Hierarchical Clustering Based on Dendrogram in Sustainable Transportation Systems

Sangaiah, Arun Kumar; Javadpour, Amir; Ja’fari, Forough; Zhang, Weizhe; Khaniabadi, Shadi Mahmoodi

doi:10.1109/tits.2022.3222789

Cited by 26 publications

(3 citation statements)

References 37 publications

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“…The algorithm can pretrain the network, which is implemented by introducing feature-based pruning strategy, so as to realize the compression of the network to adjust the parameters and reduce the complexity and the overall training time (Dubey et al 2019). The conjugate gradient secant line correction is designed in the literature, which can fully reduce the convergence time of the network, make the global classification effect of the system more rapid and accurate, and complete the adjustment of the target data through the parallel system acceleration (Mukilan and Semunigus 2021;Zheng et al 2014;Sangaiah et al 2022). The literature introduces firefly optimization algorithm to search and share information, initializes parameters based on IFAS algorithm and realizes parallel training for DCNN to improve the optimization ability of the network (Kim and Choi 2021).…”

Section: Relevant Workmentioning

confidence: 99%

RETRACTED ARTICLE: Application of big data classification effects based on neural network in video English course and relevant optimization suggestions

Suyun

Suying²

2023

Soft Comput

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Due to the improvement of Internet technology and information technology, more and more students hope to learn and consolidate knowledge through video in the classroom. Teachers are more accustomed to using video in the classroom to improve and improve their teaching quality. In the current English class, teachers and students are more accustomed to using video English for teaching. English teaching videos are informative, intuitive and efficient. Through video teaching, we can make the classroom atmosphere more interesting, thus simplifying complex problems. In this context, this paper analyzes how neural networks can improve the application effect of English video courses in the context of big data, optimizes the PDCNO algorithm by using the neural network principle, and then discusses the impact of the optimized PDCNO algorithm on classification and system performance. This improves the accuracy of English video, reduces the execution time of the algorithm and reduces the memory occupation. Compared with ordinary video, the training time required under the same training parameters is shorter, and the convergence speed of the model itself will be faster. From the students' attitude towards video teaching, we can see that students prefer video English teaching, which also reflects the effectiveness of neural network big data in English video teaching. This paper introduces the neural network and big data technology into the video English course to improve the teaching effectiveness.

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Section: Relevant Workmentioning

confidence: 99%

RETRACTED ARTICLE: Application of big data classification effects based on neural network in video English course and relevant optimization suggestions

Suyun

Suying²

2023

Soft Comput

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“…Clustering analysis is a type of unsupervised learning in machine learning (Sangaiah et al. 2023 ). Clustering involves partitioning a data set into distinct groups or clusters founded on specific criteria, such that objects within the same cluster exhibit higher similarity to one another, whereas objects assigned to different clusters exhibit a lower degree of similarity.…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Three-way decisions in generalized intuitionistic fuzzy environments: survey and challenges

Ding,

Zhang,

et al. 2024

Artif Intell Rev

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Enhancing decision-making under risks is crucial in various fields, and three-way decision (3WD) methods have been extensively utilized and proven to be effective in numerous scenarios. However, traditional methods may not be sufficient when addressing intricate decision-making scenarios characterized by uncertain and ambiguous information. In response to this challenge, the generalized intuitionistic fuzzy set (IFS) theory extends the conventional fuzzy set theory by introducing two pivotal concepts, i.e., membership degrees and non-membership degrees. These concepts offer a more comprehensive means of portraying the relationship between elements and fuzzy concepts, thereby boosting the ability to model complex problems. The generalized IFS theory brings about heightened flexibility and precision in problem-solving, allowing for a more thorough and accurate description of intricate phenomena. Consequently, the generalized IFS theory emerges as a more refined tool for articulating fuzzy phenomena. The paper offers a thorough review of the research advancements made in 3WD methods within the context of generalized intuitionistic fuzzy (IF) environments. First, the paper summarizes fundamental aspects of 3WD methods and the IFS theory. Second, the paper discusses the latest development trends, including the application of these methods in new fields and the development of new hybrid methods. Furthermore, the paper analyzes the strengths and weaknesses of research methods employed in recent years. While these methods have yielded impressive outcomes in decision-making, there are still some limitations and challenges that need to be addressed. Finally, the paper proposes key challenges and future research directions. Overall, the paper offers a comprehensive and insightful review of the latest research progress on 3WD methods in generalized IF environments, which can provide guidance for scholars and engineers in the intelligent decision-making field with situations characterized by various uncertainties.

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“…Hierarchical clustering: It is recognized as a robust technique in data analysis and clustering, orchestrating the arrangement of data into a hierarchical tree or dendrogram structure based on similarities among data points [40]. This method systematically builds clusters by iteratively merging or dividing existing clusters until a comprehensive hierarchy is established.…”

mentioning

confidence: 99%

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

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This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations.

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Hierarchical Clustering Based on Dendrogram in Sustainable Transportation Systems

Cited by 26 publications

References 37 publications

RETRACTED ARTICLE: Application of big data classification effects based on neural network in video English course and relevant optimization suggestions

RETRACTED ARTICLE: Application of big data classification effects based on neural network in video English course and relevant optimization suggestions

Three-way decisions in generalized intuitionistic fuzzy environments: survey and challenges

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

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