Improved Lion Optimization based Enhanced Computation Analysis and Prediction Strategy for Dropout and Placement Performance Using Big Data

Background: Predicting the undergraduate’s placement performance is vital as it impacts the credibility of educational institutions. Hence, it is significant to predict their performance based on placement in the early days of degree program. Objectives: The study intends to predict the undergraduate’s placement performance through the introduced ANN-R (Artificial Neural Network based Regression) as it is able to handle fault tolerance. For efficient prediction, relevant feature selection is needed that is performed by the proposed ILO (Improved Lion Optimization) algorithm as it has the ability to find nearest probable optimal solution. Methodology: Initially, the parameters and population are initialised. Subsequently, first best-agent is stated in accordance with fitness function. Subsequently, position of present search agent is updated. This iteration continues until all the features are selected and optimized result is attained. Here best score is computed using the proposed ILO for feature selection. Finally, the dropout analysis and placement performance of students is predicted using the introduced ANN-R through a train and test split. Results/Conclusion: Performance of the proposed system is analysed in accordance with loss metrics. Additionally, internal comparison is performed to find the extent to which the actual and predicted values correlate with one another during prediction using the existing and proposed system. The outcomes revealed that the proposed system has the ability to predict the student’s placement performance along with domain of interest with minimum errors than the traditional system. This makes the proposed system to be highly suitable for predicting student’s performance

Improved Lion Optimization based Enhanced Computation Analysis and Prediction Strategy for Dropout and Placement Performance Using Big Data

Background: Predicting the undergraduate’s placement performance is vital as it impacts the credibility of educational institutions. Hence, it is significant to predict their performance based on placement in the early days of degree program. Objectives: The study intends to predict the undergraduate’s placement performance through the introduced ANN-R (Artificial Neural Network based Regression) as it is able to handle fault tolerance. For efficient prediction, relevant feature selection is needed that is performed by the proposed ILO (Improved Lion Optimization) algorithm as it has the ability to find nearest probable optimal solution. Methodology: Initially, the parameters and population are initialised. Subsequently, first best-agent is stated in accordance with fitness function. Subsequently, position of present search agent is updated. This iteration continues until all the features are selected and optimized result is attained. Here best score is computed using the proposed ILO for feature selection. Finally, the dropout analysis and placement performance of students is predicted using the introduced ANN-R through a train and test split. Results/Conclusion: Performance of the proposed system is analysed in accordance with loss metrics. Additionally, internal comparison is performed to find the extent to which the actual and predicted values correlate with one another during prediction using the existing and proposed system. The outcomes revealed that the proposed system has the ability to predict the student’s placement performance along with domain of interest with minimum errors than the traditional system. This makes the proposed system to be highly suitable for predicting student’s performance

Are providers prepared for genomic medicine: interpretation of Direct-to-Consumer genetic testing (DTC-GT) results and genetic self-efficacy by medical professionals.

BACKGROUND:Precision medicine is set to deliver a rich new data set of genomic information. However, the number of certified specialists in the United States is small, with only 4244 genetic counselors and 1302 clinical geneticists. We conducted a national survey of 264 medical professionals to evaluate how they interpret genetic test results, determine their confidence and self-efficacy of interpreting genetic test results with patients, and capture their opinions and experiences with direct-to-consumer genetic tests (DTC-GT). METHODS:Participants were grouped into two categories, genetic specialists (genetic counselors and clinical geneticists) and medical providers (primary care, internists, physicians assistants, advanced nurse practitioners, etc.). The survey (full instrument can be found in the Additional file 1) presented three genetic test report scenarios for interpretation: a genetic risk for diabetes, genomic sequencing for symptoms report implicating a potential HMN7B: distal hereditary motor neuropathy VIIB diagnosis, and a statin-induced myopathy risk. Participants were also asked about their opinions on DTC-GT results and rank their own perceived level of preparedness to review genetic test results with patients. RESULTS:The rates of correctly interpreting results were relatively high (74.4% for the providers compared to the specialist's 83.4%) and age, prior genetic test consultation experience, and level of trust assigned to the reports were associated with higher correct interpretation rates. The self-selected efficacy and the level of preparedness to consult on a patient's genetic results were higher for the specialists than the provider group. CONCLUSION:Specialists remain the best group to assist patients with DTC-GT, however, primary care providers may still provide accurate interpretation of test results when specialists are unavailable

Floral stalk on date palm: a new discovery

Date palm (Phoenix dactylifera L.) is harvested for its sweet fruit mainly in the middle east and other parts of the world. It has been cultivated for several thousand years and is known to be found in Mesopotamia as well. Besides the fruit, the various parts of the tree are employed for variety of uses. The stalks of the fruit, which connect the fruit to the spikelet, are very beautiful, colourful flower like structures, which have never been described earlier. These fruit stalks could be used for decorations in houses and would then add to more economic gain to the farmer. We observed these stalks and describe here this interesting finding hitherto unreported in the world literature. DOI: http://dx.doi.org/10.3329/ijarit.v4i2.22649 Int. J. Agril. Res. Innov. & Tech. 4 (2): 53-54, December, 201

A Survey on Graph Database Management Techniques for Huge Unstructured Data

Data analysis, data management, and big data play a major role in both social and business perspective, in the last decade. Nowadays, the graph database is the hottest and trending research topic. A graph database is preferred to deal with the dynamic and complex relationships in connected data and offer better results. Every data element is represented as a node. For example, in social media site, a person is represented as a node, and its properties name, age, likes, and dislikes, etc and the nodes are connected with the relationships via edges. Use of graph database is expected to be beneficial in business, and social networking sites that generate huge unstructured data as that Big Data requires proper and efficient computational techniques to handle with. This paper reviews the existing graph data computational techniques and the research work, to offer the future research line up in graph database management

Analysis for Molecular Distinction in the Chloroplast DNA Sequences of Gymnospora montana (Celastraceae) and Belanites aegyptiaca (Balanitaceae) from Semi-arid Area

Gymnospora montana (Celastraceae) and Belanites aegyptiaca  (Balanitaceae) showed marked similarity in  their cpDNA sequences. Therefore, its detail analysis of cpDNA sequences is performed for codon use bias and its index, relative synonymous codon use value (RSCU), effective number of codons (ENC), GC content of the gene and frequencies of the nucleotides G+C at various positions in synonymous codon were calculated and compared it with Tribulus terresties. Length of the gene and ENC showed close relationship which suggest that longer genes has less codon bias. The codons for leucine, isoleucine and serine were most abundant in the studied plant species. The correlation analysis suggested that codon usage patterns in both cp genomes appear due to the different forces; natural selection, mutation pressure, GC content of gene and protein length.   Their role in gene evolution process is discussed

Adaptive And Reliable GPS Uncertain Position Estimation an Insightful Oceanography and Geography Applications

Location evaluation applications are one of the most imperative services in GPS position applications. The Global Positioning Systems (GPS) is a versatile and legacy technology has been providing a reliable and accurate position of objects on Earth. The uncertain GPS position is considered an initialization parameter for many inherent systems in today’s world. This initialization position estimate has a wide variety of applications such as Coast line maps, understanding the geo-dynamical phenomena such as volcanic eruptions, earthquakes and subsequent originating source mechanisms, Mean Sea level estimation for contours of land surfaces, Oceanic en-route as well as in mobile and Vehicular technologies etc. The validation and reliability of the results of all those applications is dependent on the accuracy of the position estimate given by GPS. In this work an attempt is made to retrieve accurate and reliable position parameters from GPS by correcting the measurement errors for all the visible satellites at every epoch. The maximum and minimum pseudo ranges in L2 signal observed are 2437404.2 meters and -76295.22 meters