Improving timing resolution of BGO for TOF-PET: a comparative analysis with and without deep learning by Francis Loignon-Houle, Nicolaus Kratochwil, Maxime Toussaint, Carsten Lowis, Gerard Ariño-Estrada, Antonio J. Gonzalez, Etiennette Auffray, Roger Lecomte

“…Deep learning, particularly convolutional neural networks (CNNs), can also enhance CTR by training with digitized waveforms. …”
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Comparison of Deep Learning-Based Auto-Segmentation Results on Daily Kilovoltage, Megavoltage, and Cone Beam CT Images in Image-Guided Radiotherapy by Zhixing Wang PhD, Chengyu Shi PhD, Carson Wong BS, Seyi M Oderinde PhD, William T Watkins PhD, Kun Qing PhD, Bo Liu PhD, Terence M Williams MD, PhD, An Liu PhD, Chunhui Han PhD

“…Deep learning auto-segmentation models using a convolutional neural network algorithm were used to generate organs-at-risk contours. …”
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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“…Overall, 24 AI technologies were identified, covering traditional algorithms such as random forest, support vector machine, and k-nearest neighbor, as well as deep learning models such as convolutional neural networks, Bidirectional Encoder Representations From Transformers, and ChatGPT. …”
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Prediction of Alzheimer’s Disease Based on Multi-Modal Domain Adaptation by Binbin Fu, Changsong Shen, Shuzu Liao, Fangxiang Wu, Bo Liao

“…However, the structure and semantics of different modal data are different, and the distribution between different datasets is prone to the problem of domain shift. Most of the existing methods start from the single-modal data and assume that different datasets meet the same distribution, but they fail to fully consider the complementary information between the multi-modal data and fail to effectively solve the problem of domain distribution difference. …”
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Tree Species Detection and Enhancing Semantic Segmentation Using Machine Learning Models with Integrated Multispectral Channels from PlanetScope and Digital Aerial Photogrammetry i... by Arun Gyawali, Mika Aalto, Tapio Ranta

“…The study mainly involved two different objectives: first, tree species detection using the latest version of You Only Look Once (YOLOv12), and second, semantic segmentation (classification) using random forest, Categorical Boosting (CatBoost), and a Convolutional Neural Network (CNN). To the best of our knowledge, this marks the first exploration utilizing YOLOv12 for tree species identification, along with the study that integrates digital aerial photogrammetry with Planet imagery to achieve semantic segmentation in young forests. …”
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Automatic Identification of Amharic Text Idiomatic Expressions Using a Deep Learning Approach by Habtamu Hunegnaw Limenih, Abebe Belay Adege, Abrham Yaregal Alene, Habtamu Tariku Demasu, Habtamu Molla Belachew

“…Few studies have been conducted to identify idiomatic expressions using K-Nearest Neighbors (KNN) and Convolutional Neural Network (CNN) algorithms for the Amharic language. …”
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Expression Dynamics and Genetic Compensation of Cell Cycle Paralogues in <i>Saccharomyces cerevisiae</i> by Gabriele Schreiber, Facundo Rueda, Florian Renner, Asya Fatima Polat, Philipp Lorenz, Edda Klipp

“…Due to the duplication of the yeast genome during evolution, most of the cyclins are present as a pair of paralogues, which are considered to have similar functions and periods of expression. …”
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Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…A range of sophisticated artificial intelligence methods, including One-Dimensional Convolutional Neural Network (1D-CNN), Artificial Neural Networks (ANN), Decision Tree (DT), Ensemble Learning (EL), Adaptive Boosting (AdaBoost), Random Forest (RF), and Least Squares Support Vector Machine (LSSVM), were utilized to model and predict pH variations with high accuracy. …”
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A Rotated Object Detection Model With Feature Redundancy Optimization for Coronary Athero-Sclerotic Plaque Detection by Xue Hao, Haza Nuzly Abdull Hamed, Qichen Su, Xin Dai, Linqiang Deng

“…Meanwhile, RFDS is introduced into spatial and channel reconstruction convolution (SCConv) to form the enhancement structure RFDS-SCConv, which refines feature extraction by adaptively regulating redundant features in both spatial and channel dimensions. …”
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Optimisation of socio-economic, environmental and public health determinants of national security for post-pandemic recovery by Jana Firstová, Alina Vysochyna

“…Thirdly, the additive-multiplicative convolution (to integrate individual parameters). The National Security Index showed a delayed COVID-19 destructive impact (in 2020 the Index decreased in 2 out of 34 countries). …”
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Advanced Hydro-Informatic Modeling Through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction by Shahariar Hossain Mahir, Md Tanjum An Tashrif, Md Ahsan Karim, Dipanjali Kundu, Anichur Rahman, Md. Amir Hamza, Fahmid Al Farid, Abu Saleh Musa Miah, Sarina Mansor

“…Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylhet, Bangladesh where transboundary water flows and climate change have increased the risk of disasters. …”
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A Radiomic-based model to predict the depth of myometrial invasion in endometrial cancer on ultrasound images by Francesca Arezzo, Annarita Fanizzi, Rosanna Mancari, Emiliano Cocco, Samantha Bove, Maria Colomba Comes, Mariangela Gianciotta, Giorgia Lanza, Salvatore Lopez, Gerardo Cazzato, Erica Silvestris, Elsa Vitale, Enrico Vizza, Gennaro Cormio, Raffaella Massafra, Vera Loizzi

“…Abstract In Europe, endometrial carcinoma was found to be the fourth most common tumor in the female population in 2022. …”
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Testing the reliability of geometric morphometric and computer vision methods to identify carnivore agency using Bi-Dimensional information by Manuel Domínguez-Rodrigo, Marina Vegara-Riquelme, Juan Palomeque-González, Blanca Jiménez-García, Gabriel Cifuentes-Alcobendas, Marcos Pizarro-Monzo, Elia Organista, Enrique Baquedano

“…Biased replication and exclusion of the most widely represented forms of non-oval tooth pits from such analyses have compromised the published results and their ensuing generalizations. …”
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Advances in soil salinity diagnosis for mangrove swamp rice production in Guinea Bissau, West Africa by Gabriel Garbanzo, Jesus Céspedes, Marina Temudo, Maria do Rosário Cameira, Paula Paredes, Tiago Ramos

“…Rice is one of the most important crops in many West African countries and has a direct impact on food security. …”
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Flexible retinomorphic vision sensors with scotopic and photopic adaptation for a fully flexible neuromorphic machine vision system by Lei Shi, Ke Shi, Zhi‐Cheng Zhang, Yuan Li, Fu‐Dong Wang, Shu‐Han Si, Zhi‐Bo Liu, Tong‐Bu Lu, Xu‐Dong Chen, Jin Zhang

“…Abstract Bioinspired neuromorphic machine vision system (NMVS) that integrates retinomorphic sensing and neuromorphic computing into one monolithic system is regarded as the most promising architecture for visual perception. …”
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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…Since the first epidemic in 1972, Nigeria has experienced recurrent outbreaks, with the most severe in 1991, resulting in over 7,000 deaths. …”
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Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas by Peng Shu, Rana Waqar Aslam, Iram Naz, Bushra Ghaffar, Dmitry E. Kucher, Abdul Quddoos, Danish Raza, M. Abdullah-Al-Wadud, Rana Muhammad Zulqarnain

“…This study presents a novel deep learning-based super-resolution framework for enhancing remote sensing imagery to assess groundwater quality and environmental conditions in Lahore, Pakistan. We developed a convolutional neural network architecture that upscales low-resolution satellite imagery to generate high-resolution (0.5 m) outputs, achieving a peak signal-to-noise ratio of 32.4 dB and structural similarity index of 0.91. …”
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A Novel Short‐Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia by Xiukuan Zhao, Guozhu Li, Haiyong Xie, Lianhuan Hu, Wenjie Sun, Yi Li, Guofeng Dai, Jianfei Liu, Yu Li, Baiqi Ning, Michi Nishioka, Septi Perwitasari, Prasert Kenpankho

“…The model integrates the convolutional neural network and long short‐term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities. …”
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Development of an Efficient Grading Model for Maize Seedlings Based on Indicator Extraction in High-Latitude Cold Regions of Northeast China by Song Yu, Yuxin Lu, Yutao Zhang, Xinran Liu, Yifei Zhang, Mukai Li, Haotian Du, Shan Su, Jiawang Liu, Shiqiang Yu, Jiao Yang, Yanjie Lv, Haiou Guan, Chunyu Zhang

“…Maize, the world’s most widely cultivated food crop, is critical in global food security. …”
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The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale by Adrian Baggström, Robert Goodsell, Laura van Dijk, Ela Iwaszkiewicz-Eggebrecht, Andreia Miraldo, Ayco J.M. Tack, Tobias Andermann

“…Here, we present a biodiversity modeling approach that utilizes metabarcoding-derived biodiversity data, remote sensing, and convolutional neural networks (CNNs). We apply CNNs to predict the spatial pattern of seasonal arthropod richness across Sweden and compare the results with other statistical models commonly used in spatial modeling. …”
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