Optimization of the road bump and pothole detection technology using convolutional neural network by Ding Haiping, Tang Qianlong

“…The training method considers variations in lighting, weather conditions, and bridge materials, ensuring the model performs well in various real-world situations. …”
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Macrostructural Analysis of the Dynamics of the Two-Sector Economy by O. S. Sukharev, E. N. Voronchikhina

“…The purpose of the study is the formation of an algorithm for macrostructural analysis of the two-sector Russian economy with the identification of the main patterns and relationships of their joint dynamics — contribution to the overall growth rate, price dynamics, and the definition of a model for further development.   …”
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Development of an artificial intelligence-based algorithm to classify images acquired with an intraoral scanner of individual molar teeth into three categories. by Nozomi Eto, Junichi Yamazoe, Akiko Tsuji, Naohisa Wada, Noriaki Ikeda

“…The availability of such a system would greatly increase the efficiency of personal identification in the event of a major disaster.…”
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Workflow for a Functional Assay of Candidate Effectors From Phytopathogens Using a TMV-GFP-based System by Peng Cao, Haotian Shi, Jialan Chen, Langjun Cui, Meixiang Zhang, Yuyan An

“…The screening system combines the TMV-GFP vector and Agrobacterium-mediated transient expression in the model plant Nicotiana benthamiana. This system enables the rapid identification of effectors that interfere with plant immunity (both activation and suppression). …”
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Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images by G. Savitha, S. Girisha, Pundarika Sughosh, Dasharathraj K. Shetty, Jayaraj Mymbilly Balakrishnan, Rahul Paul, Nithesh Naik

“…The teacher model is then trained with a student model to efficiently extract features from the labeled data. …”
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Classification Based on the Support Vector Machine for Determining Operational Targets for Controlling Electricity Usage With Conventional Meters: A Case Study of Industrial and Bu... by Galih Arisona, Alief Pascal Taruna, Dwi Irwanto, Arif Bijak Bestari, Wildan Juniawan

“…This optimized model contributes to improving PLN’s operational efficiency, offering more accurate identification of electricity theft cases, leading to substantial financial savings by reducing losses from unpaid consumption. …”
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Space Precession Target Classification Based on Radar High-Resolution Range Profiles by Yizhe Wang, Cunqian Feng, Yongshun Zhang, Sisan He

“…We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. …”
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Electronic nose and machine learning for modern meat inspection by Ivan Shtepliuk, Guillem Domènech-Gil, Viktor Almqvist, Arja Helena Kautto, Ivar Vågsholm, Sofia Boqvist, Jens Eriksson, Donatella Puglisi

“…Using the Optimizable Ensemble model, we achieved a sensitivity of 96.5% and specificity of 95.3% in categorizing fresh and urine-contaminated meat samples. …”
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Empowering voice assistants with TinyML for user-centric innovations and real-world applications by Sireesha Chittepu, Sheshikala Martha, Debajyoty Banik

“…A comparative review of current methods identifies areas of research gaps such as deployment difficulties, noise interference, and model efficiency on low-resource devices. From this study, researchers can directly identify the research gap with minimal effort, which may motivate them to focus more on solving the open problems due to optimize the problem identification time.…”
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Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSN by WAN Zhiguo, ZHAO Wei, WANG Zhiguo, DOU Yihua

“…ObjectiveAiming at the problem of poor accuracy of gearbox fault diagnosis under noise interference, a new fault diagnosis method for gearboxes based on the denoising methods of optimized variational modal decomposition (VMD）and non-local means (NLM) was constructed, combined with a one-dimensional deep residual shrinkage network (1DDRSN).MethodsFirstly, the parameters in the VMD were automatically optimized using the subtractive average-based optimization (SABO); secondly, each intrinsic mode function (IMF） after the decomposition of the VMD was filtered using sample entropy, and the noise-containing components were subjected to the NLM denoising and reconstruction; then, a residual network that combines the attention mechanism with soft thresholding was introduced to model 1DDRSN; finally, the denoised and reconstructed signals were inputted into the 1DDRSN for fault diagnosis and identification. …”
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Research on Local Obstacle Avoidance Path Planning Algorithm for Autonomous Mining Trucks by HUANG Shuai, WANG Jia, HUANG Jiade, HUANG Peng, LYU Liang

“…Autonomous mining trucks currently face several challenges in local obstacle avoidance path planning within mining environments, including difficulties in close-range identification, delays in vehicle-to-ground communication, and the absence of real-time planning capabilities on the ground. …”
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PARADIGM FOR CONTEMPORARY SYMBOLIC REALITY by Elena Korableva

“…Conclusions: 1) Persons' immersion into the space of cultural and historical symbolic processes determines the content of their socialization which is a process of self-identification in the symbolic reality, the process of entering the subject-shaped field of human culture; 2) An adequate model of signs and symbols reflects the diversity of information flows, that is the synthesis of a specific objective and true knowledge, its interpretation and value. …”
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Using Modified Intelligent Experimental Design in Parameter Estimation of Chaotic Systems by Zahra Shourgashti, Hamid Keshvari, Shirin Panahi

“…Therefore, parameter estimation is known as an important part of the modeling and system identification. It usually refers to the process of using sampled data to estimate the optimum values of parameters. …”
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Artificial Intelligence and Machine Learning Approaches for Target-Based Drug Discovery: A Focus on GPCR-Ligand Interactions by M. O. Otun

“…The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized target-based drug discovery, offering innovative approaches to predict GPCR-ligand interactions with enhanced accuracy and efficiency. This review explores the integration of AI and ML techniques in GPCR-targeted drug discovery, highlighting their potential to accelerate lead identification, optimize ligand binding predictions, and improve structure-activity relationship modeling. …”
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Intelligent Fault Detection and Self-Healing Mechanisms in Wireless Sensor Networks Using Machine Learning and Flying Fox Optimization by Almamoon Alauthman, Abeer Al-Hyari

“…This paper presents an intelligent framework based on Light Gradient Boosting Machine integration for fault detection and a Flying Fox Optimization Algorithm in dynamic self-healing. The LGBM model provides very accurate and scalable performance related to effective fault identification, whereas FFOA optimizes the recovery strategies to minimize downtown and maximize network resilience. …”
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Improving Internal Combustion Engine Performance through Inlet Valve Geometry and Spray Angle Optimization: Computational Fluid Dynamics Study by Muhammad Ahsan, Mian Noman

“…This study investigated the role of the intake port’s geometry and spray angles in creating squish and swirl, which is crucial for enhancing combustion efficiency and overall engine performance. The analysis employed the Finite Volume Method (FVM), solved within ANSYS Fluent 2021 software, utilizing the standard k-ε turbulence model. …”
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Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems by Min Wei, Min Wei, Yongqing Yu, Xing Du, Yupeng Song, Lifeng Dong, Qikun Zhou, Linfeng Wang, Longying Zhang, Yamei Wang

“…Employing automated identification of side-scan sonar (SSS) images can enhance marine geophysical survey efficiency, enabling high-frequency assessment of seabed anthropogenic footprints. …”
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ADeepWeeD: An adaptive deep learning framework for weed species classification by Md Geaur Rahman, Md Anisur Rahman, Mohammad Zavid Parvez, Md Anwarul Kaium Patwary, Tofael Ahamed, David A. Fleming-Muñoz, Saad Aloteibi, Mohammad Ali Moni, PhD

“…We believe that our developed model could be used to develop an automation system for weed identification. …”
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Research on new energy power plant network traffic anomaly detection method based on EMD by Danni Liu, Shengda Wang, YutongLi, Ji Du, Jia Li

“…Presenting simulations of the communication networks of the PV power system, this research sought to evaluate possible futures of PV power plants. We test our model on a massive PV cell dataset and show that it outperforms state-of-the-art approaches in terms of resilience, speed, and accuracy of identification.…”
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Automatic Detection of Landslide Surface Cracks from UAV Images Using Improved U-Network by Hao Xu, Li Wang, Bao Shu, Qin Zhang, Xinrui Li

“…Surface cracks are key indicators of landslide deformation, crucial for early landslide identification and deformation pattern analysis. However, due to the complex terrain and landslide extent, manual surveys or traditional digital image processing often face challenges with efficiency, precision, and interference susceptibility in detecting these cracks. …”
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