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AUTOMATIc PROTEIN SPOTS QUANTIFIcATION IN TWO-DIMENSIONAL GEL IMAGES

ADVANcES IN DATA ScIENcE AND ADAPTIVE ANALYSIS 2011年第3期3卷 401-415页

作者： Rashwan, Shams Sarhan, Amany Faheem, Muhammad Talaat Youssef, Bayumy. A. City Sci Res & Applicat Technol Informat Res Inst Alexandria Egypt Univ Tanta Fac Engn Comp & Automat Control Engn Dept Tanta Egypt

Two-dimensional (2D) polyacrylamide gel electrophoresis of proteins is a robust and reproducible technique. It is the most widely used separation tool in proteomics. current efforts in the field are directed at development of tools for expanding the range of proteins accessible with 2D gels. Proteomics was built around the 2D gel. The idea that multiple proteins can be analyzed in parallel grew from 2D gel maps. Proteomics researchers needed to identify interested protein spots by examining the gel. This is time-consuming, labor-extensive, and error-prone process. It is desired that the computer can analyze the proteins automatically by first detecting then quantifying the protein spots in the 2D gel images. In our previous work, we presented a new technique for segmentation of 2D gel images using the fuzzy c-means (FcM) algorithm using the notion of fuzzy relations. In this paper, we will describe the new relational FcM (RFcM) algorithm and use it for automatic protein spots quantification. We will also use two methods to evaluate its performance: the unsupervised evaluation method and comparison with the expert spots quantification.

关键词： 2D gel images protein spot detection protein spot quantification fuzzy c-means algorithm fuzzy relations computer vision

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Quality Evaluation for Online Distance Education Based on Grey Relational Degree

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JOURNAL OF INTERNET TEcHNOLOGY 2023年第5期24卷 1141-1148页

作者： Du, Tao Fu, Weina Jinzhong Univ Dept Informat Technol & Engn Jinzhong Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha Peoples R China

Aiming to address the problems of low accuracy and models, a new online distance education quality evaluation model based on grey correlation algorithm is proposed. Firstly, calculate the membership of education quality data, and complete the data collection through cluster mining. Secondly, construct the evaluation index system according to the set evaluation principles. Finally, the grey correlation relationship of education quality index data and complete the results show that the evaluation accuracy of the built model Therefore, the model has higher application value.

关键词： Grey correlation degree algorithm Evaluation index system fuzzy c-means algorithm Online distance education Educational quality evaluation

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Indoor Human Action Recognition Based on Dual Kinect V2 and Improved Ensemble Learning Method

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SENSORS 2023年第21期23卷 8921页

作者： Kan, Ruixiang Qiu, Hongbing Liu, Xin Zhang, Peng Wang, Yan Huang, Mengxiang Wang, Mei Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Guilin Univ Elect Technol Minist Educ Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China Guilin Univ Technol Coll Informat Sci & Engn Guilin 541004 Peoples R China State Grid Qianshan City Elect Power Supply Co Qianshan 246300 Peoples R China Natl Forestry & Grassland Adm Northwest Surveying & Planning Inst Xian 710048 Peoples R China

Indoor human action recognition, essential across various applications, faces significant challenges such as orientation constraints and identification limitations, particularly in systems reliant on non-contact devices. Self-occlusions and non-line of sight (NLOS) situations are important representatives among them. To address these challenges, this paper presents a novel system utilizing dual Kinect V2, enhanced by an advanced Transmission control Protocol (TcP) and sophisticated ensemble learning techniques, tailor-made to handle self-occlusions and NLOS situations. Our main works are as follows: (1) a data-adaptive adjustment mechanism, anchored on localization outcomes, to mitigate self-occlusion in dynamic orientations;(2) the adoption of sophisticated ensemble learning techniques, including a chirp acoustic signal identification method, based on an optimized fuzzy c-means-AdaBoost algorithm, for improving positioning accuracy in NLOS contexts;and (3) an amalgamation of the Random Forest model and bat algorithm, providing innovative action identification strategies for intricate scenarios. We conduct extensive experiments, and our results show that the proposed system augments human action recognition precision by a substantial 30.25%, surpassing the benchmarks set by current state-of-the-art works.

关键词： human action recognition Kinect V2 ensemble learning fuzzy c-means algorithm random forest binocular system

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Multi-Objective ED²MT Strategy-based clustering algorithm for Large Scale WSN

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IETE JOURNAL OF RESEARcH 2024年第8期70卷 6618-6629页

作者： Navaz, K. Sophia, S. Gnana Alarfaj, Abeer Abdulaziz Muthukumaran, N. Sangeetha, K. Priyanka, R. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Chennai India St Hindu Coll Dept Comp Sci & Applicat Nagercoil 629002 India Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Sri Eshwar Coll Engn Ctr Computat Imaging & Machine Vis Dept ECE Coimbatore 641202 TN India SNS Coll Technol Dept Informat Technol Coimbatore TN India PSR Engn Coll Dept Comp Sci & Engn Sivakasi India

Wireless Sensor Networks play a crucial role in everyday life, providing a wide range of networking structures to develop real-time applications. Energy consumption is limited by WSN devices, so reducing the energy consumption of malicious nodes is crucial to improving network performance. A main concern of WSNs from an application perspective is the prolonged lifetime of the network and reduced energy usage. To overcome these challenges a novel multi-objective ED2MT (Energy, Distance, Degree, Mobility, Time) driven strategy has been proposed which consists of three phases namely dual cluster Head Selection, data compression, and routing. In the first phase, the proposed ED2MT uses a fuzzy c-means (FcM) clustering algorithm to select dual cluster Head (cH) which has been performed in two stages, namely Inter-cluster and Intra-cluster. In the second phase, a Neighborhood Indexing Sequence (NIS) technique is utilized to compress the number of bits in the data before it is transmitted. In the third phase, a multi-objective shortest-path routing algorithm is utilized for route selection. The performance of the ED2MT method has been evaluated using network lifetime, delay, throughput, alive nodes, energy efficiency, and packet delivery ratio to provide a better result. The proposed ED2MT framework achieves 20.55%, 18.4%, and 15.2% more residual energy than c3HA, cAFL, and DHScA algorithms.

关键词： Dual cluster head selection fuzzy c-means algorithm Neighbourhood indexing sequence algorithm Shortest path routing Wireless sensor networks

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Promote clustering Accuracies With fuzzy Supervised clustering algorithm

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International Journal of Intelligent Technologies and Applied Statistics 2021年第4期14卷 205-219页

作者： Huang-Shu Fen Jeng-Ming Yih

Purpose: fuzzy algorithms of Gath-Geva (GG) and Gustafson-Kessel (GK) based on Mahalanobis distance can improve those limitations of spherical structural clusters, but GG algorithm can only be used for the data with multivariate normal distribution. GK algorithm is limited by that it must know the distribution of data. An improved supervised clustering algorithm based on fuzzy c-means (FcM) has been proposed. Methodology: We take a new threshold value and a new convergent algorithm to improve those limitations of GG and GK algorithms, delete the constraint of the determinants of covariance matrices in the GK algorithm, and replace the covariance matrix with the correlation matrix which exists in the objective function. Findings: The experimental results of real data sets show that our proposed new algorithm can promote clustering accuracies and get better performance. Value: The popular FcM algorithm based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. Adding fuzzy covariance matrices in their distance measure was not directly derived from the objective function. But it is not stable enough when some of its covariance matrices are not equal. Hence, different initializations may lead to different results.

关键词： fuzzy c-means algorithm Mahalanobis distance Spherical structural clusters Objective function

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Identification of the Dental Root canal from Micro-cT Records Using 3D curve Skeleton Extraction

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IFAc Proceedings Volumes 2011年第1期44卷 6184-6189页

作者： Balázs Benyó László Szilágyi csaba Dobó-Nagy Department of Control Engineering and Information Technology Budapest University of Technology and Economics (Tel: +36-1-463-4027) Independent Section of Radiology Semmelweis University of Budapest Hungary

Abstract This paper presents a novel image processing procedure dedicated to the automated detection of the medial axis of the root canal from dental micro cT records. The 3D model of root canal is built up from several hundreds of parallel cross sections, using image enhancement and an enhanced fuzzy c-means based partitioning, center point detection in the segmented slice, three dimensional inner surface reconstruction and curve skeleton extraction in three dimensions. The central line of the root canal can finally be approximated as a 3D spline curve. The proposed procedure can help prepare several kinds of endodontic interventions.

关键词： image processing skeleton extraction micro computed tomography fuzzy c-means algorithm

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covariance matrix approach to feature-weight selection in FcM with application to color image segmentation

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智慧科技与应用统计学报 2017年第2期15卷 13-30页

作者：蔡政霖

fuzzy c-means (FcM) algorithm is a general method for clustering analysis. When there exitsts noise variables in the data, the error rate of the FcM algorithm relatively increases. Thus, how to choose the weight of the variable to reduce the error rate is an important issue. To solve this problem, this paper presents a new method of variable weight selection, called covariance Matrix (cM) method. The simulation results show that the proposed variable selection method can effectively reduce the error rate. Finally, the proposed cM method is applied to color image segmentation.

关键词： fuzzy c-means algorithm feature-weight covariance matrix color image segmentation

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A Soft-Hard combination Decision Fusion Scheme for a clustered Distributed Detection System with Multiple Sensors

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SENSORS 2018年第12期18卷 4370-4370页

作者： Luo, Junhai He, Xiaoting Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China

In the distributed detection system with multiple sensors, there are two ways for local sensors to deliver their local decisions to the fusion center (Fc): a one-bit hard decision and a multiple-bit soft decision. compared with the soft decision, the hard decision has worse detection performance due to the loss of sensing information but has the main advantage of smaller communication costs. To get a tradeoff between communication costs and detection performance, we propose a soft-hard combination decision fusion scheme for the clustered distributed detection system with multiple sensors and non-ideal communication channels. A clustered distributed detection system is configured by a fuzzy logic system and a fuzzy c-means clustering algorithm. In clusters, each local sensor transmits its local multiple-bit soft decision to its corresponding cluster head (cH) under the non-ideal channel, in which a simple and efficient soft decision fusion method is used. Between clusters, the fusion center combines all cluster heads' one-bit hard decisions into a final global decision by using an optimal fusion rule. We show that the clustered distributed system with the proposed scheme has a good performance that is close to that of the centralized system, but it consumes much less energy than the centralized system at the same time. In addition, the system with the proposed scheme significantly outperforms the conventional distributed detection system that only uses a hard decision fusion. Using simulation results, we also show that the detection performance increases when more bits are delivered in the soft decision in the distributed detection system.

关键词： distributed detection system soft decision hard decision clustering fuzzy logic system fuzzy c-means algorithm

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Traffic coordination by reducing jamming attackers in VANET using probabilistic Manhattan Grid Topology for automobile applications

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ScIENTIFIc REPORTS 2024年第1期14卷 1-16页

作者： Santhi, G. B. Jacob, Suma Sira Sheela, D. Kumaran, P. New Prince Shri Bhavani Coll Engn & Technol Dept Comp Sci & Engn Chennai India Sri Krishna Coll Technol Dept Artificial Intelligence & Data Sci Coimbatore India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai India Wolaita Sodo Univ Coll Engn Dept Mech Engn Wolaita Sodo Ethiopia

In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamic characteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed traffic conditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the fuzzy c-means algorithm (FcM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the fuzzy c-means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor.

关键词： Vehicular adhoc network fuzzy c-means algorithm Modified fisheye state routing algorithm Modified Manhattan grid topology Particle swarm optimization

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Methods of Resource Scheduling Based on Optimized fuzzy clustering in Fog computing

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SENSORS 2019年第9期19卷 2122.-2122.页

作者： Li, Guangshun Liu, Yuncui Wu, Junhua Lin, Dandan Zhao, Shuaishuai Qufu Normal Univ Sch Informat Sci & Engn Rizhao 276800 Peoples R China

cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architecture, fog computing is an extension of cloud computing that can effectively solve the aforementioned problems. Resource scheduling is one of the key technologies in fog computing. We propose a resource scheduling method for fog computing in this paper. First, we standardize and normalize the resource attributes. Second, we combine the methods of fuzzy clustering with particle swarm optimization to divide the resources, and the scale of the resource search is reduced. Finally, we propose a new resource scheduling algorithm based on optimized fuzzy clustering. The experimental results show that our method can improve user satisfaction and the efficiency of resource scheduling.

关键词： fog computing fuzzy c-means algorithm particle swarm optimization resource clustering resource scheduling

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