Effective municipal solid waste management (MSWM) is essential for sustainable urban development, significantly impacting environmental health, economic efficiency, and social well-being. It aligns with Sustainable De...
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clustering algorithms have been used in different areas of knowledge with different goals such as noise detection, outliers, and descriptive tasks. The adsorption kinetics is a curve that describes the rate retention ...
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clustering algorithms have been used in different areas of knowledge with different goals such as noise detection, outliers, and descriptive tasks. The adsorption kinetics is a curve that describes the rate retention to the adsorbate on the adsorbent at time, which is represents as a two-dimensional graph. In this paper, we present a computational application to determine the experimental conditions that influence when equilibrium point is reached into adsorption kinetics curve using the K-means clustering algorithm and, Parallel Coordinates concept, in order to prove our method we used adsorption kinetic curves Q-PVA . Results obtained were compared with two designs of experiments (three-stage nested design and hierarchical design with crossed factors).
Based on the high dynamic of Sentiment Analysis (SA) topic among the latest publication landscape, the current review attempts to fill a research gap. Consequently, the paper elaborates on the most recent body of lite...
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Based on the high dynamic of Sentiment Analysis (SA) topic among the latest publication landscape, the current review attempts to fill a research gap. Consequently, the paper elaborates on the most recent body of literature to extract and analyze the papers that elaborate on the clustering algorithms applied on social media datasets for performing SA. The current rapid review attempts to answer the research questions by analyzing a pool of 46 articles published in between Dec 2020 - Dec 2023. The manuscripts were thoroughly selected from Scopus (Sco) and WebOf-Science (WoS) databases and, after filtering the initial pool of 164 articles, the final results (46) were extracted and read in full.
In this work, we develop a new method of setting the input to reservoir and reservoir to reservoir weights in echo state machines. We use a clustering technique which we have previously developed as a pre-processing s...
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Hierarchical addresses are fundamental to the scalability of Internet renting. The recent explosive growth of the inter-net has strained the initial two-level hierarchy and led to the development of more flexible divi...
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ISBN:
(纸本)0818690143
Hierarchical addresses are fundamental to the scalability of Internet renting. The recent explosive growth of the inter-net has strained the initial two-level hierarchy and led to the development of more flexible divisions between levels (CIDR) and larger addresses (IP nu 6). Equally important are algorithms and protocols to systematically assign addresses with appropriate hierarchical structure to allow route aggregation. This paper describes and analyzes two algorithms for clustering network nodes into a multi-level address hierarchy We evaluate the resulting address assignment with respect to routing table size path length and concentration of traffic. We also explicitly recognize the need for "robustness" or "slack" in the assignment to accommodate future changes in topology Our evaluation includes both single- and multi-domain topologies.
In our time people and devices constantly generate data. User activity generates data about needs and preferences as well as the quality of their experiences in different ways: i. e. streaming a video, looking at the ...
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ISBN:
(纸本)9783319624013;9783319624006
In our time people and devices constantly generate data. User activity generates data about needs and preferences as well as the quality of their experiences in different ways: i. e. streaming a video, looking at the news, searching for a restaurant or a an hotel, playing a game with others, making purchases, driving a car. Even when people put their devices in their pockets, the network is generating location and other data that keeps services running and ready to use. This rapid developments in the availability and access to data and in particular spatially referenced data in a different areas, has induced the need for better analysis techniques to understand the various phenomena. Spatial clustering algorithms, which groups similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this paper is to analyze the performance of three different clustering algorithms i. e. the Density-Based Spatial clustering of Applications with Noise algorithm (DBSCAN), the Fast Search by Density Peak (FSDP) algorithm and the classic K-means algorithm (K-Means) as regards the analysis of spatial big data. We propose a modification of the FSDP algorithm in order to improve its efficiency in large databases. The applications concern both synthetic data sets and satellite images.
Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. When studying the workings of a biological ce...
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Tacit assumptions have been made about the suitability of two primary data-driven deconvolution algorithms concerning large (10,000+) data sets captured using nanoindentation grid array measurements, including (1) pro...
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ISBN:
(纸本)9783030923815;9783030923808
Tacit assumptions have been made about the suitability of two primary data-driven deconvolution algorithms concerning large (10,000+) data sets captured using nanoindentation grid array measurements, including (1) probability density function determination and (2) k-means clustering and deconvolution. Recent works have found k-means clustering and probability density function fitting and deconvolution to be applicable;however, little forethought was afforded to algorithmic compatibility for nanoindentation mapping data. The present work highlights how said approaches can be applied, their limitations, the need for data pre-processing before clustering and statistical analysis, and alternatively appropriate clustering algorithms. Equally spaced apart indents (and therefore measured properties) at each recorded nanoindentation location are collectively processed via high-resolution mechanical property mapping algorithms. clustering and mapping algorithms also explored include k-medoids, agglomerative clustering, spectral clustering, BIRCH clustering, OPTICS clustering, and DBSCAN clustering. Methods for ranking the performance of said clustering approaches against one another are also considered herein.
Wireless sensor networks (WSNs) have many applications in military services, health centers, industries as well as home surveillances. In such networks energy efficiency of nodes and life time of network are main conc...
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ISBN:
(纸本)9789380544168
Wireless sensor networks (WSNs) have many applications in military services, health centers, industries as well as home surveillances. In such networks energy efficiency of nodes and life time of network are main concerns. Different clustering approaches are used to efficiently optimize the energy of sensor nodes. clustering also improves the scalability of sensor nodes. We reviewed different approaches of clustering which are centralized, distributed and hybrid used in Sensor Networks. Recently there have been many researches on developing algorithms using equal and unequal clustering techniques. These techniques use residual energy of nodes and distance to base station as parameters for selecting cluster heads. This paper aims to examine various distributed and hybrid clustering algorithm as on date reported by different authors actively working in this area. We also briefly discuss the operations of these algorithms, as well as compare on the basis of various clustering attributes.
The complexity and size of digital circuits have grown exponentially, and today's circuits can contain millions of logic elements. clustering algorithms have become popular due to their ability to reduce circuit s...
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ISBN:
(纸本)9781424413416
The complexity and size of digital circuits have grown exponentially, and today's circuits can contain millions of logic elements. clustering algorithms have become popular due to their ability to reduce circuit sizes. clustering enables circuit layout design problems, such as partitioning and placement to be performed faster and with higher quality. In this paper, current clustering algorithms and the effect of these algorithms on industry test benchmarks are studied. It is revealed that the score-based clustering algorithms are the most successful clustering techniques for circuit layout design and deserve more future research investigations.
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