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Open Access Article
1 - Extending the Lifetime of Wireless Sensor Networks Using Fuzzy Clustering Algorithm Based on Trust Model
Farshad Kiyoumarsi Behzad Zamani DehkordiWireless sensor networks (WSNs) are the safest and most widely used existing networks, which are used for monitoring and controlling the environment and obtaining environmental information in order to make appropriate decisions in different environments. One of the very MoreWireless sensor networks (WSNs) are the safest and most widely used existing networks, which are used for monitoring and controlling the environment and obtaining environmental information in order to make appropriate decisions in different environments. One of the very important features of wireless sensor networks is their lifetime. Two important factors come to mind to increase the lifetime of networks: These factors are maintaining the coverage of the network and reducing the energy consumption of sensor nodes simultaneously with the uniform consumption of energy by all of them. Clustering, as the optimal method of data collection, is used to reduce energy consumption and maintain the coverage of the network in wireless sensor networks. In clustered networks, each node transmits acquired data to the cluster head to which it belongs. After a cluster head collects all the data from all member nodes, it transmits the data to the base station (sink). Given that fuzzy logic is a good alternative for complex mathematical systems, in this study, a fuzzy logic-based trust model uses the clustering method in wireless sensor networks. In this way, cluster-head sensors are elected from among sensors with high reliability with the help of fuzzy rules. As a result, the best and most trusted sensors will be selected as the cluster heads. The simulation results in MATLAB software show that in this way, in comparison with K-Means, FCM, subtractive clustering, and multi-objective fuzzy clustering protocols, the energy consumption in clustered nodes will decrease and the network’s lifetime will increase. Manuscript profile -
Open Access Article
2 - Fuzzy Clustering Based Routing in Wireless Body Area Networks to Increase the Life of Sensor Nodes
Mohsen Abdollahzadeh Aghbolagh Mohammad Ali Pourmina -
Open Access Article
3 - Image Segmentation using spectral clustering based SuperPixel
Fatemeh Afsari Sholi Jalil azimpour Marziye DadvarOne of the sciences in order to increase the efficiency of intelligent systems to be used in the visual sense, is Machine vision science. The first step in many applications in machine vision is image segmentation. Image segmentation, refers to the grouping of pixels in MoreOne of the sciences in order to increase the efficiency of intelligent systems to be used in the visual sense, is Machine vision science. The first step in many applications in machine vision is image segmentation. Image segmentation, refers to the grouping of pixels in an image So that these pixels, the same qualities have with each other And the pixels adjacent parts, have different characteristics. The most important feature used in image segmentation, colors and features. In monochrome images, the gray level is considered as properties But color images, different color spaces used as a color feature. In this study, the color and texture features for image segmentation is considered. Clustering-based methods of are used in image segmentation methods and Gaussian function is similar measure in clustering images. Spectral clustering requires has high computational cost. To save time and accelerate the segmentation of images Using clustering with Super pixels will achieve optimal results And to achieve reliable results approximate and fuzzy algorithm is used. The proposed algorithm is applied on several standard image And the evaluation criteria,Evaluated and evaluated by the indicators are evaluated and compared. The results of the experiments were compared to other fragmentation methods, suggesting a 3.4% superiority in the segmentation accuracy of the proposed algorithm, and all the evaluation indicators of the study have increased to a satisfactory level. Manuscript profile -
Open Access Article
4 - Toward a High-Accuracy Hybrid System for Cardiac Patient Data Analysis using C-Means Fuzzy Clustering in Neural Network Structure
Mahmood Karim Qaseem Razieh Asgarnezhad -
Open Access Article
5 - Unsupervised Domain Adaptation for image classification based on Deep Neural Networks
Amirfarhad Farhadi Mitra Mirzarezaee Arash Sharifi Mohammad TeshnehlabIntroduction: Domain adaptation has become an important issue today. A high percentage of data processing domain adaptation is done with a significant percentage of studies related to deep learning. Traditional methods often ignore the distance between the intra-class i MoreIntroduction: Domain adaptation has become an important issue today. A high percentage of data processing domain adaptation is done with a significant percentage of studies related to deep learning. Traditional methods often ignore the distance between the intra-class in source domain and target domain. As a result, models can be sensitive to outliers and noisy data, additionally increasing the negative transfer in the model. This method applied GAN to extract appropriate features and then used Fuzzy c-means to cluster train datasets in the target domain. Finally, based on the WMMD metric and CNN, the model estimates the final label data. Five real datasets are selected to generate eight transfer tasks. The results show that the superiority of the proposed model lies in transferring more knowledge from the source domain to the target domain.Method: In this approach, firstly based on GAN extracting features from source domains and the target domain (without labels), then label estimation by Fuzzy c-means clustering, finding the center of Fuzzy c-means on target domain data, new data points with labels in target domain as a new input to feature extraction module and regenerate features by GAN based on new pseudo labels. Afterward, we apply WMMD metrics based on CNN to ultimately assign labels for the target domain. Consequently, classification tasks have been done.Results: Empirical results on various benchmark datasets showcase the exceptional performance of the proposed method compared to state-of-the-art DA approaches, validating the proposed Deep-Learning Unsupervised Domain Adaptation approach efficacy. Overall, the approach shows potential for advancing domain adaptation research by offering an efficient and resilient approach for addressing domain shifts in real-world applications. Experimental results on visual object recognition and a digit dataset reveal that the proposed algorithm is robust, flexible, and significantly superior regarding accuracy compared to the baseline DA approaches. Based on the three and combined digit datasets, 1.7% and 2.4% accuracy improvement are achieved, respectively, compared to the best baseline DA approach results.Discussion: In this research, we addressed the challenging issues of outlier and negative transfer in the context of domain adaptation. Despite significant progress in domain adaptation techniques, outliers and negative transfer instances continue to hinder models' generalization performance across different domains. Based on DNNs and the WMMD metric, our proposed method was designed to mitigate these issues and effectively enhance knowledge transfer between domains. Manuscript profile -
Open Access Article
6 - Improvement of adaptive neuro-fuzzy controller using by fuzzy clustering means algorithm for control of vehicle suspension system
Gholamreza Bamimohamadi Mehdi SalehiSuspension system is an important part of vehicle whose main role is to separate the vehicle body from road induced vibrations. Design and control of a suspension system that can adapt to different road conditions with high flexibility is essential. In this study, data MoreSuspension system is an important part of vehicle whose main role is to separate the vehicle body from road induced vibrations. Design and control of a suspension system that can adapt to different road conditions with high flexibility is essential. In this study, data were collected from three types of road conditions with different roughness coefficients in various forward speeds for training a suspension model. Primarily, dynamic equations were derived for a linear full model suspension system. Then, with the use of fuzzy system simulation data, two adaptive neuro-fuzzy controllers namely Grid Partitioning and Fuzzy Clustering were trained. Finally, four methods were evaluated and the results showed that decrease in linear deflection and acceleration of vehicle body is higher in adaptive neuro-fuzzy controller by Subtractive Clustering compared to other systems. Manuscript profile -
Open Access Article
7 - A bi-objective mathematical model for the patient appointment scheduling problem in outpatient chemotherapy clinics using Fuzzy C-means clustering: A case study
Masoud Rabbani Alireza Khani amirreza Zare niloofar Akbarian-Saravi -
Open Access Article
8 - Fuzzy Particle Swarm Optimization Algorithm for a Supplier Clustering Problem
esmaeil Mehdizadeh reza Tavakkoli Moghaddam -
Open Access Article
9 - A Hybrid Algorithm for Fault Diagnosis using Fuzzy Clustering Tools
Adrián Rodríguez Ramos Pedro Juan Rivera-Torres Antônio José da Silva Neto Orestes Llanes-Santiago -
Open Access Article
10 - Providing a Method to Identify Malicious Users in Electronic Banking System Using Fuzzy Clustering Techniques
Leila Pourabdi Ali Harounabadi -
Open Access Article
11 - Taxonomy of Promotion Strategies of the Prosperous Pharmaceutical Products in the Growth Stage
Mahdi Ebrahimi ali asgarhalvaeiIran's pharmaceutical industry has long been confronted with various marketing and advertising constraints Most of these constraints has arised from governmental terms and conditions, and has led to the overwhelming majority of these companies pursuing a passive and min MoreIran's pharmaceutical industry has long been confronted with various marketing and advertising constraints Most of these constraints has arised from governmental terms and conditions, and has led to the overwhelming majority of these companies pursuing a passive and minimalistic approach to exploiting promotional strategies. However,today, with the arrival of newly stablished pharmaceutical companies and with private sector support, we are observing a remarkable change in the past approaches of these companies toward their promotion strategies. In this research, presenting the latest findings of promotion strategies of human pharmaceutical companies, we aimed to identify the common types of these strategies using the taxonomic method. For this purpose, we first created a comprehensive framework for the dimensions and components of the promotion strategy of pharmaceutical companies by conducting semi-structured interviews in a qualitative research and using a content analysis method ,then, through a quantitative survey and completion of questionnaires, the promotion strategy of each of the forty pharmaceutical companies in the statistical sample of this study was identified. Finally, by performing fuzzy clustering, four clusters or distinct types of promotion strategies of pharmaceutical companies were identified, each with significant differences in some of the key characteristics of other types. A key result of study shows that pharmaceutical companies have adopted different approaches to using promotion strategies. Manuscript profile -
Open Access Article
12 - The Application of Combined Fuzzy Clustering Model and Neural Networks to Measure Valuably of Bank Customers
Raheleh Nasiri Sharifi Maryam Rastgarpour
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