Establishing complete and reliable coverage for a long time-span is a crucial issue in densely surveillance wireless sensor networks (WSNs). Many scheduling algorithms have been proposed to model the problem as a maximum disjoint set covers (DSC) problem. The goal of DSC based algorithms is to schedule sensors into several disjoint subsets. One subset is assigned to be active, whereas, all remaining subsets are set to sleep. An extension to the maximum disjoint set covers problem has also been addressed in literature to allow for more advance sensors to adjust their sensing range. The problem, then, is extended to finding maximum number of overlapped set covers. Unlike all related works which concern with the disc sensing model, the contribution of this paper is to reformulate the maximum overlapped set covers problem to handle the probabilistic sensing model. The problem is addressed as a multi-objective optimization (MOO) problem and the well-known decomposition based multi-objective evolutionary algorithm (MOEA/D) is adopted to solve the stated problem. A Multi-layer MOEA/D is suggested, wherein each layer yields a distinct set cover. Performance evaluations in terms of total number of set covers, total residual energy, and coverage reliability are reported through extensive simulations. The main aspect of the results reveals that the network's lifetime (i.e. total number of set covers) can be extended by increasing number of sensors. On the other hand, the coverage reliability can be increased by increasing sensing ranges but at the expense of decreasing the network's lifetime.
Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Mak
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The main purpose of this paper is to introduce a some concepts in fibrewise totally topological space which are called fibrewise totally mapping, fiberwise totally closed mapping, fibrewise weakly totally closed mapping, fibrewise totlally perfect mapping fibrewise almost totally perfect mapping. Also the concepts as totally adherent point, filter, filter base, totally converges to a subset, totally directed toward a set, totally rigid, totally-H-set, totally Urysohn space, locally totally-QHC totally topological space are introduced and the main concept in this paper is fibrewise totally perfect mapping in totally top
In this paper ,the problem of point estimation for the two parameters of logistic distribution has been investigated using simulation technique. The rank sampling set estimator method which is one of the Non_Baysian procedure and Lindley approximation estimator method which is one of the Baysian method were used to estimate the parameters of logistic distribution. Comparing between these two mentioned methods by employing mean square error measure and mean absolute percentage error measure .At last simulation technique used to generate many number of samples sizes to compare between these methods.
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The aim of this paper is to present a weak form of -light functions by using -open set which is -light function, and to offer new concepts of disconnected spaces and totally disconnected spaces. The relation between them have been studied. Also, a new form of -totally disconnected and inversely -totally disconnected function have been defined, some examples and facts was submitted.
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On 2-9th April 2001 the Energetic and Relativistic Nuclei and Electron (ERNE) instrument on the Solar and Heliospheric Observatory (SOHO) observed three gradual solar energetic particle (SEP) events separated by 9 hour and 7days respectively, in association with three effective solar flares and coronal mass ejections (CMEs).
In this paper, a study of MESEP events was considered. As the definition of this phenomenon suggested there might be many sources for each MESEP event. This event has been examined in order to view the different sources that might relate to suspected accelerator of the SEPs. A careful analysis for the spectra and associated emission with such eruptions was made. Soft X-ray emission was observed by the Geostationar
On 2-9th April 2001 the Energetic and Relativistic Nuclei and Electron (ERNE)
instrument on the Solar and Heliospheric Observatory (SOHO) observed three
gradual solar energetic particle (SEP) events separated by 9 hour and 7days
respectively, in association with three effective solar flares and coronal mass
ejections (CMEs).
In this paper, a study of MESEP events was considered. As the definition of this
phenomenon suggested there might be many sources for each MESEP event. This
event has been examined in order to view the different sources that might relate to
suspected accelerator of the SEPs. A careful analysis for the spectra and associated
emission with such eruptions was made. Soft X-ray emission was obse
We presented in this paper a new class containing analytic univalent functions defined on unit disk. We obtained many geometric properties , like , coefficient inequality , distortion and growth theorems, convolution property, convex set, arithmetic mean and radius of starlikness and convexity by using Gaussian hypergeometric function for the class
The main purpose of this article is to study the soft LC-spaces as soft spaces in which every soft Lindelöf subset of is soft closed. Also, we study the weak forms of soft LC-spaces and we discussed their relationships with soft LC-spaces as well as among themselves.
In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho
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