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Role of Data Science in the Field of Genomics and Basic Analysis of Raw Genomic Data Using Python
The application of genomics in identifying the nature and cause of diseases has predominantly increased in this decade. This field of study in life sciences combined with new technologies, revealed an outbreak of certain large amounts of genomic sequences. Analysis of such huge data in an appropriate way will ensure accurate prediction of disease which helps to adopt preventive mechanisms which can ultimately improve the human quality of life. In order to achieve this, efficient comprehensive analysis tools and storage mechanisms for handling the enormous genomic data is essential. This research work gives an insight into the application of data science in genomics with a demonstration using Python. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Asynchronous Method of Oracle: A Cost-Effective and Reliable Model for Cloud Migration Using Incremental Backups
Cloud Computing has reached a new level in flexibility to provide infrastructure. The proper migration method should be chosen for better cost management and to avoid overpayments to unused resources. So, the migrations from On-Premises to cloud infrastructure is a challenge. The migration can be done in synchronous or asynchronous modes. The synchronous method is mostly used to minimize downtime while doing the cloud migrations. The asynchronous methods can do the migrations in offline mode and very consistently. This paper addresses various issues related to the synchronous mode of Oracle while doing highly transactional database migrations. The proposed methodology provides a solution with a combination of asynchronous and incremental backups for highly transactional databases. This proposed method will be a more cost-effective and reliable model without compromising consistency and integrity. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A literature review on friction stir welding of dissimilar materials
Friction stir welding (FSW) employs a tool that does not require any filler materials; frictional heat is produced and performs a solid-state joining method. Severe plastic deformation causes to join similar and dissimilar materials without melting the workpiece at the welding line. Friction stir welding is the most recent friction welded joining processes with the most surprising features when welding various metal alloys, including magnesium, aluminium, copper, and steel. FSW is victorious of all the other conventional welding methods implied in many industrial applications like automobile, aerospace, fabrication, shipping, marines and robotics. It gives high-quality welds, energy input, and distortion are lower, better retention of mechanical properties; it is eco-friendly and can be performed less operating cost. This research work aims at the FSW process in Al-Cu alloys, highlighting:(a) Optimizing the welding process parameters, welding feed rate, tool rotation speed, (b) Evaluation of Electrical Conductance properties of joints, (c) Mechanical properties and metallography characteristics of joints. 2021 Elsevier Ltd. All rights reserved. -
Some New Results on Non-zero Component Graphs of Vector Spaces Over Finite Fields
The non-zero component graph of a vector space with finite dimension over a finite field F is the graph G=(V,E), where vertices of G are the non-zero vectors in V, two of which are adjacent if they have at least one basis vector with non-zero coefficient common in their basic representation. In this paper, we discuss certain properties of the non-zero component graphs of vector spaces with finite dimension over finite fields and their graph invariants. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
On Equitable Near Proper Coloring of Mycielski Graph of Graphs
When the available number of colors are less than that of the equitable chromatic number, there may be some edges whose end vertices receive the same color. These edges are called as bad edges. An equitable near-proper coloring of a graph G is a defective coloring in which the number of vertices in any two color classes differ by at most one and the resulting bad edges is minimized by restricting the number of color classes that can have adjacency among their own elements. In this paper, we investigate the equitable near-proper coloring of Mycielski graph of graphs and determine the equitable defective number of those graphs. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Non-linear Convection in Couple Stress Fluid with Non-classical Heat Conduction Under Magnetic Field Modulation
A theoretical examination of thermal convection for a couple stress fluid which is electrically conducting and possessing significant thermal relaxation time is explored under time dependent magnetic field. Fouriers law fails for a diverse area of applications such as fluids subjected to rapid heating, strongly confined fluid and nano-devices and hence a non-classical heat conduction law is employed. The heat transport in the system is examined and quantified employing the Lorenz model. The Nusselt number is deduced to quantitate the transfer of heat. 2021, Springer Nature Singapore Pte Ltd. -
Unraveling the Potential of Artificial Intelligence-Driven Blockchain Technology in Environment Management
Blockchain as an emerging technology provides a ray of hope to the most intensive environmental issues facing the planet. Blockchain with its decentralized business model has great relevance not only in the field of finance but also in environmental sustainability. The World Economic Forum has identified blockchain as a repair mechanism to the most challenging global environmental issues. It is a highly promising technology gaining traction in diverse fields. Blockchain through this technology unveils its capabilities as a decentralized ledger of all dealings across peer-to-peer networks, where participants can ratify transactions without any central authority. Blockchain technology is an indestructible electronic ledger of transactions designed to record and store everything of value in addition to financial transactions. Blockchain can ensure a shift to cleaner and more resource conserving decentralized solutions, unravel natural capital and to empower communities. This chapter attempts to study the applicability of blockchain in protecting and sustaining the global environment at various levels including life on land, life below the earth and climate changes. Deployment of blockchain technology is needed in areas like climate change, biodiversity conservation and healthy water bodies to overcome the threats they face. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Inventory Model for Growing Items with Deterioration and Trade Credit
Growing items industry plays a vital role in the economy of most of the countries. Growing item industries consists of live stocks like sheep, fishes, pigs, chickens etc. In this paper, we developed a mathematical model for growing items by considering various operational constraints. The aim of the present model is to optimize the net profit by optimizing decision variables like time after growing period and shortages. Also, the delay in payment policy has been used to maximize the profit. A numerical example is provided in support of the solution procedure. Sensitivity analysis provides some important insights. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Effective Tensor Based PCA Machine Learning Techniques for Glaucoma Detection and ASPP EffUnet Classification
Main problem in current research area focused on generating automatic AI technique to detect bio medical images by slimming the dataset. Reducing the original dataset with actual unwanted noises can accelerate new data which helps to detect diseases with high accuracy. Highest level of accuracy can be achieved only by ensuring accuracy at each level of processing steps. Dataset slimming or reduction is NP hard problems due its resembling variants. In this research work we ensure high accuracy in two phases. In phase one feature selection using Normalized Tensor Tubal PCA (NTT-PCA) method is used. This method is based on tensor with single value decomposition (SVD) for accurate dimensionality reduction problems. The dimensionality reduced output from phase one is further processed for accurate classification in phase two. The classification of affected images is detected using ASPP EffUnet. The atrous spatial pyramid pooling (ASPP) with efficient convolutional block in Unet is combined to provide ASPP EffUnet CNN architecture for accurate classification. This two phase model is designed and implemented on benchmark datasets of glaucoma detection. It is processed efficiently by exploiting fundus image in the dataset. We propose novel AI techniques for segmenting the eye discs using EffUnet and perform classification using ASPP-EffUnet techniques. Highest accuracy is achieved by NTT-PCA dimensionality reduction process and ASPP-EffUnet based classification which detects the boundaries of eye cup and optical discs very curiously. Our resulting algorithm NTT-PCA with ASPP-EffUnet for dimensionality reduction and classification process which is optimized for reducing computational complexity with existing detection algorithms like PCA-LA-SVM,PCA-ResNet ASPP Unet. We choose benchmark datasets ORIGA for our experimental analysis. The crucial areas in clinical setup are examined and implemented successfully. The prediction and classification accuracy of proposed technique is achieved nearly 100%. 2021, Springer Nature Switzerland AG. -
Multiple Approaches in Retail Analytics to Augment Revenues
Knowledge is power. The retail sector has been revolutionized around the clock by the plentiful product knowledge available to customers. Today, customers can use the knowledge available online at any time to study, compare and purchase products from anywhere. Retail companies can stay ahead of shopper trends by using retail information analytics to discover and analyze online and in-store shopper patterns. A product recommender will suggest products from a wide selection that would otherwise be very difficult to locate for the customer. The algorithm would recommend various products, increase the sales of items that would otherwise be difficult to sell. Market basket analysis is a common use scenario for the search for frequent patterns, which involves analyzing the transactional data of a retail store to decide which items are bought together. To do so data from online resource has been taken, which is analyzed and several conclusions were made. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A microstructure exploration and compressive strength determination of red mud bricks prepared using industrial wastes
The consensual view among researchers concerning building with industrial by-products is that the utilization of by-products represents green technology and sustainable development. The current investigation focuses on the utilization of an assortment of by-products for the production of bricks. The by-products include Red Mud (RM), Fly ash (FA), and Ground Granulated Blast Furnace Slag (GGBS) combined in different proportions with lime. The Red Mud employed ranged from 100% to 60% with a decrement of 10%, whereas Fly ash and GGBS varied between10% and 40% with an increment of 10%. Bricks produced from two methods namely, ambient curing and firing methods, were tested as per IS standards/ASTM norms, on both the materials and the composites of bricks. XRD, XRF, and SEM focused on both the raw materials and the composites. Because geopolymer materials are partially amorphous materials with complex composition, understanding the structural characteristics of geopolymers is opined as intricate. The results of the investigation show that the compressive strength of the bricks increased with the increment in the percentage of Fly ash and GGBS. The compressive strength of Red Mud-GGBS fired bricks attained maximum strength of 7.56 MPa. 2021 Elsevier Ltd. All rights reserved. -
Factors Influencing Online Shopping Behaviour: An Empirical Study of Bangalore
Online shopping is growing rapidly in India, predominantly driven by tremendous and substantial divulgatory activities among millennial consumers. Online shopping is becoming more popular and attracts significant attention because it has excellent potential for both consumers and vendors. The convenience of online shopping makes it more successful and makes it an emerging trend among consumers. When all the companies are striving against one another, certain factors influence the behavior of customers. This paper analyses the relationship between the critical, independent variables, including consumer behavior, cultural, social, personal, psychological, and marketing mix factors. The results revealed that the influence of Brand as a factor had positively influenced the customers decisions in shopping online and evaluates the customers level of satisfaction with Online shopping. Results provided in this research could be employed as reference information for Ecommerce app builders and marketers regarding such issues in the city. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Analytical Review on Data Privacy and Anonymity in 'Internet of Things (IoT) Enabled Services'
Nowadays, the Internet of Things (IoT) is an emerging technology, spreading all over the world so the number of devices is increasing day by day. So, the volume and data complexity has increased drastically in the past three years. The resultant system might contain a significant number of heterogeneous devices, posing integration and scalability issues that must be addressed. In such a situation, security and privacy are commonly regarded as a significant concern. On the other hand, user privacy, defined as the capacity to provide data protection and anonymity, must be protected, which is especially important when personal and/or sensitive information is involved. This paper presents the comprehensive survey, characteristics, and application of IoT and the immense number of challenges raced and faced during the implementation of IoT frameworks. 2021 IEEE. -
Comparisons of Stock Price Predictions Using Stacked RNN-LSTM
This paper seeks to identify how the RNN-LSTM can be used in predicting the rise and fall in stock markets thereby helping investors to understand stock prices. Therefore, by predicting the nature of the stock market, investors can use different machine learning techniques to understand the process of selecting the appropriate stock and enhance the return investments thereafter. Long Short-Term Memory (LSTM) is a deep learning technique that helps to analyze and predict the data with respect to the challenges, profits, investments and future performance of the stock markets. The research focuses on how neural networks can be employed to understand price changes, interest patterns and trades in the stock market sector.The datasets of companies such as IBM, Cisco, Microsoft, Tesla and GE were used to build the stacked RNN-LSTM model using timesteps of 7 and 14days. The two layered stacked RNN-LSTM models of the companies such as Microsoft and Tesla achieved their highest model accuracies after being trained over a span of one year whereas the other companies acquired their highest accuracies after being trained over a span of 4 to 5years which implies that the rate of change of economic factors affecting Microsoft and Tesla over a short span of time is high as compared to the other existing companies. 2021, Springer Nature Switzerland AG. -
A critical review of determinants of financial innovation in global perspective
Financial innovation is the widely accepted process across the globe. 'What forces drive the financial innovation?' is the research question since long. Many studies were conducted in the past to answer and each study identified some or other factors that prominently driving financial innovation landscape in their respective economy. The present study critically review existing Literatures to suggest a comprehensive list of determinants. The study uses descriptive research design. A sample of 54 literatures focusing on financial innovation and it's determinants during the time period 1983 to 2018 is included in the study. Further, content analysis and descriptive statistics are used to explore the determinants. The study identified 23 different determinants of financial innovation and classify those under two bases. First, on the basis of influencing power and second on the basis of nature of the determinant. The study found that technological development, competition, firm size and regulations are the major sources of financial innovation from different categories. The study also raised the research agenda to study determinants of financial innovation in Asian context, as there are scanty literature covering Asian economies. 2021 Elsevier Ltd. All rights reserved. -
On Improving Quality of Experience of 4G Mobile Networks A Slack Based Approach
This paper analyses Indias four top 4G Mobile network Providers with respect to five key user experience metrics Video, Games, Voice app, Download speed and Upload speed. Results using Data Envelopment Analysis show Airtel and Vodafone-Idea performing with maximum relative efficiency with respect to these metrics, while BSNL and Jio closely follow them. Further analysis using the Slack Based Measure shows where and by how much BSNL and Jio need to improve to perform at par with Airtel and Vodafone-Idea. On certain variables, for instance Voice app, BSNL and Jio perform well, with no need for improvement. On the contrary, for Upload and Download speed experiences, both BSNL and Jio lag. For Video and Games, there is still scope for improvement, although both these players are reasonable in their performance. Thus, this analysis provides an accurate and optimal benchmark for each variable whose user experience has been evaluated. 2021, Springer Nature Switzerland AG. -
Towards various applications of Big Data and related issues and challenges
A new trend in feature abstraction is Big Data Analysis combined with computational techniques. This includes gathering knowledge from reputable data sources, analyzing information quickly, and forecasting the future. Big data entails vast amounts of data that are challenging to analyze using typical database and software approaches. When using big data applications, a technological hurdle arises when transporting data across several locations, which is quite expensive and necessitates a huge primary memory for storing data for processing. Big data refers to the transaction and interaction of datasets whose size and complexity transcend the usual technical capabilities of acquiring, organizing, and processing data in a cloud environment. This article provides an in depth study of various applications of big data. It also provides a detailed view on various problems and challenges in Big Data. 2021 IEEE. -
Approaches Towards A Recommendation Engine for Life Insurance Products
Recommender engines are powerful tools in today's world to overcome the problem of over choice. As the world is moving towards information overload, the life insurance industry is no more immune than any other domain. Three broad categories of life insurance plans are namely - Endowment, Term and ULIP. This paper discusses a variety of ML models that aim to classify the right fit product category for a new customer (extendable to existing customers) on a real-time life insurance company dataset. The dataset used for the modelling were of 2 kinds. The first kind contained features of customer demographics - age, location, education and occupation. The second dataset included these customer demographics as well as the bureau information of the respective customers which included multiple features describing their credit history. By the means of clustering, collaborative filtering approaches were tried on. Also, the problem was tackled using predictive modelling techniques such as Random Forest, Decision Trees and XGBoost. 2021 IEEE. -
MuLSA-Multi Linguistic Sentimental Analyzer for Kannada and Malayalam using Deep Learning
Natural language Processing has been always a topic of interest in artificial intelligence. Opinion mining or Sentiment Analysis is an important application of Natural language Processing. Sentiment Analysis of text is to extract the sentiments underlined in the text. In this paper, a multi-linguistic sentimental analyzer (MuLSA), is implemented, a model that would address Malayalam, Kannada and English text. This model explores two languages in three categories of the text, its original script, transliterated script, and the combination of both along with English. Deep Learning, Recurrent Neural Network with LSTM is used as the basis for this model. The model exhibits 82% of prediction accuracy. 2021 IEEE. -
Scrutiny In-Utero to recognize Fetal Brain MRI Anomalies
In utero MRI distinguishes fbrain irregularities high precisely compared to ultrasonography as well as gives extra medical data during the pregnancies. fMRI is medically performed to get the knowledge of the brain in conditions where the inconsistency are perceived with the help of pre-birth sonography. These are common regularly solidify ventriculomegaly, not regular of the corpus callosum, and oddities of the back fossa. Fbrain inconsistencies can cause authentic brain hurt. Therefore, it is vital to recognize them from the get-go in their course so treatment can be managed to the mother, if conceivable. The job of imaging is to decide the presence, assuming any, and the degree of brain harm in the contaminated hatchling. Even though MRI is most generally utilized as a subordinate to sonography when clinical doubt is high in the setting of a typical ultrasound or to all the more likely characterize irregularities recognized by ultrasound, MRI is regularly utilized in toxoplasmosis seroconversion to conclusively preclude brain injuries, in any event, when the ultrasound examination is viewed as ordinary. X-ray is likewise utilized sequentially all through the pregnancy to check for the improvement of brain anomalies; clinical treatment brings about the astounding clinical result if the brain is typical. Intracranial irregularities are ordinarily speculated discoveries on antenatal US that are needed for assessment which is used by MRI. This audit portrays numerous irregularities imaged as a way to direct clinicians' inappropriate determination. 2021 IEEE.