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Analysis of visible–near infrared spectral characteristics for water layer management of rice based on the big data platform

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Multimedia Tools and Applications 2024年第18期83卷 53279-53292页

作者： Xia, Ji’An Zhang, WeiXin Zhang, WenYu Mu, WenTao Xu, RongWang Yuan, WangHao Ge, DaoKuo Zhang, Qian Ge, SiJun Cao, HongXin School of Computer and Software Nanjing Vocational University of Industry Technology Jiangsu Nanjing210023 China Institute of Agricultural Information Jiangsu Academy of Agricultural Sciences Jiangsu Nanjing210014 China School of Agricultural Engineering Jiangsu University Jiangsu Zhenjiang212013 China

The objective of this study was to evaluate the potential of big data technology for spectral feature extraction rice plants under different water management. In this study, we performed reflectance spectral analysis of rice grown under different water management schedules based on big data and machine learning approaches. We applied water management at the tillering, jointing, and heading stages of rice growth and collected visible to near-infrared reflectance spectra to analyze the spectral characteristics of rice grown under different water management schedules. Then, we constructed a data mining model based on the spectral analysis results using the Hadoop and Spark frameworks, and analyzed the characteristic bands of rice grown under each schedule using a parallel machine learning algorithm run in both local and cluster modes. The ChiSqSelector algorithm showed characteristic bands in the range of 400–1000 nm, with bands at approximately 474, 558, 632, 735, and 855 nm for water management in the tillering stage;472, 551, 627, 721, and 843 nm in the jointing stage;and 471, 545, 642, 725, and 849 nm in the heading stage. By contrast, the UnivariateFeatureSelector algorithm showed characteristic bands at approximately 462, 665, 755, 833, and 937 nm in the tillering stage;475, 671, 744, 848, and 932 nm in the jointing stage;and 470, 678, 757, 857, and 942 nm in the heading stage. These results demonstrate the feasibility and efficiency of applying big data and data mining technologies for spectral analysis of rice grown under different water management schedules. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Data mining

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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Local Optimisation Based Detection of Weak Targets in Isolated Forests 24

Local Optimisation Based Detection of Weak Targets in Isolat...

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8th International Conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Hu, Jurong Xing, Yanxiao Li, Yuxiao School of Information Science and Engineering Hohai University Jiangsu Nanjing China School of Computer and Software Hohai University Jiangsu Nanjing China

ISBN: (纸本)9798400710094

Aiming at the problem that the iForest algorithm is not sensitive enough to local anomalies and produces a large number of false alarms in the detection results on some low sea state datasets, this paper proposes the iForest algorithm based on an improved splitting strategy. The algorithm introduces the Gini impurity, incorporates the Gini impurity parameter into the splitting strategy, and is more targeted in the selection of splitting attributes and splitting points, thus better adapting to the problem of detecting weak targets under different sea states, and has an excellent performance of high accuracy, low time duration, and low false alarm rate compared with the traditional methods in anomaly detection. © 2024 Copyright held by the owner/author(s).

关键词： Optimization algorithms

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A Rearrangement and Restore-Based Mixer Model for Target-Oriented Multimodal Sentiment Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3109-3119页

作者： Jia, Li Ma, Tinghuai Rong, Huan Sheng, Victor S. Huang, Xuejian Xie, Xintong Nanjing University of Information Science and Technology School of Computer Science Nanjing Jiangsu210044 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing Jiangsu210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang Jiangsu222005 China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing Jiangsu210044 China Texas Tech University Department of Computer Science LubbockTX79409 United States

With the development of fine-grained multimodal sentiment analysis tasks, target-oriented multimodal sentiment (TMSC) analysis has received more attention, which aims to classify the sentiment of target with the help of textual and associated image features. Existing methods focus on exploring fine-grained image features and incorporate transformer-based complex fusion strategies, while ignoring the heavy computational burden. Recently, some lightweight multilayer perceptrons (MLP)-based methods have been successfully applied to multimodal sentiment classification tasks. In this article, we propose an effective rearrangement and restore mixer model (RR-Mixer) for TMSC, which dedicates the interaction of image, text, and targets along the modal-axis, sequential-axis, and feature channel-axis through rearrangement and restore operations. Specifically, we take vision transformer (ViT) and robustly optimized BERT (RoBERTa) pretrained models to extract image and textual features, respectively. Further, we adopt cosine similarity to select the most semantically relevant image features. Then, an RR-Mixer module is designed for mixed multimodal features, with the core technology consisting of rolling, grouping rearrangement and restore operations. Moreover, we introduce MLP Unit to learn the information of different modalities for intermodal interaction. The results show that our model achieves superior performance on two benchmark multimodal datasets, TWITTER-15 and TWITTER-17, with a significant improvement of 4.66% and 1.26% in terms of macro-F1. © 2020 IEEE.

关键词： Feature extraction

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A Question-and-Answer Scheme of Clinical Practice Guidelines Based on SDA 24

A Question-and-Answer Scheme of Clinical Practice Guidelines...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Zhang, Zhizheng Yang, Qian Du, Yan School of Computer Science and Engineering Southeast University Jiangsu Nanjing China Joint Graduate School Southeast University Jiangsu Nanjing China School of Software Engineering Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400717826

This paper proposes a medical diagnosis and treatment knowledge question and answer scheme based on clinical practice guidelines, aiming at the need of standardization of medical process and popularization of medical knowledge. The scheme realizes computer intelligent diagnosis and treatment assistance through diagnosis and treatment knowledge base construction, problem analysis, information retrieval and answer generation. The SDA*(State-DecisionAction) modeling method and question formal definition for hypertension diagnose and treatment are proposed, and the answers are generated based on the answer set program (ASP). For the transformation of natural language and ASP rules, large language model (LLM) technology is adopted to automatically identify question terms, generate ASP rules and process the generated answers. To verify the scheme’s ability to generate reliable answers, accuracy for problem classification and ASP rule generations are confirmed. The experimental results show that the scheme could provide the ability to solve medical problems based on the knowledge of clinical practice guidelines. © 2024 Copyright held by the owner/author(s).

关键词： Hyperthermia therapy

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Reflective clothing detection based on YOLOv5s fused with SE attention mechanism

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International Journal of Reasoning-based Intelligent Systems 2025年第1期17卷 9-16页

作者： Wang, Yuanyuan Gu, Dingyun Zhu, Yemeng Zhang, Zhihan Cao, Jiahui Zhang, Haiyan School of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Engineering Laboratory of Mobile Interconnection Technology for Internet of Things Jiangsu Province Huai’an Huaian City223001 China

This paper proposes a YOLOv5s deep learning algorithm incorporating the SE attention mechanism to address the issue of workers failing to wear reflective clothing on duty, which has resulted in casualties from time to time. The YOLOv5s model is used to train the reflective clothing dataset obtained by collecting images of construction sites, and the SE attention mechanism module is added to the network structure of YOLOv5 for improved performance. The reflective clothing detection algorithm is then deployed to the web for real-time detection. The experimental results show that the algorithm achieves mAP value of 96.70% with the SE attention module, resulting in an accuracy improvement of 1.09% compared to the model without the SE module. This enables practical applications to reduce the incidence of construction accidents at construction sites. Coрyright © 2025 Inderscience Enterрrises Ltd.

关键词： attention mechanism flask reflective clothing detection vue YOLOv5

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Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

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computers, Materials & Continua 2023年第7期76卷 849-859页

作者： Baowei Wang Neng Wang Yuxiao Zhang Zenghui Xu Junhao Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044JiangsuChina Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044JiangsuChina

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data *** speaking,EHRs are widely used in blockchain-based medical data *** are valuable private assets of patients,and the ownership belongs to *** recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit *** order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate *** paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted *** the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical *** addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical *** the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the *** analysis and security analysis illustrate the completeness and feasibility of the scheme.

关键词： Electronic health records cross-chain medical certificate blockchain data deletion and recovery

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A Trust-Based Hierarchical Consensus Mechanism for Consortium Blockchain in Smart Grid

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Tsinghua Science and Technology 2023年第1期28卷 69-81页

作者： Xingguo Jiang Aidong Sun Yan Sun Hong Luo Mohsen Guizani School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Institute of Food safety and Nutrition Jiangsu Academy of Agricultural SciencesNanjing 210000China Computer Science and Engineering Department Qatar UniversityDoha 2713Qatar

As the smart grid develops rapidly,abundant connected devices offer various trading *** raises higher requirements for secure and effective data *** centralized data management does not meet the above ***,smart grid with conventional consortium blockchain can solve the above ***,in the face of a large number of nodes,existing consensus algorithms often perform poorly in terms of efficiency and *** this paper,we propose a trust-based hierarchical consensus mechanism(THCM)to solve this ***,we design a hierarchical mechanism to improve the efficiency and ***,intra-layer nodes use an improved Raft consensus algorithm and inter-layer nodes use the Byzantine Fault Tolerance ***,we propose a trust evaluation method to improve the election process of ***,we implement a prototype system to evaluate the performance of *** results demonstrate that the consensus efficiency is improved by 19.8%,the throughput is improved by 12.34%,and the storage is reduced by 37.9%.

关键词： consortium blockchain consensus algorithm trust evaluation method smart grid Internet of Things(IoT)

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UniL: Point Cloud Novelty Detection through Multimodal Pre-training 24

UniL: Point Cloud Novelty Detection through Multimodal Pre-t...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yuhan Song, Mofei College of Software Engineering Southeast University Jiangsu Nanjing China Ministry of Education Jiangsu Nanjing China School of Computer Science and Engineering Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400706868

3D novelty detection plays a crucial role in various real-world applications, especially in safety-critical fields such as autonomous driving and intelligent surveillance systems. However, existing 3D novelty detection methods are constrained by the scarcity of 3D data, which may impede the model's ability to learn adequate representations, thereby impacting detection accuracy. To address this challenge, we propose a Unified Learning Framework (UniL) for facilitating novelty detection. During the pretraining phase, UniL assists the point cloud encoder in learning information from other modalities, aligning visual, textual, and 3D features within the same feature space. Additionally, we introduce a novel Multimodal Supervised Contrastive Loss (MSC Loss) to improve the model's ability to cluster samples from the same category in feature space by leveraging label information during pretraining. Furthermore, we propose a straightforward yet powerful scoring method, Depth Map Error (DME), which assesses the discrepancy between projected depth maps before and after point cloud reconstruction during novelty detection. Extensive experiments conducted on 3DOS have demonstrated the effectiveness of our approach, significantly enhancing the performance of the unsupervised VAE method in 3D novelty detection. Codes are avaliable at https://***/EugeneWon9/UniL. © 2024 ACM.

关键词： Self-supervised learning

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A deep neural network based feature fusion panoramic segmentation method

A deep neural network based feature fusion panoramic segment...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Fei, Hongyan Mokhtarzadeh, Amir Ali Zhou, Jiayu Geng, Junzuo Huaiyin Institute of Technology School of Computer and Software Engineering 223003 China Jiangsu Foreign Expert WorhShop Jiangsu Huaian223003 China Nanjing Institute of Technology School of Computer and Software Engineering 211167 China

Segmentation of complex and diverse scenes in the field of machine vision has the advantages of low cost and good robustness. However, in the real open intelligent driving scene, the segmented object will be affected by the change of proportion and illumination. The feature extraction of the interference algorithm causes some problems such as local information loss and feature background fusion in the process of execution. In this paper, deep neural network method is used for feature fusion panoramic segmentation. It focuses on the premise of lightweight, and improves the performance by improving the structure of semantic segmentation network layer and model calculation effect, and reducing the number of network parameters and calculations, so as to further improve the detection performance. At the same time, with the gradual deepening of deep learning network architecture, but most embedded devices can not bear a huge amount of computing workload. An attentional channel pruning based approach is proposed using a wide range of experimental and ablation studies to compare different data, including VOC2012 and ImageNet datasets. We show that the proposed method outperforms state of the art methods in terms of visual quality. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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