Enhancing chronic wound assessment through agreement analysis and tissue segmentation by Ana C. Morgado, Rafaela Carvalho, Ana Filipa Sampaio, Maria J. M. Vasconcelos

“…However, the current manual process of tissue segmentation and quantification, which is an indicator of the healing progress, is time-consuming and subject to variability, so automated methods that can effectively monitor wound healing are required. …”
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Accurate Sugarcane Detection and Row Fitting Using SugarRow-YOLO and Clustering-Based Spline Methods for Autonomous Agricultural Operations by Guiqing Deng, Fangyue Zhou, Huan Dong, Zhihao Xu, Yanzhou Li

“…In addition to addressing the problem of large variability in row spacing and plant spacing of sugarcane, this paper introduces the DBSCAN clustering algorithm and combines it with a smooth spline curve to fit the crop rows in order to realize the accurate extraction of crop rows. …”
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Noninvasive Continuous Glucose Monitoring Using Multimodal Near-Infrared, Temperature, and Pressure Signals on the Earlobe by Jongdeog Kim, Bong Kyu Kim, Mi-Ryong Park, Hyoyoung Cho, Chul Huh

“…Across all participants, results showed 90.9% CEG A-Zone accuracy and a MARD of 8.4%, with performance variations linked to individual factors such as earlobe thickness variability and physical activity. These outcomes demonstrate the potential of the MW-SE-NIRS system for noninvasive glucose monitoring and highlight the importance of future work on personalized modeling, sensor optimization, and larger-scale clinical validation.…”
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AI-driven smart agriculture using hybrid transformer-CNN for real time disease detection in sustainable farming by Zhuo Zeng, Tariq Mahmood, Yu Wang, Amjad Rehman, Muhammad Akram Mujahid

“…This study introduces the AttCM-Alex model, a novel deep-learning framework designed to boost the detection and classification of plant diseases under challenging environmental conditions. By integrating convolutional operations with self-attention mechanisms, AttCM-Alex effectively addresses the variability in light intensity and image noise, ensuring robust performance. …”
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Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…A pressing necessity exists for a study on the variability of these factors and their impact on cardiovascular disease (CVD). …”
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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

“…Despite advances, existing diagnostic methods face challenges such as resource dependency, variability in accuracy, and limited accessibility, especially in underserved regions. …”
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I-AIR: intention-aware travel itinerary recommendation via multi-signal fusion and spatiotemporal constraints by Xiao Cui, Zhihua Wang, Ping Li, Qiang Xu

“…Although traditional methods have made progress in next-POI prediction and route planning, they often rely on static user preferences and overly simplistic spatial assumptions, overlooking contextual variability and non-linear distance effects. Additionally, many approaches decouple POI selection from itinerary construction and depend heavily on limited behavioral signals, failing to capture richer feedback such as ratings, dwell times, or click histories. …”
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Deep Learning Techniques for Lung Cancer Diagnosis with Computed Tomography Imaging: A Systematic Review for Detection, Segmentation, and Classification by Kabiru Abdullahi, Kannan Ramakrishnan, Aziah Binti Ali

“…However, challenges persist, including dataset scarcity, annotation variability, and population generalizability. Hybrid architectures, such as convolutional neural networks (CNNs) and transformers, show promise in improving nodule localization. …”
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DermViT: Diagnosis-Guided Vision Transformer for Robust and Efficient Skin Lesion Classification by Xuejun Zhang, Yehui Liu, Ganxin Ouyang, Wenkang Chen, Aobo Xu, Takeshi Hara, Xiangrong Zhou, Dongbo Wu

“…Currently, skin lesion classification faces challenges such as lesion–background semantic entanglement, high intra-class variability, artifactual interference, and more, while existing classification models lack modeling of physicians’ diagnostic paradigms. …”
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Thyroid nodule segmentation in ultrasound images using transformer models with masked autoencoder pre-training by Yi Xiang, Rajendra Acharya, Quan Le, Jen Hong Tan, Chiaw-Ling Chng

“…The difficulty arises from factors such as the absence of prior knowledge about the thyroid region, low contrast between anatomical structures, and speckle noise, all of which obscure boundary detection and introduce variability in nodule appearance across different images.MethodsTo address these challenges, we propose a transformer-based model for thyroid nodule segmentation. …”
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A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data by David M. Morris, Chengjia Wang, Giorgos Papanastasiou, Calum D. Gray, Wei Xu, Samuel Sjöström, Sammy Badr, Julien Paccou, Scott IK Semple, Tom MacGillivray, William P. Cawthorn

“…Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. …”
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SIGNAL DETECTION WITH UNKNOWN PARAMETERS by Y. A. Sidorkina, V. V. Antipov

“…The effectiveness of this algorithm is determined by the sampling distribution of the maximum values of random variables distributed according to Rayleigh.…”
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Real-time classroom student behavior detection based on improved YOLOv8s by Xiaojing Sheng, Suqiang Li, Sixian Chan

“…However, the field still faces specific challenges, primarily concerning the accuracy of identifying student behaviors within complex and variable classroom environments, as well as the real-time capabilities of detection algorithms. …”
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A Lightweight Person Detector for Surveillance Footage Based on YOLOv8n by Qicheng Wang, Guoqiang Feng, Zongzhe Li

“…Next, a heterogeneous PAFPN with improved MSBlock was formed using heterogeneous convolution kernels. Finally, AKConv, a variable kernel convolution, was applied to further reduce the number of parameters and the computational cost while improving accuracy. …”
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On Symmetrical Sonin Kernels in Terms of Hypergeometric-Type Functions by Yuri Luchko

“…In this paper, a new class of kernels of integral transforms of the Laplace convolution type that we named symmetrical Sonin kernels is introduced and investigated. …”
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Deep learning (DL)‐based advancements in prostate cancer imaging: Artificial intelligence (AI)‐based segmentation of 68Ga‐PSMSA PET for tumor volume assessment by Sharjeel Usmani, Khulood Al Riyami, Subash Kheruka, Shah P Numani, Rashid al Sukaiti, Maria Ahmed, Nadeem Pervez

“…Traditional manual segmentation methods are time‐consuming processes that are further challenged by inter‐observer variability. Artificial intelligence (AI)‐based segmentation techniques offer a promising solution to these challenges. …”
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Quantum Integral Inequalities with Respect to Raina’s Function via Coordinated Generalized Ψ-Convex Functions with Applications by Saima Rashid, Saad Ihsan Butt, Shazia Kanwal, Hijaz Ahmad, Miao-Kun Wang

“…In accordance with the quantum calculus, we introduced the two variable forms of Hermite-Hadamard- (HH-) type inequality over finite rectangles for generalized Ψ-convex functions. …”
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Privacy–preserving dementia classification from EEG via hybrid–fusion EEGNetv4 and federated learning by Muhammad Umair, Muhammad Shahbaz Khan, Muhammad Hanif, Wad Ghaban, Ibtehal Nafea, Sultan Noman Qasem, Sultan Noman Qasem, Faisal Saeed

“…Electroencephalography (EEG) based diagnosis presents a non-invasive, cost effective alternative for early detection, yet existing methods are challenged by data scarcity, inter-subject variability, and privacy concerns. This study proposes lightweight and privacy-preserving EEG classification framework combining deep learning and Federated Learning (FL). …”
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A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting by Cihai Qiu, Yitian Zhang, Xunrui Qian, Chuhang Wu, Jiacheng Lou, Yang Chen, Yansong Xi, Weijie Zhang, Zhenxi Gong

“…Nonetheless, traditional single prediction models usually suffer from limited predictive performance and fail to capture complex variability of market behavior. Aiming to solve these limitations, an innovative two-stage hybrid deep integration framework that combines feature extraction and residual correction techniques is proposed with a view to predicting the gold price more accurately. …”
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Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques by Mohamed Hammad, Mohammed ElAffendi, Muhammad Asim, Ahmed A. Abd El-Latif, Radwa Hashiesh

“…However, previous deep learning models for lung cancer detection have faced challenges such as limited data, inadequate feature extraction, interpretability issues, and susceptibility to data variability. This paper presents a novel deep learning methodology that addresses these limitations. …”
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