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Meta-Learning with Task-Adaptive Selection

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wan, Quan Wang, Maofa Shan, Weifeng Wang, Bin Zhang, Lu Leng, Zhixiong Yan, Bingchen Xu, Yanlin Chen, Huiling Beijing Information Science and Technology University School of Applied Science Beijing100192 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Institute of Disaster Prevention School of Emergency Management Langfang065201 China China Geological Survey Command Center of Natural Resources Comprehensive Survey Beijing100055 China Southern University of Science and Technology Department of Earth and Space Sciences Shenzhen518055 China Wenzhou University Department of Computer Science and Artificial Intelligence Wenzhou325035 China

The gradient-based meta-learning algorithm gains meta-learning parameters from a pool of tasks. Starting from the obtained meta-learning parameters, it can achieve better results through fast fine-tuning with only a few gradient descent updates. The two-layer meta-learning approach that shares initialization parameters has achieved good results in solving few-shot learning domain. However, in the training of multiple similar tasks in the inner layer, the difficulty and benefits of the tasks have been consistently overlooked, resulting in conflicts between tasks and ultimately compromising the model to unexpected positions. Therefore, this paper proposes a task-adaptive selection metalearning algorithm called TSML. Specifically, we construct a task selection trainer to assess the difficulty of tasks and calculate their future benefits. Designing more optimal training strategies for each task based on difficulty and benefit, altering the current compromise in multi-task settings, and balancing the impact of tasks on meta-learning parameters. Additionally, the outer meta-parameter updating method for traditional meta-learning has been adjusted, enabling the meta-parameters to attain a better position. By doing so, we can rapidly improve the generalization and convergence of the meta-learning parameters on unknown tasks. Experimental results indicate a 2.1% improvement over the base model in the 4-conv setting, with a more pronounced effect as the neural network is progressively complexified, reaching a 4.1% improvement in resnet12. © 1991-2012 IEEE.

关键词： Zero-shot learning

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Medicinal Plant Recognition Using Image Processing

Medicinal Plant Recognition Using Image Processing

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Arekar, Vedanti Vaishampayan, Devashree Bachute, Mrinal Kotecha, Ketan Abraham, Ajith Department of Electronics and Telecommunication Symbiosis Institute of Technology Pune India Department of Electronics and Telecommunication Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune 412115 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune 412115 India UCSI University Kuala Lumpur Malaysia School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Center for Artificial Intelligence Innopolis University Republic of Tatarstan Innopolis 420500 Russian Federation

ISBN: (纸本)9783031810855

Indian medicinal herbs are widely used to produce traditional medicine. It has long been believed that medicinal plants may be a valuable source of therapeutic help. This entails the use of medicinal plants, maybe as a material for preserving good health as well as for the treatment of disorders. Locating an Ayurvedic plant among massive greenery is a difficult task. Based on a plant’s size, leaves, blooms, fruits, etc., we can identify it. The majority of the time, we must identify plants based on their leaves since a leaf is a component of the plant that is present on plants virtually all year round. When working with a plant’s leaf, it’s crucial to pay attention to even the smallest variations in the curves that describe the leaf’s form. In this study, we built a computerised system for recognising the enormous variety of medicinal plants important to Ayurveda. The methods required for significant leaf characteristics from images and for detection of leaves are included in the review. They are divided into group depending on their performance, categorization of leaf pictures such as shape, vein, texture. A data set of 25 plant species is created. It is converted into a gray scale image. Then analysis of the texture of the leaf is done, it is compared with the given database, and the result is given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image processing techniques Leaf recognition Medicinal plant

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Deep learning models for early and accurate diagnosis of ventilator-associated pneumonia in mechanically ventilated neonates

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computers in Biology and Medicine 2025年 189卷 109942-109942页

作者： Hsu, Jen-Fu Lin, Ying-Chih Lin, Chun-Yuan Chu, Shih-Ming Cheng, Hui-Jun Xu, Fan-Wei Huang, Hsuan-Rong Liao, Chen-Chu Fu, Rei-Huei Tsai, Ming-Horng Division of Neonatology and Pediatric Hematology/Oncology Department of Pediatrics Chang Gung Memorial Hospital Yunlin Taiwan Division of Pediatric Neonatology Department of Pediatrics Chang Gung Memorial Hospital Taoyuan Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Applied Mathematics Feng Chia University Taichung Taiwan Department of Artificial Intelligence Application Minth University of Science and Technology HsinChu County Taiwan

Background: Early and accurate confirmation of critically ill neonates with a suspected diagnosis of ventilator-associated pneumonia (VAP) can optimize the therapeutic strategy and avoid unnecessary use of empirical antibiotics. We aimed to examine whether deep learning (DL) methods can assist the diagnosis of VAP of intubated neonates in the neonatal intensive care unit (NICU). Methods: A total of 670 neonates with mechanical ventilation were prospectively observed in a tertiary-level NICU in Taiwan between October 2017 and March 2022, during which image data were collected. All neonates with clinically suspected VAP were enrolled, and various DL methods were used to test the prediction ability of VAP diagnosis. The accuracy, precision, sensitivity, specificity, F1-score, and area under curves (AUCs) of several DL methods were compared. Results: A total of 900 chest X-ray images derived from 670 neonates with VAP and/or bronchopulmonary dysplasia (BPD) were enrolled, including 399 images from patients with definite diagnosis of VAP based on the strict criteria and 501 images from neonates without VAP. Compared with conventional DNN models such as ResNet, VGG, DenseNet, the RegNetX80 achieved the best specificity of 0.8378, which facilitates a low false positive rate. For accurate diagnosis of neonatal VAP, a combinatorial model of ResNet50 and RegNetX80, created through ensemble learning, further enhanced the AUC to 0.8023 for neonates with VAP on mechanical ventilation. In addition, the consistent XAI results in the left-lower region of chest X-ray image provided informative feedback and increased confidence to AI-assisted doctors. Conclusions: Deep learning methods are applicable with good predictive accuracy using chest X-ray images to help diagnosis of VAP in the NICU, which can help clinicians make decisions regarding the choices of empiric antibiotics for critically ill neonates. Future prospective trials are warranted to document its clinical usefulness and

关键词： Contrastive Learning

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Topology Rewiring Strategies to construct robust scale-free medical Internet of Things Networks

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Simulation Modelling Practice and Theory 2025年 142卷

作者： Khan, Muhammad Awais Javaid, Nadeem Alrajeh, Nabil Bouk, Safdar Hussain Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan ComSens Lab International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Old Dominion University 5115 Hampton Blvd NorfolkVA23529 United States

The Internet of Things (IoT) network topologies are now most commonly impacted by cyberattacks. The scale-free network topologies have demonstrated great robustness against random attacks by preserving the connectedness of the nodes. The scale-free network topologies’ susceptibility to malicious attacks, however, is a significant worry. It is due to the significance of the scale-free networks in different fields of life like medical, transportation, education, agriculture, etc. Also, high-degree node removal diminishes the network's resiliency and compromises the connection of the majority of nodes. In this study, we offer several rewiring techniques for building scale-free, reliable Medical Internet of Things (MIoT) networks that can withstand malicious attacks. Initially, the scale-free MIoT network's performance optimization is ensured using a heuristic algorithm known as the Great Deluge Algorithm (GDA). Then, four rewiring strategies are formulated. The initial approach is degree dissortativity, which rewires the network if all nodes have high maximum connectivity to other neighbors with a similar degree. For the second strategy, we introduced a degree difference operation based on degree dissortativity to ensure that the edges that are connected possess low dissortativity and degree difference. Meanwhile, the remaining two strategies take into account the node load bound and enhanced GDA to increase network robustness. The performance of the proposed rewiring strategies is validated through simulations. The results prove that the proposed strategies increase network robustness by up to 25% compared to Hill Climbing (HC) and Simulated Annealing (SA). Additionally, the strategies show great success in improving network connectivity and graph density. However, their computational time is higher compared to HC and SA. © 2025 Elsevier B.V.

关键词： Network topology

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amVAE: Age-aware multimorbidity clustering using variational autoencoders

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computers in Biology and Medicine 2025年 186卷 109632-109632页

作者： Holm, Nikolaj Normann Le, Thao Minh Frølich, Anne Andersen, Ove Juul-Larsen, Helle Gybel Stockmarr, Anders Venkatesh, Svetha Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark Applied Artificial Intelligence Institute Deakin University Geelong Australia Innovation and Research Centre for Multimorbidity Slagelse Hospital Slagelse Denmark Department of Public Health University of Copenhagen Copenhagen Denmark Department of Clinical Research Copenhagen University Hospital Amager and Hvidovre Hvidovre Denmark Department of Clinical Medicine University of Copenhagen Copenhagen Denmark Emergency Department Copenhagen University Hospital Amager and Hvidovre Hvidovre Denmark

Multimorbidity, the co-occurrence of multiple chronic conditions within the same individual, is increasing globally. This is a challenge for the single patients, as these individuals are subject to a heavy disease and treatment burden, yet evidence on the epidemiology and consequences of multimorbidity remains underexplored. Historically, studies aiming to understand multimorbidity patterns predominantly utilized cross-sectional data, neglecting the essential temporal dynamics which shape multimorbidity progression. Other studies based their analyses on small datasets, or populations only targeting certain sectors of the healthcare system. In this study, we (1) introduce a novel two-step multimodal Variational Autoencoder-based approach for temporal disease-based clustering (i.e. discovering age-aware multimorbidity clusters);(2) provide quantitative experiments for the robustness of our approach and the extracted temporal clusters;and (3) demonstrate how the temporal disease clusters obtained from our model can provide novel understanding of the development of multiple conditions over time and thus generate new hypotheses for different stages of multimorbidity and their associations. We trained and evaluated our models on a dataset containing the entire adult population of Denmark in the period 1995–2015, focusing on individuals suffering from chronic heart disease, including 766,596 individuals. © 2025 The Authors

关键词： Electronic health record

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Correction: Divine Religions algorithm: a novel social-inspired metaheuristic algorithm for engineering and continuous optimization problems

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Cluster Computing 2025年第5期28卷 1-2页

作者： Mozhdehi, Ali Toufanzadeh Khodadadi, Nima Aboutalebi, Mohaddeseh El-kenawy, El-Sayed M. Hussien, Abdelazim G. Zhao, Weiguo Nadimi-Shahraki, Mohammad H. Mirjalili, Seyedali Faculty of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Civil Architectural and Environmental Engineering University of Miami Coral Gables USA Department of Computer Engineering Azad University of Lahijan Lahijan Iran School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic Isa Town Bahrain Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer and Information Science Linköping University Linköping Sweden Faculty of Science Fayoum University Fayoum Egypt School of Water Conservancy and Hydropower Handan China Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley Australia University Research and Innovation Center (EKIK) Obuda University Budapest Hungary

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Effect of Information Technology on Job Creation to Support Economic: Case Studies of Graduates in Universities (2023-2024) of the KRG of Iraq

arXiv

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arXiv 2025年

作者： Bapir, Azhi Kh. Maolood, Ismail Y. Abdullah, Dana A. Ameen, Aso K. Abdullah, Abdulhady Abas General Directorate of Sport Ministry of Youth and Culture Erbil44001 Iraq Akre University for Applied Sciences College of Administration and Economics Department of Business Administration Iraq - System Information Ministry of Higher Education and Scientific Research Kurdistan Region Iraq Department of Computer Science College of Science Knowledge University Erbil44001 Iraq Artificial Intelligence and Innovation Centre University of Kurdistan Hewlêr Erbil Kurdistan Region Iraq

The aim of this study is to assess the impact of information technology (IT) on university graduates in terms of employment development, which will aid in economic issues. This study uses a descriptive research methodology and a quantitative approach to understand variables. The focus of this study is to ascertain how graduates of Kurdistan regional universities might use IT to secure employment and significantly contribute to the nation's economic revival. The sample size was established by the use of judgmental sampling procedure and consisted of 314 people. The researcher prepared the questionnaire to collect data, and then SPSS statistical software, version 22, and Excel 2010 were used to modify, compile, and tabulate the results. The study's outcome showed that information technology is incredibly inventive, has a promising future, and makes life much easier for everyone. It also proved that a deep academic understanding of information technology and its constituent parts helps graduates of Kurdistan Regional University find suitable careers. More importantly, though, anyone looking for work or a means of support will find great benefit from possessing credentials and understanding of IT. The study's final finding was that information technology has actively advanced the country's economy. Not only is IT helping to boost youth employment, but it is also turning into a worthwhile investment for economic growth. © 2025, CC BY.

关键词： Information hiding

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LSTM new gate for computing the efficiency on inputdata

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Knowledge-Based Systems 2025年 322卷

作者： García Cabello, Julia Carbó-García, Santiago Department of Applied Mathematics Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain

The recognized learning ability of neural networks (NNs) is determined by their training process. The NN data-dependent nature makes that their success depends to a large extent on the quality of the training data sets. This paper addresses the selection of the best training sets for Long Short Time Models (LSTMs) in real-life contexts. If such selection from a theoretical standpoint is already complex, it becomes challenging when addressed for real scenarios, where effectiveness should be replaced by efficiency (i.e., effectiveness in the shortest possible time and without increasing the computational cost). Our proposal revolves around the introduction, definition and structuring of a new LSTM gate: the efficiency gate, that applies to those inputs that have passed the forget gate. The efficiency gate is responsible for ensuring that training advances only with the best inputs in the sense that the corresponding outputs match as closely as possible with a target value (best estimators). For this purpose, new mathematical results will be demonstrated here in order to prove that our efficiency gate is mathematically feasible. We also fully describe how the new gate joins the LSTM operation, blending in with it. The consequence of such natural integration is that the computational effort is practically unchanged. Our methodology also allows the user to set the admissible error thresholds (which are different in each real scenario) as part of the setting options. © 2025 The Authors

关键词： Efficiency gate New LSTM gate Real-life contexts Selection of training data sets

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Solving Copyright Infringement on Short Video Platforms: Novel Datasets and an Audio Restoration Deep Learning Pipeline

arXiv

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arXiv 2025年

作者： Oh, Minwoo Park, Minsu Park, Eunil Department of MetaBioHealth Sungkyunkwan University Korea Republic of Department of Applied Artificial Intelligence Sungkyunkwan University Korea Republic of Department of Computer Science and Engineering Spain

Short video platforms like YouTube Shorts and TikTok face significant copyright compliance challenges, as infringers frequently embed arbitrary background music (BGM) to obscure original soundtracks (OST) and evade content originality detection. To tackle this issue, we propose a novel pipeline that integrates Music Source Separation (MSS) and cross-modal video-music retrieval (CMVMR). Our approach effectively separates arbitrary BGM from the original OST, enabling the restoration of authentic video audio tracks. To support this work, we introduce two domain-specific datasets: OASD-20K for audio separation and OSVAR-160 for pipeline evaluation. OASD-20K contains 20,000 audio clips featuring mixed BGM and OST pairs, while OSVAR-160 is a unique benchmark dataset comprising 1,121 video and mixed-audio pairs, specifically designed for short video restoration tasks. Experimental results demonstrate that our pipeline not only removes arbitrary BGM with high accuracy but also restores OSTs, ensuring content integrity. This approach provides an ethical and scalable solution to copyright challenges in user-generated content on short video platforms. © 2025, CC BY-NC-SA.

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