A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling by Joel Adams, Brendon Cintas, Nakwon Sung, Anthony Abrahao, Leonel Lagos, Dwayne McDaniel

“…The results additionally demonstrate that dynamically adjusting the optimization algorithm’s exploration tendency based on the arm’s collision history improves the system’s ability to navigate cluttered environments and construct accurate radiation maps without getting stuck in unreachable areas.…”
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Image-Based Classification of Freshwater Fish Species to Support Feed Recommendation Using Random Forest by Hindayati Mustafidah, Suwarsito Suwarsito, Rahmat Setiawan, Abdul Karim

“…Feature extraction was applied to capture color characteristics (HSV), texture patterns (GLCM), and morphological features (regionprops). The model was trained on 70% of the dataset and tested on the remaining 30%. …”
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Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management by Chenxi Wang, Chenxi Wang, Xinyue Yuan, Wei Jing

“…Crucially, AI outputs require clinician verification alongside multidimensional clinical data.DiscussionFuture research must prioritize algorithm optimization, data quality improvement, and enhanced AI transparency. …”
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CSDK: A Chi-square distribution-Kernel method for image de-noising under the Internet of things big data environment by Lin Teng, Hang Li

“…It also shows a consistent accuracy, edge preservation, and detailed retention improvement compared to the classic de-noising algorithms.…”
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Graph-Regularized Orthogonal Non-Negative Matrix Factorization with Itakura–Saito (IS) Divergence for Fault Detection by Yabing Liu, Juncheng Wu, Jin Zhang, Man-Fai Leung

“…By using Itakura–Saito divergence, GONMF-IS effectively handles data with probabilistic distribution characteristics, improving the model’s ability to process non-Gaussian data. …”
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Detection-Driven Gaussian Mixture Probability Hypothesis Density Multi-Target Tracker for Airborne Infrared Platforms by Mingyu Hong, Jiarong Wang, Ming Zhu, Shenyi Cao, Haitao Nie, Xiangdong Xu

“…This study presents a multi-object tracking system specifically designed for weak-textured infrared targets, aimed at enhancing detection accuracy and tracking stability. Initially, improvements are made to the YOLOv10 model through the incorporation of modules such as DSA, c2f_fasterblock, and NMSFree, which collectively enhance detection accuracy and robustness for weak-textured targets. …”
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Using machine learning to study the population life quality: methodological aspects by E. V. Shchekotin, В. Л. Гойко, P. A. Basina, B. B. Bakulin

“…The article consistently reviews all stages of the machine learning algorithms application to assess the life quality of the population of the regions of the Russian Federation and the issues of improving neural network accuracy. …”
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Cost-Effective Multitask Active Learning in Wearable Sensor Systems by Asiful Arefeen, Hassan Ghasemzadeh

“…Multitask learning models provide benefits by reducing model complexity and improving accuracy by concurrently learning multiple tasks with shared representations. …”
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Layered energy equalization structure for series battery pack based on multiple optimal matching by Jianfang Jiao, Hongwei Wang, Feng Gao, Serdar Coskun, Guang Wang, Jiale Xie, Fei Feng

“…Matlab/Simulink software is used to build and simulate the model. The experimental results indicate that, under the same static state, the newly proposed control strategy improves efficiency by 6.08% and enhances equalization speed by 42.03% compared to the maximum value equalization method. …”
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Fed-DPSDG-WGAN: Differentially Private Synthetic Data Generation for Loan Default Prediction via Federated Wasserstein GAN by Padmaja Ramachandra, Santhi Vaithiyanathan

“…This paper uses federated learning to create synthetic data to improve loan defaulter prediction. By allowing multiple institutions to collaboratively train a shared model without directly exchanging sensitive loan data, this privacy-preserving approach allows for the utilization of more extensive and diverse datasets, potentially leading to significant improvements in prediction accuracy compared to traditional, siloed approaches. …”
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A Real-Time Signal Measurement System Using FPGA-Based Deep Learning Accelerators and Microwave Photonic by Longlong Zhang, Tong Zhou, Jie Yang, Yin Li, Zhiwen Zhang, Xiang Hu, Yuanxi Peng

“…The results show that the time of inference saves 28–31% for the DNN model and 71–73% for the LSTM + DNN model compared to running on graphic processing unit (GPU). …”
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Use of artificial intelligence in the diagnosis, treatment and surveillance of patients with kidney cancer by E. Yu. Timofeeva, С. R. Azilgareeva, A. O. Morozov, M. S. Taratkin, D. V. Enikeev

“…AI model algorithms may be used to identify patients at high risk of relapse requiring long-term follow-up, as well as to develop individual treatment and follow-up strategies. …”
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Enhancing Autonomous Driving Perception: A Practical Approach to Event-Based Object Detection in CARLA and ROS by Jingxiang Feng, Peiran Zhao, Haoran Zheng, Jessada Konpang, Adisorn Sirikham, Phuri Kalnaowakul

“…We adapted a conventional frame-based detection model (YOLOv8) for event-based inputs by training it on the GEN1 dataset, achieving a mean average precision (mAP) of 70.1%, a significant improvement over previous benchmarks. …”
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Energy Distribution Optimization in Heterogeneous Networks with Min–Max and Local Constraints as Support of Ambient Intelligence by Alessandro Aloisio, Domenico D. Bloisi, Marco Romano, Cosimo Vinci

“…Additionally, we examine the computational complexity of this model and propose two solution algorithms grounded in fixed-parameter tractability theory for specific network classes.…”
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Simulation of Minefield Installation in a Video Game Engine by Maksym Maksymov, Oleksii Neizhpapa, Oleksandr Toshev, Maksym Kiriakidi

“…The first objective of this research is to improve damage prediction algorithms, enabling the simulation to more accurately estimate the consequences of ships passing through a minefield. …”
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A secure and efficient user selection scheme in vehicular crowdsensing by Min Zhang, Qing Ye, Zhimin Yuan, Kaihuan Deng

“…The model uses PCA for data dimensionality reduction to eliminate redundant information and then employs LSTM to process time series data, capture long-term dependencies for more accurate user credit prediction, screen high-quality users, and improve perceived data quality. …”
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Chronobiologically-informed features from CGM data provide unique information for XGBoost prediction of longer-term glycemic dysregulation in 8,000 individuals with type-2 diabetes... by Jamison H Burks, Leslie Joe, Karina Kanjaria, Carlos Monsivais, Kate O'laughlin, Benjamin L Smarr

“…These findings support the use of chronobiologically-inspired and explainable features to improve glucose prediction algorithms with relatively long time-horizons.…”
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Leveraging transformers and explainable AI for Alzheimer's disease interpretability. by Humaira Anzum, Nabil Sadd Sammo, Shamim Akhter

“…Many researches have been undertaken to use computer vision on MRI images, and their accuracy ranges from 80-90%, new computer vision algorithms and cutting-edge transformers have the potential to improve this performance.We utilize advanced transformers and computer vision algorithms to enhance diagnostic accuracy, achieving an impressive 99% accuracy in categorizing Alzheimer's disease stages through translating RNA text data and brain MRI images in near-real-time. …”
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Quantum Variational Autoencoder Based on Weak Measurements With Fuzzy Filtering of Input Data by Vyacheslav Korolyov, Maksim Ogurtsov, Oleksandr Khodsinskyi

“…The purpose of the article is to improve AI through modeling of autoencoder algorithms using weak measurements and fuzzy logic. …”
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Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia by Ehab M. Almetwally, I. Elbatal, Mohammed Elgarhy, Amr R. Kamel

“…This system aims to detect and forecast motor impairment disorders early on using AI model approaches. This system is an efficient tool that properly detects and diagnoses a variety of motor impairment problems using ML algorithms. …”
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