Obtaining Approximation using Map Reduce by Comparing Inter-Tables

Size of the data increasing day by day because of digital world at unpredictable rate. Basically size of raw data is increasing so deal with such rough data is the challenging task and we need to acquire knowledge from such a colossal data. Number of techniques are used to retrieve knowledge from raw data like genetic algorithm, fuzzy sets and rough set. Rough set is very popular method and basically depends upon approximation i.e. lower approximation, upper approximation, boundary region. Hence, the effective computation of approximation is important pace in improving the performance of rough set. There are number of ways to calculate this approximation. In our system we have calculated rough approximation independent of each other. This can be achieved by dividing input dataset, so that we can also reduce number of comparison. The division of dataset based on decision attribute in the dataset. In our paper, we have explained a new method for computing rough set approximation. Using map-reduce we can deal with massive data and able to compute rough approximation for massive dataset

A Systematic Literature Review With Bibliometric Meta-Analysis Of Deep Learning And 3D Reconstruction Methods In Image Based Food Volume Estimation Using Scopus, Web Of Science And IEEE Database

Purpose- Estimation of food portions is necessary in image based dietary monitoring techniques. The purpose of this systematic survey is to identify peer reviewed literature in image-based food volume estimation methods in Scopus, Web of Science and IEEE database. It further analyzes bibliometric survey of image-based food volume estimation methods with 3D reconstruction and deep learning techniques. Design/methodology/approach- Scopus, Web of Science and IEEE citation databases are used to gather the data. Using advanced keyword search and PRISMA approach, relevant papers were extracted, selected and analyzed. The bibliographic data of the articles published in the journals over the past twenty years were extracted. A deeper analysis was performed using bibliometric indicators and applications with Microsoft Excel and VOS viewer. A comparative analysis of the most cited works in deep learning and 3D reconstruction methods is performed. Findings: This review summarizes the results from the extracted literature. It traces research directions in the food volume estimation methods. Bibliometric analysis and PRISMA search results suggest a broader taxonomy of the image-based methods to estimate food volume in dietary management systems and projects. Deep learning and 3D reconstruction methods show better accuracy in the estimations over other approaches. The work also discusses importance of diverse and robust image datasets for training accurate learning models in food volume estimation. Practical implications- Bibliometric analysis and systematic review gives insights to researchers, dieticians and practitioners with the research trends in estimation of food portions and their accuracy. It also discusses the challenges in building food volume estimator model using deep learning and opens new research directions. Originality/value- This study represents an overview of the research in the food volume estimation methods using deep learning and 3D reconstruction methods using works from 1995 to 2020. The findings present the five different popular methods which have been used in the image based food volume estimation and also shows the research trends with the emerging 3D reconstruction and deep learning methodologies. Additionally, the work emphasizes the challenges in the use of these approaches and need of developing more diverse, benchmark image data sets for food volume estimation including raw food, cooked food in all states and served with different containers

The Body Shop "Forever Against Animal Testing”

Body Shop is a well-known cruelty-free cosmetics brand company. This research paper explores how Body Shop is running the campaign 'forever against animal testing' and raising its voice for banning animal testing in cosmetics. The Body Shop has been advocating for animal rights since 1989. Qualitative analysis techniques have been used in this research paper and information is obtained through a questioner focused on convenient sampling. We have discovered in our research that most consumers do not want to purchase goods which are created by harming animals. In manufacturing cosmetics, we say companies must use alternative artificial testing like Body Shop

Anesthetic management of laparoscopic dual renal transplantation

Since the first laparoscopic nephrectomy was reported in 1991, this technique has evolved rapidly and laparoscopic donor nephrectomy has emerged as a standard of care in many institutions. However, recipient renal transplantation is still performed by the traditional open approach. There is only one case report of laparoscopic kidney transplantation (LKT) from Spain in 2009. LKT is a technically demanding surgery for a urologist and equally challenging for an anesthesiologist as he has to be vigilant because of the major perturbations in the cardiorespiratory system due to steep trendelenberg position and pneumoperitoneum; additionally, the pneumoperitoneum can have deleterious effects on blood flow and function of the transplanted kidney. We herewith present our experience with anesthesia of the first laparoscopic dual kidney transplantation from a deceased donor performed in our center

Prenatal sex and other preferences for reproductive career of final year graduation girl students

Background: Marriage of girls just after graduation is common in Western Maharashtra. This study was planned to know the views of final year graduation student towards reproductive carrier. Aim: To interact with final year girl students of various streams to know their preferences on various aspects of reproductive carrier and contraceptive awareness. Material and Methods: Study-design: Cross-sectional. Study-setting: Academic institutes of Sangli-Miraj-Kupwad Corporation area. Study-subject: All willing final year Girl students. Exclusion Criteria: Married girls. Sample size: All final year girl students Sampling Technique: Cluster sample Study-Duration: 7 months. Study-tool: Pretested questionnaire. Statistical Analysis: Percentages, Chi-square test. Results: All girls who have responded prefer marrying and having first child at right age. All feel spacing is needed, at least of 2 years. Two children was the most common choice (52.3%). Forty-three percent girls feel male child is must and 52.3% of total girls will like to have sex determination done if required. Total 47.24% girls were unaware about any contraceptive methods but 88.2% girls knew the place of its availability. Most common source of information about contraceptive was school and friends. E-pill was known to 41.5% of girls. All girls felt the need for more information about reproductive health and according to 81.3% right age for it is 15-18 years. Conclusion: Girls have correct reproductive preferences except sex of child. Sex preference and Low contraceptive awareness needs strong intervention

Venous air embolism: A complication during percutaneous nephrolithotomy

Venous air embolism during percutaneous nephrolithotomy (PCNL) following air pyelogram or saline irrigation has been occasionally reported. We present a case of suspected venous air embolism during air pyelogram in a patient undergoing PCNL. The clinical diagnosis of air embolism was made by fall in end tidal carbon dioxide, blood pressure and Oxygen saturation and was conservatively managed. Early diagnosis with rapid resuscitation is the key to management of a patient with air embolism

ONTOLOGY EXTRACTION FOR AGRICULTURE DOMAIN IN MARATHI LANGUAGE USING NLP TECHNIQUES

Ontology is defined as shared specification of conceptual vocabulary used for formulating knowledge-level theories about a domain of discourse. Dataset is created by manually collecting information about different diseases related to crops. Ontology modeling is used for knowledge representation of various domains. India is an agricultural based economic country. Majority of Indian population relies on farming but the technologies are sparsely used for the aid of farmers. Ontology based modeling for agricultural knowledge can change this scenario. The farmers can understand it easily in their native language. We proposed a system which will model and extract knowledge in Marathi language. In this paper, we review various existing agriculture ontology’s along with some of Natural Language Processing (NLP) models. Model ontology for agriculture domain system aims to retrieve relevant answers to the farmer’s query. We explored Rule-Based and Conditional Random Fields based models for Ontology extraction. The extraction methods and preprocessing phases of proposed system is discussed

Single-incision total laparoscopic hysterectomy

Single-incision laparoscopic surgery is an alternative to conventional multiport laparoscopy. Single-access laparoscopy using a transumbilical port affords maximum cosmetic benefits because the surgical incision is hidden in the umbilicus. The advantages of single-access laparoscopic surgery may include less bleeding, infection, and hernia formation and better cosmetic outcome and less pain. The disadvantages and limitations include longer surgery time, difficulty in learning the technique, and the need for specialized instruments. Ongoing refinement of the surgical technique and instrumentation is likely to expand its role in gynecologic surgery in the future. We perform single-incision total laparoscopic hysterectomy using three ports in the single transumbilical incision

A Study of the Recent Trends of Immunology : Key Challenges, Domains, Applications, Datasets, and Future Directions

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in‐depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.