Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends by Md Amran Hossen, Pg Emeroylariffion Abas

“…Despite significant progress, HAR still faces critical challenges, including handling environmental variability, ensuring model interpretability, and achieving high recognition accuracy in complex, real-world scenarios. …”
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Performance Enhancement of EEG Signatures for Person Authentication Using CNN BiLSTM Method by Ashish Ranjan Mishra, Rakesh Kumar, Rajkumar Saini

“…The broad implementation of EEG-based authentication systems has been hindered by problems such as noise, variability, and inter-subject variances despite the potential distinctiveness of EEG signals. …”
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Artificial Intelligence for data modeling in triboelectric nanogenerators by Chenjia Li, Ali Matin Nazar

“…The review also addresses key challenges such as data variability, environmental robustness, and algorithmic scalability, and future directions in hybrid energy systems, adaptive algorithms, and cross-disciplinary collaboration for sustainable, intelligent sensing technologies.…”
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MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network by Yi Zhang, Wei Song, Mingyue Shao, Xiangchun Liu

“…The current methods, whether CNNs with fixed convolutional kernel sizes or Transformers with fixed attention sizes, struggle to handle such variability effectively. …”
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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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DeepBiteNet: A Lightweight Ensemble Framework for Multiclass Bug Bite Classification Using Image-Based Deep Learning by Doston Khasanov, Halimjon Khujamatov, Muksimova Shakhnoza, Mirjamol Abdullaev, Temur Toshtemirov, Shahzoda Anarova, Cheolwon Lee, Heung-Seok Jeon

“…<b>Background/Objectives</b>: The accurate identification of insect bites from images of skin is daunting due to the fine gradations among diverse bite types, variability in human skin response, and inconsistencies in image quality. …”
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Advancement and independent validation of a deep learning-based tool for automated scoring of nail psoriasis severity using the modified nail psoriasis severity index by Stephan Kemenes, Stephan Kemenes, Liu Chang, Maja Schlereth, Rita Noversa de Sousa, Rita Noversa de Sousa, Ioanna Minopoulou, Pauline Fenzl, Pauline Fenzl, Giulia Corte, Giulia Corte, Melek Yalcin Mutlu, Melek Yalcin Mutlu, Michael Wolfgang Höner, Michael Wolfgang Höner, Ioannis Sagonas, Ioannis Sagonas, Birte Coppers, Birte Coppers, Anna-Maria Liphardt, Anna-Maria Liphardt, David Simon, Arnd Kleyer, Lukas Folle, Michael Sticherling, Michael Sticherling, Georg Schett, Georg Schett, Andreas Maier, Filippo Fagni, Filippo Fagni

“…A reader study was performed to assess inter-rater variability.ResultsIn total, 460 patients providing 4,400 nail photographs were included in the training dataset. …”
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ScarNet: Development and Validation of a Novel Deep CNN Model for Acne Scar Classification With a New Dataset by Masum Shah Junayed, Md Baharul Islam, Afsana Ahsan Jeny, Arezoo Sadeghzadeh, Topu Biswas, A. F. M. Shahen Shah

“…Dermatologists mainly recognize the type of acne scars manually based on visual inspections, which are time- and energy-consuming and subject to intra- and inter-reader variability. In this paper, a novel automated acne scar classification system is proposed based on a deep Convolutional Neural Network (CNN) model. …”
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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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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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Implications of artificial intelligence in periodontal treatment maintenance: a scoping review by Raafat Musief Sarakbi, Sudhir Rama Varma, Sudhir Rama Varma, Lovely Muthiah Annamma, Vinay Sivaswamy, Vinay Sivaswamy

“…Traditional diagnostic methods in periodontology often rely on subjective clinical assessments, which can lead to variability and inconsistencies in care. Imbibing artificial intelligence (AI) facilitates a significant solution by enhancing precision metrics, treatment planning, and personalized care. …”
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Deep Learning for Video Fluoroscopic Swallowing Study Analysis: A Survey on Classification, Detection, and Segmentation Techniques by Ahmed Fakhry, Sarah Mary Antony, Eunhee Park, Jong Taek Lee

“…Classification methods utilizing convolutional neural networks achieve high accuracy, ranging from 91.7% to 95.98%, and Area Under the ROC Curve scores between 0.71 and 0.97, thus enhancing the consistency and reliability of swallowing phase identification. …”
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