Prediction of Machining Conditions Using Machine Learning

The new blast of Machine Learning (ML) and Artificial Intelligence (AI) shows extraordinary expectations in the forward leap of additive manufacturing (AM) process displaying, which is an important step toward determining the cycle structure-property relationship. The advancement of standard AI apparatuses in information science was primarily attributed to the extraordinarily huge amount of named informational collections, that may be obtained throughout the trials or first-rate reenactments. To completely take advantage of the force of AI in AM metal while lightening the reliance on "enormous information", everybody set an Improved Neural Network (INN) structure if the wires the two information and first actual standards include the preservation laws of energy, mass, and energies, towards the NN to illuminate the growing experiences. We suggest compressed-type strategies in the Dirichlet limit regulation in light of a Heaviside capability, that may precisely uphold the BCs and speed up the growing experience. The hotel structure was applied to two agent metal assembling issues, that includes the NIST AM-Benchmark series test. The examinations show that the Motel, owing to the extra actual information, may precisely foresee the temperature and also liquefy pool elements throughout the AM processes in metal along a moderate measure of named informational collections

Prescription Based Recommender System for Diabetic Patients Using Efficient Map Reduce

Healthcare sector has been deprived of leveraging knowledge gained through data insights, due to manual processes and legacy record-keeping methods. Outdated methods for maintaining healthcare records have not been proven sufficient for treating chronic diseases like diabetes. Data analysis methods such as Recommendation System (RS) can serve as a boon for treating diabetes. RS leverages predictive analysis and provides clinicians with information needed to determine the treatments to patients. Prescription-based Health Recommender System (HRS) is proposed in this paper which aids in recommending treatments by learning from the treatments prescribed to other patients diagnosed with diabetes. An Advanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering is also proposed to cluster the data for deriving recommendations by using winnowing algorithm as a similarity measure. A parallel processing of data is applied using map-reduce to increase the efficiency & scalability of clustering process for effective treatment of diabetes. This paper provides a good picture of how the Map Reduce can benefit in increasing the efficiency and scalability of the HRS using clustering

An Investigation of Artificial Neural Network Based Prediction Systems in Rain Forecasting

The present research work is about to disaster mitigation using the applications of ANN. The ANN is used in the number of diverse fields due to its ability to model non linear patterns and self adjusting (learning) nature to produce consistent output when trained using supervised learning. This study utilizes Backpropagation Neural Network to train ANN models to mitigation of disaster through forecasting of Rainfall conditions of hilly areas of Uttarakhand (India). We used 12 learning algorithms of BPNN (as- traingd, traingdx, trainbfg, trainlm, etc.) to conduct 1296 models using supervised learning (MatLab command window) and the results are assessed using Mean Square Error (MSE). DOI: 10.17762/ijritcc2321-8169.15085

Improvement of faba bean (Vicia faba L.) yield and quality through biotechnological approach: A review

Faba bean (Vicia faba L.), an old-world grain legume, is grown approximately in 3 m/ha area world-wide from temperate, tropical to hot arid conditions. It is such a wonderful legume that it can excel even under adverse soil conditions; acidic or saline alkaline (pH 4.5 to 9.0). In favourable conditions, it gives very high yields, but low yield may result from biotic and abiotic stress. In India, it is still treated as minor legume. Genetic transformation based on Agrobacteria is possible. Several random amplified polymorphic DNA (RAPD) markers linked to a gene determining hypersensitive resistance to race 1 of the rust (Uromyces viciae-fabae) have been reported. Molecular breeding for resistance to broomrape, Ascochyta blight, rust, and chocolate spot have been obtained. The use of marker assisted selection (MAS) can complement conventional breeding by speeding up the selection of desirable traits and increasing selection efficiency. Recently, markers linked to a gene controlling growth habit or to select against traits affecting the nutritional value of seeds have also been reported. Lack of suitable cultivar can be easily overcome by application of modern tools and techniques. Several in-vitro techniques would be very useful for faba bean breeding. New techniques such as protoplast fusion, regeneration, and embryo-rescue assisted interspecific crossing could probably be introduced to V. faba L. to improve yield and quality. This review work examines the role of various techniques with reference to faba bean improvement.Key words: Vicia faba, faba bean, grain quality, resistance breeding, nitrogen fixation, zero tannin

Validation of the Wiedemann-Franz Law in solid and molten tungsten above 2000 K through thermal conductivity measurements via steady state temperature differential radiometry

We measure the thermal conductivity of solid and molten tungsten using Steady State Temperature Differential Radiometry. We demonstrate that the thermal conductivity can be well described by application of Wiedemann-Franz Law to electrical resistivity data, thus suggesting the validity of Wiedemann-Franz Law to capture the electronic thermal conductivity of metals in their molten phase. We further support this conclusion using ab initio molecular dynamics simulations with a machine-learned potential. Our results show that at these high temperatures, the vibrational contribution to thermal conductivity is negligible compared to the electronic component

Integration of EHR and PHR leveraging cloud services for approving treatments

Historically clinicians have been prescribing treatment to patients based on their visit to the hospital without referring to previous health records of the patients. This is primarily because clinicians do not have access to patient’s medical records. Though, with digital revolution in healthcare domain, patient’s medical history is available via Patient Health Records using Electronic Health Records (EHR) and Personal Health Records (PHR) systems. However, as EHR and PHR are maintained as separate systems in isolated manner, efficient accessibility of these systems is still very limited. To provide a unified view of patient data to the providers and the patients, integrated data resulting from such system not only allow to access past health records of patients, but further leverage PHRs data to provide quality treatment by examining the effect of treatment on patient’s health. Data integration techniques such as ETL and Cloud based technologies can be used to develop a system which provides real-time integration of healthcare data sourced & streamed from various healthcare systems. It will leverage the recommendations of quality treatments to the patients as well as the clinicians from cohesive view of integrated EHR and PHR data.&nbsp

Short Communication: Waterbird species distribution between natural and manmade wetland in Himalayan foothills of Uttarakhand, India

Abstract. Saini V, Joshi K, Bhatt D, Singh A, Joshi R. 2017. Short Communication: Waterbird species distribution between natural and manmade wetland in Himalayan foothills of Uttarakhand, India. Biodiversitas 18: 334-340. A comparative study on waterbird diversity and abundance was conducted at natural and man-made wetland of District Hardwar from 2010 to 2013. A total of 37 waterbirds belonging to 11 families were recorded of which 14 species were winter migrant in the wetlands of the study area. Among these waterbird species, two species viz. Black-necked Stork, Ephippiorhynchus asiaticus; River Lapwing, Vanellus duvaucelii were near threatened (IUCN status Ver. 2013.1.) and two species namely Woolly-necked Stork, Ciconia episcopus; and  Marbled Duck, Marmaronetta angustirostris were Vulnerable (IUCN status). The avian species diversity and abundance were recorded significantly high (t = 4.16, p < 0.01) at natural wetland site. It is also observed that vegetation variety and food availability is the responsible causes of waterbird species variation in the natural and manmade wetland. The results of this study suggest that freshwater natural wetland site is more suitable habitat for short and long-distance water migratory birds. This natural wetland should be protected to enhance the abundance and diversity of water migrant community. Keywords: Diversity, man-made wetland, migratory species, natural wetlan

COVID Crisis and Tourism Sustainability: An Insightful Bibliometric Analysis

The pernicious impact of COVID-19 on all the aspects of travel and tourism has posed a question of tourism sustainability before policymakers and researchers. This research aims to cast light on the bibliometric construct and knowledge structure of the contemporaneous research that evolved around tourism sustainability amid COVID-19. Bibliometric methods of performance analysis and science mapping were used to analyze a total of 440 bibliographic records retrieved from the Scopus database. The major findings showed sustainability as a trending area of tourism research amid COVID-19 and revealed the concentration of research in three prime domains: Management and sustainable development of tourism, environmental health, and mobility trends in the context of COVID-19 pandemic. These areas may be perceived as the recent domains, and they are imperative for future research