Leveraging edge computing and deep learning for the real-time identification of bean plant pathologiesBean Plant Pathologies Dataset for Deep Learning Tasks by Andrew Katumba, Wayne Steven Okello, Sudi Murindanyi, Joyce Nakatumba-Nabende, Moses Bomera, Ben Wycliff Mugalu, Amos Acur

“…Of these challenges, diseases are recognized as a key challenge, resulting in a decline in both yield quality and quantity, and inflicting substantial financial losses on farmers.This work proposes a deep learning-based approach for precise in-field identification of diseases in bean plants. …”
Get full text

Clinically validated graphical approaches identify hepatosplenic multimorbidity in individuals at risk of schistosomiasis by Yin-Cong Zhi, Simon Mpooya, Narcis B. Kabatereine, Betty Nabatte, Christopher K. Opio, Goylette F. Chami

“…Thresholds for including graph edges were found using graph kernels and tested with graph neural networks to assess predictive utility for unobserved conditions. …”
Get full text

İslam Din Öğretiminde Eşik Kavramlar Teorisi: Yeni Bir Yaklaşım by Elif Çakır

Subjects: Get full text

LDC-GAT: A Lyapunov-Stable Graph Attention Network with Dynamic Filtering and Constraint-Aware Optimization by Liping Chen, Hongji Zhu, Shuguang Han

Get full text

Machine learning message-passing for the scalable decoding of QLDPC codes by Arshpreet Singh Maan, Alexandru Paler

Get full text

An improved multi-object instance segmentation based on deep learning by Nawaf Alshdaifat, Mohd Azam Osman, Abdullah Zawawi Talib

“…The findings also revealed that in terms of average precision over IoU (AP) threshold measurements using different thresholds, the proposed approach obtained improved results compared to other well-known segmentation approaches. …”
Get full text

Machine Learning Detection of Melting Layers From Radar Observations by Yan Xie, Fraser King, Claire Pettersen, Mark Flanner

“…Traditional detection algorithms based on fixed thresholds or a priori assumptions lack general robustness across diverse weather conditions, which can be addressed by leveraging machine learning techniques. …”
Get full text

Multihead Average Pseudo-Margin Learning for Disaster Tweet Classification by Iustin Sîrbu, Robert-Adrian Popovici, Traian Rebedea, Ștefan Trăușan-Matu

“…Specifically, we investigate state-of-the-art semi-supervised learning models and focus on co-training, a less-explored approach in recent years. …”
Get full text

Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches by Hamed Ghaderi, Nasibe Alipour, Hossein Safari

“…Using GalaxyZoo2 (GZ2) fractions thresholds, we collect 545 and 11,735 samples in nongalaxy and galaxy classes, respectively. …”
Get full text

Speech Recognition System Based on Machine Learning in Persian Language by Shahed Mohammadi, Niloufar Hemati, Neda Mohammadi

Get full text

Deep Learning for Identification Malaria Diseases from Microscopic Image by Edy Victor Haryanto S, Aimi Salihah Abdul Nasir, Mohd Yusoff Mashor, Bob Subhan Riza, Zeehaida Mohamed

“…Our method includes enhancement with the AGCS method, color transformation with grayscale, adaptive thresholding for segmentation, extraction, and GoogLeNet-based classification. …”
Get full text

Loss Adaptive Curriculum Learning for Ground-Based Cloud Detection by Tianhong Qi, Yanyan Hu, Juan Wang

“…To overcome these limitations, we propose CurriCloud, a loss-adaptive curriculum framework featuring three key innovations: (1) real-time sample evaluation via Unified Batch Loss (UBL) for difficulty measurement, (2) stabilized training monitoring through a sliding window queue mechanism, and (3) progressive sample selection aligned with model capability using meteorology-guided phase-wise threshold scheduling. Extensive experiments on the ALPACLOUD benchmark demonstrate CurriCloud’s effectiveness across diverse architectures (YOLOv10s, SSD, and RT-DETR-R50), achieving consistent improvements of +3.1% to +11.4% mAP50 over both random sampling baselines and existing curriculum learning methods.…”
Get full text

RL4CEP: reinforcement learning for updating CEP rules by Afef Mdhaffar, Ghassen Baklouti, Yassine Rebai, Mohamed Jmaiel, Bernd Freisleben

“…Abstract This paper presents RL4CEP, a reinforcement learning (RL) approach to dynamically update complex event processing (CEP) rules. …”
Get full text

ALDP-FL for adaptive local differential privacy in federated learning by Lixin Cui, Xu Wu

“…Abstract Federated learning, as an emerging distributed learning framework, enables model training without compromising user data privacy. …”
Get full text

Research on online inspection of pantograph and catenary based on deep learning by ZHOU Zhaoan, LI Shuzhi

“…The mAP of inspection target before and after optimization was 0.950 and 0.961 (with the IOU threshold of 0.5) respectively. Classification of catenary dropper status based on ViT lightweight class attention model was realized at an average accuracy rate of 97.69%. …”
Get full text

The Improving Effect of Intelligent Speech Recognition System on English Learning by Qi Luo

“…To improve the effect of English learning in the context of smart education, this study combines speech coding to improve the intelligent speech recognition algorithm, builds an intelligent English learning system, combines the characteristics of human ears, and studies a coding strategy of a psychoacoustic masking model based on the characteristics of human ears. …”
Get full text

Improving Data Cleaning by Learning From Unstructured Textual Data by Rihem Nasfi, Guy de Tre, Antoon Bronselaer

“…We also suggest improving data repair by comparing the probabilities of machine learning model predictions against a threshold, replacing the actual data with higher certainty predictions. …”
Get full text

Subliminal Cues While Teaching: HCI Technique for Enhanced Learning by Pierre Chalfoun, Claude Frasson

Get full text

E-learning future trends in higher education in the 2020s and beyond by Shahid Bashir, Alexander L. Lapshun

“…Our study found five notable keywords: accessible learning, blended learning, microlearning, personalized learning, and flexible learning. …”
Get full text

Multimodal deep learning for cephalometric landmark detection and treatment prediction by Fei Gao, Yulong Tang

“…This paper presents DeepFuse, a novel multi-modal deep learning framework that integrates information from lateral cephalograms, CBCT volumes, and digital dental models to simultaneously perform landmark detection and treatment outcome prediction. …”
Get full text