Prediction of Soil Carbon Sequestration in Rangelands Regarding the Depth and Elevation of Sample Locations using Adaptive Neuro-Fuzzy Inference System (ANFIS) (Case Study: Lar Wat... by zeinab jafarian, zhila ghorbani

“…Conclusion: The increase in the correlation coefficient and the reduction of the mean error deviation in the ANFIS method compared to the linear multivariate regression show that the ANFIS method is more successful in estimating the amount of soil CS under the effects of various factors in the studied land use. …”
Get full text

Enhanced Lightweight Quantum Key Distribution Protocol for Improved Efficiency and Security by Ashutosh Bhatia, Sainath Bitragunta, Kamlesh Tiwari

“…However, the classical post-processing steps in BB84, such as sifting, error correction, and key verification, often result in significant communication overhead, limiting its efficiency and scalability. …”
Get full text

Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method by Chengcheng Wang, Zhi Yan, Qifeng Li, Zhaopeng Zhu, Chengkai Zhang

“…In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. …”
Get full text

Computational fluid dynamics and machine learning integration for evaluating solar thermal collector efficiency -Based parameter analysis by Xiaoyu Hu, Lanting Guo, Jiyuan Wang, Yang Liu

“…The study identifies a critical transition at 100–300 samples where error rates drop sharply, with optimal performance requiring more than 600 samples. …”
Get full text

AI-Powered Precision in Diagnosing Tomato Leaf Diseases by MD Jiabul Hoque, Md. Saiful Islam, Md. Khaliluzzaman

“…Conventional methods, such as visual inspection and microscopic analysis, are typically labor-intensive, subjective, and vulnerable to human error, making them infeasible for extensive monitoring. …”
Get full text

Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction by Hui Wang, LiPing Wang, QiCang Qiu, Yuyan Gao, WenJuan Li, QiuShun Wang

“…New hydrologic insights for the region:: Compared to ten other advanced prediction models, DLGNet achieved an average reduction in mean square error (MSE) by 94. 82%, 93. 35%, and 83. 13% for short-term, mid-term, and long-term predictions, respectively. …”
Get full text

Smart Agile Prioritization and Clustering: An AI-Driven Approach for Requirements Prioritization by Aya M. Radwan, Manal A. Abdel-Fattah, Wael Mohamed

“…Various machine learning algorithms are tested, with KNN and Random Forest demonstrating the highest accuracy and lowest Mean Squared Error (MSE), outperforming traditional prioritization techniques. …”
Get full text

Application research and effectiveness evaluation mechanism of hybrid intelligent algorithm integrating cognitive computing and deep learning for dynamically adjusting employee per... by Zhenlin Luo, Kebin Lu

“…The results revealed that the mean square error (MSE) of the hybrid algorithm was significantly lower than that of the KPI (Key Performance Indicators) method across all datasets, with a 43.5% improvement in accuracy. …”
Get full text

Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies for Literature Review Automation by Masood Sujau, Masako Wada, Emilie Vallée, Natalie Hillis, Teo Sušnjak

“…Despite the abundance of scientific publications, the manual extraction of these data via systematic literature reviews remains a significant bottleneck, requiring extensive time and resources, and is susceptible to human error. This study examines the application of a large language model (LLM) as an assessor for screening prioritisation in climate-sensitive zoonotic disease research. …”
Get full text

Measurement of Total Iron in Breast Tissue Samples by Inductively Coupled Plasma-Mass Spectrometry by Mete Bora Tüzüner, Burçin Alev Tüzüner

“…In addition, three cancer-free breast tissues, obtained from breast reduction surgery, were analyzed as a normal group. …”
Get full text

Multi-Attribute Data-Driven Flight Departure Delay Prediction for Airport System Using Deep Learning Method by Yujie Yuan, Yantao Wang, Chun Sing Lai

“…The case study shows that the proposed method can significantly improve the accuracy of flight prediction delay. The Mean Absolute Error (MAE) can reach 0.26, which is a 14.47% reduction compared with 2D CNN+GCN+LSTM.…”
Get full text

Patient Safety in Medication Nomenclature: Orthographic and Semantic Properties of International Nonproprietary Names. by Rachel Bryan, Jeffrey K Aronson, Pius ten Hacken, Alison Williams, Sue Jordan

“…<h4>Background</h4>Confusion between look-alike and sound-alike (LASA) medication names (such as mercaptamine and mercaptopurine) accounts for up to one in four medication errors, threatening patient safety. Error reduction strategies include computerized physician order entry interventions, and 'Tall Man' lettering. …”
Get full text

Unsupervised Multimodal UAV Image Registration via Style Transfer and Cascade Network by Xiaoye Bi, Rongkai Qie, Chengyang Tao, Zhaoxiang Zhang, Yuelei Xu

“…This paper proposes a novel multi-scale cascaded registration network based on style transfer that achieves superior performance: up to 67% reduction in mean squared error (from 0.0106 to 0.0068), 9.27% enhancement in normalized cross-correlation, 26% improvement in local normalized cross-correlation, and 8% increase in mutual information compared to state-of-the-art methods. …”
Get full text

Optimized bio-signal reconstruction and watermarking via enhanced fractional orthogonal moments by Gaber Hassan, Khalid M. Hosny, Islam S. Fathi

“…The findings indicate that the proposed methodology exhibits significantly higher performance levels than current methodologies, with a 64.3% improvement in PSNR (reaching 147.08 dB compared to 89.74 dB in existing approaches), 89.7% reduction in MSE (0.0092 versus 0.09 average), and 84.1% decrease in bit error rate (0.25 versus 1.57) for watermarking applications. …”
Get full text

Model-based hierarchal control framework for frequency and voltage stability in islanded microgrids with low inertia by Girmaw Teshager Bitew, Teketay Mulu Beza, Muhammad Shahzad

“…The main results include frequency deviation below 0.04 Hz (versus 0.12 Hz for droop control), voltage regulation error within ±1.2% (versus ±4.2% for droop control), and a 50% reduction in communication traffic. …”
Get full text

A de-embedding method based on combining time and frequency domains by Wenwen Zeng, Yaoli Wang, Zhibin Wang

“…Abstract This paper proposes an automatic fixture removal (AFR) de-embedding method to address the embedding error introduced by the fixture in radio frequency (RF) chip parameter testing and the cumbersome calibration process of the short-open-load-thru de-embedding method. …”
Get full text

Experimentally investigating the structural capacity of slender web tapered built up plate girder with web opening by Mohammed Taher, Ahmed M. Ebid, Sherif M. Ibrahim, Mohamed A. El-Aghoury

“…In many practical applications, web openings are introduced to accommodate service ducts, utilities, and weight reduction, making them an essential feature in modern steel structures. …”
Get full text

Research on obstacle avoidance for aerial work vehicle booms with light arrays based on an improved gradient projection method by Yi HUANG, Lei DENG, Kangyi DENG, Qin SHU, Guangan REN, Zhihong ZHOU

“…At the same time, the end position error reduction algorithm is added to reduce the end velocity and improve the trajectory tracking accuracy. …”
Get full text

Dynamic Modeling and Parameter Optimization of Potato Harvester Under Multi-Source Excitation by Jianguo Meng, Zhipeng Li, Zheng Li, Yanzhou Li, Wenxia Xie

“…The comparison between actual and simulated signals shows that the RMS error of acceleration is only 2.42%, indicating that introducing two degrees of freedom in pitch and roll directions to the potato harvester can accurately describe its vibration characteristics. …”
Get full text

Cosmology with Topological Deep Learning by Jun-Young Lee, Francisco Villaescusa-Navarro

“…When applied to the Quijote suite, our TNNs achieve a significant reduction in the mean squared error. Compared to our GNNs, which lack higher-order message-passing, ClusterTNNs show improvements of up to 22% in Ω _m and 34% in σ _8 jointly, while the best FullTNN achieves an improvement of up to 60% in σ _8 . …”
Get full text