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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. -
A Comparative Study of LGMB-SVR Hybrid Machine Learning Model for Rainfall Prediction
Weather forecasting is a critical factor in deter mining the crop production and harvest of any geographical location. Among various other factors, rainfall is a crucial determining component in the sowing and harvesting of crops. The aim and intent of this paper is to analyze various machine learning algorithms like LightGBM and SVR, and develop a hybrid model using LightGBM and SVR to accurately predict rainfall The hybrid model implements both LightGBM and SVR on a preprocessed dataset and then combines the predicted values of the results through an ensemble model which considers the average of these values based on a predefined weight. The weight of the model is determined by considering various combinations, and the result with the least error is taken into consideration for that particular dataset. The study shows that the hybrid model performed better than LightGBM and SVR individually, and produced the least root mean square error yielding a more accurate prediction of rainfall. 2021 IEEE. -
AI-based Power Screening Solution for SARS-CoV2 Infection: A Sociodemographic Survey and COVID-19 Cough Detector
Globally, the confirmed coronavirus (SARS-CoV2) cases are being increasing day by day. Coronavirus (COVID-19) causes an acute infection in the respiratory tract that started spreading in late 2019. Huge datasets of SARS-CoV2 patients can be incorporated and analyzed by machine learning strategies for understanding the pattern of pathological spread and helps to analyze the accuracy and speed of novel therapeutic methodologies, also detect the susceptible people depends on their physiological and genetic aspects. To identify the possible cases faster and rapidly, we propose the Artificial Intelligence (AI) power screening solution for SARS- CoV2 infection that can be deployable through the mobile application. It collects the details of the travel history, symptoms, common signs, gender, age and diagnosis of the cough sound. To examine the sharpness of pathomorphological variations in respiratory tracts induced by SARS-CoV2, that compared to other respiratory illnesses to address this issue. To overcome the shortage of SARS-CoV2 datasets, we apply the transfer learning technique. Multipronged mediator for risk-averse Artificial Intelligence Architecture is induced for minimizing the false diagnosis of risk-stemming from the problem of complex dimensionality. This proposed application provides early detection and prior screening for SARS-CoV2 cases. Huge data points can be processed through AI framework that can examine the users and classify them into "Probably COVID", "Probably not COVID"and "Result indeterminate". 2021 The Authors. Published by Elsevier B.V. -
A Review of Channel Estimation Mechanisms in Wireless Communication Networks
The fluctuating nature of wireless networks influences network performance. Estimation of channel condition is essential for many reasons. The accurate estimation and prediction help to improve the performance, like better rate adaptation in Wi-Fi, improved video streaming, reduce energy consumption, and better scheduling. There are many different approaches introduced past two decades. In this paper, we are focusing on providing a brief review of different channel estimation approaches and their importance in improving performance. 2021 IEEE. -
Impacts of Cloud Computing in Digital Marketing
In modern day of digital marketing the cloud computing is proving extremely beneficial links for businesses. Moreover, it's characteristic to access the stored data from anywhere makes it more popular among the entrepreneurs. The present paper is an exploration of the cloud computing in respect of digital marketing. The paper defines and correlates the term cloud computing, digital marketing, as well as also elaborates about benefits that can be harvested by the integration of cloud computing in digital marketing strategy. 2021 IEEE. -
Human Body Pose Estimation and Applications
Human Pose Estimation is one of the challenging yet broadly researched areas. Pose estimation is required in applications that include human activity detection, fall detection, motion capture in AR/VR, etc. Nevertheless, images and videos are required for every application that captures images using a standard RGB camera, without any external devices. This paper presents a real-time approach for sign language detection and recognition in videos using the Holistic pose estimation method of MediaPipe. This Holistic framework detects the movements of multiple modalities-facial expression, hand gesture and body pose, which is the best for the sign language recognition model. The experiment conducted includes five different signers, signing ten distinct words in a natural background. Two signs, 'blank' and 'sad, ' were best recognized by the model. 2021 IEEE. -
Model Selection Procedure in Alleviating Drawbacks of the Electronic Whiteboard
Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning and deep learning has left its mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss and accuracy. We later visualize the different results for the training and validation datasets and reach to a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with simultaneous focus on the suitable model selection procedure for the digit recognition problem. 2021 IEEE. -
An Approach for Detecting Frauds in E-Commerce Transactions using Machine Learning Techniques
This paper is primarily focused on E-commerce fraud detection using machine learning techniques. There are many different ways to detect E-commerce fraud using machine learning approach. In this work, comparison study is conducted between various available machine learning algorithms to detect the online frauds. During the comparative study, focus is underlined on comparison of all the algorithms to identify the fraud transactions. When compared to other algorithms, such as support vector machine, Decision Tree, K-nearest neighbour and Random Forest, it has been observed that Logistic regression gives better result among all machine learning algorithms. 2021 IEEE.