Vipin Kumar

Bio: Vipin Kumar is an academic researcher from University of Minnesota. The author has contributed to research in topics: Parallel algorithm & Cluster analysis. The author has an hindex of 95, co-authored 614 publications receiving 59034 citations. Previous affiliations of Vipin Kumar include University of Maryland, College Park & United States Department of the Army.

In vitro Availability of Macro Nutrients in Various North Indian Soils by Application of Biochar

Abstract: The use of organic amendments is popular these days therefore we conducted a study on biochar in various North Indian region soils under pot culture to evaluate the changes in soil characteristics. Four types of soils taken for 75 days during Kharif and Rabi season were studied, out of which Soil-1 is slightly acidic while Soil-2 to Soil-4 are alkaline. The soil samples were collected thrice at the interval of 25 days using standard procedures and analyzed for various macronutrients (pH, EC, OC, N, P, and K) essential for plants in the laboratory. The change in status of available NPK in soils and other soil properties like pH, electrical conductivity (EC), and organic carbon (OC) content due to inoculation of biochar was assessed. Results reveal that all treatments having biochar leads to an increase in the content of available N, P, and K together with OC. There recorded a slight elevation in EC initially at 25th DAI due to its inoculation. The pH during the initial period had reduced but later on rise as the number of DAI increased.

Probabilistic Inverse Modeling: An Application in Hydrology

Abstract: Rapid advancement in inverse modeling methods have brought into light their susceptibility to imperfect data. The astounding success of these methods has made it imperative to obtain more explainable and trustworthy estimates from these models. In hydrology, basin characteristics can be noisy or missing, impacting streamflow prediction. For solving inverse problems in such applications, ensuring explainability is pivotal for tackling issues relating to data bias and large search space. We propose a probabilistic inverse model framework that can reconstruct robust hydrology basin characteristics from dynamic input weather driver and streamflow response data. We address two aspects of building more explainable inverse models, uncertainty estimation and robustness. This can help improve the trust of water managers, handling of noisy data and reduce costs. We propose uncertainty based learning method that offers 6% improvement in R 2 for streamflow prediction (forward modeling) from inverse model inferred basin characteristic estimates, 17% reduction in uncertainty (40% in presence of noise) and 4% higher coverage rate for basin characteristics.

Genetic Variability, Heritability, Correlation and Path Coefficient Analysis in Amaranth (Amaranthus spp.) in Western Uttar Pradesh (India)

Abstract: The field experiment was conducted during Rabi season in 2021-22 in randomized block design with three replications. Total 19 diverse germplasm of Amaranth were studied for 12 growth and yield parameters. Analysis of variance for 19 genotypes of Amaranthrevealed significant difference for all the 12parameters, which indicated the presence of wide spectrum of variability among the genotypes. The phenotypic coefficient of variation (PCV) was higher than the respective genotypic coefficient of variation (GCV) for all the traits. High heritability and genetic advance as per cent of mean were observed for all twelve characters. The highest heritability was recorded in biological yield per plant 99.49%and lowest for days to germination 69.95%. Correlation coefficient studies indicated that genotypic correlation coefficient was found to be higher than phenotypic correlation coefficients for most of the characters, indicating a strong inherent association between various characters and due to which it is affected by environmental components in regard to phenotypic expression. Seed yield expressed highly significant and positive correlation with plant height and biological yield per plant at both genotypic and phenotypic level, which implies that these characters were the primer contributing factors to seed yield. All the combination of traits should be considered, while breeding programme for selecting high yielding genotypes and suitable for breeders to achieving improved plant type. Path coefficient analysis revealed that highest positive direct effect on seed yield kg per ha was observed for biological yield per plant, seed yield per plant, number of leaves per plant, days to maturity, days to germination, inflorescence length, fresh leaf weight and plant height. Improvement of these characters might be improved.

Deep Learning

Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Data Mining: Concepts and Techniques

Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

A Survey on Transfer Learning

Abstract: A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.