Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials by Cosimo Ricci, Agata Gadaleta, Annamaria Gerardino, Angelo Didonna, Giuseppe Ferrara, Francesca Romana Bertani

“…Methods Two species of wheat were evaluated in this study: durum wheat, Triticum turgidum var. durum, and Tritordeum (durum wheat × wild barley) together with pomegranate fruits of the variety Wonderful. Two different portable Near InfraRed (NIR) spectrometers have been used: a prototype developed in the PhasmaFood project and the commercial SCiO™ molecular sensor. …”
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A review of deep learning models to detect malware in Android applications by Elliot Mbunge, Benhildah Muchemwa, John Batani, Nobuhle Mbuyisa

“…Smartphone applications use permissions to allow users to utilize different functionalities, making them susceptible to malicious software (malware). …”
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Effect of Cypermethrin on Hematological and Histological Parameters in Male Albino Mice by Noor S. Jaafer, Adel M. Rabee

“…The mice were divided into three groups, with the control group remaining untreated and the other groups treated with different doses. The behavioral effects produced mild to moderate toxic symptoms. …”
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Vehicle Lane Change Multistep Trajectory Prediction Based on Data and CNN_BiLSTM Model by Shijie Gao, Zhimin Zhao, Xinjian Liu, Yanli Jiao, Chunyang Song, Jiandong Zhao

“…In the meanwhile, the research on multistep trajectory prediction in different prediction time domains is carried out. It was found that the longer the prediction time domain is, the lower the prediction performance of the model decreases, but the prediction accuracy still reached more than 96%, and it was able to accurately predict the lane change trajectory.…”
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Intrusion Detection System Framework for SDN-Based IoT Networks Using Deep Learning Approaches With XAI-Based Feature Selection Techniques and Domain-Constrained Features by Manlaibaatar Tserenkhuu, Md Delwar Hossain, Yuzo Taenaka, Youki Kadobayashi

“…The experimental results reveal that Shapley Additive Explanations and Random Forest feature importance are the reliable feature selection techniques, as they yield consistent results across all deep learning models and different feature subsets. Furthermore, the convolutional neural network model produced a top performance with an accuracy of 99.9% in the InSDN and 98% in the X-IIoTID datasets for multi-class classification. …”
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MS-trust: a transformer model with causal-global dual attention for enhanced MRI-based multiple sclerosis and myelitis detection by Salha M. Alzahrani

“…Further evaluation and validation are needed to assess its generalizability to different datasets and settings.…”
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Tree Species Classification at the Pixel Level Using Deep Learning and Multispectral Time Series in an Imbalanced Context by Florian Mouret, David Morin, Milena Planells, Cécile Vincent-Barbaroux

“…In our case study in central France with 10 tree species, we obtained an overall accuracy (OA) of around 95% and an F1-macro score of around 80% using three different benchmark DL architectures (fully connected, convolutional, and attention-based networks). …”
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3-D UXSE-Net for Seismic Channel Detection Based on Satellite Image Enhanced Synthetic Datasets by Xinke Zhang, Yihuai Lou, Naihao Liu, Daosheng Ling, Yunmin Chen

“…Our results show that 3-D UXSE-Net outperforms baseline methods, including the coherence-based approach and other DL models, and demonstrates strong generalization to field data even when trained solely on synthetic data. Comparisons of different methods highlight the effectiveness of the synthetic data generation approach for DL model training.…”
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A Novel Network for Choroidal Segmentation Based on Enhanced Boundary Information by Wenbo Huang, Chaofan Qu, Yang Yan

“…Moreover, the modular design of the boundary enhancement module ensures its portability across different segmentation tasks, making it a versatile component for integration into existing frameworks.…”
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Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov, Diana Kalita

“…We verified the performance of the proposed and known neural networks on different optimizers. It is shown that the proposed neural network accelerates the solving object detection problem by 44–52%. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This research enhances the precision of DR diagnosis by applying it to different publicly accessible datasets. It contributes to the broader discourse on the potential of hybrid, randomization-inspired neural networks in medical imaging. …”
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The spatial–temporal evolution characteristics and influencing factors of coordinated development in the Yellow River Basin: Based on the perspective of flood-sediment transport, e... by Ni Geng, Guiliang Tian, Hengquan Zhang

“…Secondly, Grey relational analysis and obstacle degree (OD) model were developed to identify the major factors affecting the coordinated development of the FES system. Finally, the Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model was selected to predict the CCD under different scenarios. …”
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On the effectiveness of neural operators at zero-shot weather downscaling by Saumya Sinha, Brandon Benton, Patrick Emami

“…., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. …”
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Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions by YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying

“…The model demonstrates robust performance across different forecast horizons, particularly suitable for short-term predictions of 1-3 months. …”
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Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition by Pawel Powroznik, Maria Skublewska-Paszkowska, Krzysztof Dziedzic, Marcin Barszcz

“…They are employed to extract joint information at different scales from the motion capture data. Due to focusing on relevant components, the model enriches the network’s comprehension of tennis motion data representation and allows for a more invested representation. …”
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Ground Segmentation for LiDAR Point Clouds in Structured and Unstructured Environments Using a Hybrid Neural–Geometric Approach by Antonio Santo, Enrique Heredia, Carlos Viegas, David Valiente, Arturo Gil

“…Evaluated in structured (SemanticKITTI) and unstructured (Rellis-3D) environments, two different versions of the proposed method are studied, including a purely geometric method and a hybrid approach that exploits deep learning techniques. …”
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Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning by Saud Sohail, Syed Muhammad Sajjad, Adeel Zafar, Zafar Iqbal, Zia Muhammad, Muhammad Kazim

“…The experimental results reveal a near-perfect accuracy of over 99% across different architectures, highlighting their effectiveness in forensic investigations.…”
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TCBGY net for enhanced wear particle detection in ferrography using self attention and multi scale fusion by Lei He, Haijun Wei, Cunxun Sun

“…Furthermore, multi-scale feature maps extracted by the backbone network are fed into the bidirectional feature pyramid network (BiFPN) for feature fusion to enhance the model’s ability to detect wear particle feature maps at different scales. Lastly, Ghost modules are introduced into both the backbone network and the neck network to reduce their complexity and improve detection speed. …”
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An Efficient Encoding Spectral Information in Hyperspectral Images for Transfer Learning of Mask R-CNN for Instance Segmentation of Tomato Sepals by Zeljana Grbovic, Marko Panic, Vladan Filipovic, Sanja Brdar, Hendrik de Villiers, Manon Mensink, Aneesh Chauhan

“…We investigate four different techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Probabilistic Principal Component Analysis (PPCA), and Non-Negative Matrix Factorization (NMF), to perform transfer learning for tomato sepal instance segmentation using models previously trained on RGB images. …”
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CIA-UNet: An Attention-Enhanced Multi-Scale U-Net for Single Tree Crown Segmentation by Jiapeng Duan, Chuanzhao Tian, Wansi Liu, Lixiang Cao, Teng Feng, Xiaomin Tian

“…In this model, the multiscale feature extraction capability of the InceptionV2 module is leveraged to compensate for U-Net’s limitation in capturing different scales of tree crowns and fine details using fixed convolutional kernels. …”
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