Search results for: Adarsh%20Shroff

Authors: Adarsh Shroff

Abstract:

Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining.

Keywords: big data, map reduce, incremental processing, iterative computation

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Authors: Dhruveshi B. Rana, Nirav P. Vaghela, Jigar N. Mehta

Abstract:

Objective: To assess the relationships among age, gender, anthropometrics and dynamic balance in 5 to 12 years of children in Anand city. Method: Cross-sectional study was conducted. 150 school going children of 5-12 (75-girls, 75-boys) years were recruited from the school of the Anand city-Shivam English Medium school, Veer Vithalbhai Patel school, Adarsh Primary school. Height, weight, arm length, and foot length were measured in 150 children of 5 to 12 years. Dynamic balance was assessed using Time Up and Go Test, Functional Reach Test, Pediatric Balance Scale. Results: Positive relationship (r = 0.58 and r= 0.77) were found between increasing age and FRT and PBS scores. A negative relationship (r = - 0.46) was observed between age of boys and TUG test. Significant gender by age group difference was observed in FRT. Arm length and height has the strongest influence on FRT, and age, height, foot length; and arm length has the strongest influence on PBS. Conclusions: Age and arm length have the strongest relationship with the dynamic balance (FRT, PBS). Dynamic balance ability is directly related to the age. It helps the pediatric therapists in selecting dynamic balance test according to the age.

Keywords: age, gender, anthropometric, dynamic balance

Procedia PDF Downloads 261

Authors: Yogesh Kumar, Adarsh Kumar

Abstract:

A body farm is a research facility where research is done on forensic investigation and medico-legal disciplines like forensic entomology, forensic pathology, forensic anthropology, forensic archaeology, and related areas of forensic veterinary. All the research is done to collect data on the rate of decomposition (animal and human) and forensically important insects to assist in crime detection. The data collected is used by forensic pathologists, forensic experts, and other experts for the investigation of crime cases and further research. The research work includes different conditions of a dead body like fresh, bloating, decay, dry, and skeleton, and data on local insects which depends on the climatic conditions of the local areas of that country. Therefore, it is the need of time to collect appropriate data in managed conditions with a proper set-up in every country. Hence, it is the duty of the scientific community of every country to establish/propose such facilities for justice and social management. The body farms are also used for training of police, military, investigative dogs, and other agencies. At present, only four countries viz. U.S., Australia, Canada, and Netherlands have body farms and related facilities in organised manner. There is no body farm in Asia also. In India, we have been trying to establish a body farm in A&N Islands that is near Singapore, Malaysia, and some other Asian countries. In view of the above, it becomes imperative to discuss the matter with Asian countries to collect the data on decomposition in a proper manner by establishing a body farm. We can also share the data, knowledge, and expertise to collaborate with one another to make such facilities better and have good scientific relations to promote science and explore ways of investigation at the world level.

Keywords: body farm, rate of decomposition, forensically important flies, time since death

Procedia PDF Downloads 44

Authors: Shilpa Varghese, Adarsh Eregowda

Abstract:

Introduction: The normal development and maturation of the central nervous system is significantly influenced by thyroid hormones. Small for gestational age (SGA) babies have a distinct hormonal profile than kids born at an acceptable birth weight for gestational age, according to several studies (AGA). In SGA babies, thyroid size is larger when expressed as a percentage of body weight, indicating that low thyroid hormone levels throughout foetal life may be partially compensated for. Numerous investigations have found that compared to full-term and preterm AGA neonates, SGA babies exhibit considerably decreased thyroid plasma levels. According to our hypothesis, term and preterm SGA newborns have greater thyroid-stimulating hormone (TSH) concentrations than those that are normal for gestational age (AGA) and a higher incidence of thyroid dysfunction. Need for the study: Clinically diagnosed Assessment of term SGA babies confirming thyroid dysfunction unclear Requirement and importance of ft4 along with tsh and comparative values of ft4 in SGA babies as compared to AGA babies unclear. Inclusion criteria : SGA infants including preterm (<37 weeks of gestation) term (37-40 weeks) – comparing with preterm and term AGA infants. 3.76 7.66 0 2 4 6 8 10 12 AGA Babies SGA Babies Mean Mean TSH Comparison 2.73 1.52 0 0.5 1 1.5 2 2.5 3 3.5 4 AGA Babies SGA Babies Mean Mean FT4 Comparison Discussion : According to this study, neonates with SGA had considerably higher TSH levels than newborns with AGA. Our findings have been supported by results from earlier research. The TSH level range was established to 7.5 mU/L in the study by Bosch-Giménez et al, found greater TSH concentrations in SGA newborns. Thyroid hormone levels from newborns that are tiny for gestational age were found to be higher than AGA in our investigation. According to Franco et al., blood T4 concentrations are lower in both preterm and term SGA infants, while TSH concentrations are only noticeably greater in term SGA infants compared to AGA ones. According to our study analysis, the SGA group had considerably greater FT4 concentrations. Therefore, our findings are consistent with those of the two studies that SGA babies have a higher incidence of transient hypothyroidism and need close follow-up. Conclusions: A greater frequency of thyroid dysfunction and considerably higher TSH values within the normal range were seen in preterm and term SGA babies. The SGA babies who exhibit these characteristics should have ongoing endocrinologic testing and periodic TFTs.

Keywords: thyroid hormone, thyroid function tests, small for gestationl age, appropriate for gestational age

Procedia PDF Downloads 35

Authors: Warda Khan, Remo Lachmann, Adarsh S. Garakahally

Abstract:

The increasing complexity of software and its applicability in a wide range of industries, e.g., automotive, call for enhanced quality assurance techniques. Test automation is one option to tackle the prevailing challenges by supporting test engineers with fast, parallel, and repetitive test executions. A high degree of test automation allows for a shift from mundane (manual) testing tasks to a more analytical assessment of the software under test. However, a high initial investment of test resources is required to establish test automation, which is, in most cases, a limitation to the time constraints provided for quality assurance of complex software systems. Hence, a computer-aided creation of automated test cases is crucial to increase the benefit of test automation. This paper proposes the application of machine learning for the generation of automated test cases. It is based on supervised learning to analyze test specifications and existing test implementations. The analysis facilitates the identification of patterns between test steps and their implementation with test automation components. For the test case generation, this approach exploits historical data of test automation projects. The identified patterns are the foundation to predict the implementation of unknown test case specifications. Based on this support, a test engineer solely has to review and parameterize the test automation components instead of writing them manually, resulting in a significant time reduction for establishing test automation. Compared to other generation approaches, this ML-based solution can handle different writing styles, authors, application domains, and even languages. Furthermore, test automation tools require expert knowledge by means of programming skills, whereas this approach only requires historical data to generate test cases. The proposed solution is evaluated using various multi-label evaluation criteria (EC) and two small-sized real-world systems. The most prominent EC is ‘Subset Accuracy’. The promising results show an accuracy of at least 86% for test cases, where a 1:1 relationship (Multi-Class) between test step specification and test automation component exists. For complex multi-label problems, i.e., one test step can be implemented by several components, the prediction accuracy is still at 60%. It is better than the current state-of-the-art results. It is expected the prediction quality to increase for larger systems with respective historical data. Consequently, this technique facilitates the time reduction for establishing test automation and is thereby independent of the application domain and project. As a work in progress, the next steps are to investigate incremental and active learning as additions to increase the usability of this approach, e.g., in case labelled historical data is scarce.

Keywords: machine learning, multi-class, multi-label, supervised learning, test automation

Procedia PDF Downloads 90