Article
Computer Science, Information Systems
Matthew Coulson, Christos Ferles, Simon Winberg, Kevin J. Naidoo
Summary: The Growing Hierarchical Self-Organising Representation Map (GHSORM) is a model that combines denoising autoencoder and Growing Hierarchical Self-Organising Map algorithms to represent datasets and cluster input data. It shows the ability to subgroup clusters that cannot be fully separated by a single SOM. The model is applied to clustering handwritten digits and complex digital gene expression data, outperforming linear methods and its constituent algorithms.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Kassem Bagher, Ibrahim Khalil, Abdulatif Alabdulatif, Mohammed Atiquzzaman
Summary: With the rapid growth of IoT devices and data, traditional systems struggle to meet real-time and performance requirements. Although cloud computing provides vast computing resources, it may not always be suitable for real-time analytics. Mobile edge computing is emerging as a new paradigm to run demanding applications on edge devices for real-time and low latency needs. EdgeSOM, a distributed MEC-based framework, effectively reduces network traffic and provides accurate analysis results.
COMPUTER COMMUNICATIONS
(2021)
Article
Geosciences, Multidisciplinary
Hamid Geranian, Emmanuel John M. Carranza
Summary: The sampling of stream sediments in the central part of the Lut-Block in eastern Iran has revealed the potential for poly-metallic mineralization. Hierarchical clustering analysis grouped the analyzed elements into four clusters, with strategic metals identified in the first and third clusters. The clustering algorithms used successfully determined areas with mineralization potential, with the BIRCH algorithm showing relative superiority in comparison to others.
NATURAL RESOURCES RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Yong Mi, Jian Dai, Zhenwen Ren, Xiaojian You, Yanlong Wang
Summary: This study proposes a novel multi-view clustering method that exploits the hierarchical attribute information in multi-view data to improve clustering performance. Experimental results demonstrate the advancement and effectiveness of the proposed method.
COGNITIVE COMPUTATION
(2023)
Article
Biochemical Research Methods
Xiaoqing Cheng, Chang Yan, Hao Jiang, Yushan Qiu
Summary: Advances in single-cell RNA sequencing technology have allowed for unbiased and high-throughput analysis of individual cells, enhancing the study of cellular heterogeneity. However, the data generated by this method are often sparse and noisy due to dropouts, impacting downstream analyses. In this study, a flexible and accurate two-stage algorithm called scHOIS was proposed, which effectively addressed both dropout and clustering problems and outperformed other state-of-the-art methods in determining cellular differences.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Chemistry, Analytical
Zerun Li, Qinglin Wang, Yufei Zhu, Zuocheng Xing
Summary: This paper proposes a hierarchical self-organizing map model based on high-order cumulants and amplitude moment features for automatic modulation classification, showing advantages in classification accuracy and computational resource consumption.
Article
Computer Science, Artificial Intelligence
Mingwen Shao, Junhui Dai, Ran Wang, Jiandong Kuang, Wangmeng Zuo
Summary: This paper introduces a novel filter pruning method, CSHE, for compression of convolutional neural networks by combining cluster similarity and large eigenvalues of feature maps to reduce model parameters and calculations while preserving representative information. Experimental results show that the pruned sparse deep network achieved using the CSHE method maintains almost the same accuracy as the reference network in classification tasks on CIFAR-10 and ImageNet ILSVRC-12.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Biochemistry & Molecular Biology
Shanghua Liu, Yuchao Liang, Jinzhao Li, Siqi Yang, Ming Liu, Chengfang Liu, Dezhi Yang, Yongchun Zuo
Summary: A copper ion-bound protein classifier, RPCIBP, was developed in this study by integrating reduced amino acid composition into position-specific scoring matrix. The classifier accurately predicts copper ion-binding proteins, facilitating further structural and functional studies, and aiding mechanism exploration and target drug development.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Computer Science, Artificial Intelligence
Liangshao Hou, Delin Chu, Li-Zhi Liao
Summary: This article studies symmetric nonnegative matrix factorization (SNMF), which is a powerful tool in data mining for dimension reduction and clustering. The main contributions of the work include: (i) deriving a new descent direction and strategy for choosing the step size for rank-one SNMF; (ii) developing a parameter-free progressive hierarchical alternating least squares (PHALS) method for SNMF, which updates the variables column by column and solves rank-one SNMF subproblems for each column; and (iii) proving the convergence to the Karush-Kuhn-Tucker (KKT) point set for PHALS. Experimental results demonstrate the effectiveness and efficiency of the proposed method, which outperforms several state-of-the-art SNMF methods in terms of computational accuracy, optimality gap, and CPU time.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Yao Peng, Bin Cui, Hujun Yin, Yonghong Zhang, Peijun Du
Summary: This paper proposes a SAR change detection network based on visual saliency and multi-hierarchical fuzzy clustering. By constructing a difference map and integrating hierarchical clustering, it can accurately extract difference features and identify potential changed regions for sample selection. The class-balanced adaptive focal loss is incorporated into the network training to obtain accurate predictions.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Zhege Liu, Junxing Cao, Yujia Lu, Peng Zhou, Jun Hu
Summary: This study introduces a seismic facies analysis method that applies dynamic time warp (DTW) distance to self-organizing map (SOM) to adapt to varying thickness of target layers, and uses a hierarchical clustering strategy to reduce computational cost. By combining HSV coloring and hierarchical mapping, the lateral distribution of stratigraphic can be quickly displayed.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Moshe Unger, Alexander Tuzhilin
Summary: This paper proposes a hierarchical representation of latent contextual information and introduces an algorithm to convert unstructured latent contextual information into structured hierarchical representations. It also presents two general context-aware recommendation algorithms that utilize structured and unstructured latent contextual information. Experimental results show that using hierarchical latent contextual representations leads to significantly better recommendations for datasets with high- and medium-dimensional contexts.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
Yerim Jung, Nur Suriza Syazwany, Sujeong Kim, Sang-Chul Lee
Summary: In this study, we propose a method for fine-grained visual classification by inserting an attention module that utilizes covariance characteristics. By focusing on salient areas using the covariance matrix, our method outperforms state-of-the-art models on three datasets, CUB-200-2011, Stanford Cars, and FGVC-Aircraft, with improvements of 0.4%, 1.1%, and 1.4% respectively. Ablation studies are also conducted to demonstrate the impact of suggested strategies on classification performance.
Article
Biology
Otaviano Martins Monteiro, Sandro Renato Dias, Thiago de Souza Rodrigues
Summary: This paper introduces a reduced distance matrix for protein interactions clustering by using two alpha carbon atoms as centroids, achieving high precision and optimal computational performance.
BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY
(2021)
Article
Engineering, Multidisciplinary
Neha Goyal, Kapil Gupta, Nitin Kumar
Summary: This article introduces a hierarchical framework for multiclass classification that overcomes the limitations of existing methods. The proposed approach groups classes with similar traits and achieves efficient and accurate plant recognition with low computational cost.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
Article
Automation & Control Systems
Sourav Rakshit, Srinivas Akella
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2015)
Article
Engineering, Mechanical
Sourav Rakshit, Anindya Chatterjee
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2015)
Article
Biology
Sourav Rakshit, G. K. Ananthasuresh
JOURNAL OF THEORETICAL BIOLOGY
(2010)
Proceedings Paper
Automation & Control Systems
Sourav Rakshit, Srinivas Akella
2016 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
(2016)
Article
Computer Science, Interdisciplinary Applications
Sourav Rakshit, G. K. Ananthasuresh
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2008)
Article
Biology
Iain Hunter, Raz Leib
Summary: Natural movement is related to health, but it is difficult to measure. Existing methods cannot capture the full range of natural movement. Comparing movement across different species helps identify common biomechanical and computational principles. Developing a system to quantify movement in freely moving animals in natural environments and relating it to life quality is crucial. This study proposes a theoretical framework based on movement ability and validates it in Drosophila.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Andy Gardner
Summary: Fisher's geometric model is a useful tool for predicting key properties of Darwinian adaptation, and here it is applied to predict differences between the evolution of altruistic versus nonsocial phenotypes. The results suggest that the effect size maximizing probability of fixation is smaller in the context of altruism and larger in the context of nonsocial phenotypes, leading to lower overall probability of fixation for altruism and higher overall probability of fixation for nonsocial phenotypes.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Thomas F. Pak, Joe Pitt-Francis, Ruth E. Baker
Summary: Cell competition is a process where cells interact in multicellular organisms to determine a winner or loser status, with loser cells being eliminated through programmed cell death. The winner cells then populate the tissue. The outcome of cell competition is context-dependent, as the same cell type can win or lose depending on the competing cell type. This paper proposes a mathematical framework to study the emergence of winner or loser status, highlighting the role of active cell death and identifying the factors that drive cell competition in a cell-based modeling context.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Haruto Tomizuka, Yuuya Tachiki
Summary: Batesian mimicry is a strategy in which palatable prey species resemble unpalatable prey species to avoid predation. The evolution of this mimicry plays a crucial role in protecting the unpalatable species from extinction.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Jason W. Olejarz, Martin A. Nowak
Summary: Gene drive technology shows potential for population control, but its release may have unpredictable consequences. The study suggests that the failure of suppression is a natural outcome, and there are complex dynamics among wild populations.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Hamid Ravaee, Mohammad Hossein Manshaei, Mehran Safayani, Javad Salimi Sartakhti
Summary: Gene expression analysis is valuable for cancer classification and phenotype identification. IP3G, based on Generative Adversarial Networks, enhances gene expression data and discovers phenotypes in an unsupervised manner. By converting gene expression profiles into images and utilizing IP3G, new phenotype profiles can be generated, improving classification accuracy.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Beatrix Rahnsch, Leila Taghizadeh
Summary: This study forecasts the evolution of the COVID-19 pandemic in Germany using a network-based inference method and compares it with other approaches. The results show that the network-inference based approach outperforms other methods in short-to mid-term predictions, even with limited information about the new disease. Furthermore, predictions based on the estimation of the reproduction number in Germany can yield more reliable results with increasing data availability, but still cannot surpass the network-inference based algorithm.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Rongsheng Huang, Qiaojun Situ, Jinzhi Lei
Summary: Maintaining tissue homeostasis requires appropriate regulation of stem cell differentiation. Random inheritance of epigenetic states plays a pivotal role in stem cell differentiation. This computational model provides valuable insights into the intricate mechanism governing stem cell differentiation and cell reprogramming, offering a promising path for enhancing the field of regenerative medicine.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Patrick Vincent N. Lubenia, Eduardo R. Mendoza, Angelyn R. Lao
Summary: This study compares insulin signaling in healthy and type 2 diabetes states using reaction network analysis. The results show similarities and differences between the two conditions, providing insights into the mechanisms of insulin resistance, including the involvement of other complexes, less restrictive interplay between species, and loss of concentration robustness in GLUT4.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Nuverah Mohsin, Heiko Enderling, Renee Brady-Nicholls, Mohammad U. Zahid
Summary: Mathematical modeling is crucial in understanding radiobiology and designing treatment approaches in radiotherapy for cancer. This study compares three tumor volume dynamics models and analyzes the implications of model selection. A new metric, the point of maximum reduction of tumor volume (MRV), is introduced to quantify the impact of radiotherapy. The results emphasize the importance of caution in selecting models of response to radiotherapy due to the artifacts imposed by each model.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Armindo Salvador
Summary: Michael Savageau's Biochemical Systems Analysis papers have had a significant impact on Systems Biology, generating core concepts and tools. This article provides a brief summary of these papers and discusses the most relevant developments in Biochemical Systems Theory since their publication.
JOURNAL OF THEORETICAL BIOLOGY
(2024)