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Journal articles on the topic 'Multiclass Sunflower Network'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Thilagavathi, T., and L. Arockiam. "Multi-Class Sunflower Disease Detection by Integrating Enhanced Jellyfish Search Algorithm." Indian Journal Of Science And Technology 17, no. 44 (2024): 4633–45. https://doi.org/10.17485/ijst/v17i44.2874.

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Objectives: This research aims to classify diseases observed in sunflower flowers and leaves using deep learning algorithms. The goal is to enhance agricultural output by addressing the significant issue of plant diseases, which negatively affect the security of the food supply. Methods: The study utilizes a feature selection approach to identify multi-class sunflower diseases. The Enhanced Jellyfish Search (EJFSOA) algorithm is improved in three ways: (i) optimization capabilities and convergence speed are enhanced through sine and cosine learning variables during Type B motion in the swarm,

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T, Thilagavathi, and Arockiam L. "Multi-Class Sunflower Disease Detection by Integrating Enhanced Jellyfish Search Algorithm." Indian Journal of Science and Technology 17, no. 44 (2024): 4633–45. https://doi.org/10.17485/IJST/v17i44.2874.

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Abstract <strong>Objectives:</strong> This research aims to classify diseases observed in sunflower flowers and leaves using deep learning algorithms. The goal is to enhance agricultural output by addressing the significant issue of plant diseases, which negatively affect the security of the food supply. <strong>Methods:</strong> The study utilizes a feature selection approach to identify multi-class sunflower diseases. The Enhanced Jellyfish Search (EJFSOA) algorithm is improved in three ways: (i) optimization capabilities and convergence speed are enhanced through sine and cos

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Zhang, Shuailing, Hailin Yu, Bingquan Tian, et al. "Combining UAV Multi-Source Remote Sensing Data with CPO-SVR to Estimate Seedling Emergence in Breeding Sunflowers." Agronomy 14, no. 10 (2024): 2205. http://dx.doi.org/10.3390/agronomy14102205.

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In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the

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Bonato, Jacopo, Francesco Pelosin, Luigi Sabetta, and Alessandro Nicolosi. "MIND: Multi-Task Incremental Network Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11105–13. http://dx.doi.org/10.1609/aaai.v38i10.28987.

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The recent surge of pervasive devices that generate dynamic data streams has underscored the necessity for learning systems to adapt continually to data distributional shifts. To tackle this challenge, the research community has put forth a spectrum of methodologies, including the demanding pursuit of class-incremental learning without replay data. In this study, we present MIND, a parameter isolation method that aims to significantly enhance the performance of replay-free solutions and achieve state-of-the-art results on several widely studied datasets. Our approach introduces two main contri

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You, Wu, Lee, and Liu. "Intelligent Neural Network Schemes for Multi-Class Classification." Applied Sciences 9, no. 19 (2019): 4036. http://dx.doi.org/10.3390/app9194036.

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Multi-class classification is a very important technique in engineering applications, e.g., mechanical systems, mechanics and design innovations, applied materials in nanotechnologies, etc. A large amount of research is done for single-label classification where objects are associated with a single category. However, in many application domains, an object can belong to two or more categories, and multi-label classification is needed. Traditionally, statistical methods were used; recently, machine learning techniques, in particular neural networks, have been proposed to solve the multi-class cl

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Wang, Ruxin, Jianping Fan, and Ye Li. "Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection." IEEE Journal of Biomedical and Health Informatics 24, no. 9 (2020): 2461–72. http://dx.doi.org/10.1109/jbhi.2020.2981526.

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Gonzalez-Barrios, Pablo, Marina Castro, Osvaldo Pérez, Diego Vilaró, and Lucía Gutiérrez. "Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency." Spanish Journal of Agricultural Research 15, no. 4 (2018): e0705. http://dx.doi.org/10.5424/sjar/2017154-11016.

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Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, esti

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Yang, Hai, and Hai-Jun Huang. "The multi-class, multi-criteria traffic network equilibrium and systems optimum problem." Transportation Research Part B: Methodological 38, no. 1 (2004): 1–15. http://dx.doi.org/10.1016/s0191-2615(02)00074-7.

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Liu, Jin, Chenkai Gu, Jin Wang, Geumran Youn, and Jeong-Uk Kim. "Multi-scale multi-class conditional generative adversarial network for handwritten character generation." Journal of Supercomputing 75, no. 4 (2017): 1922–40. http://dx.doi.org/10.1007/s11227-017-2218-0.

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Raovic, Nevena, Otto Anker Nielsen, and Carlo Giacomo Prato Carlo Giacomo Prato. "DYNAMIC QUEUING TRANSMISSION MODEL FOR DYNAMIC NETWORK LOADING." Transport 32, no. 2 (2015): 146–59. http://dx.doi.org/10.3846/16484142.2015.1062417.

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This paper presents a new macroscopic multi-class dynamic network loading model called Dynamic Queuing Transmission Model (DQTM). The model utilizes ‘good’ properties of the Dynamic Queuing Model (DQM) and the Link Transmission Model (LTM) by offering a DQM consistent with the kinematic wave theory and allowing for the representation of multiple vehicle classes, queue spillbacks and shock waves. The model assumes that a link is split into a moving part plus a queuing part, and p that traffic dynamics are given by a triangular fundamental diagram. A case-study is investigated and the DQTM is co

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Hsu, Yen-Chi, Cheng-Yao Hong, Ming-Sui Lee, and Tyng-Luh Liu. "Query-Driven Multi-Instance Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4158–65. http://dx.doi.org/10.1609/aaai.v34i04.5836.

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We introduce a query-driven approach (qMIL) to multi-instance learning where the queries aim to uncover the class labels embodied in a given bag of instances. Specifically, it solves a multi-instance multi-label learning (MIML) problem with a more challenging setting than the conventional one. Each MIML bag in our formulation is annotated only with a binary label indicating whether the bag contains the instance of a certain class and the query is specified by the word2vec of a class label/name. To learn a deep-net model for qMIL, we construct a network component that achieves a generalized com

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Zheng, Ya Di. "Research on the Combination of Modern Information Technology and English Teaching." Advanced Materials Research 433-440 (January 2012): 5274–76. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5274.

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With the popularization of multi-media and network technology, some Chinese schools are applying the high technology to the class. The author believes that the model of teaching and learning focus on the establishment of multi-media class environment and the after-school autonomous learning system. The utilization reflects the development process of the combination of modern information technology and English teaching. The modern teaching technology, which includes multi-media, network system and various teaching aids, enriched the choice of teaching resources and provided plenty of materials

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Lourdusamy, A., S. Kither Iammal, and I. Dhivviyanandam. "Monophonic Cover Pebbling Number \((MCPN)\) of Network Graphs." Utilitas Mathematica 121, no. 1 (2024): 11–24. https://doi.org/10.61091/um121-02.

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Given a connected graph \(G\) and a configuration \(D\) of pebbles on the vertices of \(G\), a pebbling transformation involves removing two pebbles from one vertex and placing one pebble on its adjacent vertex. A monophonic path is defined as a chordless path between two non-adjacent vertices \(u\) and \(v\). The monophonic cover pebbling number, \(\gamma_{\mu}(G)\), is the minimum number of pebbles required to ensure that, after a series of pebbling transformations using monophonic paths, all vertices of \(G\) are covered with at least one pebble each. In this paper, we determine the monopho

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Tarhuni, Naser G., Mohammed Elmusrati, and Timo Korhonenn. "MULTI-CLASS OPTICAL-CDMA NETWORK USING OPTICAL POWER CONTROL." Progress In Electromagnetics Research 64 (2006): 279–92. http://dx.doi.org/10.2528/pier06070701.

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Ford, William, Kun Xiang, Walker Land, Robert Congdon, Yinglei Li, and Omowunmi Sadik. "A Multi-class Probabilistic Neural Network for Pathogen Classification." Procedia Computer Science 20 (2013): 348–53. http://dx.doi.org/10.1016/j.procs.2013.09.284.

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Zhang, Fan, and Pramode K. Verma. "Pricing multi-class network services using the Shapley Value." NETNOMICS: Economic Research and Electronic Networking 12, no. 1 (2011): 61–75. http://dx.doi.org/10.1007/s11066-011-9058-5.

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KumarSingh, Ajay, Shamik Tiwari, and V. P.Shukla. "Wavelet based Multi Class image classification using Neural Network." International Journal of Computer Applications 37, no. 4 (2012): 21–25. http://dx.doi.org/10.5120/4597-6555.

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Deng, Xiaozheng, Shasha Mao, Jinyuan Yang, et al. "Multi-Class Double-Transformation Network for SAR Image Registration." Remote Sensing 15, no. 11 (2023): 2927. http://dx.doi.org/10.3390/rs15112927.

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In SAR image registration, most existing methods consider the image registration as a two-classification problem to construct the pair training samples for training the deep model. However, it is difficult to obtain a mass of given matched-points directly from SAR images as the training samples. Based on this, we propose a multi-class double-transformation network for SAR image registration based on Swin-Transformer. Different from existing methods, the proposed method directly considers each key point as an independent category to construct the multi-classification model for SAR image registr

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Gu, Yanan, Cheng Deng, and Kun Wei. "Class-Incremental Instance Segmentation via Multi-Teacher Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1478–86. http://dx.doi.org/10.1609/aaai.v35i2.16238.

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Although deep neural networks have achieved amazing results on instance segmentation, they are still ill-equipped when they are required to learn new tasks incrementally. Concretely, they suffer from “catastrophic forgetting”, an abrupt degradation of performance on old classes with the initial training data missing. Moreover, they are subjected to a negative transfer problem on new classes, which renders the model unable to update its knowledge while preserving the previous knowledge. To address these problems, we propose an incremental instance segmentation method that consists of three netw

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Liu, Yazhi, Xinyi Yao, Zhigang Yang, and Wei Li. "A multi-queue-based ECN marking strategy for multi-class QoS guarantee in programmable networks." PeerJ Computer Science 10 (October 31, 2024): e2382. http://dx.doi.org/10.7717/peerj-cs.2382.

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Currently, network applications are experiencing explosive growth, and various types of network applications are showing a trend of varied demands for quality of network service. However, the existing Explicit Congestion Notification (ECN) marking methods have not taken into account the diversified Quality of Service (QoS) requirements of network applications. This article introduces a multi-queue ECN marking strategy targeting multiple QoS guarantees. The strategy utilizes virtual queues and dynamic weighted round-robin scheduling to achieve traffic partitioning in a programmable data plane.

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Chen, Dan. "Multiple Linear Regression of Multi-class Images in Devices of Internet of Things." Traitement du Signal 37, no. 6 (2020): 965–73. http://dx.doi.org/10.18280/ts.370609.

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The correct classification of images is an important application in the monitoring of Internet of things (IoT). In the research of IoT images, a key issue is to recognize multi-class images at a high accuracy. As a result, this paper puts forward a classification method for multi-class images based on multiple linear regression (MLR). Firstly, the convolutional neural network (CNN) was improved to automatically generate a network from the IoT terminals, and used to classify images into disjoint class sets (clusters), which were processed by the subsequently constructed expert network. After th

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Gao, Yuanyuan. "An Empirical Analysis of Audio-Visual Teaching and Network Multi-Modal Learning Environment Theory for English Majors." Journal of Environmental and Public Health 2022 (August 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/5000501.

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In order to further investigate the network multi-modal learning environment to promote students’ ability to English audio-visual effects and improve the quality of teaching effect, an empirical analysis of audio-visual teaching and network multi-modal learning environment theory for English majors was proposed in the research. The 98 students were chosen as the experiment objects from two classes in the same school. They were divided into the experiment class and the control class. The students in the experiment class were taught according to the teaching model based on network multi-modal le

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Han, Baojin, Min Hu, Xiaohua Wang, and Fuji Ren. "A Triple-Structure Network Model Based upon MobileNet V1 and Multi-Loss Function for Facial Expression Recognition." Symmetry 14, no. 10 (2022): 2055. http://dx.doi.org/10.3390/sym14102055.

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Existing facial expression recognition methods have some drawbacks. For example, it becomes difficult for network learning on cross-dataset facial expressions, multi-region learning on an image did not extract the overall image information, and a frequency multiplication network did not take into account the inter-class and intra-class features in image classification. In order to deal with the above problems, in our current research, we raise a symmetric mode to extract the inter-class features and intra-class diversity features, and then propose a triple-structure network model based upon Mo

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Uma, Shankar Rao Erothi, and Rodda Sireesha. "Reduct ECOC Framework for Network Intrusion Detection System." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 258–66. https://doi.org/10.35940/ijeat.B4238.029320.

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Now a day’s network security is major concern for e-government and e-commerce applications. A wide range of malicious activities are increasing with the usage of internet and network technologies. Identifying novel threats and finding modern solutions for network to prevent from these threats are important. Designing an effective intrusion detection system is significant to continuously look out the network activities to efficiently thwart malicious attacks or to identify the intruders. To tackle multi class imbalance classification problem in networks, a reduct based ECOC ensemble frame

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Lee, Hansoo, Jonggeun Kim, Jungwon Yu, Yeongsang Jeong, and Sungshin Kim. "Multi-class Classification using Transfer Learning based Convolutional Neural Network." Journal of Korean Institute of Intelligent Systems 28, no. 6 (2018): 531–37. http://dx.doi.org/10.5391/jkiis.2018.28.6.531.

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Gordon, O., P. D’Hondt, L. Knijff, et al. "Scanning tunneling state recognition with multi-class neural network ensembles." Review of Scientific Instruments 90, no. 10 (2019): 103704. http://dx.doi.org/10.1063/1.5099590.

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Nie, Qingfeng, Lizuo Jin, Shumin Fei, and Junyong Ma. "Neural network for multi-class classification by boosting composite stumps." Neurocomputing 149 (February 2015): 949–56. http://dx.doi.org/10.1016/j.neucom.2014.07.039.

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Bhardwaj, Arpit, Aruna Tiwari, Harshit Bhardwaj, and Aditi Bhardwaj. "A genetically optimized neural network model for multi-class classification." Expert Systems with Applications 60 (October 2016): 211–21. http://dx.doi.org/10.1016/j.eswa.2016.04.036.

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Moya, Mary M., and Don R. Hush. "Network constraints and multi-objective optimization for one-class classification." Neural Networks 9, no. 3 (1996): 463–74. http://dx.doi.org/10.1016/0893-6080(95)00120-4.

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Yang, Jie, Yi-Xuan Wang, Yuan-Yuan Qiao, Xiao-Xing Zhao, Fang Liu, and Gang Cheng. "On Evaluating Multi-class Network Traffic Classifiers Based on AUC." Wireless Personal Communications 83, no. 3 (2015): 1731–50. http://dx.doi.org/10.1007/s11277-015-2473-4.

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Hassan, Junaid, and Umar Shoaib. "Multi-class Review Rating Classification using Deep Recurrent Neural Network." Neural Processing Letters 51, no. 1 (2019): 1031–48. http://dx.doi.org/10.1007/s11063-019-10125-6.

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Kochkar, Hedia, Takeshi Ikenaga, Kenji Kawahara, and Yuji Oie. "Multi-class QoS routing strategies based on the network state." Computer Communications 28, no. 11 (2005): 1348–55. http://dx.doi.org/10.1016/j.comcom.2005.02.015.

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Chen, Juan, Qinxuan Feng, and Qi Guo. "Multi-Class Freeway Congestion and Emission Based on Robust Dynamic Multi-Objective Optimization." Algorithms 14, no. 9 (2021): 266. http://dx.doi.org/10.3390/a14090266.

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In order to solve the problem of traffic congestion and emission optimization of urban multi-class expressways, a robust dynamic nondominated sorting multi-objective genetic algorithm DFCM-RDNSGA-III based on density fuzzy c-means clustering method is proposed in this paper. Considering the three performance indicators of travel time, ramp queue and traffic emissions, the ramp metering and variable speed limit control schemes of an expressway are optimized to improve the main road and ramp traffic congestion, therefore achieving energy conservation and emission reduction. In the VISSIM simulat

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Chiesa, Marco, and Fábio L. Verdi. "Network Monitoring on Multi-Pipe Switches." ACM SIGMETRICS Performance Evaluation Review 51, no. 1 (2023): 49–50. http://dx.doi.org/10.1145/3606376.3593554.

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Programmable switches have been widely used to design network monitoring solutions that operate in the fast data-plane level, e.g., detecting heavy hitters, super-spreaders, computing flow size distributions and their entropy. Existing works assume packets access the same memory region in a switch. However, high-speed ASIC switches deploy multiple packet processing pipes, each equipped with its own independent memory. In this work, we first quantify the accuracy degradation due to splitting a monitoring data structure across multiple pipes (e.g., up to 3000x worse flow-size estimation average

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Zheng, Mao, Chao Wang, Yu Juan Wang, and Sheng Huang. "Research on Aircraft Sortie Generation Rate Using Multi-Class Closed Queueing Network." Applied Mechanics and Materials 380-384 (August 2013): 1864–67. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1864.

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To analysis the sortie generation rate (SGR) of carrier-based aircrafts, an analytical method based on closed queueing network was carried out. A multi-class, multi-server non-preemptive (or Head of Line, HOL) closed queueing network model was developed. An approximate method based on reduced work-load assumption and mean value analysis (MVA) iteration was used to gain the performance of the network. As a result, this approximate method can provide the marginal distribution of aircrafts at each service facility of the queueing network.

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He, Haoyang, Jiangning Zhang, Hongxu Chen, et al. "A Diffusion-Based Framework for Multi-Class Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8472–80. http://dx.doi.org/10.1609/aaai.v38i8.28690.

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Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have sparked research efforts to utilize them for enhanced reconstruction of anomalous images. Nonetheless, these methods might face challenges related to the preservation of image categories and pixel-wise structural integrity in the more practical multi-class setting. To solve the above problems, we propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection, which consists of a pixel-s

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Abdulhammed, Razan, Hassan Musafer, Ali Alessa, Miad Faezipour, and Abdelshakour Abuzneid. "Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection." Electronics 8, no. 3 (2019): 322. http://dx.doi.org/10.3390/electronics8030322.

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The security of networked systems has become a critical universal issue that influences individuals, enterprises and governments. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reducti

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Guan, Huihui, Yuqing Wang, Wei Zhao, Rufei Zhang, Nannan Li, and Dongjin Li. "Multi-reference source aircraft classification network based on unknown class prototype estimation." Journal of Physics: Conference Series 2863, no. 1 (2024): 012034. http://dx.doi.org/10.1088/1742-6596/2863/1/012034.

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Abstract Gathering complete aircraft types for close-set tasks is challenging and costly in fine-grained aircraft classification, resulting in encountering unknown class aircraft images in real-world models. To address this problem, we propose a network called a multi-reference source fine-grained aircraft classification network (MRSN) to explicitly and implicitly distinguish known class boundaries and embed unknown classes into the sample space. Specifically, we propose an embedding module (UCPE) to synthesize an unknown class prototype to facilitate the modeling of unknown class data distrib

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Muhuri, Pramita Sree, Prosenjit Chatterjee, Xiaohong Yuan, Kaushik Roy, and Albert Esterline. "Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks." Information 11, no. 5 (2020): 243. http://dx.doi.org/10.3390/info11050243.

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An intrusion detection system (IDS) identifies whether the network traffic behavior is normal or abnormal or identifies the attack types. Recently, deep learning has emerged as a successful approach in IDSs, having a high accuracy rate with its distinctive learning mechanism. In this research, we developed a new method for intrusion detection to classify the NSL-KDD dataset by combining a genetic algorithm (GA) for optimal feature selection and long short-term memory (LSTM) with a recurrent neural network (RNN). We found that using LSTM-RNN classifiers with the optimal feature set improves int

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Liu, Xuyang, Bingbing Wen, and Sibei Yang. "CCQ: Cross-Class Query Network for Partially Labeled Organ Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1755–63. http://dx.doi.org/10.1609/aaai.v37i2.25264.

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Learning multi-organ segmentation from multiple partially-labeled datasets attracts increasing attention. It can be a promising solution for the scarcity of large-scale, fully labeled 3D medical image segmentation datasets. However, existing algorithms of multi-organ segmentation on partially-labeled datasets neglect the semantic relations and anatomical priors between different categories of organs, which is crucial for partially-labeled multi-organ segmentation. In this paper, we tackle the limitations above by proposing the Cross-Class Query Network (CCQ). CCQ consists of an image encoder,

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Tian, Ming, Ju Long Lan, and Peng Yi. "A Multi-next-Hop Routing Optimization Algorithm Based on Polymerization Equivalence Class." Applied Mechanics and Materials 48-49 (February 2011): 52–55. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.52.

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A multi-next-hop routing algorithm based on polymerization equivalence class proposed in this paper, which can efficiently avoid route loop. Because each node is divided into different equivalence class, message transmission direction is along reducing order of equivalent class numbers. Based on that, the link criticality is referred to depict the average task in network for every link, it can be used to avoid choosing links with heavier task and longer path length to route, making whole network utilization tend to balance, reducing package loss in the case of high load.

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Guo, Xiuwen, Weichao Yi, Liquan Dong, et al. "Multi-Class Wound Classification via High and Low-Frequency Guidance Network." Bioengineering 10, no. 12 (2023): 1385. http://dx.doi.org/10.3390/bioengineering10121385.

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Wound image classification is a crucial preprocessing step to many intelligent medical systems, e.g., online diagnosis and smart medical. Recently, Convolutional Neural Network (CNN) has been widely applied to the classification of wound images and obtained promising performance to some extent. Unfortunately, it is still challenging to classify multiple wound types due to the complexity and variety of wound images. Existing CNNs usually extract high- and low-frequency features at the same convolutional layer, which inevitably causes information loss and further affects the accuracy of classifi

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Li, Xin, Hong Huang, Guotao Yuan, Zhaolian Wang, and Rui Du. "An Intrusion Detection Method based on Fusion Neural Network." Frontiers in Computing and Intelligent Systems 4, no. 2 (2023): 124–30. http://dx.doi.org/10.54097/fcis.v4i2.10369.

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Aiming at the problems of class imbalance, insufficient feature learning, weak generalization ability, and representation capability in existing intrusion detection models, we propose a multi-scale feature fusion Intrusion Detection Model (MSFF). This model combines multi-scale one-dimensional convolution and bidirectional long short-term memory (LSTM) networks, and incorporates residual connections with identity mappings to address the problem of network degradation. The multi-scale convolution captures feature representations at different levels, thereby improving the expressive power of the

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Ma, Yanxin, Mengqi Liu, Yi Zhang, et al. "Imbalanced Underwater Acoustic Target Recognition with Trigonometric Loss and Attention Mechanism Convolutional Network." Remote Sensing 14, no. 16 (2022): 4103. http://dx.doi.org/10.3390/rs14164103.

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A balanced dataset is generally beneficial to underwater acoustic target recognition. However, the imbalanced class distribution is always meted out in a real scene. To address this, a weighted cross entropy loss function based on trigonometric function is proposed. Then, the proposed loss function is applied in a multi-scale residual convolutional neural network (named MR-CNN-A network) embedded with an attention mechanism for the recognition task. Firstly, a multi-scale convolution kernel is used to obtain multi-scale features. Then, an attention mechanism is used to fuse these multi-scale f

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Mo, Xishuo, Xin Zhang, Chaoping Bai, Ziting Wang, Shuai Zhang, and Yueqiang Sun. "CNN-LSTM Based Automatic Classification Network for Multi-Type with Multi-Energy Charged Particle Identification." Applied Sciences 15, no. 4 (2025): 1837. https://doi.org/10.3390/app15041837.

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Particle identification is a critical component of space environment detection. In this study, we propose a CNN-LSTM based multi-class automatic particle classification network, which employs 1-D CNN to extract more discriminative local features of waveforms produced by particle detector, and utilizes the ability of LSTM to capture long-range dependencies for extracting global features with stronger generalization capability. The proposed network can effectively classify the waveforms from multi-type with multi-energy charged particles, achieving an accuracy of 91.72% in simultaneously classif

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Krebs, Rocio, Sikha S. Bagui, Dustin Mink, and Subhash C. Bagui. "Applying Multi-CLASS Support Vector Machines: One-vs.-One vs. One-vs.-All on the UWF-ZeekDataFall22 Dataset." Electronics 13, no. 19 (2024): 3916. http://dx.doi.org/10.3390/electronics13193916.

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This study investigates the technical challenges of applying Support Vector Machines (SVM) for multi-class classification in network intrusion detection using the UWF-ZeekDataFall22 dataset, which is labeled based on the MITRE ATT&CK framework. A key challenge lies in handling imbalanced classes and complex attack patterns, which are inherent in intrusion detection data. This work highlights the difficulties in implementing SVMs for multi-class classification, particularly with One-vs.-One (OvO) and One-vs.-All (OvA) methods, including scalability issues due to the large volume of network

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Fuangkhon, Piyabute. "Parallel Multi-Class Contour Preserving Classification." Journal of Intelligent Systems 26, no. 1 (2017): 109–21. http://dx.doi.org/10.1515/jisys-2015-0038.

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AbstractSerial multi-class contour preserving classification can improve the representation of the contour of the data to improve the levels of classification accuracy for feed-forward neural network (FFNN). The algorithm synthesizes fundamental multi-class outpost vector (FMCOV) and additional multi-class outpost vector (AMCOV) at the decision boundary between consecutive classes of data to narrow the space of data. Both FMCOVs and AMCOVs will assist the FFNN to place the hyper-planes in such a way that can classify the data more accurately. However, the technique was designed to utilize only

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Ribone, Andrés I., Mónica Fass, Sergio Gonzalez, Veronica Lia, Norma Paniego, and Máximo Rivarola. "Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance." Plants 12, no. 15 (2023): 2767. http://dx.doi.org/10.3390/plants12152767.

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Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~40% of protein-coding genes in the model plant Arabidopsis thaliana L. are still not categorized in the Gene Ontology (GO) Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus L.), the number of BP t

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Dong, Shi. "Multi class SVM algorithm with active learning for network traffic classification." Expert Systems with Applications 176 (August 2021): 114885. http://dx.doi.org/10.1016/j.eswa.2021.114885.

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Fan, Saite, Xinmin Zhang, Zhihuan Song, and Weiming Shao. "Cumulative dual-branch network framework for long-tailed multi-class classification." Engineering Applications of Artificial Intelligence 114 (September 2022): 105080. http://dx.doi.org/10.1016/j.engappai.2022.105080.

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