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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Liu, Junyi Zhang, Yu-An Xiao, Jian Yao, Tian Guo, Zhihong Song, Rende School of Computer Technology and Application Qinghai university Xining Qinghai810016 China Qinghai university Qinghai Provincial Laboratory for Intelligent Computing and Application Xining Qinghai810016 China Academy of Animal Science and Veterinary Medicine Qinghai university Xining Qinghai810016 China Yushu Prefecture Animal Disease Prevention and Control Center Yushu Qinghai815099 China

ISBN: (纸本)9798350356328

This article collects data on echinococcosis infection in the population, dogs, livestock, and rodents of Qinghai Province through literature review. Four traditional machine learning methods are used to predict the positive rates of the population, dogs, livestock, and rodents in the next three years based on the collected disease rate data from the past six years (2016-2021). In addition, to expand the dataset of the affected population in Qinghai Province, the annual population disease rate and total number of people in Qinghai Province are analyzed to obtain the total number of people affected each year. The total number of people affected each year is dispersed to each day of the year by using Normal distribution to expand the dataset, and an improved LSTM model is used to fit the expanded dataset. After conducting experimental simulations on the prevalence data, it reveals that the prevalence of echinococcosis in the population, dogs, livestock, and rodents in Qinghai Province has been decreasing year by year since 2022. After conducting experiments on the expanded dataset of affected populations, it shows that the fitting results of the improved LSTM model were 8.7% lower than those of the original LSTM model in terms of MSE. Using the improved LSTM model to predict the daily prevalence of echinococcosis in subsequent years, it reveals that the annual prevalence of echinococcosis in the region is below 0.06 %, indicating that the overall trend of echinococcosis in Qinghai Province is controllable, and the measures taken by Qinghai Province to prevent and treat echinococcosis are effective. © 2024 IEEE.

关键词： Livestock

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Smart Waste Management and Classification System for Smart Cities using Deep Learning

Smart Waste Management and Classification System for Smart C...

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2022 International Conference on Business Analytics for technology and Security, ICBATS 2022

作者： Hasan, Mohammad Kamrul Khan, Muhammad Adnan Issa, Ghassan F. Atta, Ayesha Akram, Ali Sheraz Hassan, Muhammad Center for Cyber Security Malaysia Riphah School of Computing Innovation Riphah International University Lahore Campus Lahore Pakistan Skyline University College School of Information Technology Sharjah United Arab Emirates Government College University Lahore National College of Business Administration Lahore Pakistan NCBAandE School of Computer Science Lahore Pakistan NCBAandE Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781665409209

For modern city environments to be renewable and clean, waste management and recycling are essential. Solid waste management, disposal, and recycling are issues in many Pakistani cities, particularly Karachi and Lahore. The combination of the IoTs and deep learning offers a modular technique to data categorization and real-time examining. This article illustrates a capable 'Smart trash management and categorization system' based on the 'internet of things (IoT)' and DL. The article provides an architectural idea for a microchips-based garbage bin that uses numerous measuring instruments to connect with the method to gather wastes as quickly as possible. The 'Internet of Things (IoT)' is used in the suggested data monitoring solution to offer real-time data control. In addition, in this smart waste management and categorization scheme, a waste classification model based on convolutional neural networks was deployed. This waste classification technique will be used to sort rubbish into several categories at the waste-collecting plant to increase recycling. This proposed system offers complete trash management and recycling solution in smart cities, from waste collection to waste management and classification. © 2022 IEEE.

关键词： Internet of things

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IntNet: A novel framework using reconstructed mutation samples and a biologically informed neural network for pathway analysis

IntNet: A novel framework using reconstructed mutation sampl...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Li Zhou Jie Li Weilong Tan School of Computer Science and Technology Harbin Institute of Technology Harbin China College of Computing Harbin Institute of Technology National Key Laboratory of Smart Farming Technology and Systems Harbin China Center for Medicines Nanjing Bioengineering (Gene) Technology Center for Medicines Nanjing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Most pathway analysis methods based on simple nucleotide variations overlook mutations occurring in genes outside the pathway, fail to address mutation heterogeneity with targeted measures, and do not consider the functional relationships among gene components within biomolecular networks when modeling the contributions of biomarkers to pathways. So, we proposed a novel framework, named IntNet, for pathway analysis based on simple nucleotide variations. The core components of IntNet are sample reconstruction (SC) and a new biologically informed neural network model (BINN). To address the difficulty of extracting effective features caused by the mutation heterogeneity, IntNet reconstructs mutation samples based on the hypothesis that mutations in patients with the same phenotype may cluster in similar network regions. BINN uses the human gene network and expanded biological pathways to construct its neural network architecture. BINN implements mutation signaling propagation along the human gene network and integration within the pathways. IntNet's performance was validated using multiple cancer datasets. Experimental results demonstrated that IntNet could identify more accurate and reliable pathways.

关键词： Analytical models Accuracy Phenotypes Biological system modeling Biomarkers Feature extraction Reliability Biological neural networks Nuclear measurements Cancer

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Large Model-Based Data Augmentation for Imbalanced Text Classification

Large Model-Based Data Augmentation for Imbalanced Text Clas...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Dawei Zhang Rongxin Mi Peiyao Zhou Dawei Jin Manman Zhang Tianhang Song School of Computer Science Jiangsu University of Science and Technology Zhenjiang Jiangsu China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

This study focuses on the application of large models to deal with imbalanced data problems in text classification. In view of the central position of text in web data and the negative impact of class imbalance on classifier performance, researchers have explored the method of using large models to generate high-quality minority class samples to enhance model performance. This paper reviews the technical progress of machine learning, deep learning, and large language models and their applications in text classification tasks. Although large models perform well in complex tasks due to their excellent language understanding ability, traditional machine learning and deep learning methods are popular in text classification scenarios that require fast response due to their simple structure and higher computational efficiency. This study proposes a data augmentation technique inspired by SMOTE, which uses a large language model combined with a simple prompt engineering strategy to generate high-quality minority samples. The experimental results show that the proposed method significantly improves the macro average precision, recall and F1 score on multiple text classification models, and effectively alleviates the challenge of class imbalance.

关键词： Deep learning Seminars Computational modeling Large language models Text categorization Data augmentation Data models

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Towards Efficient Discrete Controller Synthesis: Semantics-Aware Stepwise Policy Design via LLM

Towards Efficient Discrete Controller Synthesis: Semantics-A...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Yusei Ishimizu Jialong Li Takuto Yamauchi Sinan Chen Jinyu Cai Takanori Hirano Kenji Tei School of Computing Tokyo Institute of Technology Tokyo Japan Department of Computer Science and Engineering Waseda University Tokyo Japan Center of Mathematical and Data Sciences Kobe University Kobe Japan

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

Discrete Controller Synthesis (DCS) is an automated generation technique to generate controllers from environment models and requirements. The time and space complexities of DCS are critical because they directly govern the feasibility of using DCS for real-time generation of controllers to cope with environmental changes. Stepwise DCS is considered a promising approach to prevent the construction of ineffective state spaces in the subsequent analysis of the full controller by analyzing sub-controllers that meet part of the requirements at an early stage. However, constructing a stepwise policy is a challenge, as the current methods often require domain experts to manually build it by understanding and reasoning the semantics of each sub-environment model and sub-requirement. To reduce the effort of manual construction, this paper introduces a method that leverages large language models (LLMs) to automate the generation of stepwise policies by utilizing the LLM's ability to understand and reason about the semantics of state machines. The evaluation using GPT-4 on a drone control scenario demonstrates the effectiveness of our method, with an average reduction of 82.2% in states and 91.2% in transitions.

关键词： Large language models Semantics Manuals Aerospace electronics Real-time systems Cognition Complexity theory Drones

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An Ensemble Convolutional Neural Network for Recognizing and Counting of Mango Pests using Superpixel Segmentation 10

An Ensemble Convolutional Neural Network for Recognizing and...

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10th International Conference on Information Systems and computing technology, ISCTech 2022

作者： Yang, Fuqiang Choi, Yeonsung Yang, Fuping Xin, Ke Zhao, Xuechen School of Data and Computer Science Shandong Women's University Jinan China School of Computer Information and Communication Engineering Kunsan National University Kunsan Korea Republic of Shandong Labor Vocational and Technical College Network Center Jinan China College of Information Science and Engineering Binzhou University Binzhou China School of Computing National University of Defense Technology Changsha China

ISBN: (纸本)9798350332933

In the agricultural field., the problem of pests attacking crops has long plagued farmers. In order to detect and monitor the development of pests in time, compared with traditional methods, deep learning techniques that have gradually matured and become effective in recent years can be used. The success of deep learning technology in the field of computer vision in recent years can provide support for the solution of insect recognizes and counts in images under the complex background of agriculture. In recent studies, convolutional neural networks have improved the efficiency for the reorganization of insects in specific categories of crops. This paper suggested a method for recognizing and counting mango insects. In order to improve the accuracy and practicability of the model, this paper has done the following work: First of all, optimized and upgraded the A Large-Scale Benchmark Dataset for Insect Pest Recognition IP102 dataset version. Some of the pictures were cleaned, and unclear pictures that were difficult to extract in the early insects were deleted. Secondly, this paper suggested the EnRDNet algorithm to ensemble the ResNet and DenseNet models that have outstanding performance in the field of insect recognition. Third, this paper proposes to add a layer of confidence to the trained model when used in actual scene prediction which named Smart-EnRDNet algorithm to determine whether there are insect objects in the picture, and then output the insect category if there is. Experiment shows the efficiency of the proposed algorithms. © 2022 IEEE.

关键词： Convolutional neural networks

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Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning

arXiv

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arXiv 2024年

作者： Du, Xiaohu Wen, Ming Zhu, Jiahao Xie, Zifan Ji, Bin Liu, Huijun Shi, Xuanhua Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China College of Computer National University of Defense Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab HUST Wuhan430074 China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security HUST Wuhan430074 China JinYinHu Laboratory Wuhan430077 China Cluster and Grid Computing Lab HUST Wuhan430074 China

Code Pre-trained Models (CodePTMs) based vulnerability detection have achieved promising results over recent years. However, these models struggle to generalize as they typically learn superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities, resulting in poor performance in real-world scenarios beyond the training instances. To tackle this challenge, we introduce VulLLM, a novel framework that integrates multi-task learning with Large Language Models (LLMs) to effectively mine deep-seated vulnerability features. Specifically, we construct two auxiliary tasks beyond the vulnerability detection task. First, we utilize the vulnerability patches to construct a vulnerability localization task. Second, based on the vulnerability features extracted from patches, we leverage GPT-4 to construct a vulnerability interpretation task. VulLLM innovatively augments vulnerability classification by leveraging generative LLMs to understand complex vulnerability patterns, thus compelling the model to capture the root causes of vulnerabilities rather than overfitting to spurious features of a single task. The experiments conducted on six large datasets demonstrate that VulLLM surpasses seven state-of-the-art models in terms of effectiveness, generalization, and robustness. © 2024, CC BY.

关键词： Large datasets

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SAM-PS: Zero-shot Parking-slot Detection based on Large Visual Model

SAM-PS: Zero-shot Parking-slot Detection based on Large Visu...

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IEEE Symposium on Intelligent Vehicle

作者： Heng Zhai Jilin Mei Liang Chen Fangzhou Zhao Xijun Zhao Yu Hu Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Innovation Center for Processor Technologies(SHIC) China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory (CIC) School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Large visual models have recently demonstrated their promising performance on zero-shot transfer. However, so far, none of the existing methods explicitly possess the ability to perform zero-shot transfer on parking-slot detection, which results in current deep-learning based methods relying on training datasets, and methods based on traditional computer vision exhibiting poor robustness. In this paper, we propose a large visual model-based parking-slot detection method, which utilizes a large visual model (segment anything) to segment an around-view image and infer parking-slots by analyzing the relationship of marking-points in masks. In addition, we classify real-world parking-slots into two categories, line-based and area-based. The proposed method employs a two-stage approach which has a manually designed post-processing step without training. Multiple experiments have been carried out on public benchmarks, and our method demonstrates the capability for zero-shot transfer. The code will be released at https://***/Zhai0123/SAM-PS.

关键词： Training Learning systems Visualization Image segmentation Analytical models Intelligent vehicles Computational modeling

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization

arXiv

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arXiv 2025年

作者： Huang, Xiaohan Wang, Dongjie Ning, Zhiyuan Qiao, Ziyue Long, Qingqing Zhu, Haowei Du, Yi Wu, Min Zhou, Yuanchun Xiao, Meng Computer Network Information Center Chinese Academy of Sciences University of the Chinese Academy of Sciences China Computer Network Information Center Chinese Academy of Sciences China Department of Electrical Engineering and Computer Science University of Kansas United States School of Computing and Information Technology Great Bay University Dongguan China School of Software Tsinghua University China Institute for Infocomm Research Agency for Science Technology and Research Singapore

Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream machine learning tasks. Existing frameworks, though designed to mitigate manual costs, often treat feature transformations as isolated operations, ignoring dynamic dependencies between transformation steps. To address the limitations, we propose TCTO, a collaborative multi-agent reinforcement learning framework that automates feature engineering through graph-driven path optimization. The framework’s core innovation lies in an evolving interaction graph that models features as nodes and transformations as edges. Through graph pruning and backtracking, it dynamically eliminates low-impact edges, reduces redundant operation, and enhances exploration stability. This graph also provides full traceability to empower TCTO to reuse high-utility subgraphs from historical transformations. To demonstrate the efficacy and adaptability of our approach, we conduct comprehensive experiments and case studies, which show superior performance across a range of datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Task Separation and Knowledge Sharing for Class Incremental Learning

Task Separation and Knowledge Sharing for Class Incremental ...

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Intelligent computing and Human-computer Interaction (ICHCI), International Conference on

作者： Jiali Zhang Xiaoyan Qiao Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center School of Computer Science and Technology Shandong Technology and Business University Yantai Shandong China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center School of Mathematics and Information Science Shandong Technology and Business University Yantai Shandong China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory.

关键词： Knowledge engineering Human computer interaction Degradation Incremental learning Computational modeling Memory management Information sharing Network architecture Feature extraction Knowledge transfer

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