Machine Learning Technique for Sentiment Classification

Large amount of information are available online on web.The discussion forum, review sites, blogs are some of the opinion rich resources where review or posted articles is their sentiment, or overall opinion towards the subject matter. The opinions obtained from those can be classified in to positive or negative which can be used by customer to make product choice and by businessmen for finding customer satisfaction .This paper studies online movie reviews using sentiment analysis approaches. In this study, sentiment classification techniques were applied to movie reviews. Specifically, we compared two supervised machine learning approaches SVM, Naive Bayes for Sentiment Classification of Reviews. Results states that Naïve Bayes approach outperformed the SVM. If the training dataset had a large number of reviews, Naive bayes approach reached high accuraciesas compare to other

Sustainable Water Management in the Kantli River

For centuries, rivers played important role in human life. Kantli River is a lifeline of Shekhawati region but due to negligence it’s about to die. It seems like there is nothing left except sand and sand left around but still there is hope to make an effort for her survival. No one has even tried to save this river till now. It is very sad to know that this river is slowly dying before our eyes but local authorities and government have done nothing but ignored this situation totally. Still there is a hope to save this river. There are lots of solutions to save this by using some efforts to save this river seriously. If some local and national leaders come together to sort out their differences and come together for combining Kantli River in their project and planning, definitely it can be saved

Finetuning BERT and XLNet for Sentiment Analysis of Stock Market Tweets using Mixout and Dropout Regularization

Sentiment analysis is also known as Opinion mining or emotional mining which aims to identify the way in which sentiments are expressed in text and written data. Sentiment analysis combines different study areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is quickly becoming a key concern for businesses and organizations, especially as online commerce data is being used for analysis. Twitter is also becoming a popular microblogging and social networking platform today for information among people as they contribute their opinions, thoughts, and attitudes on social media platforms over the years. Because of the large database created by twitter stock market sentiment analysis has always been the subject of interest for various researchers, investors, and scientists due to its highly unpredictable nature. Sentiment analysis can be performed in different ways, but the focus of this study is to perform sentiment analysis using the transformer-based pre-trained models such as BERT(bi-directional Encoder Representations from Transformers) and XLNet which is a Generalised autoregressive model with fewer training instances using Mixout regularization as the traditional machine and deep learning models such as Random Forest, Naïve Bayes, Recurrent Neural Network (RNN), Long short-term memory (LSTM) because fails when given fewer training instances and it required intense feature engineering and processing of textual data. The objective of this research is to study and understand the performance of BERT and XLNet with fewer training instances using the Mixout regularization for stock market sentiment analysis. The proposed model resulted in improved performance in terms of accuracy, precision, recall and f1-score for both the BERT and XLNet models using mixout regularization when given adequate and under-sampled data

Learning cloth manipulation with demonstrations

Recent advances in Deep Reinforcement learning and computational capabilities of GPUs have led to variety of research being conducted in the learning side of robotics. The main aim being that of making autonomous robots that are capable of learning how to solve a task on their own with minimal requirement for engineering on the planning, vision, or control side. Efforts have been made to learn the manipulation of rigid objects through the help of human demonstrations, specifically in the tasks such as stacking of multiple blocks on top of each other, inserting a pin into a hole, etc. These Deep RL algorithms successfully learn how to complete a task involving the manipulation of rigid objects, but autonomous manipulation of textile objects such as clothes through Deep RL algorithms is still not being studied in the community. The main objectives of this work involve, 1) implementing the state of the art Deep RL algorithms for rigid object manipulation and getting a deep understanding of the working of these various algorithms, 2) Creating an open-source simulation environment for simulating textile objects such as clothes, 3) Designing Deep RL algorithms for learning autonomous manipulation of textile objects through demonstrations.Peer ReviewedPreprin

GREEN SYNTHESIS, CHARACTERIZATION, AND IN VITRO ANTIMICROBIAL EFFICACY OF SILVER NANOPARTICLES SYNTHESIZED FROM TECTONA GRANDIS WOOD FLOUR

Objective: Silver nanoparticles (AgNPs) are widely used as an antimicrobial agent. Due to the toxicity concerns related to the synthesis of the AgNPs, there is an urgent need of the novel green techniques to synthesis the AgNPs. In the light of the above, we had synthesized the AgNPs with the help of the Tectona grandis commonly known as teak wood.Methods: The aqueous wood floor extract of the T. grandis was used as a reducing agent in the synthesis of the AgNPs. The NPs were synthesized and characterized using different techniques such as ultraviolet-visible spectroscopy, Dynamic light scattering (DLS), Transmission electron microscopy (TEM), and Scanning electron microscopy (SEM). The synthesized NPs were then evaluated for their antimicrobial efficacy against the Gram-negative and Gram-positive bacteria and antifungal activity. Further, in vitro antioxidant efficacy of the AgNPs was calculated using 2,2'-azino-bis(3- ethylbenzothiazoline-6-sulphonic) acid and 2,2-diphenyl-1-picrylhydrazyl assay.Results: From the above analyses, the formation of spherical NPs with an average size of 100 nm was confirmed. Minimum inhibitory concentration (MIC) and minimal biocidal concentration (MBC) of the AgNPs were calculated, MIC and MBC values ranged from 0.50 to 1.8 μg/mL and 0.91 to 3.6 μg/mL, respectively.Conclusion: The prepared NPs were found to be uniform in size with smooth topography. The antibacterial and antifungal efficacy of the NPs was found to be effective on the broad spectrum of microbes. The antioxidant activity of AgNPs was comparable to ascorbic acid

A study on Moving Objects Recognization in DIP using thresholiding and other Methods

The digital image processing deals with developing a digital system to performs experiments and operations on a digital image with the use of computer algorithms. An image is nothing more than a 2D mathematical function f(x,y) where x and y are two horizontally and vertically co-ordinates. Object recognition is one of the most important applications of image processing. Vehicle location from a satellite picture or aeronautical picture is a standout amongst the most fascinating and testing research themes from recent years. Vehicle location from satellite picture is one of the utilizations of protest recognition. The activity and jam is expanding ordinary in everywhere throughout the world. Satellites pictures are typically utilized for climate anticipating and geological applications. In this way, Satellites pictures might be additionally useful for the recognizing activity utilizing Image preparing. This theory utilized straightforward morphological acknowledgment strategy for vehicle recognition utilizing picture preparing procedure in Matlab which is best technique for identification of autos, trucks and transports. We can without much of a stretch register the aggregate quantities of vehicles in the coveted zone in the satellite picture and vehicles are appeared under the jumping box as a little spots. Here we look at two calculations like pixel thresholding and Otsu thresholding technique. As indicated by our outcome Pixel level thresholding is superior to Otsu technique

Istraživanje patologije i dokaz koinfekcije goveđim papiloma virusima na koži i bradavicama sisa goveda

Bovine papillomas are benign tumors of the cutaneous and mucosal epithelia and are commonly found in cattle. The productivity loss and economic impact depends on the location and degree of infection. The present study was undertaken to investigate the pathology and association of different bovine papilloma virus (BPV) types in cattle cutaneous and teat warts. Grossly, the warts were of variable size and shape (rice grain, cauliflower and finger- like horny growths or irregular). Histopathologically, the warts were diagnosed as fibropapilloma, papilloma, fibrosarcoma and hyperplasia. Fibropapilloma was the most frequent histological type observed and was characterized by hyperkeratosis, parakeratosis and acanthosis. PCR revealed the presence of either BPV-1, -2 and -5 DNA or their co-infections. Transmission electron microscopy on negative staining showed BPV-like particles. Varied copy numbers of viral DNA of BPV-1, -2 and -5 were detected by real-time PCR. Immunohistochemistry revealed the expression of Ki-67 mainly in the proliferating cells of stratum spinosum and a few basal cells in papilloma and fibropapilloma. Cyclooxygenase-2 immunostaining was observed in the cytoplasm and cell membrane of suprabasal cells. In conclusion, cutaneous and teat warts in cattle in India are more frequently associated with BPV-1/ -2 and their mixed infections, with the rare presence of BPV-5. The DNA of BPV-5 was detected for the first time in warts in India. Co-infection with two or three different viral types demonstrated the diversity of BPV types involved in warts. The frequent expression of Ki-67 in suprabasal layers may be indicative of its association with viral replication and that they are as proliferation sites.Papilomi su benigni tumori kožnog i sluzničnog epitela koji se u goveda često nalaze. Proizvodni gubici i utjecaj na ekonomičnost uzgoja ovise o lokaciji i stupnju infekcije. Ovo je istraživanje provedeno kako bi se ustanovila patologija i povezanost različitih tipova goveđih papilomavirusa (BPV) na koži i bradavicama sisa goveda. Bradavice su većinom bile različite veličine i oblika (oblika zrna riže, cvjetače i rožnate izrasline nalik na prste ili nepravilna oblika). Histopatološki su bradavice dijagnosticirane kao fibropapilomi, papilomi, fibrosarkomi i hiperplazije. Fibropapilomi su bili najčešći histološki tip, obilježeni hiperkeratozom, parakeratozom i akantozom. PCR-om je dokazan DNA virusa BPV-1, BPV-2 i BPV-5 DNA ili koinfekcije tim virusima. Negativno bojenje elektronskom transmisijskom mikroskopijom pokazalo je čestice nalik na BPV. PCR-om u stvarnom vremenu otkriveni su različiti brojevi kopija virusne DNA virusa BPV-1, BPV-2 i BPV-5. Imunohistokemijskim je pretragama pronađena ekspresija Ki-67, većinom u proliferativnim stanicama stratum spinosum i nekoliko bazalnih stanica u papilomima i fibropapilomima. Provedeno je imunobojenje ciklooksigenazom 2 u citoplazmi i staničnoj membrani suprabazalnih stanica. Zaključeno je da su u Indiji bradavice na koži i sisama u goveda mnogo češće povezane s BPV-1 i BPV-2 i mješovitim infekcijama tim virusima, dok je prisutnost virusa BPV-5 rijetka. DNA virusa BPV-5 prvi je put pronađena u bradavicama u Indiji. Koinfekcije dvama ili trima različitim virusnim tipovima pokazuje raznolikost tipova BPV-a koji se nalaze u bradavicama. Učestala ekspresija Ki-67 u suprabazalnim slojevima može upućivati na njegovu povezanost s virusnom replikacijom i mjestima proliferacije

A Review Paper on Data Mining Techniques andAlgorithms

Data mining has made a great progress in recent year but the problem of missing data has remained a great challenge for data mining algorithms. It is an activity of extracting some useful knowledge from a large data base, by using any of its techniques.Data mining is used to discover knowledge out of data and presenting it in a form that is easily understood to humans. Data mining is the notion of all methods and techniques which allow analyzing very large data sets to extract and discover previously unknown structures and relations out of such huge heaps of details. This paper studied the classification and clustering techniques on the basis of algorithms which is used to predict previously unknown class of objects

Study of Different Approaches of Creature Detection in DIP

Creature identification assumes a critical part in everyday life. In the region like an air terminal where the nearness of any sort of creature nearness is entirely confined, creature location assumes an exceptionally fundamental part in such territories. In the horticultural regions put close to the timberland numerous creatures demolishes the harvests or even assault on individuals hence there is a need of framework which identifies the animal nearness and gives cautioning about that in the perspective of security reason. What's more, it is additionally helpful in the timberlands. Where wild animal can distinguish for the wellbeing reason.