Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

Positioning the parturient from supine to the left lateral tilt position (supine-to-tilt) may not effectively displace the gravid uterus, but turning from the left lateral position to the left lateral tilt position (left lateral-to-tilt) may keep the gravid uterus displaced and prevent aortocaval compression. Fifty-one full-term parturients were randomly placed in the left lateral position, supine-to-tilt and left lateral-to-tilt positions using a Crawford wedge. Femoral vein area, femoral vein velocity, femoral artery area, pulsatility index, resistance index and right arm mean arterial blood pressure and heart rate were recorded. Our results showed a lower mean (SD) femoral vein area (82.2 (14.9) vs 96.2 (16.4) mm2), a lower pulsatility index (3.83 (1.3) vs 5.8 (2.2)), a lower resistance index (0.93 (0.06) vs 0.98 (0.57)), a higher femoral artery area (33.3 (3.8) vs 30.9 (4.4) mm2) and a higher femoral vein velocity (7.9 (1.2) vs 6.1 (1.6) cm.s-1) with left lateral-to-tilt when compared with supine-to-tilt (all p < 0.001). Our results suggest that moving a full-term parturient from the full left lateral to the lateral tilt position may prevent aortocaval compression in full-term parturients more efficiently than when positioning the parturient from a supine to left lateral tilt position.

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Department of Chemistry

ANAESTHESIA

Effect of positioning from supine and left lateral positions to left lateral tilt on maternal blood flow velocities and waveforms in full-term parturients

Cancer prognosis prediction improves the quality of treatment and increases the survivability of the patients. Conventional methods of cancer prediction deal with single class by limiting the prognosis prediction to one response variable. The SEER Public Use cancer database has more prominent variables that support better prediction approach. The objective of this paper is to find the prominent labels from cancer databases and use them in a multi-class environment. The implementation consist of three phases namely, pre-processing, prominent label identification and multi-label classification. Breast, Colorectal and Respiratory Cancer Data sets have been used for the experimentation. Also random samples from all three data sets are generated to form a mixed cancer data. Patient survival, number of primaries and age at diagnosis are the prominent labels identified from others using the Decision tree, Naive Bayes and KNN algorithms. The three prominent labels have been tested using multi-label RAkEL algorithm to find the relations between them. The results of the empirical study are comparatively better than the traditional way of cancer prediction.