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Pregnancy is a state of signicant metabolic, endocrinological, immunological and vascular changes which lead to varied cutaneous changes. The cutaneous manifestations could be physiological changes, pregnancy specic dermatoses and skin conditions that are affected by pregnancy. The aim of this study was to nd the incidence of pregnancy dermatoses and characterize the various cutaneous manifestations seen in pregnant women. Methods: It was a cross-sectional descriptive study done over 2-year period from September 2016 to September 2018. All pregnant women presenting to the dermatology outpatient with complaints of skin disorders were recruited for the study. Informed consent was taken and the detailed examination ndings recorded. A total of 200 pregnant women were included i Results: n the study. Mean age was 25.5 years. Majority of participants were primigravida 130(65%). Most common presenting complaint (126 cases, 63%) was itching. Majority of participants (113) presented in the third trimester. Physiological skin changes pertaining to pregnancy was noted in 91(45.5%) participants, 156(78%) had pregnancy non-specic dermatoses and 39(19.5%) had pregnancy specic dermatoses. Hyperpigmentation and striae gravidarum were seen in 24% pregnant women. Among the pregnancy-nonspecic dermatoses infections was the most common condition (76 cases, 38%). Fungal infections constituted the majority, followed by viral and bacterial infections. STI were seen in 5 patients. Polymorphic eruption of pregnancy was the most common pregnancy specic dermatoses seen in 38 (19%) women. Thirty-three of these patients were primi in their 3rd trimester, 5 were in 2nd trimester. Atopic eruption of pregnancy was seen only in 1(0.5%) participant. Hyperpigmentation and stria Conclusion: e gravidarum are the most common physiological changes seen in pregnancy. Pregnancy predisposes to a wide variety of infections of which fungal infections are most common, followed by viral. Polymorphic eruption of pregnancy is the most common pregnancy specic dermatoses.

We live in an age of information, therefore collected data and documentation are practically treasure resources. All about a business and its development can be estimated with clarity via statistics. Any machine that could really analyse information to predict a projected outcome is known for being extremely vital for the business. It is critical for the system to provide accurate and useful knowledge of the products in order to conduct accurate assessment. Summarisation is a technique for obtaining a rundown from series of sentences in a study or observation that facilitates us with understanding the basic content of the knowledge expressed within. Simple and brief summaries of just a product will assist the system in performing prospective product research and development. In our paper, we use a deep learning framework that provides to extract clean, relevant, brief summaries from comprehensive customer feedback. Strategies of abstractive text summarisation is used. The method of extracting the primary keyword from a statement and using them in the summary is defined as extractive text summarisation. We utilise abstractive summarisation in this case, which evolves from sample information and provides the best feasible description. Utilising Transformer with Depth Scaling MultiHeaded Attention as well as GloVe word embedding with positional encoding, we illustrate an abstractive approach to extract summaries from the Amazon fine food reviews dataset. Transformer aids in the parallelisation of workloads in order to process data more quickly. We have used an Attention layer which boost the model's quality and enables it to become more effective. The BLUE rating is used to quantify the model's potency.

BackgroundMaternal breastmilk is a source of pre- and pro-biotics that impact neonatal gut microbiota colonisation. Since oral rotavirus vaccines (ORVs) are administered at a time when infants are often breastfed, breastmilk microbiota composition may have a direct or indirect influence on vaccine take and immunogenicity.MethodsUsing standardised methods across sites, we compared breastmilk microbiota composition in relation to geographic location and ORV response in cohorts prospectively followed up from birth to 18 weeks of age in India (n= 307), Malawi (n= 119), and the UK (n= 60).ResultsBreastmilk microbiota diversity was higher in India and Malawi than the UK across three longitudinal samples spanning weeks of life 1 to 13. Dominant taxa such asStreptococcusandStaphylococcuswere consistent across cohorts; however, significant geographic differences were observed in the prevalence and abundance of common and rare genera throughout follow-up. No significant associations were identified between breastmilk microbiota composition and ORV outcomes including seroconversion, post-dose 1 vaccine shedding, and/or post-vaccination rotavirus-specific IgA level.ConclusionsOur findings suggest that breastmilk microbiota composition may not be a key factor in shaping trends in ORV response within or between countries.

The current use of social media has created incomparable amounts of social data, as it is a cheap and popular information sharing communication platform. Nowadays, a huge percentage of people depend on the accessible material on social networking in their choices. This feature on exchanging knowledge with a wide number of users has quickly prompted social spammers to exploit the network of confidence to distribute spam messages and support personal forums, advertising, phishing, scams and so on. Identifying these spammers and spam material is a hot subject of study, and while large amounts of experiments have recently been conducted to this end, so far the methodologies are only barely able to identify spam feedback, and none of them demonstrates the value of each derived function type. In this study, we have suggested a machine learning- based spam detection system that determines whether or not a specific message in the dataset is spam using a set of machine learning algorithms. Four main features have been used; including user-behavioral, user-linguistic, review-behavioral and review-linguistic, to improve the spam detection process and to gather reliable data.

Acacia nilotica is a potential source of protein, fibre and natural antioxidants. Also, the protein fractions from acacia seeds possess high functional properties that could be advantageous in food formulation. However, acacia pods possess a bitterness that hinders its utilization for value addition. We have developed composite flours in combination with raw banana flour. Two types of composite flours were developed substituting 30 and 50 per cent of banana flour with acacia flour. Acacia flour exhibited significantly higher (p≤0.05) water and oil absorption capacity, swelling power as well as sedimentation value compared to banana flour. Also, it was a significantly richer (p≤0.05) source of protein (12.95%), crude fibre (12.66 %) as well as DPPH activity (59.60 %). The two composite flours, respectively exhibited 0.86 and 1.35 g/g water absorption capacity; 0.44 and 0.76 g/g oil absorption capacity; 4.14 and 4.06 g/g swelling power; 14.22 and 15.36 ml/g sedimentation value. The crude protein content in respective composite flours was 9.36 and 11.86 % with 4.96 and 5.50 % crude fibre, 40.60 and 40.69 % dietary fibre, 27.05 and 34.06 % DPPH activity. Both composite flours were good source of available micro-minerals also.

Literature is a work of art blended with an aesthetic and artistic creation specifically in the written form. There are indeed many different kinds of literature around the globe, but Indian English Literature has its special qualities that make it stand out to readers. Indian English Literature is an amalgamation of various themes such as socio-historical, multicultural, and multilingual objectives. Amulya Malladi is an Indian women diasporic writer, she brings the concept of cultural dilemmas in her work. This paper investigates Amulya’s second fiction ‘The Mango Season’ and highlights the societal complications/ cultural confusions faced by the protagonist Priya Rao. Due to religious and cultural differences, the protagonist suffers a lot to decide between her love for her family and her love for her beloved. Cultural dilemmas are primarily the result of two living experiences that develop in one’s own country and in the host land. ‘The Mango Season’ is very naturalistic in its depiction of cultural confusions & societal norms faced by the protagonist who is well educated and graduated in the West while her parents are down-rooted in the Orient culture.

Aim: The study was planned to evaluate Indian traditional sweet ‘Laddoo’ supplemented with germinated pumpkin seed flour. Study Design: The traditional recipe was supplemented by replacing the whole wheat flour and bengalgram flour with 10, 20 and 30% of germinated pumpkin seed flour. The developed products were subjected to organoleptic and nutritional evaluation. The results were subjected to statistical analysis using ANOVA. Place and Duration of Study: The study was carried in Department of Foods & Nutrition and was part of doctoral research work carried out between 2017-19. Methodology: Sensory evaluation of developed products was carried out using 9-point hedonic scale. The samples were further analysed for moisture, ash, crude fat, crude protein and crude fiber, total, soluble and insoluble dietary fiber, total and available minerals using standard methods. Results: The supplementation resulted in a significant (P≤0.05) increase in ash, crude fat, crude protein and crude fiber while total carbohydrates decreased. The supplemented product had crude protein ranging from 19.39-25.59 g; crude fibre 2.21 -3.24 g and ash 5.70-8.19 g/100 g at different levels of incorporation as against 16.53, 1,64 and 3.66 g/100 g respectively in control product. Significant increase was observed in mineral content also. The supplemented products had calcium, magnesium, zinc, iron, potassium and phosphorus ranging from 63.47-95.46, 132.63-133.58, 3.58-7.51, 6.49-11.33, 639.53-884.34 and 343.32-356.63 mg/100 g on dry matter basis. The developed products were acceptable to judges. Conclusion: Incorporation of germinated pumpkin seed flour significantly (P≤0.05) improved the nutrient and mineral profile of whole wheat flour Bengal flour Laddoo. Such developed products can be very useful in combating the macronutrient and micronutrients deficiency problem in population of all age groups.

Study was carried out on two hundred school going adolescents, aged between thirteen and seventeen years. Dietary intake of all the subjects was recorded for three consecutive days by ‘24 Hour Recall Method’. Study revealed that the daily mean intake of cereals, pulses, green leafy vegetables, roots tubers, other vegetables, fruits, milk & milk products, fats & oils, and sugar & jaggery was significantly lower (p≤0.05) than the recommended RDIs values in the daily diet of both males and females of both the age group (13-15 years and 16-17 years) of government and private schools. Furthermore, there was significant (p≤0.05) difference between the daily mean dietary intake of government and private school adolescents. Except cereals, pulses, roots & tubers and sugar & jaggery the adequacy ratio was below 50 per cent for all food groups in majority of government school respondents. However, majority of private school respondents met the adequacy by 75 -99.9 per cent of all food groups except green leafy vegetables which was below 50 per cent. In order to improve the current dietary pattern of school going adolescents there is an urgent need of imparting focused nutrition education programmes among the school going youth and their parents.

This paper demonstrates the use of software that provides blind access to the Internet. The growth of technology today is important and makes the impossible possible. In addition, the concept of Artificial Intelligence is also growing rapidly. Artificial intelligence (AI) means imitating human intelligence in machines designed to think like humans and imitate their actions. The term can also be used in any machine that displays features related to the human mind such as learning and solving problems. Many online apps can be accessed with the mouse or keyboard and read on-screen information as a result of input. Despite this, most people in the community do not have the natural ability to see differently from us. The only way a blind person can access the internet is, with the help of a third party who is blind. Therefore, it makes retrieval difficult for the visually impaired. The technology supports them with a few new features like screen readers and braille displays. Screen readers read aloud the information on the screen. There is such a screen reading software online. In a braille display, dots are created to represent information on the screen, which the visually impaired can understand by touching a touch. Therefore, in order to improve visually impaired people in the community, we have come up with a program that gives the user access to a variety of services. applications like mail, classroom, and drive using voice commands without the dependency on a third person and without the need to remember keyboard shortcuts and mouse clicks., {"references":["1.\tBelekar, Aishwarya, et al. \"Voice-Based Email for the Visually Impaired.\" International Journal of Computer. 2.\tP. A. Tiwari, P. Zodawan, H. P. Nimkar, T. Rotke, P. G. Wanjari and U. Samarth, \"Update on Voice based. 3.\tEmail Program for the Blind, \"2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2020, pp. 435-438, doi: 10.1109 / ICICT48043.2020.9112539. 4.\tMamatha, A., et al. \"Voice-based Email System for the Blind.\" International Journal of Engineering, Science and Management Research 3.8 (2020): 51-54.S. Noel, \"Personal computer (HCI) application based on Smart Voice Email (Vmail) Application - Assistant for Visually Impaired Users (VIU), \"Third International Conference on 2020 Intelligent Systems and Innovation Technology (ICSSIT), Trunelvel, India, 2020, pp. 895-900, doi: 10.1109 / ICSSIT48917.2020.9214139. 5.\tHiralkar, M. K. R., Talole, M. M. S., Wagh, M. R. B., Dandage, S. M., & Jawandhiya, P. M. Voice Based E-mail for Blind. 6.\tPathan, Naziya, et al. \"V-Mail (Voice-Based Email Request).\" International Research Journal of Engineering and Technology (IRJET) 6.03 (2019). 7.\tJethan, Divesh, Simi Saluja, and Saurabh Gautam. \"Multilingual-Based Email System.\" (2018). 8.\tDasgupta, Tirthankar, et al. \"VoiceMail facilities for desktops and mobile devices for the blind.\" 2012 Fourth International Conference on Smart Computer Interaction (IHCI). IEEE, 2012. C. Donahue, B. Li and R. Prabhavalkar, \"Assessing Speech Development Through Generative Adversarial Networks for Robust Speech Recognition,\" 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 2018, pages 5024-5028, doi: 10.1109 /ICASSP.2018.8462581.A 9.\tB.Nassif, I. Shahin, I. Attili, M. Azzeh and K. Shaalan, \"Speech Recognition on Deep Emotional Networks: A Systematic Review,\" in IEEE Access, vol. 7, pages 19143-19165, 2019, doi: 10.1109 / ACCESS.2019.2896880. 10.\tPrasanna S., Vani K., Venkateswari V. (2021) The Design and Development of Mobile-Based SMS App The Automated SMS System Using Voice Transforming Method for Blind People. To: Raj J.S., Iliyasu A.M., Bestak R., Baig Z.A. (eds) New Data Technology and Performance. 11.\tKumar, Rajesh, \"Recognition of Complaint Processes Using Markov's Hidden Model.\" 7th International Conference on Facilities and Systems (ICSSS). IEEE, 2020. 12.\tIyer, Vinayak, et al. \"Visible assistant for the visually impaired.\" Fifth International Conference on Communications 2020 and Electronics Systems (ICCES). IEEE, 2020. 13.\tAzeta, Ambrose A., and Olawande DARAMOLA. \"A Voice-Based E-Examination Framework for Visually Impaired Students in Open and Distance Learning.\" Turkish Online Journal of Distance Education 19.2 (2018): 34-46. 14.\tRohith Raghavan ,Vishodhan Krishnan , Hitesh Nishad, and Bushra Shaikh. \"Virtual AI Assistant for Person with Partial Vision Impairment \",ITM Web of Conferences 37, 01019 (2021). 15.\tPranjal Ingle, HarshadaKanade , Arti Lanke. \"Voice based e-mail System for Blinds.\" International Journal of Research Studies in Computer Science and Engineering (IJRSCSE)."]}

Semen volume and factors influencing the same were analyzed among Murrah bulls under Tamil Nadu environmental conditions. A total of 4563 observations on first and second ejaculate collected from 53 Murrah bulls, maintained at Buffalo Frozen Semen Station, Tamil Nadu Co-operative Milk Producers Federation Ltd, Erode was utilized for this study. The possible factors such as age and body weight which influence the semen volume were studied. The statistical analysis of the data was carried out after angular transformation of the percentage as per Snedecor and Cochran. While expressing the mean and standard error, the angles were reconverted into percentages to a precision of two decimals. The statistical analysis was also carried out by using least-squares procedurewherever unequal and disproportionate numbers were encountered. The overall least-squares means (±S.E) of semen volume (ml) of first ejaculate was 3.24 ± 0.04 and the corresponding value was 2.99 ± 0.03 for the second ejaculate. Semen volume per collection among Murrah bulls increased positively with increase in age and body weight.

People have created a massive number of opinion content on the Web to support their opinions about goods and services that have a high commercial value in their everyday lives. For polarization, these remark sentences sometimes contain multiple comment components with varying sentiments, rendering the actual purpose of the sentence irrelevant. The goal of aspect-oriented sentiment analysis is just to identify the target’s perception polarities in scenario. Deep learning definitely is maturing, and it’s becoming more common to use deep learning approaches to identify emotions. The use of the convolutional neural network & the bidirectional gated recurrent device in a sentiment classification model is presented. LSTM is equivalent to BiGRU which is a time cyclic neural network with reduced processing complexity. We propose that CNN and bi-GRU be used in parallel, with weights applied to elements of the CNN and bi-GRU output. We use CNN and bi-GRU in parallel because CNN may produce more significant feature space from the original dataset than the bi-GRU components used as the CNN input. For the final sentiment categorization, the Sigmoid classifier is utilised.

Online product review plays an important role in raising the voice of customers and their purchase decision. However, a conventional method in the area has largely focused on field research and surveys to acquire information about customer preferences. The already existing frameworks largely focus on supervised learning model techniques which is not feasible in all cases as it requires a labelled corpus which might be difficult to gather at times. This paper proposes a new framework for a review of topic modelling named joint sentiment topic model. The proposed research work extracts key features of online reviews and present a detailed analysis of different approaches and techniques, comparing the reliability of diverse systems among them. Also, this research work attempts to implement topic modelling in order to find how much a customer’s occupation and interests match a particular product and accordingly give weightage to their reviews. This idea is an effort to explore different approaches and techniques of topic modelling and provide valuable executive implications for electronic goods.

People on the Internet have generated a large amount of commentary data to share their opinions about products and services in their daily lives which include large commercial value. For these comment sentences, they often include several comment aspects, and the sentiment varies on these aspects, making the overall meaning of the sentence meaningless for polarization. The purpose of the aspect-level sentiment classification is to recognize target’s sense extremity in context. Deep Learning is evolving in an increasingly mature direction, and the utilization of deep learning methods to detect emotion has become increasingly popular. A sentiment classification model is propsoed by combining a convolutional neural network and a bidirectional gated recurrent unit.Bidirectional gated recurrent unit is similar to Long short-term memory, a time cyclic neural network with a lesser processing complexity. The model first extracts sequence features of the text through the bidirectional gated recurrent unit and then extracts the local static features of the text through the convolutional neural network. Finally, the Sigmoid classifier is used for the final sentiment classification.

The recent years have witnessed a significant growth in the data collected from the reviews posted on various websites. Reviews are a direct way of getting the response of the customers and clients of any business, making it a convenient way for getting feedback for marketing, performance and other such characteristics in association with any product or service. The opinions mined from these collections of data can provide strategies to improve the sales based on how well a product is received. This is done in two steps, first being the Subjectivity Detection followed by Sentiment Analysis. For this process, various methods have been already introduced in this field. These vary from SVMs, Naive-Bayesian, deep learning etc. Since, English is the most commonly used language in the world, it is not surprising that most work done in this field focuses on the same. But it is already known that there are roughly around 6500 languages used around the world. India alone has 447 languages which ranks it fourth on the list of countries with the greatest number of languages. The proposed research work focuses on sentiment classification in Hindi language text. The proposed research work has attempted to experiment with a method that does not rely on availability language dictionaries. This is done by creating a completely numerical data corresponding to the text. The model proposed in this paper will use a combination of Recurrent Neural Network and Convolutional Neural Network model to extract the subjective data form the given dataset of movie reviews.

Most of the people express their ideas, views and opinions over social media. These feedbacks or comments carry an emotion in them. This data can be either straight forward or sarcastic. This text has to be analyzed and make the reviewers understand the exact intent of the writer. Sentiment analysis is used to analyze the perspective of text. Sarcasm can also be present in the text which is a bitter way of conveying the information. Selection of the dataset is the initial task. Dataset is retrieved from Amazon datasets. The next task is preprocessing of data which includes tokenization of data, polarity identification, stemming and lemmatization. Later, feature extraction is done, which includes term frequency, Inverse document frequency and n-gram. The classification algorithms are used such as Support Vector Machine (SVM), K Nearest Neighbors and Random forest are implemented. Further the calculation of results is evaluated by the parameter accuracy.

Sarcasm is consistently depicted as a circumstance where the customer will in general utilize positive words to pass on a negative opinion via online media. Leaps and bounds in business innovation have added to the tenacious flood and overall choice of web-based media. Which brings out plentiful information in the form of comments and tweets. Due to which it prompts a few inconsistencies in the current methodology of sentiment analysis. It's crucial to analyze the intended sarcasm in user words for effective automatic sentiment analysis of gathered information. We present a first report in distinguishing sarcasm by consolidating both Pattern-based methodology and Numerical sarcasm detection module where the tweets that are delegated non-sarcastic in Pattern-based methodology are fed into the Numerical sarcasm detection module to recognize sarcasm present in numerical bits of the content. At first, to get an understanding into the problem, we execute a rule-based methodology. Be that as it may, the genuine framework set up is the Deep learning (DL) model, which outperform other state-of-the-art methods on dataset of tweets containing numbers. It shows that extraordinary thoughtfulness regarding text containing numbers might be valuable to improve cutting edge in sarcasm detection.