Oversampling based on generative adversarial networks to overcome imbalance data in predicting fraud insurance claim by Ranu Agastya Nugraha, Hilman Ferdinandus Pardede, Agus Subekti

“…The problem of data imbalance would affect machine learning capabilities which tend to be biased towards the majority class. Recently, many efforts have been employed to use deep learning for data augmentation. …”
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Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What’s the Best Approach for Crime Forecasting? by Eugenio Cesario, Paolo Lindia, Andrea Vinci

“…This study examines the impact of various partitioning techniques on crime forecasting performance, comparing the traditional static division of the city into police districts with machine learning approaches, specifically density clustering algorithms, for detecting crime hotspots. The experimental evaluation, conducted on two real-world case studies, i.e. …”
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Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging by Youngjin Han, Inwhee Joe

“…An ensemble model combining seven machine learning algorithms—Logistic Regression, Support Vector Machine, KNN, Random Forest, XGBoost, LightGBM, and CatBoost—was applied to predict survival outcomes. …”
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Multi-View Collaborative Training and Self-Supervised Learning for Group Recommendation by Feng Wei, Shuyu Chen

“…One of the key challenges facing recommendation algorithms is data sparsity, a limitation that is even more severe in group recommendation than in traditional recommendation tasks. …”
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Nutrient estimation in the Peruvian upwelling system based on a neural network approach by Cristhian Asto, Cristhian Asto, Anthony Bosse, Alice Pietri, Raphaëlle Sauzède, Michelle Graco, Dimitri Gutiérrez, Dimitri Gutiérrez, François Colas

“…Variables representing the low-frequency variability related to ENSO were introduced in the training and significantly improved the performance of the algorithm. The performance of CANYON-PU was validated against independent datasets and demonstrated an improvement in accuracy over the global CANYON model that struggled to represent the nutrient distribution in the PUS mainly due to the lack of samples in its training. …”
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Research Progress on Data-Driven Industrial Fault Diagnosis Methods by Liang Lei, Weibin Li, Shiwei Zhang, Changyuan Wu, Hongxiang Yu

“…The content covers the main sources of industrial big data, commonly used datasets, and the construction of related platforms. …”
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Smart Wireless Sensor Networks (WSNs): A Review on Concepts, Techniques, and Applications by Ali Hassan Sial, Muhammad Shafiq, Zubair Sajid Sial, Saleem Ahmed

“… In this modern era, Wireless Sensor Networks (WSNs) are considered rapidly evolving areas of computational research that constitute energy consumption, routing algorithms, and location-based sensors with a sole focus on robustness, efficiency, and improved performance metrics. …”
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Mechanisms for Secure Access to Data of Scientometric Systems Using Virtual DBMS by Alexander Kozitsin, Maxim Zanchurin

“…It presents descriptions of the main methods of implementing virtual databases, shows their advantages and disadvantages, and describes how to provide access for export and import of data. …”
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Commercial symptom monitoring devices in Parkinson’s disease: benefits, limitations, and trends by Daniel Rodríguez-Martín, Carlos Pérez-López, Carlos Pérez-López

“…These devices utilize advanced technologies such as accelerometers, gyroscopes, and specific algorithms to provide objective data on motor symptoms like tremors, dyskinesia, and bradykinesia. …”
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A fact-checking tool based on Artificial Intelligence to fight disinformation on Telegram by Francisco-Javier Cantón-Correa, Lucia Ballesteros-Aguayo, Andrés Montoro-Montarroso

“… This article develops an automatic detection model based on natural learning by designing a useful tool for disinformation monitoring in Telegram that avoids the algorithmic bias of artificial intelligence. It is of relevance the monitoring of online platforms and messaging applications such as WhatsApp and Telegram because they have become tools for circumventing traditional verification controls, and, therefore, for conveying disinformation content. …”
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Reduction to master integrals and transverse integration identities by Vsevolod Chestnov, Gaia Fontana, Tiziano Peraro

“…We include some applications to cutting-edge integral families, showing significant improvements over traditional algorithms.…”
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Artificial intelligence-driven metabolic engineering is applied to the development of active ingredients in Traditional Chinese Medicine by Zhang Guoqi, Wang Juan

“…Nevertheless, the biochemical routes and critical catalysts that control numerous high-value target molecules have not been fully characterized, which is the main bottleneck for the heterologous synthesis of high-value chemicals. …”
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A Review of Vehicle Accident Detection and Notification Systems Based on Machine Learning Techniques by Duaa Hadi Nassar, Jamal Mustafa Al-Tuwaijari

“…The main purpose of this review article was to identify and collect all the studies that have been done previously to detect roads traffics based on using machine learning techniques particularly deep learning. …”
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Generative design for architectural spatial layouts: a review of technical approaches by Shurui Yan, Chen Wu, Yixin Zhang

“…This study reviews the current research on generative design for architectural spatial layouts, focusing mainly on technological methodologies. These methodologies can be broadly categorized into two main approaches: data-driven and knowledge-driven. …”
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Multi-agent system of a network enterprise based on the Industrie 4.0 digital platform by V. А. Kazakov, Yu. F. Telnov

“…The results of the study. The main results of the article are the construction of the component composition diagram of the digital platform conceptual model, algorithms for generating a request to produce product components and its assessment by software agents of the digital platform in the form of UML sequence diagrams. …”
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Interval price prediction of livestock product based on fuzzy mathematics and improved LSTM. by Weimin Ma, Lingling Peng, Hu Chen, Haisheng Yan

“…However, traditional point prediction techniques focus mainly on tracking or fitting, resulting in limited information and challenges in evaluating the uncertainty of future prices. …”
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Know your competitor! Analyzing and predicting the location of competing stores: The case study of Valora at Swiss railway stations by Thomas Wieland

“…Binary logit models are built for the formats examined in order to identify the determinants of location choice. Machine learning algorithms are used to check and optimize the predictive ability of the models. …”
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Artificial intelligence as one of the key drivers of the economy digital transformation by O.I. Pizhuk

“…The article describes the essence of artificial intelligence as an economic category. The main technological preconditions for the emergence of artificial intelligence as a «subversive» technology of the Fourth Industrial Revolution, the development of which can lead to a technological breakthrough in any country in the world. …”
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Translation in the Age of Artificial Intelligence: Machine Translation Markers in Journalistic Texts by Kim Lidiya, Deryabin Artem

“…After that, the authors identified the technological parameters of the translation algorithms that determined the translation quality. …”
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Metric-Based Few-Shot Transfer Learning Approach for Voice Pathology Detection by Jong-Ho Won, Deok-Hwan Kim

“…This paper proposes a noninvasive deep learning approach that utilizes voice signal analysis for diagnosis and reduces discomfort and examination time. The main contributions of this study include the implementation of few-shot learning (FSL) in voice pathology detection, addressing data scarcities and class imbalances through its integration with transfer learning. …”
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