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Cultivating Digital Fields: A Cloud-Centric Blueprint for Stakeholder Engagement in the Indian Agriculture
This paper examines the potential of cloud computing to revolutionize the Indian agricultural sector, government operations, and rural connectivity. It highlights the benefits and challenges associated with cloud computing in agriculture and proposes a structured model to implement it effectively. Cloud computing allows farmers to access real-time information, make informed decisions, and improve access to markets. The paper examines the difficulties and advantages of cloud computing for the government in transitioning to a cloud-based version of itself for its operations. Additionally, it draws attention to specific areas related to the agricultural sector in India and certain applications offered by the government to enhance the consumer experience for stakeholders. The Government of India has demonstrated its commitment to developing technology-driven agriculture through e-NAM, Kisan Suvidha, and Agri-market initiatives. However, some challenges must be addressed to ensure the successful adoption of cloud computing in the agricultural sector. The proposed implementation model outlines the essential stages of the process, including the needs assessment, the selection of cloud providers, the automation of workflow, the modernization of applications, the implementation of security measures, and the implementation of continuous improvement. The model emphasizes the importance of training, feedback mechanisms, and collaboration. Furthermore, the paper underscores the need for a specific feedback system and grievance redress for agricultural cloud applications to enhance user experiences. To reap the full benefits of cloud computing in the Indian agricultural sector, a comprehensive strategy is necessary. This strategy necessitates technology adoption, awareness-raising, education, and stakeholder engagement. Utilizing cloud technologies, the Indian agricultural sector can realize sustainable growth, increased efficiency, and equitable development. This paper emphasizes the importance of cloud computing in transforming the Indian agrarian landscape. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Cumulative istributionfunction: Stock price forecasting
In this paper, an attempt has been made to predict the movement of the stock price for the next day using Cumulative Distribution Function (CDF). For the purpose of the research, three companies from the Bearings Industry, namely - ABC Bearings Ltd, SNL Bearings Ltd and Austin Engineering Company Ltd, and two companies from the chemical industry, namely-Nocil ltd and Manali Petrochemicals Ltd were chosen. Historical prices of these companies were analyzed and by using Cumulative Distribution Function (CDF) the movement of the stock price for the next day is predicted. 2017 IEEE. -
Currency Exchange Rate Prediction Using Multi-layer Perceptron
Financial forecasting is an estimate of a future financial outcome and this outcome is related to some kind of value. We can measure this outcome for a company to predict its future stock or to detect the viability of a human for the sanction of a loan. In all these cases, we want to estimate the future outcome based on historical data. Various methods have been developed lately, to make time series predictions. In this work, we have used Multi-layer perceptron algorithm to predict the Currency Exchange rate between US dollar and EURO. The training network has been compiled using TensorFlow. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Curvature Ductility of Reinforced Masonry Walls and Reinforced Concrete Walls
Research conducted in this work proposes an equation to evaluate and compares the curvature ductility of reinforced masonry (RM) and reinforced concrete (RC) walls. The curvature ductilities are measured at varying levels of axial stresses for walls for aspect ratio (l/h) of 0.5, 1.0 and 1.5. The percentage of reinforcement is increased from 0.25% (minimum reinforcement for RC walls as per IS-13920) to 1.00%. The curvature ductilities are evaluated by plotting flexural strength (M) versus curvature (?) for the walls. The stressstrain curves of masonry, concrete and reinforcing steel are all adopted from existing literature. The compressive strength of masonry and concrete has been chosen as 10MPa and 25MPa, respectively. The yield strength of the steel is fixed as 415MPa. The height and thickness of the wall are 3000 and 230mm, respectively, and the length of the wall is varied to obtain different aspect ratios. Results obtained from this paper imply due to increase curvature ductility, RM walls provide a better alternative for the construction of structural walls compared to RC walls in regions of significant seismicity. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Customer Behavior Analysis Using Unsupervised Clustering and Profiling: A Machine Learning Approach
Now-a-days, client conduct models are reliably established on information mining of client information, and each model is supposed to answer one solicitation at one point on schedule. Anticipating client conduct is a problematic and irksome task. Thus, making client conduct models requires the right strategy and approach. Right when an estimate model has been fabricated, it is challenging to restrict it for the motivations driving the advertiser, to pick the very thing displaying moves to make for every client or for the party of clients. Notwithstanding the multifaceted nature of this arrangement, most client models are completely fundamental. As the need might arise, most client conduct investigation models ignore such endless proper factors that the gauges they make are overall not altogether strong. This paper plans to encourage a connection rule mining model to expect client conduct using a typical electronic retail store for data combination and concentrate critical examples from the client conduct data. In this undertaking, a solo grouping of information on the customer's records from a regular food item company's data set will be played out. Customer segmentation is the act of clustering customers into bunches that reflect likenesses among customers in each group. Customers are separated into sections to advance the meaning of every customer to the business. To change items as indicated by unmistakable requirements and practices of the customers. It additionally assists the business with obliging the worries of various kinds of customers. Customers were clustered using a technique known as agglomerative clustering, which is a type of hierarchical clustering. Agglomerative clustering is a method for clustering data in a hierarchical order. It entails merging cases until you reach the appropriate number of clusters. The number of clusters to be produced is determined using the Elbow Method. 2022 IEEE. -
Customer Lifetime Value Prediction: An In-Depth Exploration with Regression, Regularization and Hyperparameter Tuning
In today's dynamic business environment, companies have been strategically shifting towards a customer-centric approach from their traditional product-centric focus. The main goal of this paper is to estimate customer lifetime value of 5,000 customers in the retail industry. This research follows a step-by-step approach to construct a multiple regression machine learning model. The model used in the study is based on the nine features to predict the customer life time value. First basic train-test split model is developed, which predicted 74% of variation in the customer lifetime value. This necessitates to improve the model performance, hence to address the multicollinearity problem lasso regularization is used. After lasso regularization , final model is trained with hyperparameter turning for further model performance improvement. The results show significant improvements in predicting customer lifetime value with the final model. This study suggests that the machine learning regression models can help to businesses to better understand how much value they can generate from individual customer.This deep understanding about customers helps retail businesses to align their customer engagement strategies to create a positive impact on the profitability and maximizing overall value offered to the customers. 2024 IEEE. -
Customer Segmentation and Future Purchase Prediction using RFM measures
Winning in the E-Commerce business race at a competitive age like this requires proper usage of Customer data. Using that database and grouping it in similar segments in terms of spending expenditure, observation time, sex, and location so that every customer falls in a segment of characteristics. This mechanism is called Customer Segmentation. In the modern era of highly compatible technological advancements, Machine Learning Algorithms are being vastly used to bring solutions to these difficult yet essential services. In the field of research methods like simple clustering based on purchase behaviour, buyer targeting or automated customer promotion mechanism by dividing into two major categories, have been worked on. However, ensemble algorithms have come handy where different clustering algorithms are combined to deliver best segmentation. Lately combination techniques like clustering and classification mechanism have also delivered good results where, not only segmentation is done but also classification of existing and new customers are possible into the clusters. Depending on that an effective customer relationship management can really benefit the company to a huge extent. Unlike other studies where clustering was performed directly on RFM table, a different approach was taken in this study where, one dimensional clustering was done individually on Recency, Frequency, Monetary columns, then an overall score was calculated and customers were classified into three segments. However, for a new customer depending on his purchase behaviour he/she also can be classified into any of the categories. 2022 IEEE. -
Customer Segmentation in the Field of Marketing
The motive of this work is to classify and categorize customers depending on their familiar traits/characteristics so as to enable a company or a firm to adequately market their products to each category more attractively and competently. It is imperative for a firm to educate themselves with each and every detail about the customer, such as age group, sexuality, social class, purchase pattern etc as it paves way for customer segmentation. Businesses may utilize segmentation to make better use of their marketing resources, get a competitive advantage over competitors, and, most importantly, display a deeper understanding of their consumers' requirements and desires. Customer segmentation, when combined with customer targeting and positioning, creates the foundation for strategic marketing. A manager can find new marketing possibilities and create or adjust the product to satisfy the demands of potential clients using the notion of strategic marketing. The product's quality level determines its position in the market's overall offering. It's a crucial aspect in selecting which market segment a collection will target. The commercial world has gotten more competitive over time, as enterprises like these have to fulfil their consumers' demands and aspirations, attract new customers, and enhance their bottom lines. In this research, I have put the spotlight on the information used by firms for the purpose of customer segmentation in the most valuable manner. In addition to that, I have portrayed different models of customer segmentation and the benefits reaped by a business in implementing them. 2022 IEEE. -
Customized SEIR Mathematical Model to Predict the trends of Vaccination for Spread of COVID-19
The uncertainty in life plans, restrictions on physical classrooms, loss of jobs, large number of infections and deaths due to COVID-19 are some significant causes of concern for the public as well as Governments all over the globe. Moreover, the exponential increase in the number of infected people in a short time is responsible for the collapse of the health industry during the pandemic caused by COVID-19. The health experts recommended that the quick and early diagnosis followed by treatment of patients in isolation is a way to minimize its spread and save lives. The objective of this research is to propose a customized SEIR model to predict the trends of vaccination in the USA. The experimental results prove that the Moderna vaccine reports the efficacy of 93%, which is higher than the Pfizer and Johnson and Johnson vaccines. 2022 ACM. -
Cyber-Secure Framework for the Insecure Designs in Healthcare Industry
Sensitive data protection has been a top priority in the healthcare industry. This has led to the investigation of safe data storage and transaction. Despite various attempts to address this issue, data breaches continue to plague the healthcare industry. This study aims to investigate prevalent storage practices and security methodologies in the healthcare, recognizing the need for a robust framework. The work further extends with design of new security framework for healthcare industry. This framework identifies critical data and implement measures to prevent unauthorized access and data tempering. The industrial hype towards the implementation of adaptive machine learning craves the need for hybrid machine learning approaches to be adapted in the cyber secure framework. In order to improve security and confidentiality in the healthcare sector. Blockchain is used in the proposed cyber secure framework promising integrity of data with the features of immutability. This proposal aims to provide a comprehensive solution to the ongoing problem of protecting medical data. Grenze Scientific Society, 2024. -
Cybersecurity Threats Detection in Intelligent Networks using Predictive Analytics Approaches
The modern scenario of network vulnerabilities necessitates the adoption of sophisticated detection and mitigation strategies. Predictive analytics is surfaced to be a powerful tool in the fight against cybercrime, offering unparalleled capabilities for automating tasks, analyzing vast amounts of data, and identifying complex patterns that might elude human analysts. This paper presents a comprehensive overview of how AI is transforming the field of cybersecurity. Machine intelligence can bring revolution to cybersecurity by providing advanced defense capabilities. Addressing ethical concerns, ensuring model explainability, and fostering collaboration between researchers and developers are crucial for maximizing the positive impact of AI in this critical domain. 2024 IEEE. -
Dampers to Suppress Vibrations in Hydro Turbine-Generator Shaft Due to Subsynchronous Resonance
There are numerous applications to evaluate the damage caused by subsynchronous resonance (SSR) to a turbine-generator shaft. Despite multiple applications, there are relatively few studies on shaft misalignment in the literature. In this paper, stresses in the existing turbine-generator shaft due to subsynchronous resonance were studied using finite element analysis (FEA). The 3D finite element model reveals that the most stressed part of the shaft is near the generator terminal. A new nonlinear damping scheme is modeled to reflect the torsional interaction and to suppress the mechanical vibration caused by subsynchronous resonance (SSR). Stresses developed due to the addition of capacitors in the system at high rotational speeds and deformation of the shaft during various modes of oscillations were evaluated. Experimental investigations are carried out in reaction turbine connected to a 3kVA generator. Simulation is carried out for the experimental setup using ANSYS. According to the simulation results, the damper installed near the generator terminal provides satisfactory damping performance and the subsynchronous oscillations are suppressed. 2021, Springer Nature Singapore Pte Ltd. -
Data Analysis and Machine Learning Observation on Production Losses in the Food Processing Industry
Food wastage and capturing lineage from production to consumption is a bigger concern. Yielding, storage and transportation areas have evolved to a great extent associated to manufacturing and automation which lead to technical advancements in food processing industry. In such situation, losses are generally observed in the crop production which are sometimes minimal and ignored. However, in some cases these losses are huge and are becoming a threat to the both producers and consumers. Here we considered data related to dairy products and analysed the production losses especially while processing them in the treating unit. Literature on parameters and associated data analysis in the form of graphical representation are provided in the appropriate sections of the paper. Linear regression and correlation were envisaged in view of incorporating machine learning techniques understanding production losses. Karl Pearson's correlation provides an observation related to association of parameters which are desired to be less coupled in terms of employing proposed newer methodology. 2023 IEEE. -
Data Analysis on Hypothyroid Profiles using Machine Learning Algorithms
Machine learning algorithms enable computers to learn from data and continuously enhance performance without explicit programming. Machine learning algorithms have significantly improved the accuracy and efficacy of thyroid diagnosis. This study identified and analysed the usefulness of several machine-learning algorithms in predicting hypothyroid profiles. The main goal of this study was to see the extent to which the algorithms adequately assessed whether a patient had hypothyroidism. Age, sex, health, pregnancy, and other factors are among the many factors considered. Extreme Gradient Boosting Classifier, Logistic Regression, Random Forest, Long-Term Memory, and K-Nearest Neighbors are some of the machine learning methods used. For this work, two datasets were used and analysed. Data on hypothyroidism was gathered via DataHub and Kaggle. These algorithms were applied to the collected data based on metrics such as Precision, Accuracy, F1 score and Recall. The findings showed that the Extreme Gradient Boosting classification method outperformed the others regarding F1 score, accuracy, precision, and recall. The research demonstrated how machine learning algorithms might predict thyroid profiles and identify thyroid-related illnesses. 2023 IEEE. -
Data Augmentation for Handwritten Character Recognition of MODI Script Using Deep Learning Method
Deep learning-based methods such as convolutional neural networks are extensively used for various pattern recognition tasks. To successfully carry out these tasks, a large amount of training data is required. The scarcity of a large number of handwritten images is a major problem in handwritten character recognition; this problem can be tackled using data augmentation techniques. In this paper, we have proposed a convolutional neural network-based character recognition method for MODI script in which the data set is subjected to augmentation. The MODI script was an official script used to write Marathi, until 1950, the script is no more used as an official script. The preparation of a large number of handwritten characters is a tedious and time-consuming task. Data augmentation is very useful in such situations. Our study uses different types of augmentation techniques, such as on-the-fly (real-time) augmentation and off-line method (data set expansion method or traditional method). A performance comparison between these methods is also performed. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Economy: Data and Money
The article explores the concept of data economy, which is based on the sharing of data across platforms and ecosystems. Data has evolved from factual information to a new asset for companies worldwide, and the article discusses its evolution from brittle paper records to complex databases and algorithms like blockchain. With a prediction of a data explosion of about 175 zettabytes by 2025, data is used extensively in various domains, from agriculture to healthcare. The article also discusses how the data economy is not domain-specific but is a universal shift as all companies transition to become technology-driven companies. The data network effect is a cycle that uses data to acquire service users and generate more data. This has become a B2B service model that has added profits to various tech giants balance sheets. The article concludes by exploring the current need for data sharing across organizations and the future scope of the data economy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Data Encryption and Decryption Techniques Using Line Graphs
Secure data transfer has become a critical aspect of research in cryptography. Highly effective encryption techniques can be designed using graphs in order to ensure secure transmission of data. The proposed algorithm in this paper uses line graphs along with adjacency matrix and matrix properties to encrypt and decrypt data securely in order to arrive at a ciphertext using a shared-key. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Ingestion - Cloud based Ingestion Analysis using NiFi
Data Ingestion has been an integral part of Data Analysis. Bringing the data from various heterogeneous sources to one common place and ensuring the data is captured in the appropriate format is the key for performing any Big data task. Data ingestion is performed using multiple frameworks across the industry and they all have their own set of benefits and drawbacks. Apache NiFi is one popular ingestion framework which is used widely and does Ingestion effectively. Ingestion is performed on various sources and the data is generally stored in clusters or cloud storage. In this paper, we have done the File Data Ingestion using the NiFi framework on a local machine and then on two cloud-based platforms, namely Google Cloud Platform (GCP) and Amazon Web Services (AWS). The objective is to understand the latency and performance of the NiFi tool on Cloud-based Ingestion and provide a comparative study against the typical Data Ingestion. The entire setup was done on a local machine and two corresponding cloud platforms namely GCP and AWS. The findings from the comparative analysis have been compiled in a tabular format and graphs are created for easy reference. The paper places emphasis on the significance of NiFi's data ingestion performance on Cloud Platform and attempts to present it as a major activity on the data ingestion platform for Cloud Ingestion Solution. 2023 IEEE. -
Data linearity using Kernel PCA with Performance Evaluation of Random Forest for training data: A machine learning approach
In this study, Kernel Principal Component Analysis is applied to understand and visualize non-linear variation patterns by inverse mapping the projected data from a high-dimensional feature space back to the original input space. Performance Evaluation of Random Forest on various data sets has been compared to understand accuracy and various statistical measures of interest. 2016 IEEE. -
Data Mining Approaches forHealthcare Decision Support Systems
Data mining is a user-friendly approach to locating previously unknown or hidden information in data. The employment of data mining technologies in the healthcare system may result in the finding of relevant data. Data mining is used in healthcare medicine to construct learning models that predict a patients condition. Data mining technologies have the potential to benefit all stakeholders in the healthcare industry. For example, data mining may aid health providers in detecting theft and fraud, medical organizations in making customer service management decisions, physicians in discovering effective therapies and best practices, and customers in obtaining suitable and less expensive healthcare. Contemporary systems, due to their complexity and size, are unable to control and analyze the huge amounts of data generated by healthcare operations. Data mining is a technique and mechanism for converting a large amount of data into useful information. The fundamental purpose of this research is to look at what makes clinical data mining unique, to give an overview of existing clinical decision support systems, to identify and select the most common data mining algorithms used in modern Health and Demographic Surveillance System (HDSS), and to compare different data mining algorithms. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.