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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Structure, short-range order, and phase stability of the Al x CrFeCoNi high-entropy alloy: insights from a perturbative, DFT-based analysis by Christopher D. Woodgate, George A. Marchant, Livia B. Pártay, Julie B. Staunton

“…When the underlying lattice is fcc, at low concentrations of Al, depending on the value of x, we predict either an L12 or D022 ordering emerging below approximately 1000 K. …”
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Travel route recommendation with a trajectory learning model by Xiangping Wu, Zheng Zhang, Wangjun Wan

“…Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. …”
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Identification of differentially expressed genes associated with ferroptosis in ulcerative colitis. by Fang Zhang, Xin Jiang, Xuyu Chen, Zheng Wang, Jianlei Xia, Bingcheng Wang, Mei Wang, Yanbing Ding

“…<h4>Results</h4>Eleven ferroptosis-related DEGs were identified (nine upregulated and two downregulated genes) in UC, with eight genes chosen from the PPI network. MCC algorithm demonstrated that SLC7A11, PSAT1, SLC7A5, ACSF2, and ACSL4 were hub genes, predicting TFs, miRNAs and drugs. …”
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Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques by Rami Mustafa A. Mohammad, Malak Aljabri, Menna Aboulnour, Samiha Mirza, Ahmad Alshobaiki

“…With the constant increase in the number of severe COVID-19 infections, an essential area of research has been directed towards predicting the mortality rate of these patients, in order to make informed medical decisions about the necessary healthcare priorities. …”
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Constructing a web recommender system using web usage mining and user’s profiles by T. Mombeini, A. Harounabadi, J. Rezaeian Sheshdeh

“…Therefore, recommender servers use the web usage mining technique to predict users’ browsing patterns and recommend those patterns in the form of a suggestion list. …”
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Incorporation of Sub‐Resolution Porosity Into Two‐Phase Flow Models With a Multiscale Pore Network for Complex Microporous Rocks by Sajjad Foroughi, Branko Bijeljic, Ying Gao, Martin J. Blunt

“…We then show that our model can successfully predict steady‐state relative permeability measurements on a water‐wet Estaillades carbonate sample within the uncertainty of the experiments and modeling. …”
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Quantitative structure-activity relationship study of some angiotensin-converting enzyme inhibitor drugs in the treatment of hypertension based on Monte Carlo optimization method by Shahram Lotfi, Shahin Ahmadi, Ali Azimi

“…Materials & Methods: In this study, quantitative structure-activity relationship to predict the inhibitory activity of the data set containing 255 angiotensin-converting enzyme inhibitor compounds based on the algorithm Monte Carlo was studied. …”
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Spatial transcriptomics and scRNA-seq: decoding tumor complexity and constructing prognostic models in colorectal cancer by Wei Song, Yatao Wang, Min Zhou, Fengqin Guo, Yanliang Liu

“…We developed a 13-gene prognostic signature (PS) using machine learning algorithm (StepCox backward), which predicts the prognosis of CRC patients. …”
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Variance exchange process: overcoming the problem of singular information matrices in quadratic three−variable response designs by Okim Ikpan, Felix Nwobi

“…For such matrices, the variances of predicted responses at variance points cannot be evaluated, and the variance exchange process, not possible. …”
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Methodological levels of research the post-industrial development of the city by Olha Suptelo

“…The main vector of these transformations is the transition to the post-industrial stage of development, which was predicted by a number of scientists from many industries at the turn of the nineteenth and twentieth centuries. …”
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