Fourier Transform Infrared Spectroscopy (FTIR) is a relatively new technique that has been frequently applied now a days in cancer pathology including breast cancer. The long term aim of this work is to develop novel ...
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Fourier Transform Infrared Spectroscopy (FTIR) is a relatively new technique that has been frequently applied now a days in cancer pathology including breast cancer. The long term aim of this work is to develop novel techniques using machine learning methods for the analysis of FTIR data sets. This paper presents the preliminary work with a case study of a FTIR data set of breast cancer with two commonly used clustering algorithms of fuzzy c-means and k-means to differentiate between different cancer grades. We also discuss the complexities involved in the analysis of spectral data sets and need to find new methods. Future work will involve efforts towards development of a novel frame work with advanced machine learning methods to extract valuable information from complex spectral data sets.
We review main graph clustering algorithms which are MST-based, Shared Nearest Neighbor and Edge-Betweenness algorithms and show novel algebraic graph implementations using Python. We compare them using randomly gener...
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ISBN:
(纸本)9781665407601
We review main graph clustering algorithms which are MST-based, Shared Nearest Neighbor and Edge-Betweenness algorithms and show novel algebraic graph implementations using Python. We compare them using randomly generated scale-free graphs and provide pointers for parallel processing
As one of the most widely investigated topology control mechanisms of wireless sensor networks (WSNs), the clustering algorithm provides energy efficient communications by reducing transmission overhead and enhancing ...
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ISBN:
(纸本)9781424445608
As one of the most widely investigated topology control mechanisms of wireless sensor networks (WSNs), the clustering algorithm provides energy efficient communications by reducing transmission overhead and enhancing transmission reliability. Through the previous forms of noncooperative games, the behavior of each sensor node (SN) is individual in WSNs; accordingly, it engenders uneven distribution of residual energy across SNs and expedites network partition. To balance energy consumption of SNs and increase network lifetime and stability, a cooperative game theoretic model of clustering algorithms is provided for assigning feasible allocations of energy cost. Moreover, from the outcome of this model, we propose and analyze a cooperative clustering approach for global optimization with the capacity of sensing data transmission and energy efficiency. The key idea is that SNs should trade off individual cost with network-wide cost. In the algorithm, we develop conditions to form coalitions considering residual energy, transmission distance and number of SNs in a cluster adapting to various WSNs. Furthermore, we present performance evaluation and comparison of the existing clustering algorithms with our approach quantitatively with respect to network lifetime, data transmission capacity and energy efficiency. Comparing with other approaches through the simulation, our scheme can surely guarantee to prolong network life-time and improve data transmission capacity up to 5.8% and 35.9%, respectively.
For a decade swarm Intelligence, an artificial intelligence discipline, is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviors of social insects and other a...
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For a decade swarm Intelligence, an artificial intelligence discipline, is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviors of social insects and other animal societies. Swarm Intelligence is a successful paradigm for the algorithm with complex problems. This paper focuses on the procedure of most successful methods of optimization techniques inspired by Swarm Intelligence: Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). This paper also gives a comparative analysis of PSO and ACO for data clustering.
Data mining is the method which is useful for extracting useful information and data is extorted, but the classical data mining approaches cannot be directly used for big data due to their absolute complexity. The dat...
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ISBN:
(纸本)9781509011124
Data mining is the method which is useful for extracting useful information and data is extorted, but the classical data mining approaches cannot be directly used for big data due to their absolute complexity. The data that is been formed by numerous scientific applications and incorporated environment has grown rapidly not only in size but also in variety in recent era. The data collected is of very large amount and there is difficulty in collecting and assessing big data. clustering algorithms have developed as a powerful meta learning tool which can precisely analyze the volume of data produced by modern applications. The main goal of clustering is to categorize data into clusters such that objects are grouped in the same cluster when they are “similar” according to similarities, traits and behavior. The most commonly used algorithm in clustering are partitioning, hierarchical, grid based, density based, and model based algorithms. A review of clustering and its different techniques in data mining is done considering the criteria's for big data. Where most commonly used and effective algorithms like K-Means, FCM, BIRCH, CLIQUE algorithms are studied and compared on big data perspective.
In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and ...
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Two novel measurement-based, quantum clustering algorithms are proposed basedon quantum parallelism and entanglement. The first algorithm follows a divisiveapproach. The second algorithm is based on unsharp measuremen...
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Wireless Sensor Networks (WSNs) are pivotal in modern applications, yet challenges such as energy efficiency and network lifetime remain critical. This paper proposes a Dynamic Energy-Efficient clustering Algorithm (D...
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ISBN:
(数字)9798331529635
ISBN:
(纸本)9798331529642
Wireless Sensor Networks (WSNs) are pivotal in modern applications, yet challenges such as energy efficiency and network lifetime remain critical. This paper proposes a Dynamic Energy-Efficient clustering Algorithm (DEECA) utilizing hybrid metaheuristics combining Egret Swarm Optimization and Genetic algorithms. The proposed method dynamically selects cluster heads and optimizes clustering based on node energy, distance, and traffic conditions. Simulations were conducted on diverse WSN scenarios to evaluate energy consumption, network lifetime, packet delivery ratio, and throughput. Results demonstrate that DEECA outperforms traditional algorithms such as LEACH, HEED, and PSO by achieving a 20% increase in network lifetime and a 15% improvement in packet delivery ratio. Visual analyses include energy consumption trends, coverage heatmaps, and multi-metric radar plots. The findings validate DEECA's capability to enhance energy efficiency and scalability in WSNs, offering a robust framework for real-world deployments in resource-constrained environments.
With the development of information technology and the arrival of the era of big data, China's logistics industry has been developing rapidly, especially in modern logistics management information system, In order...
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ISBN:
(数字)9781728152561
ISBN:
(纸本)9781728152578
With the development of information technology and the arrival of the era of big data, China's logistics industry has been developing rapidly, especially in modern logistics management information system, In order to make full use of all kinds of resources and reduce logistics cost, this project extensively applies various information technologies related to mechanical learning. Taking the logistics distribution system of YifengWeiye Group as the research object, this paper puts forward the theory of K-means clustering algorithm and mileage-saving algorithm, adopts the idea of spatial clustering analysis regionalization, applies Python technology to process the special attribute data of transportation contained in the process of logistics distribution, and applies entity recognition technology based on attribute value partitioning algorithm and K-means clustering algorithm to realize the data area.
Context. The census of open clusters in the Milky Way is in a never-before seen state of flux. Recent works have reported hundreds of new open clusters thanks to the incredible astrometric quality of the Gaia satellit...
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