Enhanced Lightweight YOLO Model for Efficient Vehicle Detection in Satellite Imagery by Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Elmi Abu Bakar, Ahmad Faizul Hawary

“…However, these models have complex architectures that require powerful processing units to train while generating a large number of parameters and achieving slow detection speed on embedded devices. …”
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Cover classifications in wetlands using Sentinel-1 data (Band C): a case study in the Parana river delta, Argentina by Mariela Rajngewerc, Rafael Grimson, Lucas Bali, Priscilla Minotti, Patricia Kandus

“…Considering the datasets formed by the intensity values, for the winter dates the achieved kappa index values (κ) were higher than 0.8, while all summer datasets achieved κ up to 0.76. Including feature textures based on the GLCM showed improvements in the classifications: for the summer datasets, the κ improvements were between 9% and 22% and for winter datasets improvements were up to 15%. …”
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Real-Time Acoustic Detection of Critical Incidents in Smart Cities Using Artificial Intelligence and Edge Networks by Ioannis Saradopoulos, Ilyas Potamitis, Stavros Ntalampiras, Iraklis Rigakis, Charalampos Manifavas, Antonios Konstantaras

“…These devices host an audio transformer model trained on the AudioSet dataset, enabling the real-time classification and timestamping of audio events with high accuracy. …”
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Deep Residual Network With Integrated StarDist Nuclei Segmentation for Papillary Thyroid Cancer Identification: A Pathologist-Inspired Approach by Nabila Husna Shabrina, Dadang Gunawan, Mia Rizkinia, Agnes Stephanie Harahap, Mohammad Ikhsan, Rifai Chai, Maria Francisca Ham

“…Numerous studies have demonstrated that deep learning-based methods yield promising results; however, current approaches often overlook the nuclei, a key feature in PTC diagnosis. …”
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LLM4WM: Adapting LLM for Wireless Multi-Tasking by Xuanyu Liu, Shijian Gao, Boxun Liu, Xiang Cheng, Liuqing Yang

“…These tasks can leverage joint learning based on channel characteristics to share representations and enhance system design. …”
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A Hybrid Efficient U-Net Framework for Detection of Anterior Belly of the Digastric Muscle on Ultrasonography by Sule Erdem, Suheda Erdem, Muammer Turkoglu, Abdulkadir Sengur, Nebras M. Sobahi

“…The model was trained on 198 ultrasound images from 99 participants. …”
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TrAQ: A novel, versatile, semi-automated, two-dimensional motor behavioural tracking software by Davide Di Censo, Ilaria Rosa, Brigida Ranieri, Tiziana Di Lorenzo, Marcello Alecci, Tiziana M. Florio, Angelo Galante

“…Within free software an innovative feature of TrAQ is the automated counting of in-plane rotations, an important parameter in the 6-hydroxydopamine hemiparkinsonian rat model and in many rodent models of neurodegenerative diseases, and a very time-consuming manual task for highly trained human operators. …”
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Over-the-air federated learning: Status quo, open challenges, and future directions by Bingnan Xiao, Xichen Yu, Wei Ni, Xin Wang, H. Vincent Poor

“…The development of applications based on artificial intelligence and implemented over wireless networks is increasingly rapidly and is expected to grow dramatically in the future. …”
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Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique by Giovana Mancilla Pivato, Gustavo Venâncio da Silva, Beatriz Granetti Peres, Stelio Pacca Loureiro Luna, Monique Danielle Pairis-Garcia, Pedro Henrique Esteves Trindade

“…We used behavioral observations from databases of surgically castrated pre-weaned and weaned pigs. We trained a random forest algorithm using the pain-free (pre-castration) and painful (post-castration) conditions as target variable and the 17 UPAPS pain-altered behaviors as feature variables. …”
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Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir, Mousab A. E. Adiel

“…The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. …”
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Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability by Attuluri R. Vijay Babu, N. Bharath Kumar, Rajanand Patnaik Narasipuram, Soundhar Periyannan, Alireza Hosseinpour, Aymen Flah

“…Historical solar power and weather datasets were used to train and evaluate the models across multiple performance metrics. …”
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Explainable AI-driven assessment of hydro climatic interactions shaping river discharge dynamics in a monsoonal basin by Prashant Parasar, Akhouri Pramod Krishna

“…The main findings of this study are (1) KAN demonstrated high predictive performance with root mean squared error (RMSE) values ranging from 42.7 to 58.3 m3/s, Nash–Sutcliffe efficiency (NSE) between 0.80 and 0.87, mean absolute error (MAE) between 28.9 to 52.7 and R2 values between 0.84 and 0.90 across stations. (2) SHAP based feature contribution analysis identified Relative humidity (hurs), specific humidity (huss), and temperature (tas) as key predictors, while (pr) showed limited contribution due to spatial inherent inconsistencies in GCM precipitation data. (3) The bootstrapped SHAP distributions highlighted substantial variability in feature importance, particularly for humidity variables, revealing station specific uncertainty patterns in model interpretation. (4) The KAN framework results indicate strong temporal alignment and physical realism, confirming KAN’s robustness in capturing seasonal discharge dynamics and extreme flow events under monsoon influence environments. (5) In this study KAN with SHAP (SHapley additive exPlanations) is implemented for hydrological modeling under monsoon-influenced and data-limited regions such as SRB, offering improved accuracy, functional precision and efficiency compared to traditional models. …”
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Convolution of the physical point cloud for predicting the self-assembly of colloidal particles by Seunghoon Kang, Young Jin Lee, Kyung Hyun Ahn

“…In the field of pattern recognition, GCNs are widely utilized to classify arbitrary 3D objects by learning multidimensional relationships within feature spaces defined by spatial coordinates. In contrast, our study constructs a feature space based on the micromechanical stresses imparted on colloidal particles during their self-assembly, rather than relying on spatial information. …”
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LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection by Jyoti Parashar, Rituraj Jain, Mahesh K. Singh, Ashwani Kumar, Premananda Sahu, Kamal Upreti

“…Using a large dataset of Low Dose CT (LDCT) scans, the system is built with fine-tuned pre-trained Convolutional Neural Networks (CNNs) such that feature extraction is reliable though minimal reducing radiation exposure. …”
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11.2 V Supply, 0.21% Biphasic Stimuli Charge-Balanced Neurostimulator With Switching Spike Suppression: An Application to Intraspinal Microstimulation for Restoring Motor Function by Mostafa Katebi, Abbas Erfanian, Mohammad Azim Karami, Mohamad Sawan

“…In addition, two methods based on a gate driver circuit and zero current switching are proposed securing advanced stimulator feature which are validated through extensive testing phase, including a simulation of 29 million periodic stimulation cycles delivering a current of 3.2 mA. …”
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Geo-parsing and analysis of road traffic crash incidents for data-driven emergency response planning by Patricia Ojonoka Idakwo, Olubayo Adekanmbi, Anthony Soronnadi, Amos David

“…These models were trained on a dataset of Nigerian RTC news articles. …”
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Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway by Esmaeil S. Nadimi, Jan-Matthias Braun, Benedicte Schelde-Olesen, Smith Khare, Vinay C. Gogineni, Victoria Blanes-Vidal, Gunnar Baatrup

“…The characterisation DNN trained on an unaugmented database of 317 images featuring neoplastic polyps and 162 images of non-neoplastic polyps reached a sensitivity of $$84.3\%$$ and a specificity of $$81.5\%$$ in classifying polyps. …”
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Adapting SAM2 Model from Natural Images for Tooth Segmentation in Dental Panoramic X-Ray Images by Zifeng Li, Wenzhong Tang, Shijun Gao, Yanyang Wang, Shuai Wang

“…To address these challenges, this paper proposes a tooth segmentation method based on the pre-trained SAM2 model. We employ adapter modules to fine-tune the SAM2 model and introduce ScConv modules and gated attention mechanisms to enhance the model’s semantic understanding and multi-scale feature extraction capabilities for medical images. …”
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An AI‐Enabled Data Processing Pipeline for Ingesting Borehole Data in Peridotite Environments by John M. Aiken, Elliot Dufornet, Hamed Amiri, Lotta Ternieten, Oliver Plümper

“…The study focuses on the alteration of peridotite core segments taken from Borehole BA1B, utilizing a gradient‐boosted trees (CatBoost) regression model trained on an integrated data set of machine‐learning segmented core images, physical measurements, geological, lithographic data, and AI‐summarized expert texts and feature selection. …”
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Predicting the thermal conductivity of polymer composites with one-dimensional oriented fillers using the combination of deep learning and ensemble learning by Yinzhou Liu, Weidong Zheng, Haoqiang Ai, Lin Cheng, Ruiqiang Guo, Xiaohan Song

“…This strategy provides valuable insights and guidance for machine learning-based material property prediction.…”
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