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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Li, Yuan Pang, Zhaobin College of Information and Electronic Engineering Shandong Technology and Business University Yantai China School of Computer Science Xi'an Shiyou University Xi'an China

ISBN: (纸本)9798350376548

In order to counter linear frequency modulation (LFM) radar, a novel jamming method based on sinusoidal phase modulation is proposed from the perspective of the range profile of the target. In this paper, firstly, the jamming method based on sinusoidal phase modulation is designed. Then, the frequency domain characteristics of the jamming signal are analyzed in detail. It shows that the range profile of the jamming signal is similar to that of the real target, making it difficult for the enemy to distinguish between true and false targets. Based on real radar echo data from a real aircraft, the simulation experiments have been done to justify the validity and efficiency of the new jamming technique at last. Compared to radio frequency (RF) noise interference, the novel technique can achieve effective jamming at lower power. © 2024 IEEE.

关键词： Frequency domain analysis

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Research on multimodal relation extraction method based on contrast learning 3

Research on multimodal relation extraction method based on c...

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3rd International Conference on Electronic information engineering, Big Data, and computer technology, EIBDCT 2024

作者： Zhao, Jingsheng Ni, Qihui Yan, Shuai Zhu, Qiaoming School of Information and Control Engineering Qingdao University of Technology Qingdao Shandong266000 China School of Computer Science and Technology Suzhou University Suzhou Jiangsu215006 China

ISBN: (纸本)9781510680449

Traditional relation extraction methods are usually based on single text data, and other modality information such as image and video can improve the effect of text relation extraction. Aiming at the problem of heterogeneity between multi-modal data, a multi-modal relation extraction model MRECL based on contrastive learning is constructed. Firstly, the feature representation of image and text is obtained through ViT and BERT models;secondly, contrastive learning is introduced to align image-text features to reduce modality heterogeneity in advance;then image is used as prefix guidance information, and multi-modal interaction fusion is carried out with text information through attention mechanism;finally, the probability of all relation classification is calculated through Softmax layer and the prediction relation is output. Experimental results show that the precision, recall, and F1 values of MRECL on the MNRE dataset reach 83.23%, 82.19%, and 82.70%, respectively, achieving better performance than the current SOTA model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Enhance Cancer Text Classification Using Multi Word Embedding and Ensemble Learning 17

Enhance Cancer Text Classification Using Multi Word Embeddin...

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17th International Conference on Developments in eSystems engineering, DeSE 2024

作者： Mahdi, Mohammed Fadhil Abd, Dhafar Hamed Abdalkafor, Ahmed Subhi Hussain, Abir Jaafar Al-Mansour University College Baghdad Iraq College of Computer Science and Information Technology University Of Anbar Ramadi Iraq Department of Computer Sciences College of Computer and Information Technology University Of Anbar Anbar Iraq Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350368697

The explosion of medical literature over the past decade has resulted in efficient and accurate techniques for text categorization to handle huge amount of data. This work combines ensemble learning methods with coupled multi-word embedding techniques to improve cancer text classification. The intricate semantic links present in medical tests are frequently outside the scope of traditional word embedding models, resulting in not ideal categorization results. To address this problem, we employ e continuous bag-of-words and Skip-gram approaches that yield more complete word representations capturing multiple linguistic nuances. Subsequently, such embeddings are passed through LGBM, CatBoost, and NGBoost ensemble learning classifiers to enhance classification accuracy. With 99.868% accuracy rate, LGBM and CatBoost were the most successful ensemble approaches examined. These approaches provide solid foundation for future work on the use of ensemble methods with complex word representations and for advancing the field of medical text classification. © 2024 IEEE.

关键词： Embeddings

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Event-Triggered Control for Obstacle Avoidance in Second-Order Nonlinear Multi-Agent Systems with Directed Graph 8

Event-Triggered Control for Obstacle Avoidance in Second-Ord...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Zhu, Zhipeng Zhang, Zhao Wang, Chenyu Zhou, Hongyan School of Computer Science and Software Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

In this paper, we study the obstacle avoidance problem of second-order nonlinear multi-agent systems (MASs) with directed graph based on event-triggered control. Firstly, the consensus requirement is accomplished by using the event-triggered consensus algorithm, and experiments are given for verification. Secondly, an improved artificial potential field (IAPF) method is proposed to solve the obstacle avoidance problem, and comparison experiments are made, from which the experimental results show that the IAPF method is significantly better than the traditional artificial potential field (TAPF) method under the same conditions. © 2024 SPIE.

关键词： Directed graphs

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Exploring the Potential of Discrete Chaotic Evolution Algorithm for Combinatorial Optimization

Exploring the Potential of Discrete Chaotic Evolution Algori...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Yi Meng, Xiang Pei, Yan Li, Jianqiang Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu Japan Beijing University of Technology Faculty of Information Beijing100124 China

ISBN: (纸本)9781665410205

We propose an extension of the chaotic evolution algorithm into the discrete domain to address combinatorial optimization problems. In this study, we leverage the discrete chaotic evolution algorithm to tackle the Traveling Salesman Problem (TSP) for assessment purposes. The chaotic evolution algorithm exploits the ergodicity of chaos to facilitate the search process within the optimization algorithm. It incorporates a mathematical mechanism into the iterative evolution process, simulating ergodic motion within a search space based on a simple principle. To manage the discrete mutation operation within the chaotic evolution algorithm, we introduce a specifically designed chaotic operation. This operation is tailored for its application in solving combinatorial optimization problems. The chaotic sequence plays a crucial role in determining the mutation location. Our evaluation involves the comparison of our proposed discrete chaotic evolution algorithm with the outcomes of the simulated annealing algorithm and the tabu search algorithm. The assessment serves to demonstrate and validate that the discrete chaotic evolution algorithm yields superior optimization performance within the discrete domain. © 2024 IEEE.

关键词： Optimization algorithms

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Visualizing Requirements: Data-Driven Dashboards for Stakeholder Alignment in Requirements Gathering 5

Visualizing Requirements: Data-Driven Dashboards for Stakeho...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sengupta, Arijit Singha, Anupam Symbiosis Centre for Information Technology Pune India R. P. Saha University Department of Computer Science and Engineering Narayanganj Bangladesh

ISBN: (纸本)9798331518097

Stakeholders' management is an essential component of the requirements elicitation process, and communication with them must be appropriately organized in order to achieve the goal decision of the software development project. Requirement visualizations improve communication, interaction, and decision-making due to their readily understandable and tangible form presented by dashboards primarily based on data. This research looks at the applicability of data-driven dashboards in enhancing the process of requirement depiction and stakeholders' synchronism. This paper employs qualitative research and comparative analysis to investigate the dashboards' strengths and limitations for filling communication gaps, attaining consensus, and enhancing decision-making. The proposed approach intends to raise stake stakeholders and participation, especially in the ideas stage of software development, minimize potential misinterpretation and produce a better outcome for the project. The study also reveals that dashboards are helpful during the requirements elicitation life cycle to facilitate better stakeholder coordination and enhance nroiect outcomes. © 2024 IEEE.

关键词： Software design

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An Effective TF-IDF Model to Improve the Text Classification Performance 13

An Effective TF-IDF Model to Improve the Text Classification...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Jain, Shitanshu Jain, Sapan Kumar Vasal, Somil Manipal University Department of CSE-DSE School of Information Technology Jaipur India Gyan Ganga Institute of Technology and Sciences Department of Computer Science and Engineering Jabalpur India

ISBN: (纸本)9798350305463

Term weight is utilized as a baseline classifier with text classification and other text mining techniques used for a significant increase in efficiency. The words, documents, and datasets are taken into consideration while applying various term weighting techniques. In this paper, various TF-IDF models are evaluated for text classification performance, and a proposed TF-IDF is suggested. The TF-IDF model's mathematical definition that we put forward was based on the computation of term frequency, term occurrence, TF-IDF Variants, and typical TF-IDF. Our aim is to test various phrase weighting techniques on the Amazon review dataset. With respect to accuracy performance parameters employing various classifiers, the suggested method outperforms the other weighting system. This A research indicates that modifications to the original TF-IDF weighting techniques offer considerable gains in © 2024 IEEE.

关键词： Text mining

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Sustainable Algorithms using Artificial Intelligence and Various Stages for Precision Agricultural Cultivation 2

Sustainable Algorithms using Artificial Intelligence and Var...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kalaiselvi, V.K.G. Gopalakrishnan, Jagadeesh Anand, Sahana Shri Hariharan, Shanmugasundaram Saravanan, Sivasankaran Annamalai, Hemalatha Sri Sairam Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350325799

India, the largest exporter of farm products, faces low agricultural productivity, resulting in reduced income for farmers. To boost revenue, performance improvement is crucial. Irrigation plays a vital role in farming, necessitating careful management of soil moisture levels. However, traditional irrigation methods often lead to water wastage. Efficient utilization of land and resources is essential in addressing this issue. Pest attacks pose a significant threat to crop yield and quality, making pest identification critical. Farmers and agronomists now utilize sensors and IoT technology to remotely monitor crops and manage them in controlled environments. However, crop productivity is still affected by weather conditions and disease variations. Precise detection of plant deficiencies and accurate fertilizer deployment is crucial for efficient farming. Modern computer vision techniques can be leveraged for positive financial and environmental outcomes in these tasks. Good crop selection significantly impacts yield, especially in developing countries. Additionally, growing vegetables in greenhouses or hydroponics offers a promising solution to discontinuous and costly supply chains. This abstract highlights the importance of addressing productivity challenges and implementing innovative farming methods to enhance agricultural outcomes. © 2023 IEEE.

关键词： Crops

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