An Improved Model for Detecting the Presence of Pesticide Residues in Edible Parts of Tomatoes, Cabbages, Carrots, and Green Pepper Vegetables Using Batch Image Analysis by Nabaasa, Evarist, Natumanya, Deborah, Mabirizi, Vicent

“…Our method uses a deep learning–based convolutional neural network (CNN), an advanced image processing, feature extraction, and a dataset of 1,094 photographs collected from Mbarara city markets to accurately identify the presence of pesticides in the edible parts of vegetables. …”
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Large language models for depression recognition in spoken language integrating psychological knowledge by Yupei Li, Shuaijie Shao, Manuel Milling, Manuel Milling, Björn W. Schuller, Björn W. Schuller, Björn W. Schuller, Björn W. Schuller

“…We extract the audio features using the pre-trained model Wav2Vec, and map them to text-based LLMs for further processing. …”
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Design of experiments for the stochastic unit commitment with economic dispatch models by Nahal Sakhavand, Jay Rosenberger, Victoria C.P. Chen, Harsha Gangammanavar

“…To address this challenge, we develop a novel DACE approach that exploits the structure of the first-stage unit commitment decision space in a design of experiments, uses features based upon solar generation, and trains a multivariate adaptive regression splines model to approximate the second stage of the stochastic unit commitment-economic dispatch problem. …”
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Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment by Rui Liu, Yuanyuan Jia, Xiangqian He, Zhe Li, Jinhua Cai, Hao Li, Xiao Yang

“…We utilized the normalized histogram, mean value, and variance of each image as input features to train the classification model, based on ensemble learning with multiple classifiers. 600 left-hand radiographs with the bone age ranging from 1 to 18 years old were included in the dataset. …”
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Enhanced Wheat Head Detection in Images Using Fourier Domain Adaptation and Random Guided Filter: Détection améliorée des têtes de blé dans les images à l’aide de l’adaptation du d... by Sylvester C. Okafor, Linjing Wei, Solomon Boamah, Le Zhang, Mamadou B. Diallo

“…An EfficientDet model was trained on the publicly available wheat dataset and the results were analyzed and compared to a baseline model. …”
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The Cart-Pole Application as a Benchmark for Neuromorphic Computing by James S. Plank, Charles P. Rizzo, Chris A. White, Catherine D. Schuman

“…Spiking neural networks are the basis of brain-based, or neuromorphic computing. They are attractive, especially as agents for control applications, because of their very low size, weight and power requirements. …”
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Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India by Thamizh Vendan Tarun Kshatriya, Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Nivas Raj Moorthi, Dhanaraju Muthumanickam, Kaliaperumal Ragunath, Jagadeeswaran Ramasamy

“…A total of 43 SCORPAN-based environmental covariates were considered, of which 37 covariates were retained after the recursive feature elimination (RFE) process. …”
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Lncident: A Tool for Rapid Identification of Long Noncoding RNAs Utilizing Sequence Intrinsic Composition and Open Reading Frame Information by Siyu Han, Yanchun Liang, Ying Li, Wei Du

“…Here, we present a novel method for rapid identification of lncRNAs utilizing sequence intrinsic composition features and open reading frame information based on support vector machine model, named as Lncident (LncRNAs identification). …”
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Efficient Moving Object Segmentation in LiDAR Point Clouds Using Minimal Number of Sweeps by Zoltan Rozsa, Akos Madaras, Tamas Sziranyi

“…The model is trained on a dataset of LiDAR point clouds and related camera images. …”
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Improving microvascular brain analysis with adversarial learning for OCT–TPM vascular domain translation by Nadia Badawi, Jaloliddin Rustamov, Zahiriddin Rustamov, Frederic Lesage, Nazar Zaki, Rafat Damseh

“…We investigate the use of 2D and 3D cycle generative adversarial networks (CycleGANs) trained on unpaired image samples. We evaluate the generated TPM vascular structures based on image similarity and signal-to-noise ratio. …”
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Predicting the diffusion coefficients of rejuvenators into bitumens using molecular dynamics, machine learning, and force field atom types by Eli I. Assaf, Xueyan Liu, Peng Lin, Shisong Ren, Sandra Erkens

“…The manuscript presents three case studies to illustrate how the model can be used for the iterative design of rejuvenators by optimizing molecular structures based on critical chemical features, such as rejuvenator oxygen content, bitumen sulfur content, and molecular weights. …”
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Enhancing Brain Tumor Diagnosis with L-Net: A Novel Deep Learning Approach for MRI Image Segmentation and Classification by Lehel Dénes-Fazakas, Levente Kovács, György Eigner, László Szilágyi

“…These two units are coupled such a way that the CNN classifies the MRI images based on the features extracted by the U-net while segmenting the tumor, instead of relying on the original input images. …”
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Point Defect Detection and Classification in MoS₂ Scanning Tunneling Microscopy Images: A Deep Learning Approach by Shiru Wu, Guoyang Chen, Si Shen, Jiaxu Yan

“…This deep learning pipeline is trained on augmented STM image data and evaluated against manual annotations. …”
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Annotated intraoral image dataset for dental caries detection by Syed Muhammad Faizan Ahmed, Muhammad Huzaifa Ghori, Aamna Khalid, Ayesha Nooruddin, Niha Adnan, Abhishek Lal, Fahad Umer

“…This work advances AI-based dental diagnostics and sets a benchmark for caries detection. …”
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A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone by Amine Lagzouli, Peter Pivonka, David M. L. Cooper, Vittorio Sansalone, Alice Othmani

“…DBAHNet’s hierarchical structure combines transformers and convolutional neural networks to capture long-range dependencies and local features for improved contextual representation. We trained DBAHNet on a limited dataset of 3D µCT scans of mouse tibiae and evaluated its performance on a diverse dataset collected from seven different research studies. …”
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Speech as a Biomarker for Disease Detection by Catarina Botelho, Alberto Abad, Tanja Schultz, Isabel Trancoso

“…The classification strategy explored is based on Neural Additive Models, a type of glass-box neural network, which enables interpretability. …”
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MCAUnet: a deep learning framework for automated quantification of body composition in liver cirrhosis patients by Jiening Wang, Shuqi Xia, Jie Zhang, Xinyi Wang, Cai Zhao, Wen Zheng

“…A total of 11,362 L3-level lumbar CT slices were collected to train and validate the segmentation model. The proposed model incorporates an attention mechanism from the channel perspective, enabling adaptive fusion of critical channel features. …”
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Genomics and integrative clinical data machine learning scoring model to ascertain likely Lynch syndrome patients by Ramadhani Chambuso, Takudzwa Nyasha Musarurwa, Alessandro Pietro Aldera, Armin Deffur, Hayli Geffen, Douglas Perkins, Raj Ramesar

“…Group regularisation methods in combination with 10-fold cross-validation were performed for feature selection on the training data. Results Out of 4800 CRC patients frorm the TCGA datasets with clinicopathological and somatic genomics data, we ascertained 524 patients with complete data. …”
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Enhancing Breast Cancer Diagnosis With Multi-Resolution Vision Transformers and Robust Decision-Making by Margo Sabry, Hossam Magdy Balaha, Khadiga M. Ali, Tayseer Hassan A. Soliman, Dibson Gondim, Mohammed Ghazal, Norah Saleh Alghamdi, Ayman El-Baz

“…The proposed framework uses multiscale feature extraction at three magnification levels (L0, L1, L2 or 16x, 4x, 2x) to capture both fine-grained and high-level tumor features. …”
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Physics-guided attention-aware convolutional neural networks for identification of magnetic islands in the tearing mode on EAST tokamak by Feifei Long, Yian Zhao, Yunjiao Zhang, Chenguang Wan, Yinan Zhou, Ziwei Qiang, Kangning Yang, Jiuying Li, Tonghui Shi, Bihao Guo, Yang Zhang, Hailing Zhao, Ang Ti, Adi Liu, Chu Zhou, Jinlin Xie, Zixi Liu, Ge Zhuang, the EAST Team

“…An attention mechanism is designed to couple with the convolutional neural networks (CNNs) to improve the capability of feature extraction of signals. During the model training process, we utilized adaptive learning rate adjustment and early stopping mechanisms to optimize performance of AA-CNN. …”
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