A joint learning approach for automated diagnosis of keratinocyte carcinoma using optical attenuation coefficients by Lei Zhang, Xiaoran Li, Wen Chen, Yuanjie Gu, Hao Wu, Zhong Lu, Biqin Dong

“…Abstract Keratinocyte carcinoma, such as Actinic Keratosis (AK) and Basal Cell Carcinoma (BCC), share similar clinical presentations but differ significantly in prognosis and treatment, highlighting the importance of effective screening. …”
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Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel Attacks by Amjed A. Ahmed, Mohammad Kamrul Hasan, Azana H. Aman, Nurhizam Safie, Shayla Islam, Fatima A. Ahmed, Thowiba E. Ahmed, Bishwajeet Pandey, Leila Rzayeva

“…In this paper, we have presented a review on deep learning models for encryption techniques against side channel attacks with a comparison table. …”
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Multimodal hate speech detection: a novel deep learning framework for multilingual text and images by Furqan Khan Saddozai, Sahar K. Badri, Daniyal Alghazzawi, Asad Khattak, Muhammad Zubair Asghar

“…Detecting multimodal hate speech in low-resource multilingual contexts poses significant challenges. This study presents a deep learning framework that integrates bidirectional long short-term memory (BiLSTM) and EfficientNetB1 to classify hate speech in Urdu-English tweets, leveraging both text and image modalities. …”
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Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks by William Villegas-Ch, Rommel Gutierrez, Alexandra Maldonado Navarro, Aracely Mera-Navarrete

“…Federated learning is presented as an effective solution to train artificial intelligence models on the Internet of Things networks without centralizing data, thus preserving privacy and minimizing security risks. …”
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Automated detection of broiler vocalizations a machine learning approach for broiler chicken vocalization monitoring by Patricia de Carvalho Soster, Tomasz Grzywalski, Yuanbo Hou, Pieter Thomas, Annelike Dedeurwaerder, Maarten De Gussem, Frank Tuyttens, Paul Devos, Dick Botteldooren, Gunther Antonissen

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Emerging SMOTE and GAN Variants for Data Augmentation in Imbalance Machine Learning Tasks: A Review by Amadi G. Udu, Marwah T. Salman, Maryam K. Ghalati, Andrea Lecchini-Visintini, David R. Siddle, Hongbiao Dong

“…Class imbalance is a pervasive challenge in real-world machine learning (ML) applications, where the minority class, often the class of interest, is significantly underrepresented. …”
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Alphabet Handwriting Recognition: From Wood‐Framed Hydrogel Arrays Design to Machine Learning Decoding by Guihua Yan, Xichen Hu, Ziyue Miao, Yongde Liu, Xianhai Zeng, Lu Lin, Olli Ikkala, Bo Peng

“…Nonetheless, the design of such a system from scratch with sustainable materials and an easily accessible computing network presents significant challenges. In pursuit of this goal, a flexible, and electrically conductive wood‐derived hydrogel array is developed as a handwriting input panel, enabling recognizing alphabet handwriting assisted by machine learning technique. …”
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Improving student chemistry laboratory performance through Nyamplung ethnoscience-oriented learning of the Sasak tribe by Yusran Khery, Aliefman Hakim, Joni Rokhmat, Aa Sukarso

“…Evaluating student performance in open-ended laboratory settings presents challenges compared to the structured format of typical lab exercises, which often resemble recipes. …”
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An inventory of industrial solid waste in 337 cities of China: Applying machine learning for data completion by Qian Jia, Kunsen Lin, Jiawei Zhuang, Dengyu Yang, Wei Wei, Xiong Xiao, Huanzheng Du, Tao Wang

“…We further developed six machine learning models to complete the dataset across all the 337 cities in China for the period 1990–2022. …”
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CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation by Shu Feng, Luhan Gao, Leyi Shi

“…The effectiveness of pattern detection in models is diminished as a result of the difficulty in extracting attack information from extremely large datasets and obtaining an adequate number of examples for specific types of attacks. A robust Federated Learning method, CGFL, is introduced in this study to resolve the challenges presented by data distribution discrepancies and client class imbalance. …”
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Deep Water Subsea Energy Storage, Lessons Learned from the Offshore Oil and Gas Industry by Rasmus Juhlin, Alexander H. Slocum, Mohsen Assadi

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Machine Learning‐Based Mobile Application for Predicting Posterior Canal Benign Paroxysmal Positional Vertigo by Emre Soylemez, Sait Demir, Kasım Ozacar

“…Methods This study retrospectively analyzed the medical records of patients who presented to the Audiology and Balance Clinic with complaints of dizziness or vertigo between 04/01/2021 and 09/16/2023. …”
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Machine learning based skin lesion segmentation method with novel borders and hair removal techniques. by Mohibur Rehman, Mushtaq Ali, Marwa Obayya, Junaid Asghar, Lal Hussain, Mohamed K Nour, Noha Negm, Anwer Mustafa Hilal

“…The proposed method searches for the presence of corner borders in the given dermoscopc image and removes them if found otherwise it starts searching for the presence of hairs on it and eliminate them if present. Next, it enhances the resultant image using state-of-the-art image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method. …”
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Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints by Ryong-Gyu Lee, Yong-Hoon Kim

“…Abstract The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution (ρ) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can shorten or bypass the SCF loop represents a critical question in electronic structure theory from both practical and fundamental standpoints. Herein, a machine learning strategy, DeepSCF, is presented in which the map between the SCF ρ and the initial guess density (ρ 0) constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks (CNNs). …”
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Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets. by Der-Chiang Li, Susan C Hu, Liang-Sian Lin, Chun-Wu Yeh

“…It is difficult for learning models to achieve high classification performances with imbalanced data sets, because with imbalanced data sets, when one of the classes is much larger than the others, most machine learning and data mining classifiers are overly influenced by the larger classes and ignore the smaller ones. …”
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Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning by Tanvir Alam Shifat, Ryan Coe, Gioegio Bacelli, Ted Brekken

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Spatial sample weighted machine learning for multitemporal land cover change modeling with imbalanced datasets by Alysha van Duynhoven, Suzana Dragićević

“…The RF-SSW, NN-SSW, and XGB-SSW models forecasted more realistic changes across multiple timesteps with fewer errors than baseline configurations. The presented methodology provides a step toward establishing spatialized cost-sensitive learning strategies and extending classical ML models to multitemporal LC datasets.…”
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Development of Virtual Tour Media of Sambisari Temple as a History Learning Media in High Schools by Anisa Nurbaiti Islami Chotimah, Sariyatun Sariyatun, Deny Tri Ardianto

“… This study aims to develop a Virtual-based learning media centered on Sambisari Temple  to enhance the quality of history education. …”
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Multi-task deep learning framework for enhancing Mayo endoscopic score classification in ulcerative colitis by Jaehyuk Lee, Eunchan Kim

“…This study proposes a multi-task learning (MTL) framework inspired by the coarse-to-fine processing mechanism of the human brain to address these challenges. …”
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Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…Additionally, a novel graph learning module is designed to adaptively capture potential correlations between nodes during training. …”
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