Sentiment Analysis of Movie Review using Machine Learning Approach

With development of Internet and Natural Language processing, use of regional languages is also grown for communication. Sentiment analysis is natural language processing task that extracts useful information from various data forms such as reviews and categorize them on basis of polarity. One of the sub-domain of opinion mining is sentiment analysis which is basically focused on the extraction of emotions and opinions of the people towards a particular topic from textual data. In this paper, sentiment analysis is performed on IMDB movie review database. We examine the sentiment expression to classify the polarity of the movie review on a scale of negative to positive and perform feature extraction and ranking and use these features to train our multilevel classifier to classify the movie review into its correct label. In this paper classification of movie reviews into positive and negative classes with the help of machine learning. Proposed approach using classification techniques has the best accuracy of about 99%

Identity Retention of Multiple Objects under Extreme Occlusion Scenarios using Feature Descriptors

Identity assignment and retention needs multiple object detection and tracking. It plays a vital role in behavior analysis and gait recognition. The objective of Multiple Object Tracking (MOT) is to detect, track and retain identities from an image sequence. An occlusion is a major resistance in identity retention. It is a challenging task to handle occlusion while tracking varying number of person in the complex scene using a monocular camera. In MOT, occlusion remains a challenging task in real world applications. This paper uses Gaussian Mixture Model (GMM) and Hungarian Assignment (HA) for person detection and tracking. We propose an identity retention algorithm using Rotation Scale and Translation (RST) invariant feature descriptors. In addition, a segmentation based optimum demerge handling algorithm is proposed to retain proper identities under occlusion. The proposed approach is evaluated on a standard surveillance dataset sequences and it achieves 97 % object detection accuracy and 85% tracking accuracy for PETS-S2.L1 sequence and 69.7% accuracy as well as 72.3% precision for Town Centre Sequence

Development of Rate Expression for Glycerol Hydrogenation Reaction

AbstractThis paper highlights various routes for glycerol reaction and also pathway for heterogeneous catalyst and also being obtained as by product of biodiesel making it cost effective and easy availability. Whereby it also aims at development of rate model and compares values of calculated and experimentally obtained rate constant. It also aims at hydrogenation carried out at liquid phase and gas phase and also various types of catalyst, support and promoter that can be used in hydrogenation of glycerol, and also advantage of various by-products and intermediate formed by glycerol hydrogenation reaction. It also aims at various intermediate formed by acid/solid base catalyst

IoT based Automated Plant Disease Classification using Support Vector Machine

Leaf - a significant part of the plant, produces foodusing the process called photosynthesis. Leaf disease can causedamage to the entire plant and eventually lowers crop production.Machine learning algorithm for classifying five types of diseases,such as Alternaria leaf diseases, Bacterial Blight, Gray Mildew,Leaf Curl and Myrothecium leaf diseases, is proposed in theproposed study. The classification of diseases needs front faceof leafs. This paper proposes an automated image acquisitionprocess using a USB camera interfaced with Raspberry PI SoC.The image is transmitted to host PC for classification of diseasesusing online web server. Pre-processing of the acquired image byhost PC to obtain full leaf, and later classification model basedon SVM is used to detect type diseases. Results were checkedwith a 97% accuracy for the collection of acquired images

Network Intrusion Detection Using Multiclass Support Vector Machine

Intrusion detection is a topic of interest in current scenario. Statistical IDS overcomes many pitfalls present in signature based IDS. Statistical IDS uses models such as NB, C4.5 etc for classification to detect Intrusions. Multiclass Support Vector Machine is able to perform multiclass classification. This paper shows the performance of MSVM (1-versus-1, 1-versusmany and Error Correcting Output Coding (ECOC)) and it’s variants for statistical NBIDS. This paper explores the performance of MSVM for various categories of attack

Study of feto-maternal outcome in patients of jaundice in third trimester of pregnancy

Background: Jaundice in pregnancy is an important medical disorder, more commonly seen in developing countries than developed ones. It comprises of a formidable list of complications that may adversely affect the pregnant woman and her fetus. Objective of current study was to study causes and feto-maternal outcome in pregnancies with jaundice in 3rd trimester.Methods: This was a retrospective study of 49 patients admitted in department of Obstetrics & Gynaecology at a tertiary care hospital with jaundice in 3rd trimester of pregnancy during the period from September 2008 to September 2010.Results: Out of 9972 deliveries, 49 patients were admitted with jaundice in 3rd trimester of pregnancy. Out of them 91.1% patients delivered. Vaginal delivery occurred in 82.2% and Cesarean section done in 17.7%. Preterm delivery occurred in 68.8%, low birth weight (LBW) was found in 82.2%, perinatal mortality occurred in 34.6% and maternal mortality occurred in 16.3% of patients.Conclusions: Jaundice in 3rd trimester of pregnancy leads to preterm delivery, fetal distress, intra uterine fetal death (IUFD) and high perinatal & maternal morbidity and mortality. Early diagnosis & aggressive management at tertiary care center help in reducing maternal & perinatal morbiditiy and mortality

Self-medication of abortion pills and its complications: an observational study

Background: Medical termination of pregnancy has been legalized in India since 1971. Medical abortion pill is well effective in early weeks of pregnancy. It is safe only when it is used under medical supervision. This study was carried out to analyse the complications following self-medication of abortion pills and to suggest measures to prevent such practice.Methods: This was a retrospective observational study conducted at our hospital from March 2017 to July 2017.Results: In present study 30 (75%) patients were in age group of 20-30 years. Illiterate patients were 22 (55%). Half of the patients, 20 (50%) were having three or more than three children. Majority of women 30 (75%) had consumed the abortion pills 1-10 days before coming to the hospital and 14 (35%) of patients had come with complain of excessive bleeding per vagina. Incomplete abortion was present in 32 (80%) of patients. Instrumental evacuation was required in 28 (87.5%) patients. Laparotomy for ruptured ectopic and rupture uterus was performed in 1 (2.5%) of each patient. 6 (15%) patients were severely anaemic. Transfusion of blood was required in 9 (22.5%) of patients.Conclusions: Medical abortion is effective and safe when carried out under medical supervision. Unsupervised use of medical abortion pills was associated with many complications like incomplete abortion, rupture ectopic and ruptured uterus. So, over the counter sale of medical abortion pill should be restricted

Effect of Long-Term Exposure to Lower Low-Density Lipoprotein Cholesterol Beginning Early in Life on the Risk of Coronary Heart Disease A Mendelian Randomization Analysis

ObjectivesThe purpose of this study was to estimate the effect of long-term exposure to lower plasma low-density lipoprotein cholesterol (LDL-C) on the risk of coronary heart disease (CHD).BackgroundLDL-C is causally related to the risk of CHD. However, the association between long-term exposure to lower LDL-C beginning early in life and the risk of CHD has not been reliably quantified.MethodsWe conducted a series of meta-analyses to estimate the effect of long-term exposure to lower LDL-C on the risk of CHD mediated by 9 polymorphisms in 6 different genes. We then combined these Mendelian randomization studies in a meta-analysis to obtain a more precise estimate of the effect of long-term exposure to lower LDL-C and compared it with the clinical benefit associated with the same magnitude of LDL-C reduction during treatment with a statin.ResultsAll 9 polymorphisms were associated with a highly consistent reduction in the risk of CHD per unit lower LDL-C, with no evidence of heterogeneity of effect (I2 = 0.0%). In a meta-analysis combining nonoverlapping data from 312,321 participants, naturally random allocation to long-term exposure to lower LDL-C was associated with a 54.5% (95% confidence interval: 48.8% to 59.5%) reduction in the risk of CHD for each mmol/l (38.7 mg/dl) lower LDL-C. This represents a 3-fold greater reduction in the risk of CHD per unit lower LDL-C than that observed during treatment with a statin started later in life (p = 8.43 × 10−19).ConclusionsProlonged exposure to lower LDL-C beginning early in life is associated with a substantially greater reduction in the risk of CHD than the current practice of lowering LDL-C beginning later in life