Industrialization 2.0 in the Russian Federation: Professional Aspect by P'yankova Lyudmila

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CLOUD MANUFACTURING: IDENTIFICATIONS AND PRIORITIZATION OF OPPORTUNITIES USING AHP by Mohneesh Kumar SHARMA

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Generative Adversarial Network for Damage Identification in Civil Structures by Zahra Rastin, Gholamreza Ghodrati Amiri, Ehsan Darvishan

“…In recent years, many efforts have been made to develop efficient deep-learning-based structural health monitoring (SHM) methods. …”
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DYMOS: A New Software for the Dynamic Identification of Structures by Fabrizio Gara, Simone Quarchioni, Vanni Nicoletti

“…By using CAD drawings as input, it streamlines model construction, making the process faster, more intuitive, and efficient. …”
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Acoustic cues for person identification using cough sounds by Van-Thuan Tran, Ting-Hao You, Wei-Ho Tsai

“…While recent works have demonstrated the feasibility of cough-based PID (CPID), most report accuracies around 80–90 % and could face limitations in terms of model efficiency, generalization, or robustness. …”
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Risk Identification and Spatiotemporal Evolution in Rural Land Trusteeship by Jianying Xiao, Xinran Fang

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Memory-corrected quantum repeaters with adaptive syndrome identification by Alena Romanova, Peter van Loock

“…To this end, we introduce the check matrix model and quantify the resilience of stabilizer codes of up to eleven qubits against Pauli noise, obtaining analytical expressions for effective logical error probabilities. …”
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ICRSSD: Identification and Classification for Railway Structured Sensitive Data by Yage Jin, Hongming Chen, Rui Ma, Yanhua Wu, Qingxin Li

“…To improve the efficiency of regular expression generation, we developed an auxiliary tool with the help of large language models and a well-designed prompt framework. …”
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Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models by Yasmine Gaaloul, Olfa Bel Hadj Brahim Kechiche, Houcine Oudira, Aissa Chouder, Mahmoud Hamouda, Santiago Silvestre, Sofiane Kichou

“…This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss analysis and predictive models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. …”
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Ensemble of Efficient Vision Transformers for Insect Classification by Marius Alexandru Dinca, Dan Popescu, Loretta Ichim, Nicoleta Angelescu

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Structural health monitoring of railway truss bridge under moving train load using decision tree models and residual neural networks by Sharan Kumar Sunchikala, S. C. Mohan, Sai Dheeraj Gopala, P. Swetha

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RFID-Based Real-Time Salt Concentration Monitoring with Adaptive EKF by Renhai Feng, Xinyi Lin

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Analysis of selected methods of person identification based on biometric data by Marcin Rudzki, Paweł Powroźnik

“…The research employs three ground-breaking convolutional neural network architectures: ResNet50, EfficientNetB0, and VGG16. The project's objective was to examine the influence of critical factors, such as image quality and data processing techniques, on the performance of face identification systems. …”
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Machine Learning with Administrative Data for Energy Poverty Identification in the UK by Lin Zheng, Eoghan McKenna

“…Energy poverty continues to be a critical challenge, and this requires efficient and scalable identification methods to support targeted interventions. …”
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Proof of storage with corruption identification and recovery for dynamic group users by Tao JIANG, Hang XU, Liangmin WANG, Jianfeng MA

“…The outsourced storage mode of cloud computing leads to the separation of data ownership and management rights of data owners, which changes the data storage network model and security model.To effectively deal with the software and hardware failures of the cloud server and the potential dishonest service provider and also ensure the availability of the data owners’ data, the design of secure and efficient data availability and recoverability auditing scheme has both theoretical and practical importance in solving the concern of users and ensuring the security of cloud data.However, most of the existing studies were designed for the security and efficiency of data integrity or recoverability schemes, without considering the fast identification and reliable recovery of damaged data under dynamic group users.Thus, to quickly identify and recover damaged data, a publicly verifiable proof of storage scheme was proposed for dynamic group cloud users.The designed scheme enabled a trusted third-party auditor to efficiently identify the damaged files through a challenge-response protocol and allowed the cloud storage server to effectively recover them when the degree of data damage is less than an error correction ability threshold.The scheme combined association calculation and accumulation calculation, which effectively reduced the number of calculations for the identification of damaged data.By combining erasure coding and shared coding technology, the scheme achieved effective recovery of damaged data of dynamic group users.At the same time, the scheme also supported dynamic user revocation, which ensured the integrity audit and reliable recovery of the collective data after user revocation.The network model and threat model of the designed scheme were defined and the security of the scheme under the corresponding security model was proved.Through the prototype implementation of the scheme in the real environment and the modular performance analysis, it is proved that the proposed scheme can effectively identify the damaged data and reliably recover the cloud data when the data is damaged.Besides, compared with other schemes, it is also proved that the proposed scheme has less computational overhead in identifying and recovering damaged data.…”
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Identification of rice leaf disease based on DepMulti-Net by Kui Hu, Kui Hu, Xinying Zheng, Xinying Zheng, Xinyao Su, Lei Wu, Yongmin Liu, Yongmin Liu, Zhenhua Deng

“…This research presents DepMulti-Net, a novel rice disease and pest identification model, designed to overcome the challenges of complex background interference, difficult disease feature extraction, and large model parameter volume in rice leaf disease identification. …”
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ACFM: Adaptive Channel Feature Matching for Pedestrian Re-Identification by Zhengcai Lu, Zhengwei Tian

“…Image misalignment is a significant challenge in the field of pedestrian re-identification. Previous studies typically align pedestrian features using additional models or by leveraging auxiliary information. …”
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A Convolutional Neural Network for Early Supraventricular Arrhythmia Identification by Emilio J. Ochoa, Luis C. Revilla

“…This approach not only offers a valuable tool for healthcare professionals engaged in telemonitoring and early intervention strategies but also represents a significant contribution to the field of cardiac health monitoring. By facilitating efficient and precise identification of SVEs, our research sets the stage for improved patient outcomes and the prevention of severe SVAs, marking substantial advancements in this critical domain.…”
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Scoring System for Quantifying the Privacy in Re-Identification of Tabular Datasets by Jakob Folz, Manjitha D. Vidanalage, Robert Aufschlager, Amar Almaini, Michael Heigl, Dalibor Fiala, Martin Schramm

“…SCORR extends conventional metrics such as k-anonymity, l-diversity, and t-closeness with novel extended metrics, including uniqueness-only risk, uniformity-only risk, correlation-only risk, and Markov Model risk, to identify a broader range of re-identification threats. …”
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Convolutional Neural Networks for Automatic Identification of Individuals at Terrestrial Terminals by Darwin Yarango-Farro, Alex Mondragon-Fernandez, Heber I. Mejia-Cabrera, Juan Arcila-Diaz

“…Finally, the trained models were integrated into a web application that enables real-time identification using IP cameras, leveraging YOLO version 8 architecture for tracking identified individuals. …”
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