Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study by Mengting Gu, Wenjie Zou, Huilin Chen, Ruilin He, Xingyu Zhao, Ningyang Jia, Wanmin Liu, Peijun Wang

“…Conclusion We mainly developed a fusion model (Radiology MLP model) that integrated radiology and radiomics features using MLP deep learning algorithm to predict vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) patients, which yield an incremental value over the radiology and the MLP model.…”
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Digital twin-based modeling of natural gas leakage and dispersion in urban utility tunnels [version 2; peer review: 2 approved] by Jiansong Wu, Jitao Cai, Ziqi Han, Yanzhu Hu, Ming Fu, Yuefei Li, Xin Wang, Xiaofu Zou

“…Based on this model, real-time integration of gas sensor data from utility tunnels is achieved through the DA algorithm. …”
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A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder by Xingfa Zi, Feiyi Liu, Mingyang Liu, Yang Wang

“…Additionally, we explore the TiDE model’s sensitivity to input environmental variables, algorithmic versatility, and the implications of forecasting errors on PV grid integration. …”
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Evaluation of stroke sequelae and rehabilitation effect on brain tumor by neuroimaging technique: A comparative study. by Xueliang Guo, Lin Sun

“…The development of the 3D CNN model utilizes the ADAM optimization algorithm to facilitate the training process. The Bra2020 dataset is utilized to assess the accuracy of the proposed deep learning neural network. …”
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Optimization of Remote Backup Protection Coordination Logic Based on Dynamic Identification of Faulty Components in Transmission Grids by LIN Xiangning, JI Jihao, DING Yifan, LI Zhengtian, WENG Hanli

“…[Objective] The integration of a high proportion of renewable energy generation has reduced the amplitude of fault currents and changes in their directionality in power grids. …”
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Cloud-edge hybrid deep learning framework for scalable IoT resource optimization by Umesh Kumar Lilhore, Sarita Simaiya, Yogesh Kumar Sharma, Anjani Kumar Rai, S. M. Padmaja, Khan Vajid Nabilal, Vimal Kumar, Roobaea Alroobaea, Hamed Alsufyani

“…The hybrid algorithm's primary characteristic is its capacity to simultaneously fulfil multiple objectives, including reducing response times, enhancing resource efficiency, and decreasing operational costs. …”
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Research and Development of an IoT Smart Irrigation System for Farmland Based on LoRa and Edge Computing by Ying Zhang, Xingchen Wang, Liyong Jin, Jun Ni, Yan Zhu, Weixing Cao, Xiaoping Jiang

“…In response to the current key issues in the field of smart irrigation for farmland, such as the lack of data sources and insufficient integration, a low degree of automation in drive execution and control, and over-reliance on cloud platforms for analyzing and calculating decision making processes, we have developed nodes and gateways for smart irrigation. …”
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Accuracy of warm ischemia time measurement using a surgical intelligence software in partial nephrectomies: A validation study by Archan Khandekar, Joao G. Porto, Jean C. Daher, Pedro F. S. Freitas, Dotan Asselman, Maritza M. Suarez, Mark L. Gonzalgo, Dipen J. Parekh, Sanoj Punnen

“…The findings support the use of AI‐based surgical time measurement for precise intraoperative documentation and highlight the potential of integrating these data with EHRs to advance research on surgical outcomes.…”
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Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur, Gurjit S. Randhawa, Farhat Abbas, Mumtaz Ali, Travis J. Esau, Aitazaz A. Farooque, Rajandeep Singh

“…It has been learned that image-processing techniques overwhelm the existing research and have the potential to integrate meteorological data. The most widely used algorithms incorporate Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and MobileNet with accuracy rates between 64.3 and 100%. …”
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A novel hybrid fuzzy logic and federated learning framework for enhancing cybersecurity and fraud detection in IoT-enabled metaverse transactions by Amjad Rehman, Kamran Ahmad Awan, Amal Al-Rasheed, Anees Ara, Fahad F. Alruwaili, Shaha Al-Otaibi, Tanzila Saba

“…Increasing integration of the Internet of Things (IoT) with virtual environments like the Metaverse has opened up new avenues in the applicability of technologies but faces severe challenges to security and fraud detection. …”
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Coordinated Truck Loading and Routing Problem: A Forestry Logistics Case Study by Cristian Oliva, Manuel Cepeda, Sebastián Muñoz-Herrera

“…The algorithm is tested on a real forestry dispatch scenario over six working days. …”
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Aggregation-Based Ensemble Classifier Versus Neural Networks Models for Recognizing Phishing Attacks by Wojciech Galka, Jan G. Bazan, Urszula Bentkowska, Kamil Szwed, Marcin Mrukowicz, Pawel Drygas, Lech Zareba, Marcin Szpyrka, Piotr Suszalski, Sebastian Obara

“…Aggregation functions are employed to integrate the prediction values of classification models applied in the email phishing problem. …”
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Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model by Jintak Choi, Zuobin Xiong, Kyungtae Kang

“…When rolling pins are machining with CNC equipment, a sensor system is installed to collect acoustic data, analyze failure patterns, and apply RUL prediction algorithms. The AI-based predictive maintenance system developed ensures the reliability and operational efficiency of CNC equipment, while also laying the foundation for a smart factory monitoring platform, thus enhancing competitiveness in intelligent manufacturing environments.…”
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Artificial Intelligence in Journalism: Bibliometric Analysis of Publication Activity by Violetta A. Zorina

“…Artificial intelligence is changing the algorithms for the development of various spheres of society, is actively being introduced into the media industry, producing new opportunities and social risks. …”
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Research on the Method of Artificial Intelligence for Identifying Urban Land-Use Types Based on Areas of Interest (AOI) and Multi-Source Data by Miaoyi Li, Ningrui Zhu

“…Departing from existing data weighting algorithms, this research applies artificial intelligence techniques, utilizing the categorical information of AOI data as labels. …”
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Future Low-Cost Urban Air Quality Monitoring Networks: Insights from the EU’s AirHeritage Project by Saverio De Vito, Antonio Del Giudice, Gerardo D’Elia, Elena Esposito, Grazia Fattoruso, Sergio Ferlito, Fabrizio Formisano, Giuseppe Loffredo, Ettore Massera, Paolo D’Auria, Girolamo Di Francia

“…Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. …”
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An Approach for Multi-Source Land Use and Land Cover Data Fusion Considering Spatial Correlations by Jing Yang, Yiheng Jiang, Qirui Song, Zheng Wang, Yang Hu, Kaiqiang Li, Yizhong Sun

“…Although existing research has explored land use type recognition from remote sensing imagery, interpretation algorithms, and other perspectives, significant spatial discrepancies exist between these data products. …”
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Optimizing Stroke Risk Prediction: A Primary Dataset‐Driven Ensemble Classifier With Explainable Artificial Intelligence by Md. Maruf Hossain, Md. Mahfuz Ahmed, Md. Rakibul Hasan Rakib, Mohammad Osama Zia, Rakib Hasan, Md. Rakibul Islam, Md. Shohidul Islam, Md Shahariar Alam, Md. Khairul Islam

“…Methods We applied several preprocessing techniques, including outlier detection, data normalization, k‐means clustering, and missing value detection, to refine the datasets. A novel ensemble classifier was developed, combining AdaBoost, Gradient Boosting Machine (GBM), Multilayer Perceptron (MLP), and Random Forest (RF) algorithms to enhance predictive accuracy. …”
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An approach that utilizes blockchain to effectively and securely preserve data privacy for location data from IoT in smart cities by D. Rawal, J. Seedorf, B. Patil

“…The results show the proposed crypto-enhanced blockchain framework can significantly enhance geospatial data security while still maintaining operational efficiency. Integration with zero-knowledge proofs may be explored in future work to enhance privacy, mitigate energy costs through alternative consensus algorithms, and enhance resilience in multi-network ecosystems through cross-chain interoperability.…”
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Theoretical justification of the model of search for optimal solutions in complex resource management systems by Roman A. Khalturin, Roman O. Sudorgin, Nikita S. Akinshin

“…In multicriteria optimization under uncertainty, the lack of a unified mathematical framework requires the use of diverse methods such as combinatorics, graph theory, heuristics, linear and dynamic programming, and search algorithms. The research highlights the challenges of applying these methods in complex systems, where external and internal uncertainties complicate the formulation of constraints and the integration of qualitative and quantitative criteria. …”
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