Encryption–decryption-based state estimation for nonlinear complex networks subject to coupled perturbation by Peixia Gao, Chaoqing Jia, Aozhan Zhou

“…Furthermore, a uniformly bounded condition is elaborated to evaluate the algorithm performance of EDBSE. Finally, an illustrative example is conducted to verify the validity of the introduced EDBSE method.…”
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Kernel Negative ε Dragging Linear Regression for Pattern Classification by Yali Peng, Lu Zhang, Shigang Liu, Xili Wang, Min Guo

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On-chip differential mode group delay manipulation based on 3D waveguides by Xiaofeng Liu, Quandong Huang, Jiaqi Ran, Jiali Zhang, Ou Xu, Di Peng, Yuwen Qin

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Integration of Accelerometers and Machine Learning with BIM for Railway Tight- and Wide-Gauge Detection by Jessada Sresakoolchai, Chayutpong Manakul, Ni-Asri Cheputeh

“…These data are processed and analyzed using supervised machine-learning algorithms to classify and predict potential tight- and wide-gauge events. …”
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An accuracy-privacy optimization framework considering user’s privacy requirements for data stream mining by Waruni Hewage, R. Sinha, M. Asif Naeem

“…Additionally, a data fitting module using kernel regression is integrated, a unique approach that predicts accuracy levels based on user-defined privacy thresholds. …”
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Design of Trabecular Bone Mimicking Voronoi Lattice-Based Scaffolds and CFD Modelling of Non-Newtonian Power Law Blood Flow Behaviour by Haja-Sherief N. Musthafa, Jason Walker

“…Designing scaffolds similar to the structure of trabecular bone requires specialised algorithms. Existing scaffold designs for bone tissue engineering have repeated patterns that do not replicate the random stochastic porous structure of the internal architecture of bones. …”
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Analysing the effectiveness of unsignalized crossing infrastructure in improving pedestrian safety using multiple data-driven approaches by Shengqi Liu, Harry Evdorides

“…While numerous studies have applied predictive models to traffic crash data, few have systematically analysed pedestrian crash severity at unsignalized crossings using multiple machine learning algorithms. …”
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Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption by Manar Ahmed Hamza, Maha M. Althobaiti, Fahd N. Al-Wesabi, Rana Alabdan, Hany Mahgoub, Anwer Mustafa Hilal, Abdelwahed Motwakel, Mesfer Al Duhayyim

“…In the existing system, random forest and ANN methods were used for TC and SMX for predicting the quantities of antibiotics in the CBMs. …”
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Topology aware multitask cascaded U-Net for cerebrovascular segmentation. by Pierre Rougé, Nicolas Passat, Odyssée Merveille

“…This loss requires computing the skeletons of both the manual annotation and the predicted segmentation in a differentiable way. Currently, differentiable skeletonization algorithms are either inaccurate or computationally demanding. …”
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Higgs boson production in association with massive bottom quarks at NNLO+PS by Christian Biello, Javier Mazzitelli, Aparna Sankar, Marius Wiesemann, Giulia Zanderighi

“…We present an extensive phenomenological analysis both at the inclusive level and considering bottom jets using flavour-tagging algorithms. By comparing four-flavour and five-flavour scheme predictions at NNLO+PS, we find that the NNLO corrections in the four-flavour scheme resolve the long-standing tension between the two schemes. …”
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Transcriptome associated with single-cell analysis reveal the role of S-palmitoylation in coronary artery disease by Yiming Xing, Xianhe Lin

“…Nomogram and ROC curve analyses revealed robust diagnostic predictive capabilities grounded in the three hub genes. …”
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Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 by Amit Kaimkuriya, S. Balaguru

“…WOA outperformed FEA in predictive accuracy, achieving error margins as low as 1.87% for minimum load and 2.31% for compressive strength. …”
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Big Data Analytics in IoT, social media, NLP, and information security: trends, challenges, and applications by Kamal Taha

“…A novel taxonomic framework is introduced to classify and analyze the suitability of algorithms based on empirical, experimental, and computational perspectives. …”
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Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations by Bogdan Felician Abaza, Vlad Gheorghita

“…The integration of intelligent monitoring systems and self-learning algorithms is reshaping machining processes, enabling higher efficiency, precision, and sustainability. …”
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Explicit and Implicit Knowledge in Large-Scale Linguistic Data and Digital Footprints from Social Networks by Maria Pilgun

“…This research aims to develop and test algorithms for analyzing both explicit and implicit information in user-generated content and digital interactions. …”
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Proteína LIC10494 de Leptospira interrogans serovar Copenhageni: modelo estructural y regiones funcionales asociadas by Orlando Emilio Acevedo, George Emílio Barreto, Janneth González-Santos

“…The secondary structure was obtained by consensus of the algorithms SOPM,PREDATOR GOR4, DPM and DSC. The approach to the tertiary structure was obtained using the algorithm MUSTER. …”
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Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 by Ozcan Ebru Ifakat

“…In this study, predictions were made on the growth performance of Alburnus sellal Heckel, 1843 from the Munzur River using back propagation artificial neural networks and ANN algorithms. …”
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Exploring entropy measures with topological indices on colorectal cancer drugs using curvilinear regression analysis and machine learning approaches. by Maria Fazal, Salma Kanwal, Muhammad Taskeen Raza, Asima Razzaque

“…Additionally, we propose the integration of machine learning (ML) techniques to further enhance the predictive accuracy and robustness of our models. By leveraging advanced ML algorithms, we aim to uncover more complex, non-linear relationships between topological indices and drug efficacy, potentially leading to more accurate predictions and better-informed drug design strategies.…”
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High-dimensional outlier detection based on deep belief network and linear one-class SVM by Haoqi LI, Na YING, Chunsheng GUO, Jinhua WANG

“…Aiming at the difficulties in high-dimensional outlier detection at present,an algorithm of high-dimensional outlier detection based on deep belief network and linear one-class SVM was proposed.The algorithm firstly used the deep belief network which had a good performance in the feature extraction to realize the dimensionality reduction of high-dimensional data,and then the outlier detection was achieved based on a one-class SVM with the linear kernel function.High-dimensional data sets in UCI machine learning repository were selected to experiment,result shows that the algorithm has obvious advantages in detection accuracy and computational complexity.Compared with the PCA-SVDD algorithm,the detection accuracy is improved by 4.65%.Compared with the automatic encoder algorithm,its training time and testing time decrease significantly.…”
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The analysis of entrepreneurship evaluation system for talent cultivation in artistic creativity and animation under artificial intelligence by Hao Xie, Zijian Zhao

“…Moreover, this work intends to propose a new perspective for talent cultivation in animation majors in higher education institutions. Addressing the strong subjectivity and lack of quantitative indicators in traditional artistic creativity evaluation methods, this work constructs an artistic creativity innovation evaluation model based on the Backpropagation Neural Network (BPNN) integrated with the Style-Based Generative Adversarial Network (StyleGAN) algorithm. …”
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