Clustering of Non-Associated Item Sets for Analyzing Show Room Sales Dataset

Market basket analysis (MBA) is a well-liked method for identifying relationships between products that people purchase in a database. It is predicated on association rule mining (ARM), a data mining technique that pulls valuable data from huge databases. Due to consumers using internet applications for online shopping and insurance, an ever-increasing amount of data is generated online. It produces large amounts and, if mined effectively, will greatly benefit society as a whole as well as individuals. So, numerous data science and machine learning-related techniques have been created to gradually unlock the potential. The Clustering of Non-Associated Item Sets (CNAIS) of the Sales dataset used in the Showroom for choosing customers for benefits and web application design is discussed in this study. The CNAIS algorithm implementation process and dataset for this study are discussed

Plant regeneration from leaf-derived callus in Plectranthus barbatus Andr. [Syn.: Coleus forskohlii (Wild.) Briq.]

A protocol was developed for Plectranthus barbatus for high frequency shoot organogenesis from leaf derived callus of aseptically grown plantlets derived from mature plant explants of high yielding clones (yield of forskolin 1.5 to 1.9%) on Gamborg medium (B5) medium supplemented with 2 mg/l 2,4- dichloro phenoxy acetic acid (2,4-D). Shoots were regenerated from the callus on MS medium supplemented with 6-benzyl amino purine (BAP) (2 mg/l) + naphthalene acetic acid (NAA) (1 mg/l). The highest rate of shoot multiplication was achieved at the 6th subculture and more than 2000 shoots were produced from callus clump. Roots were induced from shoots of in vitro grown plantlets on basal half strength MS medium and high rooting frequencies were obtained. Regenerated plants were easily acclimatized in greenhouse conditions and later transferred to soil with 100% survival. The procedure here allows the micropropagation of P. barbatus in five months of culture and proliferated cell masses which could be used for studies of organic compounds of pharmaceutical interest.Keywords: Callus culture, medicinal plant, root induction, shoot organogenesisAfrican Journal of Biotechnology Vol. 12(18), pp. 2441-244

Minimizing the Cost and Enhancing the Security for Cloud Hosting Service

Cloud computing is a new emerging technology which provides different kinds of services through internet and reduces maintenance cost as well as provides data reliability. Now a days many cloud service providers are providing services with different policies and prices. But it leads difficult to customers to choose cloud services with less price. That clients as a rule put their information into a single cloud and after that just trust to luckiness. Proposing new data hosting technique termed as CHARM which supports two strong functionalities.The first is selecting a few suitable clouds and a fitting excess system to store information with minimized financial expense and ensured accessibility.The second is setting off a move procedure to re-disperse information as indicated by the varieties of information access example and evaluating of clouds.In this project defining secret sharing algorithm to reduce the risk of data intrusion and the loss of service availability in the cloud and ensure data integrity. Finally proposed scheme not only saves the cost and also Maintains data and price adjustments

Encapsulation of Contaminated Soil in Concrete Mortar

A mixture of arsenic-contaminated soil and mortar was developed to study the effect of arsenic-contaminated soil on the strength of mortar and the effectiveness of the mortar in containing the arsenic. Preliminary laboratory analyses were conducted with spiked soil samples to verify percent recovery of the toxicity characteristic leaching procedure (TCLP). Five parameters were chosen for variation during the investigation: amount of contaminated soil (as a weight percentage of sand content), concentration of arsenic in the contaminated soil, water-cement ratio (w/ c), amount of fly ash, and amount of silica fume. Compressive strength tests and TCLP tests were conducted on mortar samples. The test results showed that with the changes in the w/ c, silica fume, and fly ash contents in the presence of arsenic, the compressive strength slightly increased from 7 to 28 to 90 days but did not show any major effect on the TCLP tests. The TCLP results were at the level (3 to 20 ppb) predicted to be established as the Drinking Water Equivalent Level (DWEL) by the United States Environmental Protection Agency (USEPA), indicating that mortar containing arsenic and contaminated soil may be a valid disposal solution

Kurtosis-Based Feature Selection Method using Symmetric Uncertainty to Predict the Air Quality Index

Feature selection is vital in data pre-processing in machine learning, and it is prominent in datasets with many features. Feature selection analyses the relevant, irrelevant, and redundant features in the dataset. Feature selection removes the irrelevant features, which improves both the accuracy and prediction performance. The significant advantages of reducing the number of features from the dataset are reducing the training time, reducing overfitting, decreasing the curse of dimensionality, and simplifying the prediction model. The filter feature selection techniques can handle the issues with the high number of features, and this paper uses the symmetric uncertainty coefficient to verify the relevance of the independent features. In this paper, a new feature selection method named as kurtosis-based feature selection has been proposed to select the relevant features which affect the air pollution. Kurtosis-based feature selection is compared with seven filter feature selection techniques on air pollution dataset and validated the performance of the proposed algorithm. It has been observed that the kurtosis-based feature selection extracts only PM2.5 as the key feature and has been compared to the accuracy of the five existing methods. The experimental results illustrate that the kurtosis-based feature selection algorithm reduces the original feature set up to 91.66\%, but the existing filter feature selection techniques reduce the feature set to only 50\%

Neurodevelopmental assessment of neonates with congenital hypothyroidism in a tertiary care center

Background: Thyroid hormones play a crucial role in early neurodevelopment so that untreated severe congenital hypothyroidism (CH) results in neurological and psychiatric deficits, including intellectual disability, spasticity, and disturbances of gait and coordination. Objective: The aim of this study is to assess the neuromotor and neurocognitive development of babies at 12 months of age who are diagnosed to have CH and initiated on thyroxine treatment at birth. Furthermore, to estimate the occurrence of CH among babies delivered in our hospital through thyroid screening. Study Design: This was a descriptive study. Setting: The study was conducted in Sree Gokulam Medical College Hospital and Research Foundation, a tertiary care center, located in Trivandrum, Kerala. Participants: All babies delivered in the hospital during the study period and neonatal screening was done for CH. Methods: Cord blood was collected as the direct flow of blood from cord, labeled and sent to laboratory from delivery room for analysis (by chemiluminescent assay) of thyroid-stimulating hormone (TSH). Babies with cord blood TSH level ≥20 uIU/ml were repeated at 72 h of age for venous TSH and FT4 level. Venous TSH level ≥20 uIU/ml with low FT4 (below normal range for age) was considered as abnormal and initiated on treatment before discharge from the hospital. These babies were followed till 12 months of age for neurodevelopmental assessment. Results: The occurrence of CH among babies delivered in our hospital was 0.6 in 100 live births. Neurodevelopmental assessment of babies with CH on follow-up has shown normal neuromotor and neurocognition at 12 months of age with early detection and prompt initiation of L-thyroxine at a higher dose range within 5 days of age. Conclusion: In our study, early detection and initiation of treatment in infants with CH have shown normal neuromotor and neurocognitive development at 12 months of age

Oxytocin and neonatal hyperbilirubinemia: A prospective cohort study

Objective: To study the incidence of neonatal jaundice requiring phototherapy in babies exposed to maternal oxytocin for inductionof labor and to compare the increase in neonatal jaundice in oxytocin group with the jaundice in babies born of spontaneous labor.Materials and Methods: This prospective cohort study was conducted in the neonatal unit of a tertiary care center in Trivandrum,Kerala over a period of 6 months. After exclusion, 308 babies were divided in two groups, Group A (babies exposed to maternaloxytocin for induction of labor) or Group B (babies born of spontaneous labor with oxytocin use for augmentation of labor). Babies wereobserved daily for clinical jaundice till discharge. Results: Incidence of neonatal jaundice in Group A was 52% and in Group B was 12%with relative risk 4.3 (95% confidence interval: 2.69-6.73). Conclusion: Our study shows that maternal oxytocin used for inductionof labor increase the incidence of neonatal jaundice, and it is logical to prevent hyperbilirubinemia by reducing the dose of oxytocin

Parallel and Distributed Closed Regular Pattern Mining in Large Databases

Abstract Due to huge increase in the records and dimensions of available databases pattern mining in large databases is a challenging problem. A good number of parallel and distributed FP mining algorithms have been proposed for large and distributed databases based on frequency of item set. Not only the frequency, regularity of item also can be considered as emerging factor in data mining research. Current days closed itemset mining has gained lot of attention in data mining research. So far some algorithms have been developed to mine regular patterns, there is no algorithm exists to mine closed regular patterns in parallel and distributed databases. In this paper we introduce a novel method called PDCRP-method (Parallel and Distributed closed regular pattern) to discover closed regular patterns using vertical data format on large databases. This method works at each local processor which reduces inter processor communication overhead and getting high degree of parallelism generates complete set of closed regular patterns. Our experimental results show that our PDCRP method is highly efficient in large databases

Studies on the Energy Band Discontinuities in SnS/ZnMgO Thin Film Heterojunction

AbstractTin sulfide (SnS), with a direct energy band gap of 1.3eV and high absorption coefficient >104cm-1 is a promising candidate for application as an absorber material for solar cell fabrication. Although several research groups have fabricated SnS-related solar cells, the reported efficiencies were low (< 3.0%). One of the reasons for this low efficiency is the mismatch at the band edges in the heterojunction. In the present work, SnS films have been synthesized by sulfurization of Sn layers deposited by evaporation and RF sputtering onto SnO2 coated glass. The structural properties of the layers were discussed. All RF sputtered Zn0.8Mg0.2O (ZMO) and ZnO:Ga were coated on SnS as buffer and window layers respectively. The band offsets at the SnO2/SnS and ZMO/SnS heterojunctions were evaluated using X-ray photoelectron spectroscopy measurements. The conduction band (ΔEc) and valence band (ΔEv) discontinuities were determined as approximately 1.0eV and 3.5eV for SnO2/SnS and 0.4eV and 1.7eV for ZMO/SnS junction. The related energy band diagram was constructed and the results discussed