Dynamic Classification of Sentiments from Restaurant Reviews Using Novel Fuzzy-Encoded LSTM

User reviews on social media have sparked a surge in interest in the application of sentiment analysis to provide feedback to the government, public and commercial sectors. Sentiment analysis, spam identification, sarcasm detection and news classification are just few of the uses of text mining. For many firms, classifying reviews based on user feelings is a significant and collaborative effort. In recent years, machine learning models and handcrafted features have been used to study text classification, however they have failed to produce encouraging results for short text categorization. Deep neural network based Long Short-Term Memory (LSTM) and Fuzzy logic model with incremental learning is suggested in this paper. On the basis of F1-score, accuracy, precision and recall, suggested model was tested on a large dataset of hotel reviews. This study is a categorization analysis of hotel review feelings provided by hotel customers. When word embedding is paired with LSTM, findings show that the suggested model outperforms current best-practice methods, with an accuracy 81.04%, precision 77.81%, recall 80.63% and F1-score 75.44%. The efficiency of the proposed model on any sort of review categorization job is demonstrated by these encouraging findings

Aspect Based Opinion Mining & Sentiment Analysis

Opinion mining is a relatively new field that refers to the practice of collecting feedback in the form of online reviews and ratings left by users on various topics. Researchers are now able to monitor the states of consciousness of individuals in real-time because to this development. Just lately, a number of research papers for sentiment analysis were implemented, each of which was based on a unique categorization and ranking procedure. However, the amount of time necessary for the newline performing class has not decreased in any way. Sentiment Sensitivity newline word list SST was provided as a solution to the problem of function mismatch in the go-domain sentiment class across the source area and the target domain; however, achieving improved accuracy and identifying distributional similarities of words became less effective as time went on. Hidden Markov’s persistent development may be seen at the beginning. Cosine In order to achieve more effective and clean pre-processing, a method that is conceptually quite similar to HM-CPCS has been devised. The HM-CPCS methodology, which has recently been suggested, makes use of the POS tagger, a variant of which is based on the Hidden Markov algorithm. Evaluations are created using data from a wide variety of different domains. Similar to a newline, the tags that come before and after it compute the possibility of transitions and the existence of the term newline among the tags in order to increase capability. This is done in order to improve capability

An Insilco Approach to Restrain HIV Replication Through Clustering and Virtual Screening

Abstract Human Immunodeficiency Virus (HIV) type-1 non-nucleoside and nucleoside reverse transcriptase inhibitors (NNRTIs) are key drugs to inhibit replication of virus, we have used virtual screening and docking resulted in inhibit replication effectively at active binding site, 884 ligands were extracted and docking analysis resulted in 59 best molecules further by clustering analysis have paved the way for innovative drug design which is better than existing nevirapine ,top three molecules (ZINC04923148, ZINC05442451 and ZINC04923002) were reported as possible novel HIV-RT inhibitors

BOBMEX: the Bay of Bengal monsoon experiment

The first observational experiment under the Indian Climate Research Programme, called the Bay of Bengal Monsoon Experiment (BOBMEX), was carried out during July-August 1999. BOBMEX was aimed at measurements of important variables of the atmosphere, ocean, and their interface to gain deeper insight into some of the processes that govern the variability of organized convection over the bay. Simultaneous time series observations were carried out in the northern and southern Bay of Bengal from ships and moored buoys. About 80 scientists from 15 different institutions in India collaborated during BOBMEX to make observations in most-hostile conditions of the raging monsoon. In this paper, the objectives and the design of BOBMEX are described and some initial results presented. During the BOBMEX field phase there were several active spells of convection over the bay, separated by weak spells. Observation with high-resolution radiosondes, launched for the first time over the northern bay, showed that the magnitudes of the convective available potential energy (CAPE) and the convective inhibition energy were comparable to those for the atmosphere over the west Pacific warm pool. CAPE decreased by 2-3 kJ kg-1 following convection, and recovered in a time period of 1-2 days. The surface wind speed was generally higher than 8 m s-1. The thermohaline structure as well as its time evolution during the BOBMEX field phase were found to be different in the northern bay than in the southern bay. Over both the regions, the SST decreased during rain events and increased in cloud-free conditions. Over the season as a whole, the upper-layer salinity decreased for the north bay and increased for the south bay. The variation in SST during 1999 was found to be of smaller amplitude than in 1998. Further analysis of the surface fluxes and currents is expected to give insight into the nature of coupling

Seed microflora of five ICRISAT mandate crops

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) supplies seeds of sorghum, pearl millet, pigeonpea, chickpea, and groundnut for research globally. The export of seeds of these crops is channelized through the regional station of the National Bureau of Plant Genetic Resources (NBPGR), Rajendranagar, Hyderabad. However, the tests for quarantine clearance of seeds for export are done at the Export Certification Laboratory at the ICRISAT Center. During the period from June 1989 to December 1997, ICRISAT exported 371,818 samples of its mandate crops to 136 countries. The largest number of exported samples were of sorghum (140,143) followed by chickpea (119,308). A total of 1786 samples (sorghum, 571; pearl millet, 120; pigeonpea, 311; chickpea, 199; Groundnut, 585) were detained due to heavy seed infection by fungi and/or bacteria (>80% seed infection). Pigeonpea appeared to be the most popular crop exported to 105 countries followed by sorghum (91 countries) and groundnut (88 countries). A total of 182 fungal spp. belonging to 71 genera were recorded. Largest number of fungi-132 fungal species across the years, were found associated with sorghum crop. The corresponding figures for pearl millet, chickpea, pigeonpea, and groundnut were 94, 91, 96, and 60, respectively. Aspergillus spp. were more on pulses and groundnut than on sorghum and pearl millet; however, Curvularia spp. showed the reverse trend. Fusarium and Alternaria spp. occurred most frequently on pigeonpea followed by on sorghum. Also, there was a total absence of three graminicolous fungi - Dreschlera, Biopolaris and Exserohilum spp. on groundnut. There were 31 fungi associated with all the five crops. Aspergillus niger (3.8%) and Cladosporium spp. (3.6%) were the most commonly occurring fungi being most predominant on groundnut and sorghum, respectively

Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study

18% of the world's population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016

Flue curing of Virginia tobacco by a tube-in-basket (TiB) burner using rice husk as fuel and barn insulation

Virginia tobacco crop is flue-cured by individual farmers in the field. This highly energy-intensive process consumes enormous quantities of firewood with serious ecological implications. A tube-in-basket (TiB) burner, developed recently, makes it possible to use rice husk, a locally available agrowaste, as an alternative energy source. Tobacco curing trials carried out in an instrumented research barn showed that the fuel cost per kilogram of tobacco leaf cured was lower with rice husk as compared to firewood. The fuel utilization efficiency index could be improved by about 30% by insulating the barn roof with husk. Comparison of detailed temperature profiles indicated that, with insulation, heat losses through the roof were virtually eliminated, vertical temperature spreads inside the barn were much narrower, and the daynight cyclic variations, as well as inversion of temperatures, were either totally suppressed or greatly reduced. It is postulated that the more stable temperature profiles contributed to fuel economy and also to the quality of the cured leaf