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3rd International Symposium on Electrical and Electronics Engineering (ISEEE)

作者： Moldovanu, Simona Moraru, Luminita Fac Sci Galati Romania

ISBN: (纸本)9781424484096

This study highlights the connection between fractal dimension of small tumors of ultrasound breast and extraction knowledge from data using k-mean algorithm which provides the capability to discover new and meaningful information by using existing data. Fractal dimension was calculated by box count method which was applied on a breast ultrasound filtrated with five different filters. The study emphasizes the usefulness of fractal analysis and clustering fractal dimension in medicine.

关键词： Fractal dimension k-mean algorithm

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Unsupervised Learning Approach for Clustering Source Code based on Functionalities

Unsupervised Learning Approach for Clustering Source Code ba...

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International Conference on Decision Aid Sciences and Application (DASA)

作者： Ifham, Mohamed kumara, B. T. G. S. kuhaneswaran, Banujan Sabaragamuwa Univ Sri Lanka Dept Comp & Informat Syst Belihuloya Sri Lanka

ISBN: (纸本)9781665416344

The source codes of a software system are one of the most valuable resources in software development. A developer spends more time and money on development if they start coding from fresh for each similar functionality project. Topic modelling is crucial for software reuse since it can be difficult for developers to remove obsolete source code from large software systems with a lot of code. Topic modelling techniques has been used to evaluate and model source codes in several ways. Several studies on this domain have been used to extract topics from source codes using various statistical approaches and methodologies. These topic extraction methods are interconnected, and if software best practices aren't followed in older systems, it may ensue unreliability of the outcome. In response to these findings, the author conducted a study in which he extracted source code using a java parser library and predicted the source code functionality name using an unsupervised learning approach such as k-mean algorithm and Expectation Maximization (EM) clustering approach. This is the first effort to develop a clustering model to predict the semantic function name of source code using an unsupervised learning approach and compare the algorithms to get the optimal model.

关键词： k-mean algorithm Parser library Unsupervised learning Software reuse Expectation maximization clustering

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A graph-based method for blood vessel segmentation of retinal images 9th

A graph-based method for blood vessel segmentation of retina...

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9th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)

作者： Zhang, J. D. Jiang, W. H. Zhang, C. X. Cui, Y. J. Shenzhen Inst Informat Technol Dept Elect & Commun Shenzhen Peoples R China

ISBN: (纸本)9781315683010;9781138027848

Segmentation of blood vessels from retinal images plays a crucial role for diagnosing eye diseases. In this paper, we propose an automatic segmentation method for blood vessels in retinal images. Firstly, we preprocess the retinal images to enhance vessels and overcome the lighting variation. Then, a graph-based algorithm is exploited to partition the retinal image into multiple regions. Finally, we classify each region as retinal region or non-vessel region with k-mean algorithm. The proposed method is validated on the publicly available DRIVE database, and compared with the state-of-the-art algorithms.

关键词： medical image segmentation retinal images graph-based algorithm k-mean algorithm

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An Effective Disease Prediction algorithms Using Machine Learning Techniques 5

An Effective Disease Prediction Algorithms Using Machine Lea...

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5th International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Sirivanth, Paladugu Rao, N. V. krishna Manduva, Jenvith Thirupathi, J. kavya, S. P., V Tejaswini, M. Sruthi, k. Sai SRM Univ Dept Comp Sci & Engn Guntur 522502 Andhra Pradesh India Inst Aeronaut Engn Dept Comp Sci & Engn Hyderabad 500043 India

ISBN: (纸本)9781665426428

Diagnosis is an important factor in every hospital to gut patient proposer treatment The earlier models only concentrate on conventional models like as film-based segmentation, CT image extraction, ultrasound scanning and etc. The following models are only depending on old techniques, so that guanine information is not getting. There for a machine learning (ML) based diagnosis models are required to cross over above limitations In this work an advanced ML technique are analyzed for disease prediction and clear classification. The performance measures sensitivity 98.45%, accuracy 98.42% and recall 97.987% had been attained which are out performance the methodology.

关键词： k-mean algorithm Decision Tree Random Forest Support Vector Machine Sample data

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Customer Market Segmentation using Machine Learning algorithm 6

Customer Market Segmentation using Machine Learning Algorith...

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6th International Conference on Trends in Electronics and Informatics (ICOEI)

作者： Regmi, Swesh Raj Meena, Jasraj kanojia, Utkarsh kant, Vishesh Delhi Technol Univ Dept Informat Technol Delhi India

ISBN: (纸本)9781665483285;9781665483292

Everything is becoming more mechanized in today's times, especially in this age of data. Data is the world's new electricity. Many firms can benefit from this information in their marketing efforts. Every company wanted to grow and be better than the competition, so they employed innovative ideas and invested in product development, but only to keep their customers and expand their market. Customers are segmented in today's commercial world. They analyze their consumers' behaviors and purchasing patterns and can assist them in generating profit. Customer segmentation refers to the process of categorizing a company's customers into groups based on their commonalities. Client segmentation is the process of determining how to interact with consumers in different groups to amplify the value of each customer to the company. Unsupervised machine learning is used to segment customers. There are many algorithms that can partition customers based on their behavior and other parameters, and our study's main goal is to cluster customers into various clusters based on their behavior patterns. This research study has compared five clustering algorithms and selected the best of them for further research.

关键词： Machine Learning Classification Clustering Customer Segmentation Spending behavior k-mean algorithm

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Bug Predicting Survey Using Advanced Machine Learning algorithms 5

Bug Predicting Survey Using Advanced Machine Learning Algori...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Chandra Bhan Gupta, Namit Dalal, Surjeet Tmu U.P Moradabad India Amity University Haryana Gurugram India

ISBN: (纸本)9798350383522

In dynamic software development, ensuring high reliability and quality is an important goal. The biggest challenge in this field is the early detection and correction of errors, which if not addressed in time can lead to significant cost and time savings The aim of this study is to investigate the use of advanced machine learning (ML) algorithms to predict bugs in software systems to increase the efficiency and accuracy of the software development cycle. Analysis performs comprehensive analyzes using a variety of sophisticated ML algorithms including neural networks, random Forests, support vector machines (SVMs), and gradient enhancement machines. These algorithms are used to predict the probability of a bug in the code. The methodology includes a comprehensive approach to data acquisition, preprocessing, feature engineering, model training, and validation. They use a rich data-set, including code parameters, change logs, and developer activity, obtained from several open-source software repositories and defect-tracking systems © 2024 IEEE.

关键词： Decision tree Deep Learning k-mean algorithm Machine Learning PCA

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A Study and Comparison of Different Image Segmentation algorithms 2

A Study and Comparison of Different Image Segmentation Algor...

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2nd International Conference on Advances in Computing, Communication, and Automation (ICACCA) (Fall)

作者： kumar, Vinod Lal, Tauj Dhuliya, Piyush Pant, Diwaker Tulas Inst Dept Elect & Commun Dehra Dun Uttarakhand India

ISBN: (纸本)9781509034802

Image segmentation is a process, which divide an image into different regions, which are homogeneous in some characteristics. Image segmentation is the initial step in many image processing applications like Pattern recognition and image analysis. Image analysis includes object characterization and representation and feature measurement. Higher order task follows the classification of object. Hence, characterization, visualization of region of interest in any image, delineation plays a significant role in image segmentation. There are many segmentation algorithms available in the literature, which divide an image into number of regions based on some image features like pixel intensity value, color, texture, etc. These all algorithms are categorized based on the segmentation method used. They are Segmentation based on single or multiple thresh holding, Segmentation based on edge detection, Segmentation based on similar region and Segmentation based on clustering, Segmentation based on ANN and fuzzy logic technique etc. In this work, we have chose one algorithm from one segmentation category and implement the algorithm in MATLAB. The chosen algorithms are Otsu's algorithm, k-means, quad tree, Delta E, Region growing and fth algorithms. To check the performance of the algorithms, we have used 6 simple and complex images available in the literature. The obtained results shows the efficacy of the segmentation algorithms.

关键词： Image segmentation algorithms Otsu's algorithm k-mean algorithm fth algorithm region growing algorithm Delta E algorithm

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Improvements in LEACH based on k-means and Gauss algorithms

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Procedia Computer Science 2015年 73卷 460-467页

作者： Elkamel Rabiaa Baccar Noura Cherif Adnene National Engineering School of Carthage Innov’Com laboratory National Engineering School Of Tunis Innov’Com laboratory Faculty of science of tunis Signal Processing Laboratory

Several methods have been developed for routing in wireless sensor networks; the most popular is LEACH protocol. It owes its popularity to its simplicity and ability to handle large data sets. However, the main limitation of this method is its dependency to probabilistic calculations. In this paper, we propose a new improved version of the LEACH protocol. Our main contribution consists on the integration of an improved unsupervised algorithm which is the k-means on clusters and the use of the Gaussian elimination algorithm for choosing the Cluster Head. Based on Davies–Bouldin index (DBI), the k-means finds the optimal number of clusters k. The Davies-Bouldin index makes calculations much less complex, which is a potential purpose in clustering. Thus, DBI based k-means offers more simple parallel distributed computing of k and allows faster auto-classification of sensor nodes. In this manner a balanced energy clusters is created and distributed energy consumption is guaranteed. Simulations in a wireless sensor network show that our new approach greatly reduces the computation time thanks to k-means and minimize energy consumption thanks to Gauss.

关键词： WSN Clustering k-mean algorithm optimal number of cluster Energy efficiency

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Design of Enterprise Financial Management Platform Based on Multi System Data Linkage

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Procedia Computer Science 2024年 243卷 619-627页

作者： Hongli Wu Guangdong Innovative Technical College Dongguan 523960 China

With the continuous expansion of enterprise scale and the continuous improvement of management level, the traditional financial management system centered on the finance department can no longer meet the needs of modern enterprise development. Simple financial management aims to achieve the daily financial management of the enterprise, but without effective supervision and management of the enterprise's finance, it cannot organically combine the enterprise's finance and business management. The financial management of enterprises has put forward higher requirements for information integration, with 70% of the data provided by financial management systems. The financial management system is centered around the entire business of the enterprise, achieving the integration of enterprise resources, scientifically coordinating and allocating it, and fully reflecting the financial information of each link in the production and sales of the enterprise. This article explored the design of an enterprise financial management platform based on multi-system data linkage, which enhanced the consistency of multi-system data and the timeliness of data updates in financial management. The experimental results showed that the consistency level of the multi system data linkage management platform was between 93% and 98%. The consistency level of financial management platforms with data silos ranged from 77% to 80%.

关键词： Design of Enterprise Financial Management Platform. Multi System Data Linkage Integration of Enterprise Data Resources k-mean algorithm

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A Cluster-based Stratified Hybrid Decision Support Model under Uncertainty: Sustainable Healthcare Landfill Location Selection

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Applied intelligence (Dordrecht, Netherlands) 2022年第12期52卷 13614-13633页

作者： Erfan Babaee Tirkolaee Ali Ebadi Torkayesh Department of Industrial Engineering Istinye University Istanbul Turkey. School of Business and Economics RWTH Aachen University 52072 Aachen Germany.

Nowadays, healthcare waste management has become one of the significant environmental, health, and social problems. Due to population and urbanization growth and an increase in healthcare waste disposals according to the growing number of diseases and pandemics like COVID-19, disposal of healthcare waste has become a critical issue. Authorities in big cities require reliable decision support systems to empower them to make strategic decisions to provide safe disposal methods with a prospective vision. Since inappropriate healthcare waste management systems would definitely bring up dangerous environmental, social, health, and economic issues for every city. Therefore, this paper attempts to address the landfill location selection problem for healthcare waste using a novel decision support system. Novel decision support model integrates k-means algorithms with Stratified Best-Worst Method (SBWM) and a novel hybrid MARCOS-CoCoSo under grey interval numbers. The proposed decision support system considers waste generate rate in medical centers, future unforeseen but potential events, and uncertainty in experts' opinion to optimally locate required landfills for safe and economical disposal of dangerous healthcare waste. To investigate the feasibility and applicability of the proposed methodology, a real case study is performed for Mazandaran province in Iran. Our proposed methodology could efficiently deal with 79 medical centers within 4 clusters addressing 9 criteria to prioritize candidate locations. Moreover, the sensitivity analysis of weight coefficients is carried out to evaluate the results. Finally, the efficiency of the methodology is compared with several well-known methods and its high efficiency is demonstrated. Results recommend adherence to local rules and regulations, and future expansion potential as the top two criteria with importance values of 0.173 and 0.164, respectively. Later, best location alternatives are determined for each cluster of medical

关键词： CoCoSo Grey Numbers Healthcare Waste Management k-mean algorithm MARCOS Stratified BWM

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