrence, enabling foundries to enhance product quality, reduce waste, and augment overall production process efficiency. …”
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Bias-aware degradation models for reinforced concrete bridges based on XAI by Francesca Marsili, Filippo Landi, Rade Hajdin, Sylvia Kessler

“…The analysis comprises four steps: (1) cluster analysis of damage transition times using the k-means algorithm to identify damage patterns with similar damage evolution rates (fast, normal, slow, corresponding to bridge components with a fragile, normal, and robust deterioration behavior); (2) Random Forest classification to predict the cluster based on bridge inventory data; (3) SHAP analysis to explain the predictions of the Random Forest classifier; (4) application of the gamma process to the grouped damage transition times to assess damage evolution. …”
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