Proposing a New Method for Customer Segmentation Based on Their Level of Loyalty and Defining Appropriate Strategies for Each Segment by Samira KHodabandehlou, Ali Akbar Niknafs

“…The obtained data have been analyzed using Clementine 14.2 software application using MLP and RBF neural networks as well as the K-means algorithm. The results of the study show that the proposed method provides the highest level of accuracy for predicting the customers’ loyalty. …”
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A novel re-entrant circular star-shaped auxetic honeycomb with enhanced energy absorption and anisotropic Poisson’s ratio by Danrong Shi, Zhuangzhuang Wang, Yongwei Li, Ruyuan Huo, Jin Zhang, Jianguo Cai

“…A theoretical framework based on plastic dissipation was developed to predict the plateau stresses, and the influence of key geometric parameters on deformation modes and energy absorption was systematically examined. …”
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An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment by Julian R. Rice, G. Andrew Fricker, Jonathan Ventura

“…Specifically, we develop and train a novel PointNet-based neural network architecture to predict tree locations directly from LiDAR data augmented with multi-spectral imagery. …”
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Integrated Simulation Process Chain: From 3D Roll Forming Design to Crash Analysis by Sedlmaier Albert, Dietl Thomas, Abee André, Harrasser Johann, Pereira Miguel Sousa

“…This holistic approach enhances the fidelity of product performance predictions, offering a robust framework for optimizing forming processes and end-use structural integrity.…”
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Optimal Report Strategies for WBANs Using a Cloud-Assisted IDS by Shigen Shen, Keli Hu, Longjun Huang, Hongjie Li, Risheng Han, Qiying Cao

“…Experiments show the effectiveness of the dynamic multistage IDSRG in predicting the type and optimal strategy of a malicious body sensor.…”
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Inverse Identification of Constituent Elastic Parameters of Ceramic Matrix Composites Based on Macro–Micro Combined Finite Element Model by Sheng Huang, Le Rong, Zhuoqun Jiang, Yuriy V. Tokovyy

“…A BP neural network was used to predict the multiscale stiffness, considering the influence of the porous structure on the macroscopic stiffness of the material. …”
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Enhancement of joint quality for laser welded dissimilar material cell-to-busbar joints using meta model-based multi-objective optimization by Andreas Andersson Lassila, Tobias Andersson, Rohollah Ghasemi, Dan Lönn

“…Artificial neural network-based meta models, trained on numerical results from computational fluid dynamics simulations of the laser welding process, are used to predict and evaluate the joint quality. A set of optimized process parameters is identified, in order to simultaneously maximize the interface width for the joints, and minimize the formation of undercuts and in-process temperatures. …”
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A model of feature extraction for well logging data based on graph regularized non-negative matrix factorization with optimal estimation by Kehong Yuan, Youlin Shang, Haixiang Guo, Yongsheng Dong, Zhonghua Liu

“…Abstract Reservoir oil-bearing recognition is the process of predicting reservoir types based on well logging data, which determines the accuracy of recognition. …”
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Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter by Matteo Perini, Teresa K. Yamana, Marta Galanti, Jiyeon Suh, Roselyn Kaondera-Shava, Jeffrey Shaman

“…Background: Understanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics and pandemics, such as COVID-19. …”
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Identification of Road Black Spots Based on the Sliding Window Optimization and Safety Performance Function Development by Shahin Shabani, Jalal Ayoubinejad, Nassir Baradaran Rahmanian

“…The optimization methodology employed in this study is as follows: Firstly, the road is segmented, and for each segment, different scenarios of window lengths are chosen using the Density-Based Spatial Clustering of Applications with Noise algorithm. Next, a Safety Performance Function is developed to calculate the predicted and expected number of crashes, as well as the Potential Safety Improvement, for each window movement across all selected scenarios within the segment. …”
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Enhancing the Accuracy of Image Classification for Degenerative Brain Diseases with CNN Ensemble Models Using Mel-Spectrograms by Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim

“…The proposed ternary classification algorithm integrates the predictions of binary CNN classifiers through a majority voting ensemble strategy. …”
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Trajectory-Based Optimal Area Forwarding for Infrastructure-to-Vehicle Data Delivery with Partial Deployment of Stationary Nodes by Liang-Yin Chen, Song-Tao Fu, Jing-Yu Zhang, Xun Zou, Yan Liu, Feng Yin

“…To adapt with the real world, TOAF supposes that stationary nodes are partially installed at intersections in VANETs, and nodes' trajectories can be calculated and predicted, such as using cloud services and GPS, to find the optimal area where the destination vehicle may receive a packet timely. …”
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U-net based approach for pectoralis muscle segmentation in digital mammography by Francesca Angelone, Alfonso Maria Ponsiglione, Roberto Grassi, Francesco Amato, Mario Sansone

“…The U-Net network was therefore implemented on patches extracted along the straight line with which the muscle-breast edge was first estimated. The predicted patches are repositioned to perform an edge refinement and obtain the total breast mask, using histogram-based thresholding to segment the background from the breast. …”
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Driving Factors and Future Trends of Wildfires in Alberta, Canada by Maowei Bai, Qichao Yao, Zhou Wang, Di Wang, Hao Zhang, Keyan Fang, Futao Guo

“…This study analyzed the relationship between climate and wildfire and used a random forest algorithm to predict future wildfire frequencies in Alberta, Canada. …”
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Outdoor Dataset for Flying a UAV at an Appropriate Altitude by Theyab Alotaibi, Kamal Jambi, Maher Khemakhem, Fathy Eassa, Farid Bourennani

“…We used deep learning (DL) to develop a model to classify and predict the image types. Eleven experiments performed with the Gazebo simulator using a drone and a convolution neural network (CNN) proved the database’s effectiveness in avoiding different types of obstacles while maintaining an appropriate altitude and the drone’s ability to navigate in a 3D environment.…”
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Artificial Intelligence in Chest Radiography—A Comparative Review of Human and Veterinary Medicine by Andrea Rubini, Roberto Di Via, Vito Paolo Pastore, Francesca Del Signore, Martina Rosto, Andrea De Bonis, Francesca Odone, Massimo Vignoli

“…Deep learning-based models assist radiologists by detecting patterns, generating probability maps, and predicting outcomes like heart failure. However, AI is still supplementary to clinical expertise due to challenges such as data limitations, algorithmic biases, and the need for extensive validation. …”
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Design Optimization of the Multi-layer Switched Reluctance Motor to Minimize Torque Ripple and Maximize Average Torque by Payam Vahedi, Babak Ganji, Seyed Ebrahim Afjei

“…In the present paper, this waveform is predicted using an analysis of the motor based on finite element method (FEM). …”
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Is human-like decision making explainable? Towards an explainable artificial intelligence for autonomous vehicles by Jiming Xie, Yan Zhang, Yaqin Qin, Bijun Wang, Shuai Dong, Ke Li, Yulan Xia

“…An empirical investigation using real driving trajectory data from Kunming, China, confirms the ability of our approach to predict potential driving decisions with high accuracy while providing the rationale implicit AV decisions. …”
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Experimental study on mechanical properties of triaxial geogrid reinforced marine coral sand-clay mixture based on 3D printing technology by Danda Shi, Kaiwei Xu, Zhiming Chao, Zhiming Chao, Peng Cui, Peng Cui

“…This model was applied for the first time to predict the strength of MCCM. Results show that lower clay content, more reinforcement layers, and higher confining pressure significantly enhance the strength and cohesion of MCCM, with little effect on the internal friction angle. …”
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Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing by Thomas Leditznig, Hermann Klug

“…The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.…”
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