Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Zhang, Caizhi; Huang, Weifeng; Niu, Tong; Liu, Zhitao; Li, Guofa; Cao, Dongpu

doi:10.1007/s42154-022-00205-0

Cited by 14 publications

(10 citation statements)

References 181 publications

(234 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Cluster analysis has diverse applications, including market segmentation, image processing, and pattern recognition. It provides insights into the natural groupings within data ( Zhang et al, 2023 ). 8.…”

Section: The Significance Of Qsarmentioning

confidence: 99%

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Bhowmik,

Kant,

Manaithiya

et al. 2023

Front. Pharmacol.

View full text Add to dashboard Cite

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

show abstract

Section: The Significance Of Qsarmentioning

confidence: 99%

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Bhowmik,

Kant,

Manaithiya

et al. 2023

Front. Pharmacol.

View full text Add to dashboard Cite

show abstract

“…The hierarchical clustering method aims to create clusters with members that have the same characteristics in one cluster and different characteristics between clusters. This concept requires the cluster creation process to consider the distance between objects [44]. The formation of multilevel clusters helps present information on the potential of livestock areas in a tiered manner, starting from the cluster with the lowest livestock population to the cluster consisting of areas with the highest livestock population.…”

Section: Analysis Of the Potential Of The Livestock Areas Based On Le...mentioning

confidence: 99%

Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm

Sumaryanti,

Nurcholis,

Muchlis

2024

MMEP

View full text Add to dashboard Cite

Merauke Regency is the largest area in Papua Province and includes potential in the livestock sector. Regional potential analysis based on leading livestock population aims to provide regional information based on livestock sector potential, which can be used as information in policy making in government programs. A hybrid algorithm combining LQ and complete linkage can map potential livestock areas based on leading populations. The results of the LQ analysis show that there are six leading types of livestock: cows, buffaloes, horses, kampong chickens, laying chickens, and ducks. The leading livestock types can be used as a source of information regarding regional potential in the livestock business and classified into four clusters. The clustering of regional potential using a complete linkage hierarchical algorithm with a livestock population dataset by conducting four trials and yielding information that Semangga and Tanah Miring sub-districts have potential in the livestock sector. The proposed method used a hybrid approach to analyze the potential of livestock areas in Merauke and determine the leading types of livestock in the area to classify areas in each cluster and map the potential of livestock areas using GIS techniques.

show abstract

“…A dataset is decomposed into several cells using a hierarchical structure. Then, the mean, variance, minimum, and maximum of each cell are computed 46 . In this article, K3CM is proposed to identify the most cohesive clusters, mentioned in the same section in Figure 4.…”

Section: Theoretical Developmentmentioning

confidence: 99%

Cloud migration framework clustering method for social decision support in modernizing the legacy system

Aslam,

Rahim,

Watada

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Cloud solutions accelerate the large‐scale acceptance of IoT projects. By diminishing the need for maintaining on‐premises infrastructure, the cloud has enabled corporations to surpass the traditional applications of IoT (e.g., in‐home appliances) and opened the doors for large‐scale deployment of IoT applications on the cloud. However, shifting legacy systems to the cloud environment can be considerably difficult. Accordingly, this article proposes a method that may support organizations in deciding to modernize their legacy systems. The main concept of this study is to discuss the modernization strategies in detail and to support organizations in selecting the most accurate and appropriate cloud migration strategy, based on their requirements of the legacy system. This article introduces a novel research process, called the K‐means cosine cloud clustering method (K3CM). K3CM is a statistical knowledge‐based method for identifying and clustering the most relevant and similar cloud migration strategies. The quality of a cluster is evaluated by measuring intra‐cohesiveness. Simulation experiments statistically analyzed, evaluated, and verified the quality of K3CM clusters. Correspondence analysis explored the similarity and relationship among cloud migration frameworks and validated the proposed technique. The statistical and simulation results of this study focus on the analytics and decision support system implementation that provides a reliable, valid, and robust clustering method for modernizing the legacy system.

show abstract

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Cited by 14 publications

References 181 publications

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm

Cloud migration framework clustering method for social decision support in modernizing the legacy system

Contact Info

Product

Resources

About