A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision by Rongyong Zhao, Feng Hua, Bingyu Wei, Cuiling Li, Yulong Ma, Eric S. W. Wong, Fengnian Liu

“…Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. …”
Get full text

Distributed hybrid consensus–based square-root cubature quadrature information filter and its application to maneuvering target tracking by Jun Liu, Yu Liu, Kai Dong, Ziran Ding, You He, Qichao Li

“…The proposed hybrid consensus–based square-root cubature quadrature information filter exploits fifth-order spherical simplex-radial quadrature rule to tackle system nonlinearities and incorporates a novel measurement update strategy into the hybrid consensus filtering framework, which takes the predicted measurement error into account and hence produces more accurate estimates. …”
Get full text

Software Supporting the Visualization of Hazardous Substance Emission Zones During a Fire at an Industrial Enterprise by Yuri Matveev, Fares Abu-Abed, Olga Zhironkina, Sergey Zhironkin

“…The possible chain-like nature of fires and explosions of containers with toxic substances inside increases the importance of predicting changes in hazardous zone parameters in real time. …”
Get full text

Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring by Eugenia Tîrziu, Ana-Mihaela Vasilevschi, Adriana Alexandru, Eleonora Tudora

“…This approach, integrated into the NeuroPredict platform developed by our team, advances fall detection technology, supporting better care and safety for older adults.…”
Get full text

A CART-Based Model for Analyzing the Shear Behaviors of Frozen–Thawed Silty Clay and Structure Interface by Fengpan Zhu, Bo Wang, Zhiqiang Liu

“…The CART-based model offers a scientific basis for predicting the shear behavior of the frozen–thawed soil–structure interface.…”
Get full text

Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street by Tianning Yao, Yao Xu, Liang Sun, Pan Liao, Jin Wang

“…It then gathers multidimensional perception data from selected locations and ultimately analyses and predicts the results employing machine learning techniques, specifically Random Forest and XGBoost. …”
Get full text

A Numerical Laboratory for Simulation of Flanged Reinforced Concrete Shear Walls by Alireza Khaloo, Mohammad Tabiee, Hatef Abdoos

“…In order to validate FlashLab, this paper numerically simulates T-, H-, and L-shaped RC shear walls and compares the results to the experimental data, indicating good agreement between the numerical and experimental results to an extent that the proposed numerical laboratory is capable of predicting the backbone curve with an accuracy more than 90 percent. …”
Get full text

An IoT-Driven Real-Time Skin Disease Detection System using Machine Learning by Aliya Aftab, Dr.Hina Sattar, Muhammad Farhan Aslam, Touseef Hussain, Syeda Sitara Waseem

“…IoT will measure body temperature, and a deep learning algorithm and CNN algorithm predict the diseases. The output is provided on the GUI as an Android application, in order to help them understand whether they have skin diseases and allergies early. …”
Get full text

Neural Networks in Forecasting Disease Dynamics by A. G. Hasanov, D. G. Shaybakov, S. V. Zhernakov, A. M. Men’shikov, F. F. Badretdinova, I. F. Sufiyarov, J. R. Sagadatova

“…The best convergence of the network learning process is provided by the quasi-Newton and conjugated gradient algorithms. In order to assess the effectiveness of the proposed neural network in predicting the dynamics of inflammation, a comparative analysis was carried out using a number of conventional methods, such as exponential smoothing, moving average, least squares and group data handling.Conclusion.The proposed neural network based on approximation and extrapolation of variations in the patient’s medi‑ cal history over fixed time window segments (within the ‘sliding time window’) can be successfully used for forecasting the development and outcome of erysipelas.…”
Get full text

Healthcare Economics-Vital Threat Detection System Efficiency: A Business Process Analysis Methodology by Salma Elhag, Amal Alghamdi, Amal Alahmari, Abeer Alharbi

“…The data is sent to the cloud, where it is stored and analyzed using artificial intelligence (AI) algorithms to predict critical health conditions. When any abnormalities are detected, immediate alerts are sent to family members or caregivers via mobile applications, enabling a quick response. …”
Get full text

Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…The method is tested using a North American utility feeder data, achieving an average accuracy of 95.56% for forecasting aggregate load power, 93.70% for feeder flow predictions, and 97.53% for estimating the aggregate capacity of DERs.…”
Get full text

Analysis of State-of-Charge Estimation Methods for Li-Ion Batteries Considering Wide Temperature Range by Yu Miao, Yang Gao, Xinyue Liu, Yuan Liang, Lin Liu

“…Accurate state-of-charge (SOC) estimation is critical for optimizing battery performance, ensuring safety, and predicting battery lifetime. However, SOC estimation faces significant challenges under extreme temperatures and complex operating conditions. …”
Get full text

Toward Street‐Level Nowcasting of Flash Floods Impacts Based on HPC Hydrodynamic Modeling at the Watershed Scale and High‐Resolution Weather Radar Data by Pierfranco Costabile, Carmelina Costanzo, John Kalogiros, Vasilis Bellos

“…This approach paves the way for the development of new operational systems focused on impact‐based flash‐floods nowcasting, wherein hydrodynamic simulations directly model the spatial and temporal variability of measured or predicted rainfall on impacts even at a street scale. …”
Get full text

A survey on exploring the challenges and applications of wireless body area networks (WBANs) by Arun Sekar Rajasekaran, L. Sowmiya, Azees Maria, R. Kannadasan

“…Due to the arrival of the Micro Electromechanical System (MEMS) and several intelligent sensors, collaboration with WBAN makes accurate predictions of parameters in the human body. WBAN has numerous applications in medical and non-medical fields. …”
Get full text

The role of hematological parameters as predictors of mortality in elderly patients in the hospital period by L. V. Borisova, S. A. Rukavishnikova, A. S. Pushkin, T. A. Ahmedov, V. V. Yakovlev

“…The development of algorithms for predicting adverse outcomes, including death in elderly and senile patients, is an urgent issue. …”
Get full text

Biosynthesis, applications, and mathematical modeling dynamics of lactic acid bacteria exopolysaccharides: A review by Ankit Barot, Kalyan Das, Yogesh Patel, et al.

“…Global models, such as flux balance analysis (FBA), integrate genome-scale networks to predict cellular behavior and optimize production conditions. …”
Get full text

Implementation framework for AI deployment at scale in healthcare systems by Hassan Sami Adnan, Amitis Shidani, Lei Clifton, Clare R. Bankhead, Rafael Perera-Salazar

“…This design thinking approach promotes clinical utility beyond model prediction, combining privacy preservation with clinical parameters to establish a reward function for reinforcement learning, ranking competing models. …”
Get full text

Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation by Matthew J. McCarthy, Jesus D. Gomez-Velez, David Hughes, Shannon Meade

“…To address this data gap, we developed a protocol that combined satellite-based time-series measurements of river width at seven locations throughout Ukraine from 2013 to 2023 with reanalysis data, climate-model predictions, and hydrologic models to both provide a means of monitoring a proxy for near-real-time discharge and also predict near-term (i.e., 2023–2030) hydrologic patterns for the region. …”
Get full text

Differences in parameter estimates derived from various methods for the ORYZA (v3) Model by Jun-wei TAN, Qing-yun DUAN, Wei GONG, Zhen-hua DI

“…The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. …”
Get full text

A Machine Learning-Based Diagnostic Nomogram for Moyamoya Disease: The Validation of Hypoxia-Immune Gene Signatures by Cunxin Tan, Xilong Wang, Zhenyu Zhou, Yutong Liu, Shihao He, Yuanli Zhao

“…Further, our study constructed the ceRNA network and predicted potential drug targets for MMD. We obtained the top 10 drugs in the interaction of the four key genes that might serve as potential targets for the treatment of MMD. …”
Get full text