Clustering

In this study , a new model of combining variables affecting the classification of customers is introduced which is based on a distribution system of goods and services. Given the problems that the RFM model has in various distribution systems, a new model for resolving these problems is presented. The core of this model is the older RFM. The new model that has been proposed as PRFM, consists of four dimensions: Profit margins (P), time period from customer's last purchase (R), Frequency of transactions (F) and the Monetary Value (M). Adding variable (P) makes a huge change in customer clustering and classification systems and makes it more optimized for future planning. For review and approval, the model was implemented in one of the largest and most diversified distribution companies in Iran. Using Ward's clustering, the optimal number of clusters was prepared and entered by hierarchical clustering and based on Euclidian distance customers are clustered and separated. One of the most important results of this study is introducing a new model and resolving the problems of the old RFM model in determining customer's value.      

Smart applications with interconnected intelligent devices for sharing services arise serious security problems to the stability of this IoT complex and heterogeneous environment. Unless security considerations are analyzed and implemented properly in real time then IoT cannot be perceived as a pervasive network for the possible stakeholders. Current state of the art has analyzed trust-based security solutions as additional feature to application layer of the system which can identify and filter out the malicious nodes. In this paper we are proposing holistic trust management with edge computing mechanism to create trustworthy zones comprising different clusters, where Gateway on behalf of clusters will initiate migration of their nodes if falls below the defined Zone trust threshold level. The created zones are self-resilient against any malicious attacks and saves lots processing usage time and energy to address the security issues. By analyzing our proposed algorithm with other contemporary approaches to handle IoT security issues using trust mechanism, this approach is more precise in terms of protecting system against incurring malicious behavior, and also prolong the application operation duration by reducing communication and processing overhead.

In the current paper, we have assimilated fuzzy techniques and optimization techniques, namely differential evolution, to put forward a modern archive-based fuzzy evolutionary algorithm for multi-objective optimization using clustering. The current work account for the application of a cluster associated approach. Specific quantitative cluster validity measures, i.e., J-measure and Xie-Beni, have been referenced to carry out the appropriate partitioning. The proposed algorithm introduces a new form of strategy which attempts to benefit the feasible search domain of the algorithm by minimizing the analysis and exploration of less beneficial search scope. This clustering method yields a group of trade-off solutions on the ultimate optimal pare to front. Eventually, these solutions are united and maintained in an archive for further evaluation. The current work summarizes and organizes an archive concerned with excellent and diversified solutions in an effort to outline comprehensive non-dominated solutions. The degree of efficiency is revealed with respect to partitioning on gene expression and real-life datasets. The proposed algorithm seeks to reduce the function assessment analysis and maintains a very small working population size. The effectiveness of the proposed method is presented in comparison with some state-of-art methods.

In order to achieve the sensing, communication and processing tasks of Wireless Sensor Networks, an energy-efficient routing protocol is required to manage the dissipated energy of the network and to minimalize the traffic and the overhead during the data transmission stages. Clustering is the most common technique to balance energy consumption amongst all sensor nodes throughout the network. In this paper, a multi-objective bio-inspired algorithm based on the Firefly and the Shuffled frog-leaping algorithms is presented as a clustering-based routing protocol for Wireless Sensor Networks. The multi-objective fitness function of the proposed algorithm has been performed on different criteria such as residual energy of nodes, inter-cluster distances, cluster head distances to the sink and overlaps of clusters, to select the proper cluster heads at each round. The parameters of the proposed approach in the clustering phase can be adaptively tuned to achieve the best performance based on the network requirements. Simulation outcomes have displayed average lifetime improvements of up to 33.95%, 32.62%, 12.1%, 13.85% compared with LEACH, ERA, SIF and FSFLA respectively, in different network scenarios.

Heart disease pretends great danger to people, as heart disease has recently become a dangerous disease that acts as a threat to humans. It usually affects all groups from young to old. The biggest challenge in this paper is data pre-processing and discovering a solution to the failure of records Clinical heart, where an effective high-performance model is proposed to enhance heart disease and treat failure in the clinical heart failure records. The current authors applied the techniques of clustering with k-means, expectation-maximization clustering, DBSCAN, support vector clustering, and random clustering herein. Using cluster techniques, we gained good enough results for significantly predicting and improving the performance of heart disease. The goal of the model is a suggestion of a reduction method to find features of heart disease by applying several techniques. Our most important results are to predict faster and better. It indicates that the proposed model is excellent and gives excellent results. This model demonstrated a great superiority over its counterparts through the results obtained in this research. We obtained some values of 130, 980, 183, 125.133, 133, 203, and 125.800. It confirms that this model will predict significantly and improve the performance of the data that we have worked on this.

The random walk technique, which has a reputation for excellent performance, is one method for complex networks sampling. However, reducing the input data size is still a considerable topic to increase the efficiency and speed of this algorithm. The two approaches discussed in this paper, the no-retracing and the seed node selection algorithms, inspired the development of random walk technique. The Google PageRank method is integrated with these different approaches. Input data size is decreased while critical nodes are preserved. A real database was used for this sampling. Significant sample characteristics were also covered, including average clustering coefficient, sampling effectiveness, degree distribution, and average degree. The no-retracing method, for example, performs better. The efficiency increases even further when the no-retracing technique is combined with the Google PageRank. When choosing between public transportation and aircraft, for example, these algorithms might be used since time is crucial. Additionally, these algorithms are more energy-efficient methods that were looked at.

The majority of wireless sensor network (WSN) security protocols state that a direct connection from an attacker can give them total control of a sensor node. A high level of security is necessary for the acceptance and adoption of sensor networks in a variety of applications. In order to clarify this issue, the current study focuses on identifying abnormalities in nodes and cluster heads as well as developing a method to identify new cluster heads and find anomalies in cluster heads and nodes. We simulated our suggested method using MATLAB tools and the Database of the Intel Research Laboratory. The purpose of the performed simulation is to identify the faulty sensor. Using the IBRL database, sensors that fail over time and their failure model is the form that shows the beats in the form of pulses, we find out that the sensor is broken and is of no value. Of course, this does not mean that the sensor is invasive or intrusive. We have tried by clustering through Euclidean distance that identify disturbing sensors. But in this part of the simulation, we didn't have any data that shows disturbing sensors, it only shows broken sensors. We have placed the sensors randomly in a 50 x 50 space and we want to identify the abnormal node.

In recent decades, lifestyle has been one of the most important concepts in marketing, and management studies. Hence, this study aimed to identify the values and lifestyles of Iranian consumers. According to Cochran’s formula, 1580 people were selected as the statistical sample. Eighteen values were identified by examining the theoretical foundations and interviewing 77 Iranian citizens. The data collected by Likert-scale questionnaire were evaluated using R software, clustering method. Considering the importance of Iranian values, three clusters were identified: “normal lifestyle,” “phlegmatic lifestyle,” and “ideal lifestyle.” This study is one of the first attempts to create a general typology of Iranian consumers based on values and lifestyle and focusing on cultural, ideological and belief differences of Iranian consumers. Researchers are suggested to take advantage of the lifestyles identified in this research in order to identify and deeply understand the consumption behaviors specific to each style in different markets.

Innovation capability refers to a complete set of characteristics of the organization that facilitates innovation strategies, and the food industry plays a pivotal role in the processing of agricultural raw materials and food supply. The aim of the present study was to Identify and Analyze of the dimensions of innovation capability, clustering and ranking indexes in Urmia food industries. The sample size of the statistical population encompassing 221 companies was equal to 143 according to Cochran's formula. Obtaining required information was based on field study method. In the applied part of the study, exploratory factor analysis, clustering with K-MEANS algorithm and cluster ranking based on AHP technique were used. The results of the exploratory factor analysis showed that the relationship of each item with the factors classified into three categories was properly expressed. Companies with similar features but different from other clusters were put into 8 clusters. Finally, according to the weights of study variables, "human", "operability" and "structural” factors ranked first, second and last respectively.

Wireless Sensor Networks (WSNs) have been employed in various real-time applications and addressed fundamental issues, such as limited power resources and network life. Several sensor nodes in a WSN monitor the actual world and relay discovered data to base stations. The biggest issue with WSN is that the sensors have a limited lifetime and use much electricity to relay data to the base station. This paper proposes an improved PSO-based Enhanced Distributed Energy Efficient Clustering (EDEEC) algorithm to extend the network's life and reduce power consumption. Clustering is the process of forming groups of sensor nodes. The cluster aims to improve the network's scalability, energy efficiency, and other characteristics. The particle swarm optimization algorithm is modified to obtain energy-efficient WSNs. The assessment is based on the essential WSN characteristics, including network lifetime and energy efficiency (power consumption). Compared to LEACH, HEED, and DEEC, our proposed IPSO-EDEEC uses less energy.

A greater community of researchers widely studies fog computing as it reduces the massive data flow to the existing cloud-connected network and performs better for real-time systems that expect a quick response. As the fog layer plays a significant role in a fog-cloud system, all of the devices participating in fog computing must be balanced with appropriate load to upstretched the system performance. The proposed method is founded on a tree-based dynamic resources arrangement mechanism that refreshes the fog clusters created using Fuzzy C Mean (FCM) to increase the speed of resource allocation. With the help of Fuzzy rule-based load calculation and intra-cluster job allocation, the load inside the group is maintained. The system also has the facility of inter-cluster job forwarding, which works on demand. A novel load balancing strategy, Real-Time Flexi Forwarded Cluster Refreshing System (RTFRS) is proposed by which all the tasks can be handled efficiently within the fog cloud system. The proposed system is designed so that overall complexity is not upraised and becomes suitable for fog computing architecture with low processing capacity by maintaining the quality of service. Experimental results show that the proposed model outperforms standard methods and algorithms used in fog computing concerning average turnaround time, average waiting time, resource utilization, average failure rate, and the load on the gateway.

Introduction: The aim of this study was to determine the information retrieval and information therapy behavior of Asthma and allergy specialists in the country, based on cohonen self-organized neural network model. Methods: The methodology of the present study, which is an applied study in terms of purpose, has been done by descriptive-survey method using neural network technique. The tool of this research is a researcher-made questionnaire that was distributed among a sample of people in the community (149 people). After collecting the data, the neural network was selected for data clustering and using MATLAB software, Asthma and allergy specialists were clustered based on the main components of the research. Then, by removing each of the main sub-components of the research, the most effective and least effective option in their information-seeking behavior in working with information resources in this specialized field was determined. Results: The most effective component in clustering information barriers, was "lack of time due to workload" and the least was "distance of libraries and information centers". About information retrieval skills, the most effective component is " knowing what keywords to use when searching the Internet, and to be familiar with synonyms and terms related to the information I need." Conclusion: By studying the clustering of information behaviors resulting from the information needs of Asthma and allergy specialists, their needs are recognized, and this is one of the measures that provides the basis for effective research, appropriate findings and ,consequently, informational decision-making for those involved in this field.

Purpose: The main purpose of this study is to design and evaluate a book recommender system in digital and public libraries. The solution has been provided by receiving and reviewing the preferences and experiences of users and profile information and studying the background of each user, as well as considering groups of features recorded in the recommendation process. Method: This research is applied in terms of purpose and survey method. The statistical population studied in this research consists of 263 questionnaires of users and 30 questionnaires of librarian experts. In order to find similarity between users and books, clustering and grouping have been used. Findings: There are two criteria for grouping: users grouping that can be used on the three indicators of age, gender, educational level, and thematic classification of books can be based on scope, branch, and sub-category. In analyzing the data in the descriptive statistics section, Excel software is used and in the analytical section, SPSS software. Findings indicate that the accuracy criterion has been improved by calculating MAE and RSME in the proposed method compared to the basic method in this field. The results also showed that classification can have a significant impact on the forecast and performance of book forecasting systems. Conclusion: The evaluation of the conceptual design showed that by focusing on user characteristics and obtaining real feedback of Iranian libraries, the recommender can serve as a key and effective element in the service of the Iranian readership community and play a good role as a virtual reference librarian.