Forest classification and carbon stock estimation with integration of airborne LiDAR and satellite Gaofen-6 data in a subtropical region by Ruoqi Wang, Dengsheng Lu, Guiying Li, Yisa Li, Wenjing Liu

“…Another important factor influencing FCS estimation accuracy is the quality of forest classification. To address these limitations, this study developed a multi-scale decision-level fusion framework combined with a ResNet deep learning algorithm for fine forest classification, and proposed an HBA-based FCS estimation model by incorporating different stratification schemes –single-stratum (based on either forest type or canopy height distribution (CHD)) and double-strata (integrating both forest type and CHD) based on airborne LiDAR and satellite GaoFen-6 data in a subtropical region, the Baisha State-owned Forest Farm of Fujian Province, China. …”
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An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification by M. Sundara Srivathsan, S. Alden Jenish, K. Arvindhan, R. Karthik

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Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning by Morteza Fattahi, Milad Esmaeil-Zadeh, Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, Reza Rostami, Jamil Mansouri, Gholam-Ali Hossein-Zadeh, Gholam-Ali Hossein-Zadeh

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Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence by Mehak Arshad, Muhammad Attique Khan, Nouf Abdullah Almujally, Areej Alasiry, Mehrez Marzougui, Yunyoung Nam

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Examining Deep Learning Pixel-Based Classification Algorithms for Mapping Weed Canopy Cover in Wheat Production Using Drone Data by Judith N. Oppong, Clement E. Akumu, Samuel Dennis, Stephanie Anyanwu

“…Deep learning models offer valuable insights by leveraging large datasets, enabling precise and strategic decision-making essential for modern agriculture. …”
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A lifeline or a label? lived experience perspectives on the severe and enduring eating disorder (SEED) classification in eating disorder treatment by Gabriel Lubieniecki, Isabella McGrath, Gemma Sharp

“…Reflexive thematic analysis examined participants’ experiences of SEED. Results Three key themes emerged: [1] SEED as a paradoxical classification, with participants describing the term as both validating and restrictive; [2] SEED as a justification for treatment withdrawal, with clinicians and services interpreting the classification as an indicator of treatment futility, contributing to reduced care opportunities and systemic exclusion; and [3] redefining SEED through recovery-oriented frameworks, with participants advocating for alternative terminology, such as “longstanding eating disorder,” and treatment models prioritising harm reduction, step-down care, and sustained engagement. …”
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BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers by Cheng Lv, Xu-Jun Shu, Xu-Jun Shu, Quan Liang, Jun Qiu, Zi-Cheng Xiong, Jing bo Ye, Shang bo Li, Cheng Qing Liu, Jing Zhen Niu, Sheng-Bo Chen, Hong Rao

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A Hybrid Deep Learning Framework for Early-Stage Alzheimer’s Disease Classification From Neuro-Imaging Biomarkers by Samina Akram, Muhammad Amjad Iqbal, Muhammad Rashid, Muhammad Shahid Bhatti, Benish Fida

“…Existing diagnostic tools have yet to fully harness the combined strengths of neuroimaging and modern deep-learning architectures for nuanced, early-stage AD classification. In this study, we propose a novel hybrid deep learning framework that ensembles three state-of-the-art architectures—EfficientNetB7, Xception, and MobileNetV3Large—through a soft-voting strategy to classify Alzheimer’s disease across multiple stages. …”
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Transforming Early Breast Cancer Detection: A Deep Learning Approach Using Convolutional Neural Networks and Advanced Classification Techniques by Arun Kumar Singh, Bireshwar Dass Mazumdar, Rohit Raja, Kamred Udham Singh, Ankit Kumar, Mohd Asif Shah

“…Even without PCA implementation, the Decision Tree classifier also performed well, scoring 99.4% accuracy. …”
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Objective approach to diagnosing attention deficit hyperactivity disorder by using pixel subtraction and machine learning classification of outpatient consultation videos by Yi-Hung Chiu, Ying-Han Lee, San-Yuan Wang, Chen-Sen Ouyang, Rong-Ching Wu, Rei-Cheng Yang, Lung-Chang Lin

“…A classification analysis based on six machine learning models was performed to compare the performance indices and the discriminatory power of various features. …”
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Cross-Project Multiclass Classification of EARS-Based Functional Requirements Utilizing Natural Language Processing, Machine Learning, and Deep Learning by Touseef Tahir, Hamid Jahankhani, Kinza Tasleem, Bilal Hassan

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Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models by Ju-Hwan Lee, Dang Thanh Vu, Nam-Kyung Lee, Il-Hong Shin, Jin-Young Kim

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Comparison of Deep and Machine Learning Approaches for Quebec Tree Species Classification Using a Combination of Multispectral and LiDAR Data by Omid Reisi Gahrouei, Jean-François Côté, Philippe Bournival, Philippe Giguère, Martin Béland

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Predicting Suicide or Self-Harm Crises Based on Decision Tree Analysis of Life Events and Coping Style: A Population-Based Study in China by Shumeng Ma, Ping Li, Liwen Ren, Ning Jia

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A comparative study of machine learning algorithms for fall detection in technology-based healthcare system: Analyzing SVM, KNN, decision tree, random forest, LSTM, and CNN by Afuan Lasmedi, Isnanto R. Rizal

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Swarm intelligence based classification rule induction (CRI) framework for qualitative and quantitative approach: An application of bankruptcy prediction and credit risk analysis by J. Uthayakumar, T. Vengattaraman, P. Dhavachelvan

“…Bankruptcy prediction and credit risk analysis is one of the most significant problems in the field of accounting and financial decision making. Developing an effective classification rule induction (CRI) framework for bankruptcy prediction and credit risk analysis in appropriate time is essential to prevent the business communities from being bankrupt. …”
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A Novel Self-Attention-Enabled Weighted Ensemble-Based Convolutional Neural Network Framework for Distributed Denial of Service Attack Classification by Shravan Venkatraman, S. Kanthimathi, K. S. Jayasankar, T. Pranay Jiljith, R. Jashwanth

“…Traditional approaches, such as single Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) algorithms like Decision Trees (DTs) and Support Vector Machines (SVMs), struggle to extract the diverse features needed for precise classification, resulting in suboptimal performance. …”
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One size does not fit all: a support vector machine exploration of multiclass cognitive state classifications using physiological measures by Jonathan Vogl, Kevin O'Brien, Paul St. Onge

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Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy by Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde

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Interacting Large Language Model Agents. Bayesian Social Learning Based Interpretable Models by Adit Jain, Vikram Krishnamurthy

“…Throughout the paper, we numerically demonstrate the effectiveness of our methods on real datasets for hate speech classification and product quality assessment, using open-source models like LLaMA and Mistral and closed-source models like ChatGPT. …”
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