AUTO-CDD: automatic cleaning dirty data using machine learning techniques

Cleaning the dirty data has become very critical significance for many years, especially in medical sectors. This is the reason behind widening research in this sector. To initiate the research, a comparison between currently used functions of handling missing values and Auto-CDD is presented. The developed system will guarantee to overcome processing unwanted outcomes in data Analytical process; second, it will improve overall data processing. Our motivation is to create an intelligent tool that will automatically predict the missing data. Starting with feature selection using Random Forest Gini Index values. Then by using three Machine Learning Paradigm trained model was developed and evaluated by two datasets from UCI (i.e. Diabetics and Student Performance). Evaluated outcomes of accuracy proved Random Forest Classifier and Logistic Regression gives constant accuracy at around 90%. Finally, it concludes that this process will help to get clean data for further analytical process

An Efficient Microcontroller Based Sun Tracker Control for Solar Cell Systems

The solar energy is fast becoming a different means of electricity resource. Now in world Fossil fuels are seriously depleting thus the need for another energy source is a necessity. To create effective utilization of its solar, energy efficiency must be maximized. An attainable way to deal with amplifying the power output of sun-powered exhibit is by sun tracking. This paper presents the control system for a solar cell orientation device which follows the sun in real time during daytime

Embryonic development and growth performances of an endangered fish species Nandus nandus: effects of dietary polyunsaturated fatty acids supplementation

The present study explored the embryonic and larval development of an endangered fish species Nandus nandus and resolved larval growth performances with the dietary supplementation of different types of lipids. Fertilized eggs were collected from fiber glass tanks immediately after spontaneous spawning of N. nandus, which were fed with a 1% phospholipid (squid meal) supplemented diet for 3 months. Fertilized eggs were transparent, spherical, yellowish and sticky in nature. The first cleavages of eggs were observed 0.3±0.01 h post fertilization at 26°C water temperature. Hatching started around 18 h post-fertilization and newly hatched larvae were found to be 1.2±0.1 mm in length. First feeding started 64.0±0.30 h post hatching. After rearing for 10 days, they were divided into 4 groups and separately fed with only dry tubificid worms, 1% docosahexaenoic (DHA) supplemented with dry tubificid worm, 1% phospholipid supplemented with dry tubificid worm and live tubificid worms as treatment I, treatment II, treatment III and treatment IV, respectively. After 50 days of the trial, larvae of treatment II showed significantly (p<0.01) higher growth performances in length: 3.18±0.13 cm, weight: 339.8 ± 36.94 mg and survival rate: 78±2 % which were comparable to that of treatment IV, which showed the highest (p<0.01) length of 3.4±0.1 cm, weight of 406.6±27.99 mg and survival rate of 97±1 %. Larvae in treatment I showed the lowest growth performances in length: 2.73 ± 0.16 cm, weight: 259.8±29.97 mg and survival rate of 63±3 %. As this is the first record for the determination of embryonic and larval development of N. nandus with different lipid supplemented diets, it might be possible to save this endangered fish species by adopting this technology at field level

A Survey of Machine Learning Techniques for Self-tuning Hadoop Performance

The Apache Hadoop framework is an open source implementation of MapReduce for processing and storing big data. However, to get the best performance from this is a big challenge because of its large number configuration parameters. In this paper, the concept of critical issues of Hadoop system, big data and machine learning have been highlighted and an analysis of some machine learning techniques applied so far, for improving the Hadoop performance is presented. Then, a promising machine learning technique using deep learning algorithm is proposed for Hadoop system performance improvement

Biophysical and socioeconomic state and links of deltaic areas vulnerable to climate change: Volta (Ghana), Mahanadi (India) and Ganges-Brahmaputra-Meghna (India and Bangladesh)

We examine the similarities and differences of specific deltaic areas in parallel, under the project DEltas, vulnerability and Climate Change: Migration and Adaptation (DECCMA). The main reason for studying Deltas is their potential vulnerability to climate change and sea level rise, which generates important challenges for livelihoods. We provide insights into the current socioeconomic and biophysical states of the Volta Delta (Ghana), Mahanadi Delta (India) and Ganges-Brahmaputra-Meghna (India and Bangladesh). Hybrid methods of input-output (IO) construction are used to develop environmentally extended IO models for comparing the economic characteristics of these delta regions with the rest of the country. The main sources of data for regionalization were country level census data, statistics and economic surveys and data on consumption, trade, agricultural production and fishing harvests. The Leontief demand-driven model is used to analyze land use in the agricultural sector of the Delta and to track the links with final demand. In addition, the Hypothetical Extraction Method is used to evaluate the importance of the hypothetical disappearance of a sector (e.g., agriculture). The results show that, in the case of the Indian deltas, more than 60% of the cropland and pasture land is devoted to satisfying demands from regions outside the delta. While in the case of the Bangladeshi and Ghanaian deltas, close to 70% of the area harvested is linked to internal demand. The results also indicate that the services, trade and transportation sectors represent 50% of the GDP in the deltas. Still, agriculture, an activity directly exposed to climate change, plays a relevant role in the deltas'' economies-we have estimated that the complete disappearance of this activity would entail GDP losses ranging from 18 to 32%

Discrimination of Parkinsonian Tremor From Essential Tremor by Voting Between Different EMG Signal Processing Techniques

Parkinson's disease (PD) and essential tremor (ET) are the two most common disorders that cause involuntary muscle shaking movements, or what is called "tremor”. PD is a neurodegenerative disease caused by the loss of dopamine receptors which control and adjust the movement of the body. On the other hand, ET is a neurological movement disorder which also causes tremors and shaking, but it is not related to dopamine receptor loss; it is simply a tremor. The differential diagnosis between these two disorders is sometimes difficult to make clinically because of the similarities of their symptoms; additionally, the available tests are complex and expensive. Thus, the objective of this paper is to discriminate between these two disorders with simpler, cheaper and easier ways by using electromyography (EMG) signal processing techniques. EMG and accelerometer records of 39 patients with PD and 41 with ET were acquired from the Hospital of Kiel University in Germany and divided into a trial group and a test group. Three main techniques were applied: the wavelet-based soft-decision technique, statistical signal characterization (SSC) of the spectrum of the signal, and SSC of the amplitude variation of the Hilbert transform. The first technique resulted in a discrimination efficiency of 80% on the trial set and 85% on the test set. The second technique resulted in an efficiency of 90% on the trial set and 82.5% on the test set. The third technique resulted in an 87.5% efficiency on the trial set and 65.5% efficiency on the test set. Lastly, a final vote was done to finalize the discrimination using these three techniques, and as a result of the vote, accuracies of 92.5%, 85.0% and 88.75% were obtained on the trial data, test data and total data, respectively

GREEN SYNTHESIS AND CHARACTERIZATION OF SILVER NANOPARTICLES USING CORIANDRUM SATIVUM LEAF EXTRACT

Development of biologically inspired experimental processes for the synthesis of nanoparticles is evolving into an important branch of nanotechnology. To meet the increasing demands for commercial nanoparticles new eco-friendly “green” methods of synthesis are being discovered. In this study, synthesis of stable silver nanoparticles (AgNPs) was done using Coriandrum sativum leaf extract. UV-Vis spectrometer uses to monitor the reduction of Ag ions and formation of AgNPs in medium. XRD and SEM have been used to investigate the morphology of prepared AgNPs. The peaks in XRD pattern are associated with that of face-centered-cubic (FCC) form of metallic silver. The average grain size of silver nanoparticles is found to be 6.45 nm. TGA/DTA result associated with weight loss and exothermic reaction due to desorption of chemisorbed water. FTIR was performed to identify the functional groups of carbonyl, hydroxyl, amine and protein molecule which form a layer covering AgNPs and stabilize the AgNPs in medium

Temporal variation of condition and prey-predator status for a schilbid catfish Eutropiichthys vacha (Hamilton, 1822) in the Ganges River, northwestern Bangladesh through multi-model inferences

1229-1237The current study provide the baseline information on the temporal (monthly) variations of condition through multiple functions (allometric, KA; Fulton’s, KF; relative, KR) and prey-predator status through relative weight (WR) for Eutropiichthys vacha (Hamilton, 1822) from the Ganges River, northwestern Bangladesh over one year. The smallest individuals were 6.5 and 6.2 cm in TL, whereas the largest were 19.9 and 20.6 cm in TL for males and females, respectively. No significant differences were observed in the length frequency distribution, LFDs (p = 0.8152) for both sexes. KF was significantly correlated with TL for both sexes (p KF was treated as the best condition factor therefore, well-being of E. vachaa. There was no significant correlation among TL vs KA, TL vs KR and TL vs WR for males and females, respectively. But BW showed highly significant correlations with all condition factors, i.e., BW vs KA; BW vs KF; BW vs KR, and BW vs WR (p WR revealed no significant dissimilarities from 100 for males (p = 0.432) unlike females (p = 0.023), based on Wilcoxon signed-rank tests, suggesting that habitat was more suitable for males than females for food availability relative to predator presence. Moreover, this study assessed for the first time the effect of temperature and rainfall on monthly KF for E. vacha in the Ganges River. The Pearson correlation test found no significant relation between temperature and KF (r = 0.2226, p = 0.4868 for males; r = 0.2172, p = 0.4977 for females), but significant correlations were found between rainfall and KF (r = 0.6357, p = 0.0263 for males and r = 0.6983, p = 0.0115 for females)

Diagnosis of chronic conditions with modifiable lifestyle risk factors in selected urban and rural areas of Bangladesh and sociodemographic variability therein

<p>Abstract</p> <p>Background</p> <p>Bangladesh suffers from a lack of healthcare providers. The growing chronic disease epidemic's demand for healthcare resources will further strain Bangladesh's limited healthcare workforce. Little is known about how Bangladeshis with chronic disease seek care. This study describes chronic disease patients' care seeking behavior by analyzing which providers diagnose these diseases.</p> <p>Methods</p> <p>During 2 month periods in 2009, a cross-sectional survey collected descriptive data on chronic disease diagnoses among 3 surveillance populations within the International Center for Diarrheal Disease Research, Bangladesh (ICDDR, B) network. The maximum number of respondents (over age 25) who reported having ever been diagnosed with a chronic disease determined the sample size. Using SAS software (version 8.0) multivariate regression analyses were preformed on related sociodemographic factors.</p> <p>Results</p> <p>Of the 32,665 survey respondents, 8,591 self reported having a chronic disease. Chronically ill respondents were 63.4% rural residents. Hypertension was the most prevalent disease in rural (12.4%) and urban (16.1%) areas. In rural areas chronic disease diagnoses were made by MBBS doctors (59.7%) and Informal Allopathic Providers (IAPs) (34.9%). In urban areas chronic disease diagnoses were made by MBBS doctors (88.0%) and IAP (7.9%). Our analysis identified several groups that depended heavily on IAP for coverage, particularly rural, poor and women.</p> <p>Conclusion</p> <p>IAPs play important roles in chronic disease care, particularly in rural areas. Input and cooperation from IAPs are needed to minimize rural health disparities. More research on IAP knowledge and practices regarding chronic disease is needed to properly utilize this potential healthcare resource.</p