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Principles Of Random Walk

Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything

The Harmony Search Algorithm (HSA) is a swarm intelligence optimization algorithm which has been successfully applied to a broad range of clustering applications, including data clustering, text clustering, fuzzy clustering, image processing, and wireless sensor networks. We provide a comprehensive survey of the literature on HSA and its variants, analyze its strengths and weaknesses, and suggest future research directions.

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A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications

Thank you for reading an introduction to chemoinformatics. Maybe you have knowledge that, people have search hundreds times for their chosen readings like this an introduction to chemoinformatics, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful bugs inside their laptop. an introduction to chemoinformatics is available in our book collection an online access to it is set as public so you can get it instantly. Our books collection saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the an introduction to chemoinformatics is universally compatible with any devices to read.

An Introduction To Chemoinformatics

Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions

Cluster based routing strategies are popular categories of routing protocols among wireless sensor networks (WSNs). In a typical cluster based routing protocols, few nodes are elected as cluster Head (CH) nodes and they form clusters with other nodes of the networks known as cluster members. The data sensed by cluster members is sent to CH node for data aggregation and further processing. Data Aggregation is considered to be one of the important methods used to prolong the network lifetime of WSN. Data aggregation is quite popular among cluster based routing protocols for WSN, where the data packets are aggregated by intermediate nodes till it reaches to BS. Data aggregation ultimately helps in reducing the number of data messages thereby assisting in network life prolongation. In this paper, an intra-cluster data aggregation technique (ICA) for WSNs is proposed. ICA constructs the intra cluster data aggregation path from a source node to its CH node in an energy efficient way. The data packets are aggregated along the aggregation path by intermediate relay nodes till the message reaches to designated CH node. Comparative analysis of ICA, LEACH and LEACH-C protocols are carried by considering the parameters such as number of alive nodes, energy consumption, number of data packets received by BS. Results obtained show that ICA performs better compared to LEACH and LEACH-C protocols.

Energy efficient intra-cluster data aggregation technique for wireless sensor network

Design of energy efficient routing protocols for Wireless Sensor Network (WSN) is a great challenge for researchers. Recently, WSNs have gained lot of popularity and many energy efficient routing solutions are proposed. Most of the existing routing protocols focus on cluster head election and ignoring other important aspects of routing such cluster formation, data aggregation, etc. This research article presents a hybrid cluster head election for WSN based on firefly and harmony search algorithms. The contributions of the proposed protocols are (1) two level cluster head election strategy. In the first stage harmony search algorithm is used to determine initial set of energy efficient cluster head nodes that are sufficiently separated from on another by certain optimal distance. Then tentatively elected cluster head nodes are refined by firefly algorithm by considering the parameters such as node density, cluster compactness and energy to be consumed. Sometimes nature inspired optimization techniques may end up in early convergence and to avoid such problems, cluster head election scheme is divided at two levels. (2) a refined cluster formation strategy is designed where a normal node has privilege of joining to cluster head node either based on distance based metric or based on residual energy of cluster heads. This process of cluster formation helps in reduced energy consumption. The presented protocol is compared with some of the well-known clustering protocols such as LEACH, LEACH-C, EOICHD, and simple firefly based routing protocol based on the evaluation metrics such as number of alive nodes, energy consumption of network, number of packets received by Base Station, First Node Dead, Half Node Dead and Last Node Dead. Implementation is carried out using Network Simulator (NS 2.34) and results show that proposed hybrid cluster head election scheme outperforms the mentioned routing protocols.

Hybrid Cluster Head Election for WSN Based on Firefly and Harmony Search Algorithms

Twitter is the most popular microblogging and social media services used by millions of web users worldwide to read and write short messages about the topic related to their current status. Such social media sites usually has lots of irrelevant, unwanted and also huge information to carry out the study of human behaviour, new trends and so on. Highly influential, popular persons and public users tweet their status that are retweeted or replied by set of the audiences those view the tweets. By applying Sentiment analysis on the tweets made by the popular users and the audiences, we can discover whether the audiences are in favour to the views expressed. This work focuses on mining the information posted on twitter site about unemployment using hash tags. The proposed system is implemented using R language which makes it easy to mine data from Twitter with its support for twitter mining through wide range of APIs available. Mined twitter data is written into CSV files as the input dataset. Sentiment analysis is performed on the input dataset that initially performs data cleaning by removing the stop words, followed by classifying the tweets as positive and negative by matching with the positive-word and negative-word text dictionary to assigns the scores for every tweet. Next, Text mining is performed on the related hash-tags to generate the word cloud. Finally the analysis of the unemployment rate is performed on the individual hash tags and the merged hash tags based on the scores recorded for every tweets during the Sentiment analysis stage.

Twitter data analysis for unemployment crisis

Major e-Commerce service provider offers additional product recommendation to its customers, while they access the application, and enough evidence existing that such recommendations are cost effective for both consumer and service provider. For maximizing profit and to satisfy the user, existing e-Commerce platforms use long-term context for recommendations. In actual scenario, the recommendation can aid the user for other reason such as when the product is reminded of recent interest in or, point customer to currently discounted items. Furthermore, user preference changes over time due to weather, location, etc. As a result, the recommendation must be made based on the present behavior of the ongoing session. Many research based on location and session-based approaches has been presented to forecast user’s next-item requirement. However, these models are not efficient, as they are designed either to model short-term or long-term preferences. Recently, some hybrid recommendation algorithms have been presented to model both short-term and long term, but these models are designed considering static behavior and finds difficulty in revealing the correlations among behaviors and items. Furthermore, these models do not consider location-centric information for performing recommendation. To overcome the above-mentioned challenges, our research work presents hybrid location-centric prediction (HLCP) model by considering the dynamic behavior traits of users. HLCP model can learn both short-term and long-term context efficiently. Experiment results show that HLCP attains significant performance over existing models in terms of mean reciprocal rate (MMR) and hit rate (HR).

Hybrid location-centric e-Commerce recommendation model using dynamic behavioral traits of customer

Most of the existing cluster-based routing protocols focus either on cluster head election schemes or on refinement of routing process ignoring process of cluster formation. In this paper, firefly algorithm inspired energy aware clustering protocol for WSN is presented. The idea of the proposed protocol is to elect the cluster head nodes by assigning them intensity value defined through firefly algorithm. Another contribution of this paper is energy efficient cluster formation where cluster members join to cluster heads based on intensity parameter associated with cluster head. The presented protocol ensures that the elected cluster heads are usually located in densely populated area of the network resulting in low distance intra-cluster data transmissions and ultimately leading to low energy consumption. The firefly-based protocol is compared with popular routing protocols such as LEACH, LEACH-C and EOICHD and comparative results indicate that proposed protocol is highly energy efficient than other protocols.

C. R. Nirmala

Papers

Energy efficient intra-cluster data aggregation technique for wireless sensor network

Hybrid Cluster Head Election for WSN Based on Firefly and Harmony Search Algorithms

Twitter data analysis for unemployment crisis

Hybrid location-centric e-Commerce recommendation model using dynamic behavioral traits of customer

Firefly algorithm inspired energy aware clustering protocol for wireless sensor network