IchthyNet: An Ensemble Method for the Classification of In Situ Marine Zooplankton Shadowgraph Images by Brittney Slocum, Bradley Penta

“…The networks were trained on a training set of 187,000 ROIs augmented with random rotations and pixel intensity thresholding to increase data variability and evaluated against two datasets. While the performance of each individual model is examined, the best approach is to use the ensemble, which performed with an F1-score of 98% and an area under the curve (AUC) of 99% on both test datasets while its accuracy, precision, and recall fluctuated between 97% and 98%.…”
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Deep Learning Classification of Simulated Surface EMG Signals across Maximum Voluntary Contraction Levels by Radhouane Hammach, Samia Belkacem, Noureddine Messaoudi, Raïs El’hadi Bekka

“…Unlike previous studies, which focus primarily on binary classification of fatigue and non-fatigue states, our approach employs a deep convolutional neural network for the classification of sEMG signals into ten MVC levels, where the model outputs categorical predictions, with each class representing a specific MVC level. sEMG signals were generated using a computer muscle model that we developed using MATLAB, which allows for greater control over variability, ensuring robustness and generalizability of the model. …”
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Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk by Alejandro Peña, Lina M. Sepúlveda-Cano, Juan David Gonzalez-Ruiz, Nini Johana Marín-Rodríguez, Sergio Botero-Botero

“…The stability provided by the DFCS model could be evidenced through the structure exhibited by the aggregate loss distributions (ALDs), which are obtained as a result of the convolution process between frequency and severity random variables for each database and which are expected to achieve similar structures to the probability distributions suggested by Basel II agreements (lean, long tail, positive skewness) against the OR modeling. …”
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Devanagari Character Recognition: A Comprehensive Literature Review by Sandhya Arora, Latesh Malik, Sonakshi Goyal, Debotosh Bhattacharjee, Mita Nasipuri, Ondrej Krejcar

“…Challenges remain, including handwriting variability, noise, and the need for real-time performance. …”
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Linking European Temperature Variations to Atmospheric Circulation With a Neural Network: A Pilot Study in a Climate Model by Enora Cariou, Julien Cattiaux, Saïd Qasmi, Aurélien Ribes, Christophe Cassou, Antoine Doury

“…This exploratory work opens up promising prospects for estimating the contribution of atmospheric variability to observed temperature variations.…”
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Approaches to Proxy Modeling of Gas Reservoirs by Alexander Perepelkin, Anar Sharifov, Daniil Titov, Zakhar Shandrygolov, Denis Derkach, Shamil Islamov

“…On average, the ST-GNN method reduces computational time by a factor of 4.3 compared to traditional hydrodynamic models, with a median predictive error not exceeding 10% across diverse datasets, despite variability in specific scenarios. The ST-GNN framework demonstrates promising potential as a tool for operational and strategic planning.…”
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Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall by Kalpesh R. Patil, Takeshi Doi, J.V. Ratnam, Swadhin K. Behera

“…Analysis of saliency-based heatmaps indicated the high skill to be due to the model capturing the leading modes of climate variability, such as the Indian Ocean Dipole and El Niño-Southern Oscillation, realistically. …”
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Depression detection methods based on multimodal fusion of voice and text by Zhenrong Xu, Yuan Gao, Fang Wang, Longqian Zhang, Li Zhang, Junke Wang, Jie Shu

“…Traditional diagnostics often rely on subjective judgments, leading to variability and inefficiency. This study proposes a fusion model for automated depression detection, leveraging bimodal data from voice and text. …”
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Application of Modified Laplace Variational Iteration Hybrid Approach for Solving Time-Fractional Fourth-Order Parabolic PDEs by Mehari Fentahun Endalew, Xiaoming Zhang

“…The current study is aimed at obtaining analytical solutions of fourth-order parabolic partial differential equations of time-fractional derivative with variable coefficients. The modified Laplace variational iteration approach and the homotopy perturbation method were used to treat nonlinear, fourth-order, time-fractional partial differential equations with time-fractional derivatives. …”
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Federated learning for privacy-enhanced mental health prediction with multimodal data integration by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

“…This study addresses these challenges by utilising a multimodal dataset comprising physiological signals (heart rate variability, sleep patterns) and behavioural data (online activity, social media interactions). …”
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Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions by Asmaa A. Abdelwahab, Mustafa A. Elattar, Sahar Ali Fawzi

“…Furthermore, we address ongoing challenges, such as dataset variability and the generalization of models to novel chemical spaces. …”
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Mapping of soil sampling sites using terrain and hydrological attributes by Tan-Hanh Pham, Kristopher Osterloh, Kim-Doang Nguyen

“…Traditional site selection methods are labor-intensive and fail to capture soil variability comprehensively. This study introduces a deep learning-based tool that automates soil sampling site selection using spectral images. …”
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Hybrid deep learning for IoT-based health monitoring with physiological event extraction by Sivanagaraju Vallabhuni, Kumar Debasis

“…For better feature extraction, the proposed method implements Physiological Event Extraction (PEE), which is aimed at identifying important physiological events such as heart rate variability and respiratory changes from raw sensor data samples. …”
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Handwritten Text Recognition for Documentary Medieval Manuscripts by Sergio Torres Aguilar, Vincent Jolivet

“…However, several challenges must be addressed, including the scarcity of relevant training corpora, the consequential variability introduced by different scribal hands and writing scripts, and the complexity of page layouts. …”
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A systematic literature review on the role of artificial intelligence in citizen science by Germain Abdul-Rahman, Andrej Zwitter, Noman Haleem

“…However, challenges such as data quality variability, algorithmic opacity, and scalability constraints persist. …”
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Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…The proposed system comprises (1) a Cow Lameness Feature Map (CLFM) model extracting holistic gait kinematics (hoof trajectories and dorsal contour) from walking sequences, and (2) a DenseNet-Integrated Convolutional Attention Module (DCAM) that mitigates inter-individual variability through multi-feature fusion. …”
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Computer Vision Meets Generative Models in Agriculture: Technological Advances, Challenges and Opportunities by Xirun Min, Yuwen Ye, Shuming Xiong, Xiao Chen

“…However, challenges persist, including environmental variability, edge deployment limitations, and the need for interpretable systems. …”
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RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture by Jinye Gao, Jun Sun, Xiaohong Wu, Chunxia Dai

“…Current manual observation paradigms face critical limitations in temporal resolution, inter-observer variability, and scalability. This study presents RDM-YOLO, a computationally efficient deep learning framework derived from YOLOv5s architecture, specifically designed for the automated detection of three essential behaviors (resting, wriggling, and eating) in fourth instar silkworms. …”
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Automated Risser Grade Assessment of Pelvic Bones Using Deep Learning by Jeoung Kun Kim, Donghwi Park, Min Cheol Chang

“…Challenges arose from class imbalance in less frequent grades. (4) Conclusions: CNN models effectively automated Risser grade assessment, reducing clinician workload and variability.…”
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Zoon’s Balanitis – Update of Clinical Spectrum and Management by Vineet Relhan, Abhinav Kumar, Aneet Kaur

“…It reveals lozenge-shaped keratinocytes with siderophages, haemorrhages and variable plasma cell infiltrate in the dermis. Dermoscopy shows spermatozoa-like, convoluted vessels with structureless red orange areas. …”
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