Using data analytics to distinguish legitimate and illegitimate shell companies by Milind Tiwari, Adrian Gepp, Kuldeep Kumar

“…We use a hybrid approach combining graph analytics and supervised machine learning. The resulting detection models have an impressive classification accuracy ranging between 88.17 % and 97.85 %. …”
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Methods for User-Controlled Synthesis of Blood Vessel Trees in Medical Applications: A Survey by Nikolaus Rauch, Matthias Harders

“…This ranges from anatomical visualizations, via surgical training systems, to machine learning-based anatomical segmentation frameworks. …”
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Predictive Modeling of Chronic Kidney Disease Progression with Ensemble Learning Techniques by priha bhatti

“…Utilizing an extensive dataset, the study employs ten carefully designed stages, covering data analysis, missing data management, normalization, and training of machine learning models. The model that we have proposed exhibits superior performance compared to the existing approaches, attaining a noteworthy accuracy rate of 98.74%. …”
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Multiple Feature Fusion Based on Co-Training Approach and Time Regularization for Place Classification in Wearable Video by Vladislavs Dovgalecs, Rémi Mégret, Yannick Berthoumieu

“…We propose to combine several machine learning approaches in a time regularized framework for image-based place recognition indoors. …”
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DroidBet:event-driven automatic detection of network behaviors for Android applications by Song-jie WEI, Gao-xiang WU, Na LUO, Zhao-wei SHI, Zi-yang ZHOU

“…The most Android applications connect to Internet to communicate with the outside world.Applications’ network-related activities were reflected and described with network traffic.By analyzing and modeling network traffic of Android applications,network behaviors of Android applications could be subsequently characterized.Therefore,DroidBet:an event-driven network behavior automatic detection system was presented,to test and evaluate Android applications automatically.Firstly,a scenario simulation event library was built to simulate the events that applications may be executed in the process,so as to trigger the network behavior of the application as much as possible.Then,the test sequence based on the state transition analysis method was automatically generated,and the network behavior was dynamically collected during the application testing process.Finally,the machine learning method was used to learn and train the collected network behavior,and the network behavior model based on BP neural network was generated to detect the behavior of the unknown Android application.The experimental results show that DroidBet can effectively trigger and extract the network behavior of the application,which has the advantages of high accuracy and low resource cost.…”
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Modelling potential impacts of climate change on the oak spatial distribution (Case study: Ilam and Lorestan provinces) by Mohammad Safaei, Hani Rezayan, Parviz Zeaiean Firouzabadi, Ali asghar Torahi

“…For this purpose, five regression-based and machine learning approaches, four climatic variables related to temperature and precipitation and two optimistic (RCP 2.6) and pessimistic (RCP 8.5)  greenhouse-gas scenarios were used. …”
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Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies by Paul Bosch, Mauricio Herrera, Julio López, Sebastián Maldonado

“…We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolution of math problems. …”
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Error-driven upregulation of memory representations by Alexander Weuthen, Hans Kirschner, Markus Ullsperger

“…A localizer-based machine learning model displayed a network of cognitive control regions, including posterior medial frontal and dorsolateral prefrontal cortices, whose activity was related to face-processing evidence in the fusiform face area. …”
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Assessment of resilient modulus of soil using hybrid extreme gradient boosting models by Xiangfeng Duan

“…However, experimental methods tend to be time-consuming and costly; regression equations and constitutive models usually have limited applications, while the predictive accuracy of some machine learning studies still has room for improvement. …”
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Model of Automatic Classification and Localization of Images by L. V. Serebryanaya, K. Y. Bochkarev, A. Y. Popitich

“…Analysis of models, methods and algorithms has shown that for solving the set task it is preferable to use machine learning, an artificial neural network and a genetic algorithm. …”
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Rapid review of the COVID-19 pandemic’s impact on the digitalization of higher education by Chukwuere Joshua

“…Many digitalization tools such as social media, augmented reality, machine learning, and other emerging technologies are used to accelerate the higher education process during the COVID-19 pandemic (new normal).…”
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Artificial Intelligence in Emotion Quantification : A Prospective Overview by Feng Liu

“…Multi-modal data sources, including facial expressions, speech, text, gestures, and physiological signals, are combined with machine learning and deep learning methods in modern emotion recognition systems. …”
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Generalization of neural network models for complex network dynamics by Vaiva Vasiliauskaite, Nino Antulov-Fantulin

“…A recently employed machine learning tool for studying dynamics is neural networks, which can be used for solution finding or discovery of differential equations. …”
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The effect of digital economy on rural workforce occupation transformation ability: Evidence from China by Bin Xiong, Qi Sui

“…Further research revealed that the digital economy has a “spillover effect” on the labor force’s occupation transformation ability. Meanwhile, machine learning analysis revealed that age, house value, and total household income capacity are the primary elements driving heterogeneity in the digital economy’s ability to impact the occupation transformation ability. …”
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Data Mining Classification Techniques for Diabetes Prediction by Hindreen Rashid Abdulqadir, Adnan Mohsin Abdulazeez, Dilovan Assad Zebari

“…Many analyses include multiple Machine Learning algorithms for various disease assessments and predictions to improve overall issues. …”
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Towards data-driven electricity management: multi-region uniform data and knowledge graph by Vid Hanžel, Blaž Bertalanič, Carolina Fortuna

“…This data enables machine learning tasks such as disaggregation, demand forecasting, appliance ON/OFF classification, etc. …”
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Fine-Grained Arabic Post (Tweet) Geolocation Prediction Using Deep Learning Techniques by Marwa K. Elteir

“…Existing approaches for Arabic geolocation are limited in accuracy and often rely on basic machine learning techniques. Additionally, advancements in tweet geolocation for other languages often rely on distinct datasets, hindering direct comparisons and assessments of their relative performance on Arabic datasets. …”
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An Algorithm for Discretization of Real Value Attributes Based on Interval Similarity by Li Zou, Deqin Yan, Hamid Reza Karimi, Peng Shi

“…Discretization algorithm for real value attributes is of very important uses in many areas such as intelligence and machine learning. The algorithms related to Chi2 algorithm (includes modified Chi2 algorithm and extended Chi2 algorithm) are famous discretization algorithm exploiting the technique of probability and statistics. …”
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Global urban activity changes from COVID-19 physical distancing restrictions by Srija Chakraborty, Eleanor C. Stokes, Olivia Alexander

“…Here we use satellite-derived nighttime lights to quantify and map daily changes in human activity that are atypical for each urban area globally for two years after the onset of the pandemic using machine learning anomaly detectors. Metrics characterizing changes in lights from pre-COVID baseline in human settlements and quality assurance measures are reported. …”
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A dataset of Uniswap daily transaction indices by network by Nir Chemaya, Lin William Cong, Emma Jorgensen, Dingyue Liu, Luyao Zhang

“…Additionally, we developed an open-source Python framework for calculating decentralization indices, making this dataset highly useful for advanced machine learning research. Our work provides valuable resources for data scientists and contributes to the growth of the intelligent Web3 ecosystem.…”
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