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Journal articles on the topic 'Deep Embedded Clustering'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Miklautz, Lukas, Dominik Mautz, Muzaffer Can Altinigneli, Christian Böhm, and Claudia Plant. "Deep Embedded Non-Redundant Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5174–81. http://dx.doi.org/10.1609/aaai.v34i04.5961.

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Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple

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Wada, Yuichiro, Shugo Miyamoto, Takumi Nakagama, Léo Andéol, Wataru Kumagai, and Takafumi Kanamori. "Spectral Embedded Deep Clustering." Entropy 21, no. 8 (2019): 795. http://dx.doi.org/10.3390/e21080795.

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We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space. We use a conditional discrete probability distribution defined by a deep neural network as a statistical model. Our strategy is first to estimate the cluster labels of unlabeled data points selected from a high-density region, and then to conduct semi-supervised learning to train the model by using the estimated cluster labels and the remaining unlabeled data points. Lastly, by u

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3

Ren, Yazhou, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, and Zenglin Xu. "Semi-supervised deep embedded clustering." Neurocomputing 325 (January 2019): 121–30. http://dx.doi.org/10.1016/j.neucom.2018.10.016.

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Chen, Zhikui, Chaojie Li, Jing Gao, Jianing Zhang, and Peng Li. "Semisupervised Deep Embedded Clustering with Adaptive Labels." Scientific Programming 2021 (January 16, 2021): 1–12. http://dx.doi.org/10.1155/2021/6613452.

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Deep embedding clustering (DEC) attracts much attention due to its outperforming performance attributed to the end-to-end clustering. However, DEC cannot make use of small amount of a priori knowledge contained in data of increasing volume. To tackle this challenge, a semisupervised deep embedded clustering algorithm with adaptive labels is proposed to cluster those data in a semisupervised end-to-end manner on the basis of a little priori knowledge. Specifically, a deep semisupervised clustering network is designed based on the autoencoder paradigm and deep clustering, which well mine the clu

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Mautz, Dominik, Claudia Plant, and Christian Böhm. "DeepECT: The Deep Embedded Cluster Tree." Data Science and Engineering 5, no. 4 (2020): 419–32. http://dx.doi.org/10.1007/s41019-020-00134-0.

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Abstract The idea of combining the high representational power of deep learning techniques with clustering methods has gained much attention in recent years. Optimizing a clustering objective and the dataset representation simultaneously has been shown to be advantageous over separately optimizing them. So far, however, all proposed methods have been using a flat clustering strategy, with the actual number of clusters known a priori. In this paper, we propose the Deep Embedded Cluster Tree (DeepECT), the first divisive hierarchical embedded clustering method. The cluster tree does not need to

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Cahyadi, Danu Julian, Hendri Murfi, Yudi Satria, Sarini Abdullah, and Yekti Widyaningsih. "Analisis Performa Deep Embedded Clustering untuk Pendeteksian Topik." Techno.Com 24, no. 1 (2025): 56–67. https://doi.org/10.62411/tc.v24i1.11841.

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Pendeteksian topik adalah solusi untuk mengungkap struktur laten dalam sebuah dokumen. Kerangka umum pendeteksian topik berbasis clustering terdiri dari dua langkah: pembelajaran representasi dan pendeteksian topik melalui clustering. Dalam penelitian ini, Bidirectional Encoder Representations from Transformers (BERT) digunakan untuk pembelajaran representasi karena BERT mampu menangkap konteks setiap kata berdasarkan kata-kata di sekitarnya. Representasi teks yang diperoleh dari BERT digunakan untuk pendeteksian topik dengan clustering. Deep Embedded Clustering (DEC) dan Improved DEC (IDEC) a

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Ozanich, Emma, Aaron Thode, Peter Gerstoft, Lauren A. Freeman, and Simon Freeman. "Deep embedded clustering of coral reef bioacoustics." Journal of the Acoustical Society of America 149, no. 4 (2021): 2587–601. http://dx.doi.org/10.1121/10.0004221.

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Reeves Ozanich, Emma, Aaron M. Thode, Peter Gerstoft, Lauren A. Freeman, and Simon E. Freeman. "Deep embedded clustering of coral reef bioacoustics." Journal of the Acoustical Society of America 149, no. 4 (2021): A113. http://dx.doi.org/10.1121/10.0004685.

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9

Zhou, Wen'An, and Qiang Zhou. "Deep Embedded Clustering With Adversarial Distribution Adaptation." IEEE Access 7 (2019): 113801–9. http://dx.doi.org/10.1109/access.2019.2935388.

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10

Gozet, Melisa, Mehmet Karakose, and Asim Egemen Yilmaz. "Deep embedded clustering using crowd density map." IET Conference Proceedings 2024, no. 37 (2025): 758–63. https://doi.org/10.1049/icp.2025.0888.

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11

Duan, Yanting, Xiaodong Zheng, Lianlian Hu, and Luping Sun. "Seismic facies analysis based on deep convolutional embedded clustering." GEOPHYSICS 84, no. 6 (2019): IM87—IM97. http://dx.doi.org/10.1190/geo2018-0789.1.

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Seismic facies classification takes a two-step approach: attribute extraction and seismic facies analysis by using clustering algorithms, sequentially. In general, it is clear that the choice of feature extraction is critical for successful seismic facies analysis. However, the choice of features is customarily determined by the seismic interpreters, and so the clustering result is affected by the difference in the seismic interpreters’ experience levels. It becomes challenging to extract features and identify seismic facies simultaneously. We have introduced deep convolutional embedded cluste

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12

Xu, Jie, Yazhou Ren, Guofeng Li, Lili Pan, Ce Zhu, and Zenglin Xu. "Deep embedded multi-view clustering with collaborative training." Information Sciences 573 (September 2021): 279–90. http://dx.doi.org/10.1016/j.ins.2020.12.073.

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13

Kim, Yong Hwi, and Kwan H. Lee. "Data Driven SVBRDF Estimation Using Deep Embedded Clustering." Electronics 11, no. 19 (2022): 3239. http://dx.doi.org/10.3390/electronics11193239.

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Photo-realistic representation in user-specified view and lighting conditions is a challenging but high-demand technology in the digital transformation of cultural heritages. Despite recent advances in neural renderings, it is still necessary to capture high-quality surface reflectance from photography in a controlled environment for real-time applications such as VR/AR and digital arts. In this paper, we present a deep embedding clustering network for spatially-varying bidirectional reflectance distribution function (SVBRDF) estimation. Our network is designed to simultaneously update the ref

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14

Eskandarnia, Elham, Hesham M. Al-Ammal, and Riadh Ksantini. "An embedded deep-clustering-based load profiling framework." Sustainable Cities and Society 78 (March 2022): 103618. http://dx.doi.org/10.1016/j.scs.2021.103618.

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15

Prasetio, Barlian Henryranu, Hiroki Tamura, and Koichi Tanno. "Semi-Supervised Deep Time-Delay Embedded Clustering for Stress Speech Analysis." Electronics 8, no. 11 (2019): 1263. http://dx.doi.org/10.3390/electronics8111263.

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Real stressed speech is affected by various aspects (individual characteristics and environment) so that the stress patterns are diverse and different on each individual. To this end, in our previous work, we performed an unsupervised clustering method that able to self-learning manner by mapping the feature representations of the stress speech and clustering tasks simultaneously, called deep time-delay embedded clustering (DTEC). However, DTEC has not confirmed yet the compatibility between the output class and informational classes. Therefore, we proposed semi-supervised time-delay embedded

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16

Wang, Shuang, Amin Beheshti, Yufei Wang, et al. "Learning Distributed Representations and Deep Embedded Clustering of Texts." Algorithms 16, no. 3 (2023): 158. http://dx.doi.org/10.3390/a16030158.

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Instructors face significant time and effort constraints when grading students’ assessments on a large scale. Clustering similar assessments is a unique and effective technique that has the potential to significantly reduce the workload of instructors in online and large-scale learning environments. By grouping together similar assessments, marking one assessment in a cluster can be scaled to other similar assessments, allowing for a more efficient and streamlined grading process. To address this issue, this paper focuses on text assessments and proposes a method for reducing the workload of i

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17

Malhi, Umar Subhan, Junfeng Zhou, Cairong Yan, Abdur Rasool, Shahbaz Siddeeq, and Ming Du. "Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images." Applied Sciences 13, no. 5 (2023): 2828. http://dx.doi.org/10.3390/app13052828.

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Fashion image clustering is the key to fashion retrieval, forecasting, and recommendation applications. Manual labeling-based clustering is both time-consuming and less accurate. Currently, popular methods for extracting features from data use deep learning techniques, such as a Convolutional Neural Network (CNN). These methods can generate high-dimensional feature vectors, which are effective for image clustering. However, high dimensions can lead to the curse of dimensionality, which makes subsequent clustering difficult. The fashion images-oriented deep clustering method (FIDC) is proposed

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18

Cheng, Yue, Yanchi Su, Zhuohan Yu, Yanchun Liang, Ka-Chun Wong, and Xiangtao Li. "Unsupervised Deep Embedded Fusion Representation of Single-Cell Transcriptomics." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 5036–44. http://dx.doi.org/10.1609/aaai.v37i4.25631.

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Cell clustering is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data, which allows us to characterize the cellular heterogeneity of transcriptional profiling at the single-cell level. Single-cell deep embedded representation models have recently become popular since they can learn feature representation and clustering simultaneously. However, the model still suffers from a variety of significant challenges, including the massive amount of data, pervasive dropout events, and complicated noise patterns in transcriptional profiling. Here, we propose a Single-Cell Deep Embed

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19

Fu Xingwu, 付兴武, 吕明明 Lü Mingming, 刘万军 Liu Wanjun, and 魏宪 Wei Xian. "Structured Deep Discriminant Embedded Coding Network for Image Clustering." Laser & Optoelectronics Progress 58, no. 6 (2021): 0610016. http://dx.doi.org/10.3788/lop202158.0610016.

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20

Ding, Deqiong, Dan Zhuang, Xiaogao Yang, Xiao Zheng, and Chang Tang. "Latent Features Embedded Dynamic Graph Evolution Deep Clustering Network." Signal Processing 205 (April 2023): 108892. http://dx.doi.org/10.1016/j.sigpro.2022.108892.

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21

Ajay, P., B. Nagaraj, R. Arun Kumar, Ruihang Huang, and P. Ananthi. "Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm." Scanning 2022 (June 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/1200860.

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Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produced using k -means clustering. Many researchers are now interested in biosignals. Uncertainty limits the algorithms’ capacity to evaluate these signals for further information. Even while AI systems can answer puzzles, they remain limited. Deep learning is used when machine learning is inefficient. Su

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22

Farmaha, Ihor, Marian Banaś, Vasyl Savchyn, Bohdan Lukashchuk, and Taras Farmaha. "Wound image segmentation using clustering based algorithms." New Trends in Production Engineering 2, no. 1 (2019): 570–78. http://dx.doi.org/10.2478/ntpe-2019-0062.

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Abstract Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can't reach good re

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23

Binbusayyis, Adel. "Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System." Intelligent Automation & Soft Computing 33, no. 1 (2022): 501–13. http://dx.doi.org/10.32604/iasc.2022.022239.

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24

Lu, Hu, Chao Chen, Hui Wei, Zhongchen Ma, Ke Jiang, and Yingquan Wang. "Improved deep convolutional embedded clustering with re-selectable sample training." Pattern Recognition 127 (July 2022): 108611. http://dx.doi.org/10.1016/j.patcog.2022.108611.

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25

Zheng, Yimei, Caiyan Jia, Jian Yu, and Xuanya Li. "Deep embedded clustering with distribution consistency preservation for attributed networks." Pattern Recognition 139 (July 2023): 109469. http://dx.doi.org/10.1016/j.patcog.2023.109469.

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26

Karre Shankar and Dr.K.Pavan Kumar. "OPTIMIZING EMERGENCY RESPONSE WITH DEEP EMBEDDED CLUSTERING FOR AMBULANCE POSITIONING." International Journal of Information Technology and Computer Engineering 13, no. 2 (2025): 541–51. https://doi.org/10.62643/ijitce.2025.v13.i2.pp541-551.

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The number of individuals killed and wounded in traffic accidents is one of the largest problems confronting the contemporary world. Instead of only sending ambulances out when required, pre-positioning them may expedite response times and provide prompt medical treatment. Deep learning techniques hold great potential and have shown to be essential for making decisions and addressing problems in the healthcare sector. This research presents a deep-embedded clustering-based approach to ambulance positing location prediction. Since many patterns and causes within a geographic region have a subst

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Boubekki, Ahcène, Michael Kampffmeyer, Ulf Brefeld, and Robert Jenssen. "Joint optimization of an autoencoder for clustering and embedding." Machine Learning 110, no. 7 (2021): 1901–37. http://dx.doi.org/10.1007/s10994-021-06015-5.

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AbstractDeep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternates between the training of a deep autoencoder and a k-means clustering of the autoencoder’s embedding. The diachronic setting, however, prevents the former to benefit from valuable information acquired by the latter. In this paper, we present an alternative where the autoencoder and the clustering are learned simultaneously. This is achieved by providing novel theoretical insight, where we show that the objec

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Bao, Huynh Quang, Le Van Vinh, and Tran Van Hoai. "A Deep Embedded Clustering Algorithm for the Binning of Metagenomic Sequences." IEEE Access 10 (2022): 54348–57. http://dx.doi.org/10.1109/access.2022.3176954.

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Scanning. "Retracted: Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm." Scanning 2023 (June 21, 2023): 1. http://dx.doi.org/10.1155/2023/9890786.

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30

Ferles, Christos, Yannis Papanikolaou, Stylianos P. Savaidis, and Stelios A. Mitilineos. "Deep Self-Organizing Map of Convolutional Layers for Clustering and Visualizing Image Data." Machine Learning and Knowledge Extraction 3, no. 4 (2021): 879–99. http://dx.doi.org/10.3390/make3040044.

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The self-organizing convolutional map (SOCOM) hybridizes convolutional neural networks, self-organizing maps, and gradient backpropagation optimization into a novel integrated unsupervised deep learning model. SOCOM structurally combines, architecturally stacks, and algorithmically fuses its deep/unsupervised learning components. The higher-level representations produced by its underlying convolutional deep architecture are embedded in its topologically ordered neural map output. The ensuing unsupervised clustering and visualization operations reflect the model’s degree of synergy between its

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Vallenari, Antonella, Rosanna Sordo, and Emanuela Chiosi. "Pre-main-sequence stars in the stellar association N 11 in the Large Magellanic Cloud: clustering properties." Proceedings of the International Astronomical Union 5, S266 (2009): 545–48. http://dx.doi.org/10.1017/s174392130999202x.

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AbstractMagellanic Clouds are of extreme importance to study the star-formation process in low-metallicity environments. Here, we discuss the clustering properties of the pre-main-sequence candidates and young embedded stellar objects in N 11, located in the Large Magellanic Cloud. Deep archival HST/ACS photometry is used to derive color–magnitude diagrams of the associations in N 11 and of the foreground field population. These data are complemented by archival infrared Spitzer data which allow detection of young embedded stellar objects. The spatial distribution of the pre-main-sequence cand

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32

Chen, Mulin, Bocheng Wang, and Xuelong Li. "Deep Contrastive Graph Learning with Clustering-Oriented Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11364–72. http://dx.doi.org/10.1609/aaai.v38i10.29016.

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Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. To handle the general clustering scenario without a prior graph, these models estimate an initial graph beforehand to apply GCN. Throughout the literature, we have witnessed that 1) most models focus on the initial graph while neglecting the original features. Therefore, the discriminability of the learned representation may be corrupted by a low-quality initial graph; 2) the training procedure lacks effective clustering guidance, which may lead to the incorporation of clustering-irrelevan

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Li, Yu, Aiping Liu, Taomian Mi, et al. "Striatal Subdivisions Estimated via Deep Embedded Clustering With Application to Parkinson's Disease." IEEE Journal of Biomedical and Health Informatics 25, no. 9 (2021): 3564–75. http://dx.doi.org/10.1109/jbhi.2021.3083879.

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34

Cai, Jinyu, Shiping Wang, and Wenzhong Guo. "Unsupervised embedded feature learning for deep clustering with stacked sparse auto-encoder." Expert Systems with Applications 186 (December 2021): 115729. http://dx.doi.org/10.1016/j.eswa.2021.115729.

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35

Yan, Yuanjie, Hongyan Hao, Baile Xu, Jian Zhao, and Furao Shen. "Image Clustering via Deep Embedded Dimensionality Reduction and Probability-Based Triplet Loss." IEEE Transactions on Image Processing 29 (2020): 5652–61. http://dx.doi.org/10.1109/tip.2020.2984360.

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36

Enguehard, Joseph, Peter O'Halloran, and Ali Gholipour. "Semi-Supervised Learning With Deep Embedded Clustering for Image Classification and Segmentation." IEEE Access 7 (2019): 11093–104. http://dx.doi.org/10.1109/access.2019.2891970.

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37

Kassem, Hassan, Sally El Hajjar, Fahed Abdallah, and Hichem Omrani. "Multi-view Deep Embedded Clustering: Exploring a new dimension of air pollution." Engineering Applications of Artificial Intelligence 139 (January 2025): 109509. http://dx.doi.org/10.1016/j.engappai.2024.109509.

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38

Zu, Yue. "Deep learning parallel computing and evaluation for embedded system clustering architecture processor." Design Automation for Embedded Systems 24, no. 3 (2020): 145–59. http://dx.doi.org/10.1007/s10617-020-09235-5.

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39

Zheng, Jianguo, Yongqiang Tang, Xiaoxia Peng, et al. "Indirect estimation of pediatric reference interval via density graph deep embedded clustering." Computers in Biology and Medicine 169 (February 2024): 107852. http://dx.doi.org/10.1016/j.compbiomed.2023.107852.

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40

Zhu, Donglin, Jingbin Cui, Yan Li, Zhonghong Wan, and Lei Li. "Adaptive Gaussian mixture model and convolution autoencoder clustering for unsupervised seismic waveform analysis." Interpretation 10, no. 1 (2022): T181—T193. http://dx.doi.org/10.1190/int-2021-0087.1.

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Seismic facies analysis can effectively estimate reservoir properties, and seismic waveform clustering is a useful tool for facies analysis. We have developed a deep-learning-based clustering approach called the modified deep convolutional embedded clustering with adaptive Gaussian mixture model (AGMM-MDCEC) for seismic waveform clustering. Trainable feature extraction and clustering layers in AGMM-MDCEC are implemented using neural networks. We fuse the two independent processes of feature extraction and clustering, such that the extracted features are modified simultaneously with the results

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Nalavade, Jagannath E., Chandra Sekhar Kolli, and Sanjay Nakharu Prasad Kumar. "Deep embedded clustering with matrix factorization based user rating prediction for collaborative recommendation." Multiagent and Grid Systems 19, no. 2 (2023): 169–85. http://dx.doi.org/10.3233/mgs-230039.

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Conventional recommendation techniques utilize various methods to compute the similarity among products and customers in order to identify the customer preferences. However, such conventional similarity computation techniques may produce incomplete information influenced by similarity measures in customers’ preferences, which leads to poor accuracy on recommendation. Hence, this paper introduced the novel and effective recommendation technique, namely Deep Embedded Clustering with matrix factorization (DEC with matrix factorization) for the collaborative recommendation. This approach creates t

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Ueno, Fumihiko, Ippei Takahashi, Hisashi Ohseto, et al. "Deep-embedded clustering by relevant scales and genome-wide association study in autism." PLOS One 20, no. 5 (2025): e0322698. https://doi.org/10.1371/journal.pone.0322698.

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Autism spectrum disorder (ASD) presents with heterogeneous phenotypic and genetic characteristics. Despite investigation into the molecular mechanisms underlying ASD, its etiology remains elusive. In our previous investigation within the Simons Simplex Collection (SSC), we noted increased signals through a genome-wide association study (GWAS) by clustering patients with ASD and reducing the sample size. This study seeks to validate our previous study in a different population, the Simons Foundation Powering Autism Research for Knowledge (SPARK) population, while probing further into the geneti

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Chen, Zhen, Weijie Liu, Di Zhou, Tangbin Xia, and Ershun Pan. "Inconsistency identification for Lithium-ion battery energy storage systems using deep embedded clustering." Applied Energy 388 (June 2025): 125677. https://doi.org/10.1016/j.apenergy.2025.125677.

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Wani, Aasim Ayaz. "Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions." PeerJ Computer Science 10 (August 29, 2024): e2286. http://dx.doi.org/10.7717/peerj-cs.2286.

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This survey rigorously explores contemporary clustering algorithms within the machine learning paradigm, focusing on five primary methodologies: centroid-based, hierarchical, density-based, distribution-based, and graph-based clustering. Through the lens of recent innovations such as deep embedded clustering and spectral clustering, we analyze the strengths, limitations, and the breadth of application domains—ranging from bioinformatics to social network analysis. Notably, the survey introduces novel contributions by integrating clustering techniques with dimensionality reduction and proposing

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Rajendran, Arunkumar, Nagaraj Balakrishnan, and Ajay P. "Deep embedded median clustering for routing misbehaviour and attacks detection in ad-hoc networks." Ad Hoc Networks 126 (March 2022): 102757. http://dx.doi.org/10.1016/j.adhoc.2021.102757.

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46

Feng, Xiao, and Yongdong Xu. "Multi-hop Information-based Graph Convolutional Network for Clustering." Journal of Physics: Conference Series 2555, no. 1 (2023): 012012. http://dx.doi.org/10.1088/1742-6596/2555/1/012012.

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Abstract Clustering is an essential and demanding undertaking in data analysis. The combination of traditional neural networks and graph convolutional networks (GCNs) has been extensively discussed in clustering tasks, in which the deep clustering methods learn useful content information and the graph convolutional networks mine the structured neighboring information in the graph data. However, the existing works equally consider the importance of different features to clustering and only focus on the nearest neighboring information in the structured features, ignoring the features of the long

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Pu, Jingyu, Chenhang Cui, Xinyue Chen, et al. "Adaptive Feature Imputation with Latent Graph for Deep Incomplete Multi-View Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14633–41. http://dx.doi.org/10.1609/aaai.v38i13.29380.

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In recent years, incomplete multi-view clustering (IMVC), which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Previous IMVC methods suffer from the following issues: (1) the inaccurate imputation for missing data, which leads to suboptimal clustering performance, and (2) most existing IMVC models merely consider the explicit presence of graph structure in data, ignoring the fact that latent graphs of different views also provide valuable information for the clustering task. To overcome such challenges, we present a novel method

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Rumman, Mahfujul Islam, Naoaki Ono, Kenoki Ohuchida, MD Altaf-Ul-Amin, Ming Huang, and Shigehiko Kanaya. "Information maximization-based clustering of histopathology images using deep learning." PLOS Digital Health 2, no. 12 (2023): e0000391. http://dx.doi.org/10.1371/journal.pdig.0000391.

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Pancreatic cancer is one of the most adverse diseases and it is very difficult to treat because the cancer cells formed in the pancreas intertwine themselves with nearby blood vessels and connective tissue. Hence, the surgical procedure of treatment becomes complicated and it does not always lead to a cure. Histopathological diagnosis is the usual approach for cancer diagnosis. However, the pancreas remains so deep inside the body that experts sometimes struggle to detect cancer in it. Computer-aided diagnosis can come to the aid of pathologists in this scenario. It assists experts by supporti

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Shi, Jiahao, Zhijun Xie, Li Dong, Xianliang Jiang, and Xing Jin. "IDS-DEC: A novel intrusion detection for CAN bus traffic based on deep embedded clustering." Vehicular Communications 49 (October 2024): 100830. http://dx.doi.org/10.1016/j.vehcom.2024.100830.

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Et. al., Vaishali Fulmal,. "The Implementation of Question Answer System Using Deep Learning." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 176–82. http://dx.doi.org/10.17762/turcomat.v12i1s.1604.

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Abstract:

Question-answer systems are referred to as advanced systems that can be used to provide answers to the questions which are asked by the user. The typical problem in natural language processing is automatic question-answering. The question-answering is aiming at designing systems that can automatically answer a question, in the same way as a human can find answers to questions. Community question answering (CQA) services are becoming popular over the past few years. It allows the members of the community to post as well as answer the questions. It helps users to get information from a comprehen

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