Incremental Pyraformer–Deep Canonical Correlation Analysis: A Novel Framework for Effective Fault Detection in Dynamic Nonlinear Processes by Yucheng Ding, Yingfeng Zhang, Jianfeng Huang, Shitong Peng

“…However, capturing nonlinear and temporal dependencies in dynamic nonlinear industrial processes poses significant challenges for traditional data-driven fault detection methods. To address these limitations, this study presents an Incremental Pyraformer–Deep Canonical Correlation Analysis (DCCA) framework that integrates the Pyramidal Attention Mechanism of the Pyraformer with the Broad Learning System for incremental learning in a DCCA basis. …”
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ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques by Parisa Ebrahimpour Moghaddam Tasouj, Gökhan Soysal, Osman Eroğul, Sinan Yetkin

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Leveraging hybrid model of ConvNextBase and LightGBM for early ASD detection via eye-gaze analysis by Ranjeet Bidwe, Sashikala Mishra, Simi Bajaj, Ketan Kotecha

“…This research introduces a novel method for eye gaze analysis to identify autistic traits in children. This proposed work offers • A novel method of ConvNextBase and LightGBM leveraging eye position as a feature for early detection of autistic traits. • A new ConvNextBase architecture proposed with few unfreezed layers and extra dense layers with units of 512 and 128, respectively, and dropout layers with a rate of 0.5 that extract rich, high-level, and more complex features from the images to improve generalization and mitigate overfitting. • A LightGBM model performed classification using 3-fold cross-validation and found the best parameters for bagging_function, feature_fraction, max_depth, Number_of_leaves and learning_rate with values of 0.8, 0.8, −1, 31 and 0.1 respectively, to improve the model's robustness on unseen data.This proposed method is trained and tested on the publicly available Kaggle dataset, and results are benchmarked with other state-of-the-art methods. …”
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Motif clustering and digital biomarker extraction for free-living physical activity analysis by Ya-Ting Liang, Charlotte Wang

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Comparative analysis of data-driven models on detection and classification of electrical faults in transmission systems: Explainability, applicability and industrial implications by Chibueze D. Ukwuoma, Dongsheng Cai, Chiagoziem C. Ukwuoma, Chinedu I. Otuka, Qi Huang

“…To address these issues, this study presents an in-depth comparative analysis of data-driven models, including machine learning, neural networks, and deep learning techniques, for detecting and classifying electrical faults in transmission lines. …”
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Construction of a transfer learning-based depression detection model for female breast cancer patients: text sentiment analysis by Jiaqi Fu, Shisi Deng, Wanting Zheng, Chunrao Zheng, Jianhong Liu, Wenji Li, Yinghua Zeng, Hongpo Xie, Yuchang Mai, Chaixiu Li, Jie Lai, Yujie Zhang, Zihan Guo, Jianyao Tang, Chuhan Zhong, Huihui Zhao, Yanni Wu

“…However, the unstructured and vast nature of these textual expressions poses challenges for manual analysis. To address this, our research team employed transfer learning methods to efficiently process and analyze large-scale text data for depression detection. …”
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Development of a transportable High-Performance Liquid Chromatograph with Ultraviolet detection and a method for the rapid analysis of 13 carbonyl compounds hydrazones by Audrey Grandjean, Anaïs Becker, Mathilde Mascles, Franck Amiet, Jean-Philippe Amiet, Damien Bazin, Stéphane Le Calvé

“…This study proposes a simultaneous analysis of 13 carbonyl compounds, according to the ISO 16,000–3 reference method, adapted for a novel transportable High-Performance Liquid Chromatograph (HPLC) system. …”
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Efficient Deep Learning-Based Cyber-Attack Detection for Internet of Medical Things Devices by Abigail Judith, G. Jaspher W. Kathrine, Salaja Silas, Andrew J

“…The study utilizes principal component analysis (PCA) for feature reduction and employs multi-layer perceptron to classify unforeseen cyber-attack IoT-based healthcare devices. …”
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Photodiagnosis with deep learning: A GAN and autoencoder-based approach for diabetic retinopathy detection by Kerem GENCER, Gülcan GENCER, Tuğçe Horozoğlu CERAN, Aynur ER BİLİR, Mustafa DOĞAN

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EmotionNet-X: An Optimized CNN Architecture for Robust Facial Emotion Analysis by Syed Muhammad Aqleem Abbas, Qaisar Abbas, Syed Muhammad Naqi

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A Neural Network Model for Intelligent Classification of Distal Radius Fractures Using Statistical Shape Model Extraction Features by Xing‐bo Cai, Ze‐hui Lu, Zhi Peng, Yong‐qing Xu, Jun‐shen Huang, Hao‐tian Luo, Yu Zhao, Zhong‐qi Lou, Zi‐qi Shen, Zhang‐cong Chen, Xiong‐gang Yang, Ying Wu, Sheng Lu

“…We established the distal radius SSM by combining mean values with PCA (Principal Component Analysis) features and proposed six morphological indicators across four groups. …”
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Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy Conditions by Mauro Larrat, Claudomiro Sales

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Haralick Texture Analysis for Differentiating Suspicious Prostate Lesions from Normal Tissue in Low-Field MRI by Dang Bich Thuy Le, Ram Narayanan, Meredith Sadinski, Aleksandar Nacev, Yuling Yan, Srirama S. Venkataraman

“…This study evaluates the feasibility of using Haralick texture analysis on low-field, T2-weighted MRI images for detecting prostate cancer, extending current research from high-field MRI to the more accessible and cost-effective low-field MRI. …”
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Clinicopathological Features and Prognostic Factors of Renal Cell Carcinoma in Young Patients Under 45 Years: A Single-Center Retrospective Study by Gao Y, Yan H, Zhang T, Lu G, Ma L

“…PN should be prioritized when feasible, and effective management of comorbidities like hypertension is essential.Keywords: young patients, renal cell carcinoma, clinical features, pathological features, prognosis…”
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Analysis of vehicle and pedestrian detection effects of improved YOLOv8 model in drone-assisted urban traffic monitoring system. by Huili Dou, Sirui Chen, Fangyuan Xu, Yuanyuan Liu, Hongyang Zhao

“…The multi-scale feature fusion module enhances the model's detection ability for targets of different sizes by combining feature maps of different scales; the improved non-maximum suppression algorithm effectively reduces repeated detection and missed detection by optimizing the screening process of candidate boxes. …”
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Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…The GMU_D model constructed by discriminative analysis based on machine learning screening features had an excellent discriminative performance (AUC = 0.866, 95% CI: 0.858–0.874), and higher accuracy than the PKUPH model (AUC = 0.559, 95% CI: 0.552–0.567) and the Block model (AUC = 0.823, 95% CI: 0.814–0.833). …”
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Technology and Emotions: AI-Driven Software Prototyping for the Analysis of Emotional States and Early Detection of Risky Behaviors in University Students by Alba Catherine Alves-Noreña, María-José Rodríguez-Conde, Juan Pablo Hernández-Ramos, José William Castro-Salgado

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Validation of a deep learning model for cattle lameness detection: Comparison of human scorer performance and automated gait analysis by Sandra Reitmaier, Serhan Narli

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Morphological Analysis and Subtype Detection of Acute Myeloid Leukemia in High-Resolution Blood Smears Using ConvNeXT by Mubarak Taiwo Mustapha, Dilber Uzun Ozsahin

“…A deep learning pipeline was developed, integrating Stochastic Weight Averaging (SWA) for model stability, Mixup data augmentation to enhance generalization, and Grad-CAM for model interpretability, ensuring biologically meaningful feature visualization. Various models, including ResNet50 and Vision Transformers, were benchmarked for comparative performance analysis; (3) Results: ConvNeXt outperformed ResNet50, achieving a classification accuracy of 95% compared to 91% for ResNet50 and 81% for transformer-based models (Vision Transformers). …”
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Electroencephalography-Based Pain Detection Using Kernel Spectral Connectivity Network with Preserved Spatio-Frequency Interpretability by Santiago Buitrago-Osorio, Julian Gil-González, Andrés Marino Álvarez-Meza, David Cardenas-Peña, Alvaro Orozco-Gutierrez

“…(iii) Further, to account for subject variability, we conduct cross-subject analysis and grouping, clustering individuals based on similar pain detection performance, functional connectivity patterns, sex, and age. …”
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