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In this context it is to be mentioned that the weak contraction has already been used to obtain proximity point theorem by Sankar Raj [16].
The purpose of the paper is to obtain proximity point results in partially ordered metric spaces by utilizing the weak contractive inequality obtained in [24].
These networks can be thresholded by the received signal strength index (RSSI) to obtain proximity networks with a distance of 1 m (by setting RSSI (-75) dBm).
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The results show that while the proposed hierarchical clustering algorithm provides acceptable results, it is also conveniently appropriate for using interval type-2 fuzzy sets and obtaining proximity of suppliers.
If the minimized value is (d(A, B)), then we obtain a proximity point at which the proximity pair is realized.
To obtain a proximity measure closer to face-to-face interactions, we set T=30 min (lower threshold w.r.t. literature [11]).
Using the above theorem, we obtain the proximity of LMS attractors and the WFs in the following.
The purpose of this paper is to obtain best proximity point theorems for a weakly contractive mapping and a weakly Kannan mapping in partial metric spaces.
In this section, we first obtain best proximity point theorems for weakly contractive mapping and weakly Kannan mapping in metric spaces.
Consequently, the drainage map was used to obtain the proximity to water bodies map using the buffering option of ArcGIS 10.0 software.
In this paper, we first obtain best proximity point theorems for a weakly contractive mapping and a weakly Kannan mapping in partial metric spaces.
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