Transfer Learning Based on Multi-Branch Architecture Feature Extractor for Airborne LiDAR Point Cloud Semantic Segmentation with Few Samples by Jialin Yuan, Hongchao Ma, Liang Zhang, Jiwei Deng, Wenjun Luo, Ke Liu, Zhan Cai

“…The experimental results show that the proposed method achieved the highest OA of 85.5% and an average F1 score of 74.0% using only 10% training samples, which means the proposed framework can reduce manual annotation by 90% while keeping competitive classification accuracy.…”
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Two-dimensional QSAR-driven virtual screening for potential therapeutics against Trypanosoma cruzi by Naseer Maliyakkal, Sunil Kumar, Ratul Bhowmik, Harish Chandra Vishwakarma, Prabha Yadav, Bijo Mathew

“…Trypanosoma cruzi is the cause of Chagas disease (CD), a major health issue that affects 6–7 million individuals globally. …”
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On the black-box explainability of object detection models for safe and trustworthy industrial applications by Alain Andres, Aitor Martinez-Seras, Ibai Laña, Javier Del Ser

“…Explainable Artificial Intelligence methods aim to address this issue, but many existing techniques are model-specific and designed for classification tasks, making them less effective for object detection and difficult for non-specialists to interpret. …”
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BSAGIoT: A Bayesian Security Aspect Graph for Internet of Things (IoT) by Zeinab Lashkaripour, Masoud Khosravi-Farmad, AhmadReza Montazerolghaem, Razieh Rezaee

“…IoT is a dynamic network of interconnected things that communicate and exchange data, where security is a significant issue. Previous studies have mainly focused on attack classifications and open issues rather than presenting a comprehensive overview on the existing threats and vulnerabilities. …”
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A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X) by Liu Yang, Jiang Guofan, Zhang Yixin, Wei Qianze, Zhang Jian, Roohallah Alizadehsani, Pawel Plawiak

“…Our model employs a series of convolutional neural networks (CNNs) to extract significant features from textual content, which are then utilized for sentiment classification. We also incorporate an advanced artificial bee colony (ABC) optimization technique to refine the model's hyperparameters. …”
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Detection of viral contamination in cell lines using ViralCellDetector by Rama Shankar, Shreya Paithankar, Suchir Gupta, Bin Chen, Bin Chen, Bin Chen

“…To further enable the detection of viral contamination from unknown sources, we identified host genes that are differentially expressed during viral infection and used these markers to train a machine learning model for classification.ResultsUsing ViralCellDetector, we found that approximately 10% (110 samples) of RNA-seq datasets involving MCF7 cells were likely contaminated with viruses. …”
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Out-of-distribution reject option method for dataset shift problem in early disease onset prediction by Taisei Tosaki, Eiichiro Uchino, Ryosuke Kojima, Yohei Mineharu, Yuji Okamoto, Mikio Arita, Nobuyuki Miyai, Yoshinori Tamada, Tatsuya Mikami, Koichi Murashita, Shigeyuki Nakaji, Yasushi Okuno

“…We expect that this classification will help standardize measuring instruments. …”
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YOLOv5-MDS: Target Detection Model for PCB Defect Inspection Based on YOLOv5 Integrated With Mamba Architecture by Deming Guo, Qi Xi, Long Zhang, Jing Wang, Yilong Ye, Ziyi Zeng

“…Existing methods struggle to effectively detect these minute and scattered defects, especially in the context of industrial production, where high accuracy and speed are paramount. To address this issue, YOLOv5-MDS has been proposed, a convolutional neural network (CNN) specifically designed for image segmentation and classification. …”
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Optimizing IoT intrusion detection with cosine similarity based dataset balancing and hybrid deep learning by Arvind Prasad, Wael Mohammad Alenazy, Naved Ahmad, Gauhar Ali, Hanaa A. Abdallah, Sadique Ahmad

“…Additionally, we developed a hybrid deep learning model integrating RegNet and FBNet architectures to enhance feature extraction and classification performance. Experimental results on multiple IDS datasets confirm that balancing the dataset to a 1:1 ratio optimally prevents overfitting and improves model interpretability. …”
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High-precision lung cancer subtype diagnosis on imbalanced exosomal data via Exo-LCClassifier by Siyu Zhan, Siyu Zhan, Hao Yu, Shuang Liu, Ke Qin, Lu Guo

“…Next, the enhanced WGAN was applied to augment the dataset, addressing the issue of sample imbalance and increasing the diversity of effective samples. …”
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Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning by Ramzi Al-Sharawi, Abdelfatah Ali, Mostafa Shaaban, Nasser Qaddoumi, Mohamed S. Abdalzaher

“…Thus, this paper proposes a novel approach by formulating this issue as a tri-objective functions optimization problem. …”
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An Improved Spatiotemporal Fusion Framework for Land-Cover Temporal Harmonization of High-Resolution Remote Sensing Images by Kangning Li, Zilin Xie, Xiaojun Qiao, Jinzhong Yang, Jinbao Jiang

“…Furthermore, STF-TH significantly improved the subsequent classification accuracy, with the <italic>F</italic>1-score and the overall accuracy improved to 89.88&#x0025; and 93.87&#x0025;, respectively. …”
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Protocol for a comparative cross-sectional study on characterisation of auditory impairment in sickle cell disease and sickle cell trait and its impact on health-related quality of... by Obiageli Nnodu, Titus S Ibekwe, Christine Rogers, Lebogang Ramma

“…Hearing levels will be assessed through audiometric assessments and categorised by type and severity of hearing impairments using WHO classifications. HRQoL will also be assessed using WHO Disability Assessment Schedule 2.0. …”
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Mapping of the responsible investments instruments in sdg 3 ‘good health and well-being’ financing: EU and US experience by Yulia Sepreninova, Inna Makarenko, Alex Plastun, Angela Babko, Gunnay Gasimova

“…Systematization of scientific sources and approaches on the investigated issue showed no unique approach to forming a list of responsible investment instruments to finance health and well-being in Europe and United States. …”
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Explainable AI Meets Synthetic Data: A Deep Learning Framework for Detecting Network Intrusion in NextG Network Infrastructure by Md Junayed Hossain, Khorshed Alam, Md Fahad Monir, Md Mozammal Hoque, Tarem Ahmed

“…These conventional methods not only struggle to detect unknown attack patterns but are also hindered by the issue of imbalanced datasets, where minority attack classes are underrepresented and frequently overlooked. …”
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A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers by Yunendah Nur Fuadah, Ali Ikhsanul Qauli, Muhammad Adnan Pramudito, Aroli Marcellinus, Ulfa Latifa Hanum, Ki Moo Lim

“…Abstract This study addresses the critical issue of drug‐induced torsades de pointes (TdP) risk assessment, a vital aspect of new drug development due to its association with arrhythmia and sudden cardiac death. …”
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Pollutants generation characteristics from retired wind turbine blades treated with thermochemical methods by Xiaohe XIONG, Yinan ZHANG, Liangyu LI, Jingjing ZHANG, Houzhang TAN

“…Considering that the oxygen generated by NO mainly comes from the blades themselves, traditional air classification is not effective in reducing nitrogen oxides.…”
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Medical expenditure of women during pregnancy, childbirth and puerperium at the beginning of China’s universal two-child policy enactment: a population-based retrospective study by Xin Wang, Ying Zhang, Yu Chen, Shuang Zang, Meizhen Zhao, Yalan Zhu

“…Reducing medical expenditure by shortening the length of stay could be a feasible way to effectively address the issue of cost in women during pregnancy, childbirth and puerperium.…”
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Domain adaptation of deep neural networks for tree part segmentation using synthetic forest trees by Mitch Bryson, Ahalya Ravendran, Celine Mercier, Tancred Frickey, Sadeepa Jayathunga, Grant Pearse, Robin J.L. Hartley

“…Supervised deep learning algorithms have recently achieved state-of-the-art performance in the classification, segmentation and analysis of 3D LiDAR point cloud data in a wide-range of applications and environments. …”
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A Spatial–Frequency Combined Transformer for Cloud Removal of Optical Remote Sensing Images by Fulian Zhao, Chenlong Ding, Xin Li, Runliang Xia, Caifeng Wu, Xin Lyu

“…These approaches lead to edge blurring and noticeable color distortions. To address this issue, we propose a spatial–frequency collaborative enhancement Transformer network named SFCRFormer, which significantly improves cloud removal performance. …”
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