The thermal contact resistance is a principal parameter interfering with heat transfer in a fin tube heat exchanger. The objective of the present study is to examine the heat transfer rate and thermal contact resistance of hot and cold fluids. The heat transfer rate and thermal contact resistance of hot and cold fluids have been investigated through the experimental numerical method.

[1]. Kim Chang Nyung and Jeongjin and YounBaek (2003). Evaluation of thermal contact conductance using a new experimental-numerical method in fin-tube Heat exchangers. Journal of refrigeration, 26:900-908.
[2]. Jeongjin and Kim Chang Nyung and YounBaek and Kim Young Saeng (2004). A study on the correlation between the thermal contact conductance and effective factors in fin-tube Heat exchangers with 9.52 mm tube. Journal of Heat and fluid flow, 25:1006-1014.
[3]. Jeongjin and Kim Chang Nyung and YounBaek (2006). A study on the thermal contact conductance in fin-tube Heat exchangers with 7 mm tube. Journal of Heat and mass transfer, 49:1547-1555.
[4]. Eisherbini A.I and Jacobi A.M and Hrnjak P.S (2003). Experimental investigation of TCR in plain-fin-and-tube evaporators with collarless fins. Journal of Refrigeration, 26:527-536.
[5]. MadhusudanaChakravatri and Cheng Wui-wai (2006). Effect of electroplating on the thermal conductance of fin-tube interface. Journal of applied thermal engineering, 26:2119-2131.

Nowadays the use of automatic computer aided technology for the extraction of brain tumor by image segmentation process becomes greatly important. To improve the performance and reduce the complexity, the combined K-means and fuzzy C-means (FCM) clustering are investigated based on segmentation for brain tumor detection and support vector machine (SVM) is used for the classification of tumor types. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier; relevant features are extracted from each segmented tissue of brain MRI images. The experimental results of the proposed method have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity. The experimental results achieved 94.37% accuracy, 98% specificity, and 98.19% sensitivity, demonstrating the effectiveness of the proposed method for identifying normal and abnormal(i.e. Benign or Malignant) tissues from brain MR images.

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[2] Nag, Sanjay & Maitra, Dr. Indra&Sudipta, Maitra& Roy, Sudipta&Bandyopadhyay, Kumar (2014),"A Review of Image Segmentation Methods On Brain MRI For Detection Of Tumor And Related Abnormalities" International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 5, May 2014, ISSN: 2277 128X.
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[5] IntedharShakir Nasir,"The proposed image segmentation method based on adaptive k-means algorithm",Journal of Theoretical and Applied Information Technology,15th November 2018. Vol.96. No 21.