Effectiveness of Group Investigation Cooperative Learning Method Based on Curipod Application For Class II Students At MTs Uswatun Hasanah by Nurhalimah, Surya Bakti

Subjects: “…cooperative method…”
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Ripe-Detection: A Lightweight Method for Strawberry Ripeness Detection by Helong Yu, Cheng Qian, Zhenyang Chen, Jing Chen, Yuxin Zhao

“…To address these limitations, this study proposes Ripe-Detection, a novel lightweight object detection framework integrating three key innovations: a PEDblock detection head architecture with depth-adaptive feature learning capability, an ADown downsampling method for enhanced detail perception with reduced computational overhead, and BiFPN-based hierarchical feature fusion with learnable weighting mechanisms. …”
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Auto-Probabilistic Mining Method for Siamese Neural Network Training by Arseniy Mokin, Alexander Sheshkus, Vladimir L. Arlazarov

“…However, it has its own shortcomings due to the known imperfections of widely used loss functions such as contrastive loss and triplet loss, as well as sample mining methods. This paper address these issues by proposing a novel mining method and metric loss function. …”
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3D fault detection method using TransVNet by Yang Lei, Chenqiang Zhang, Wenjing Wu, Mingchun Chen, Mingchun Chen, Xiaotao Wen, Xilei He, Chenggang Bai, Siping Qin, Ying Li, Lijing Wang

“…Without employing transfer learning, the fault detection results demonstrate that our proposed method exhibits superior fault detection capability, higher prediction accuracy, and better continuity compared to existing approaches.DiscussionThe method proposed in this article demonstrates that deep learning can be applied to fault detection in complex regions, which can enhance the accuracy and continuity of fault detection.…”
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Encrypted traffic classification method based on convolutional neural network by Rongna XIE, Zhuhong MA, Zongyu LI, Ye TIAN

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Method of Webshell detection based on multi-view feature fusion by Feng LIN, Liujing XU, Xiaohua CHEN, Weiqiang QI, Ke CHEN, Tiantian ZHU

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Zero-shot Image Classification Method Based on Discriminator Feedback by FAN Yufei, DING Bo, HE Yongjun

“…Zero-shot learning (ZSL) strives to classify unseen categories for which no data is available during training.At present, among generative methods, zero-shot learning based on joint generative model VAEGAN is a research hotspot.On this basis, we propose a zero-shot image classification method based on Discriminator Feedback VAEGAN (DF-VAEGAN).This method introduces a feedback module in the discriminator part, which can improve the overall performance of the model in the training stage.In the feature generation stage, it can be combined with the generator to jointly improve the quality of feature generation.Finally, the classifier is trained through high quality synthetic features to improve classification accuracy.The method also reconstructs attribute features through the decoder and uses a cycle consistency loss to ensure semantic consistency of the generated feature.Experiments on ZSL and generalized zero-shot learning (GZSL) show that our method outperforms existing methods on five classical datasets, effectively enhancing the quality of feature synthesis and reducing the goal of between categories in the zero-shot image classification task.…”
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MolAnchor method for explaining compound predictions based on substructures by Alec Lamens, Jürgen Bajorath

“…Therefore, we adapt principles underlying the anchor concept from explainable artificial intelligence (XAI) and alter them for molecular machine learning. The resulting method, termed MolAnchor, systematically identifies substructures in test compounds that determine property predictions, thus ensuring chemical interpretability. …”
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A novel method for power transformer fault diagnosis considering imbalanced data samples by Jun Chen, Yong Wang, Lingming Kong, Yilong Chen, Mianzhi Chen, Qian Cai, Gehao Sheng

“…IntroductionMachine learning-based power transformer fault diagnosis methods often grapple with the challenge of imbalanced fault case distributions across different categories, potentially degrading diagnostic accuracy. …”
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STUDENTS’ PERSPECTIVES ON THE IMPACT OF ARTIFICIAL INTELLIGENCE IN LEARNING BUSINESS ENGLISH by Cristina Laura ABRUDAN

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Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods by Bhaktishree Nayak, Pallavi Nayak

“…Various ML and DL approaches are analyzed with respect to fault segmentation, adaptive learning, and fault detection models. These techniques, benchmarked against established seismic datasets, reveal significant improvements over classical methods in terms of accuracy and computational efficiency. …”
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Malicious code within model detection method based on model similarity by Degang WANG, Yi SUN, Chuanxin ZHOU, Qi GAO, Fan YANG

“…The privacy of user data in federated learning is mainly protected by exchanging model parameters instead of source data.However, federated learning still encounters many security challenges.Extensive research has been conducted to enhance model privacy and detect malicious model attacks.Nevertheless, the issue of risk-spreading through malicious code propagation during the frequent exchange of model data in the federated learning process has received limited attention.To address this issue, a method for detecting malicious code within models, based on model similarity, was proposed.By analyzing the iterative process of local and global models in federated learning, a model distance calculation method was introduced to quantify the similarity between models.Subsequently, the presence of a model carrying malicious code is detected based on the similarity between client models.Experimental results demonstrate the effectiveness of the proposed detection method.For a 178MB model containing 0.375MB embedded malicious code in a training set that is independent and identically distributed, the detection method achieves a true rate of 82.9% and a false positive rate of 1.8%.With 0.75MB of malicious code embedded in the model, the detection method achieves a true rate of 96.6% and a false positive rate of 0.38%.In the case of a non-independent and non-identically distributed training set, the accuracy of the detection method improves as the rate of malicious code embedding and the number of federated learning training rounds increase.Even when the malicious code is encrypted, the accuracy of the proposed detection method still achieves over 90%.In a multi-attacker scenario, the detection method maintains an accuracy of approximately 90% regardless of whether the number of attackers is known or unknown.…”
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A Malware Classification Method Based on Knowledge Distillation and Feature Fusion by Xin Guan, Guodong Zhang

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Data quality optimized online task allocation method for mobile crowdsensing by Wei ZHANG, Zhuo LI, Xin CHEN

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Intelligent detection method on network malicious traffic based on sample enhancement by Tieming CHEN, Chengqiang JIN, Mingqi LYU, Tiantian ZHU

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Vision-Based Localization Method for Picking Points in Tea-Harvesting Robots by Jingwen Yang, Xin Li, Xin Wang, Leiyang Fu, Shaowen Li

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Application of Structure Tensor-Based Image Fusion Method in Marine Exploration by Xiaoyi MA, Yihong CHEN, Fei WANG, Shuo XIE

“…To obtain high-quality color fusion images with a prominent performance in detecting targets and obtaining comprehensive information, the deep learning-based image fusion method using structure tensors was optimized based on the characteristics of marine targets. …”
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Exploration of intelligent education method:mobile game assisted research teaching by Hao SHENG, Li RUAN, Ke XU, Jun HAN, Zhang XIONG, Xiaopeng GAO, Weifeng LYU

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Cooperative control method for multi-agent ground fracturing truck group based on offline reinforcement learning by RuYi Wang, HuiShen Jiao, YingCheng Tian, Yi Zhao, SiQi Wang, Ke Zhang, Bo Huang, QinRui Sun, DanDan Zhu

“…Furthermore, the proposed method has been experimentally compared with both classical and cutting-edge models of machine learning and reinforcement learning, resulting in 37.5% to 48.6% reductions in pump speed deviation. …”
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A Federated Weighted Learning Algorithm Against Poisoning Attacks by Yafei Ning, Zirui Zhang, Hu Li, Yuhan Xia, Ming Li

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