Image Reconstruction Algorithm Based on Extreme Learning Machine for Electrical Capacitance Tomography by SU Ziheng, CHEN Deyun, WANG Lili

“…Aiming at the problem that the traditional ECT is not accurate in complex situations, this paper proposes a depth learning based inversion method Through the improvement and optimization of the traditional extreme learning machine, the image feature information obtained by the reconstructed image method is used as the training data, and the result obtained by inputting the data into the predictive model is used as the prior information The cost function is used to encapsulate the prior knowledge and domain expertise, and spatial regularizers and time regularizers are introduced to enhance sparsity The separated Bregman (SB) algorithm and the iterative shrinkage threshold (FIST) method are used to solve the specified cost function The final imaging result is obtained The simulation results show that the image reconstructed by this method has less than 10% error compared with the original flow pattern, and reduces artifacts and distortion, which improves the reconstructed image quality…”
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Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm by Kuan-Cheng Lin, Sih-Yang Chen, Jason C. Hung

“…The proposed method was used to identify the critical features that determine the pattern of botnet. The experimental results indicated that the method can be used for identifying the essential botnet features and that the performance of the proposed method was superior to that of genetic algorithms.…”
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Synthesis and Functional Optimization of a Vibratory Machine with a Parallel Mechanism Structure by Mircea-Bogdan Tătaru, Alexandru Rus, Tiberiu Vesselényi, Mariana Raţiu, Ioan Ţarcă

“…Vibrations are used in this case to increase the technological speed of separation of these materials. Vibratory machines are limited in functioning to fixed oscillation patterns. …”
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Connecting metal-organic framework synthesis to applications using multimodal machine learning by Sartaaj Takrim Khan, Seyed Mohamad Moosavi

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Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies by Qi Liu, Wanjing Ma

“…The results indicate that performance degradation follows a diminishing-return pattern, well modeled by an exponential function. …”
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The Synergy of Minds and Machines: Rethinking the AI-HI Relationship through Dialectical Reconstruction by Ridwan Ishola MOGAJI, Adewale Oluwaseun MOTADEGBE

“…Artificial intelligence on the other hand is the simulation of human cognitive functions by machines, especially in tasks such as problem-solving, pattern recognition, and decision-making, which often operates based on algorithms and data, both of which are unique and important in themselves. …”
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Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea by Maximilian Berthold, Pascal Nieters, Rahel Vortmeyer-Kley

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Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map by Satoshi Suzuki, Fumio Harashima

“…We present an intention estimator algorithm that can deal with dynamic change of the environment in a man-machine system and will be able to be utilized for an autarkical human-assisting system. …”
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Detecting expert’s eye using a multiple-kernel Relevance Vector Machine by Giuseppe Boccignone, Mario Ferraro, Sofia Crespi, Carlo Robino, Claudio de’Sperati

“…Decoding mental states from the pattern of neural activity or overt behavior is an intensely pursued goal. …”
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The nursing process and total health cost variability: an analysis using machine learning by Maria Consuelo Company-Sancho, Victor M. González-Chordá, Maria Isabel Orts-Cortés

“…To improve prediction accuracy and account for non-linear relationships, the analysis was completed using two machine learning models. Results 58% (n = 980,437) of the population presented some data from the nursing process, for individuals with an assessed pattern, the average cost was €2304.17 compared with €950.93 for those who had none; with a nursing diagnosis, the average cost was €1,666 versus €840 without it. …”
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Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh. by Arman Hossain Chowdhury, Md Siddikur Rahman

“…Exploratory spatial analysis, spatial regression and tree-based machine learning models were utilized to analyze the data.…”
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Remote sensing with machine learning for multi-decadal surface water monitoring in Ethiopia by Mathias Tesfaye, Lutz Breuer

“…Mann–Kendall trend analysis does not confirm a general pattern over time for the four sites, suggesting that local site characteristics, water management and anthropogenic impacts are superimposed on the likely effects of climate change. …”
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Interpretable Machine Learning for Population Spatialization and Optimal Grid Scale Selection in Shanghai by Yuan Cao, Hefeng Wang, Lanxuan Guo, Anbing Zhang, Xiaohu Wu

“…The population density estimates across different grid scales consistently exhibited a spatial gradient pattern of decreasing density from the urban center toward suburban areas. …”
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Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review by Florin-Stefan Zamfir, Madalina Carbureanu, Sanda Florentina Mihalache

“…This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. …”
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Forecasting Delivery Time of Goods in Supply Chains Using Machine Learning Methods by V. K. Rezvanov, O. M. Romakina, E. V. Zaytseva

“…The research objective is to describe a pattern of appropriate selection of the least resource-intensive delivery forecasting model based on the analysis of machine learning algorithms.Materials and Methods. …”
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Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning by Paul A. Constable, Javier O. Pinzon-Arenas, Luis Roberto Mercado Diaz, Irene O. Lee, Fernando Marmolejo-Ramos, Lynne Loh, Aleksei Zhdanov, Mikhail Kulyabin, Marek Brabec, David H. Skuse, Dorothy A. Thompson, Hugo Posada-Quintero

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Applying Causal Machine Learning to Spatiotemporal Data Analysis: An Investigation of Opportunities and Challenges by Christian M. Mulomba, Vogel M. Kiketa, David M. Kutangila, Pescie H. K. Mampuya, Junior N. Mukenze, Landry M. Kasunzi, Kyandoghere Kyamakya, Tasho Tashev, Selain K. Kasereka

“…To bridge this gap, we review causal machine learning (CML) techniques for spatiotemporal data, aiming to provide robust insights into their unique advantages. …”
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Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…Three widely applicable machine learning algorithms (Random Forests, Gradient Boosting Machine, and XGBoost) were used to screen the metrics, and then the corresponding predictive models were constructed using discriminative analysis, and the best performing model was selected as the target model. …”
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Machine learning for human mobility during disasters: A systematic literature review by Jonas Gunkel, Max Mühlhäuser, Andrea Tundis

“…However, existing models are limited in their applicability to disasters, as they are typically restricted to describing regular mobility patterns. Machine learning models trained to capture patterns observable in provided training data also face this limitation. …”
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Integrating remote sensing, GIS, and machine learning for zoonotic cutaneous leishmaniasis modelling by Fatemeh Parto Dezfooli, Mohammad Javad Valadan Zoej, Fahimeh Youssefi, Ebrahim Ghaderpour

“…Zoonotic Cutaneous Leishmaniasis (ZCL) is a vector-borne disease (VBD) characterized by distinct spatiotemporal patterns. Accurate evaluation of ZCL risk patterns necessitates the utilization of comprehensive epidemiological and ecological data. …”
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