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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 encryption and decryption using graph plotting
Cryptography plays a vital role in today's network. Security of data is one of the biggest concern while exchanging data over the network. The data need to be highly confidential. Cryptography is the art of hiding data or manipulating data in such a way that where no third party can understand the original data while transmission from source to destination. In this paper, a modified affine cipher algorithm has been used to encrypt the data. The encrypted data will be plot onto a graph. Later, graph will be converted into image. This system allows sender to select his/her own keys to encrypt the original data before plotting graph. Then, Receiver will use the same key to decrypt the data. This system will provide the better security while storing the data in cloud in the form of secret message embedding in graphical image file in network environment. IAEME Publication. -
Data encryption in public cloud using multi-phase encryption model
Cloud computing the most used word in the world of Information Technology, is creating huge differences in IT industry. Nowadays huge amount of data is being generated and the researchers are finding new ways of managing these data. Basically, the word cloud refers to a virtual database that stores huge data from various clients. There are three types of cloud public, private and hybrid. Public cloud is basically for general users where users can use cloud services free or by paying. Private cloud is for any particular organizations and hybrid one is basically combine of both. Cloud offers various kind of services such as IAAS, PAAS, SAAS where services like platform for running any application, accessing the huge storage, can use any application running under cloud are given. The cloud also has a disadvantage regarding the security for the data storage facility. Basically, the public cloud is prone to data modification, data hacking and thus the integrity and confidentiality of the data is being compromised. Here in our work the concern is to protect the data that will be stored in the public cloud by using the multi-phase encryption. The algorithm that we have proposed is a combination of Rail Fence cipher and Play Fair cipher. 2018 Snata Choudhury, Dr. Kirubanand V.B. -
Data Engineering and Data Science: Concepts and Applications
DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the one-stop shop for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesnt need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library. 2023 Scrivener Publishing LLC. -
Data Ethics
Ethics is all about living an ethical life. As rational beings, humans have always been in the pursuit of ethical life in spite of the contrary temptations. Society and engagement in society are helpful for a person to be ethical. However, it is the choice one makes in critical situations that define the ethical nature of a person. Swarmed by a vast pool of data, the complex nature of ethical decision-making is getting far more complex for human beings with autonomous cognitive faculty. One needs to be conscious and focused to face any dilemma in ones life. Dealing prudently with private and public data and understanding the science of data would help the homo sapiens to prove her/his relevance in this data-driven world. 2022, 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 journalists perception and practice of transparency and interactivity in Indian newsrooms
Data journalism research recorded exponential growth during the last decade. However, the extant literature lacks comparative perspectives from the Asian region as it has been focused on select geographies (mainly Europe and the US). In this backdrop, the present study examined data journalism practices in the Indian media industry by conducting intensive interviews with 11 data journalists to investigate their perception of transparency and interactivity which are two of the core aspects of data journalism practice. Further, a content analysis of data stories published by two Indian news organizations for two years was conducted to assess the status of transparency and interactivity options in these stories. The findings showed that Indian data journalists acknowledge the importance of transparency and interactivity, but exhibit a cautious approach in using them. There is general apathy in practicing transparency among journalists in legacy organizations, drawing a stark contrast with their counterparts in digitally-native organizations. 2022 Asian Media Information and Communication Centre. -
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. -
Data Mining Techniques to Enhance Customer Segmentation and Targeted Marketing Strategies
The retail industry is facing an ever-increasing challenge of effectively identifying and targeting its customers. Using traditional segmentation techniques to fully capture the intricate and ever-changing character of customer behavior is difficult. This project will examine sales data from a general shop using an assortment of data mining technologies in order give insights into customer habits and purchasing trends. Retail sales records builds the dataset. K-means clustering, association rule mining, and regency, the frequency, and monetary (RFM) analysis will all be employed to look into the data. This study contributes to create something of focused marketing strategies and consumer segmentation by identifying high-value and atrisk clients. Association rule mining illuminates consumer taste and actions by identifying hidden patterns and correlations in large datasets. These discoveries extend the scope of our comprehension of consumer purchasing habits and offer data for more targeted advertising initiatives. Additionally, the K-means clustering algorithm divides customers according to their purchasing habits and behavior, allowing profound knowledge to enhance marketing and sales strategies. Findings from the research will give an extensive awareness of customer behavior and purchasing dynamics, which will improve the efficacy of the general store's marketing and sales campaigns. The most effective technique for exploiting insights from sales data will be discovered by contrasting the outcomes of RFM analysis, K-means clustering, and association rule mining. This work promises to make substantial improvements to data mining and buyer behavior research algorithms, and it has the capacity to be implemented across an extensive selection of corporate restrictions intended to improve their sales strategies. 2024 IEEE. -
Data Mining-Based Variant Subset Features
A subset of accessible variants data is chosen for the learning approaches during the variant selection procedure. Itincludes the important one with the fewest dimensions and contributes the most to learner accuracy. The benefit of variant selection would be that essential information about a particular variant isnt lost, but if just a limited number of variants are needed,and the original variants are extremely varied, there tends to be a risk of information being lost since certain variants must be ignored. Dimensional reduction, also based on variant extraction, on the other hand, allows the size of the variant space to be reduced without losing information from the original variant space.Filters, wrappers, and entrenched approaches are the three categories of variant selection procedures. Wrapper strategies outperform filter methods because the variation selection procedure is suited for the classifier to be used. Wrapper techniques, on the other hand, are too expensive to use for large variant spaces due to their high computational cost;therefore each variant set must be evaluated using the trained classifier, which slows down the variant selection process. Filter techniques have a lower computing cost and are faster than wrapper procedures, but they have worse classification reliability and are better suited to high-dimensional datasets. Hybrid techniques, which combine the benefits of both filters and wrappers approaches, are now being organized. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Data Modeling and Analysis for the Internet of Medical Things
Smart biomedical technology greatly assists in rapid disease screening and diagnosis within hospitals. One innovative device, a smart inhaler, incorporates sensors to track medication doses, usage patterns, and effectiveness. These inhalers provide valuable support to asthma sufferers, allowing for improved condition management and better patient outcomes. Asthma, a chronic respiratory disease affecting millions worldwide, causes airway constriction and swelling, resulting in breathing difficulties. Typically, medication such as inhaled corticosteroids and bronchodilators is used for management. However, medication adherence is often inadequate, leading to worsened outcomes and exacerbations. Smart inhalers aim to address this challenge by enabling users to monitor medication usage and compliance. Equipped with sensors, the inhalers track when, how much, and how frequently the prescribed medication is taken. The collected data is then transmitted to a mobile app or web portal, accessible to patients and healthcare providers. This integration facilitates medication tracking and provides personalized coaching for improved asthma control. The gathered data serves multiple purposes, including helping patients monitor their medication use and adherence. Patients can receive feedback on their treatment plan adherence and utilize the app to set medication reminders, promoting adherence and enhancing outcomes. 2024 CRC Press. -
Data Privacy
Data privacy is a private and public phenomenon and its operations have implications for the individual and the society. This understanding of privacy ceases it from being viewed as a simple technological process and highlights different factors that are associated with it. While on one hand, the right to privacy is seen as integral to the freedom of the individual, on the other hand, it is also seen as the ability to hide certain information for malpractice. This chapter delves into this existing dichotomy of data privacy and simplifies various terms and operations that have emerged in the field of study. A discussion is facilitated on the concept and its associated areas. The chapter looks at privacy regimes in different countries to note emerging developments and also presents a critique of the practice to bring forth shortcomings and enable change. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Reduction Techniques in Wireless Sensor Networks with AI
Due to their numerous uses in practically every part of life and their related problems, such as energy saving, a longer life cycle, and better resource usage, the research of wireless sensor networks is ongoing. Its extensive use successfully saves and processes a considerable volume of sensor data. Since the sensor nodes are frequently placed in challenging locations where less expensive resources are required for data collection and processing, this presents a new difficulty. One method for minimizing the quantity of sensor data is data reduction. A review of data reduction methods has been provided in this publication. The different data reduction approaches that have been put forth over the years have been examined, along with their advantages and disadvantages, ways in which they can be helpful, and whether or not using them in contexts with limited resources is worthwhile. 2022 IEEE. -
Data Science in the Medical Field
Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Data science: simulating and development of outcome based teaching method
The educational researcher has a wealth of options to apply analytics to extract meaningful insights to improve teaching and learning due to the growing availability of educational data. Teaching analytics, in contrast to learning analytics, examines the quality of the classroom environment and the efficacy of the instructional methods used to improve student learning. To investigate the potential of analytics in the classroom without jeopardizing students' privacy, we suggest a data science strategy that uses simulated data using pseudocode to build test cases for educational endeavors. Hopefully, this method's findings will contribute to creating a teaching outcome model (TOM) that can be used to motivate and evaluate educator performance. In Splunk, the study's simulated methodology was carried out. Splunk is a real-time Big Data dashboard that can gather and analyze massive amounts of machine-generated data. We provide the findings as a set of visual dashboards depicting recurring themes and developments in classroom effectiveness. Our study's overarching goal is to help bolster a culture of data-informed decision-making at academic institutions by applying a scientific method to educational data. 2023 IEEE. -
Data science: the Artificial Intelligence (AI) algorithms-inspired use cases
The data science field is growing fast with the faster maturity and stability of its implementation technologies. We had been fiddling with traditional data analytics methods. But now, with Artificial Intelligence (AI), it is possible to embark on predictive and prescriptive insights generation in time. There are several data science (DS) use cases emerging with the wider adoption and adaptation of AI technologies and tools. This chapter is dedicated to illustrate various AI-inspired use cases. The Institution of Engineering and Technology 2022. -
Data Security
Corporates, industries, and governments have completely digitized their infrastructure, processes, and data or running towards completed digitalization. This data could be text files, different types of databases, accounts, and networks. The data living in the digital format needs to be preserved and also protected from unauthorized access. If this data remains open for access, any unauthorized user can destroy, encrypt, or corrupt the data, making the data unusable. There are implications of data security threats such as data breaches and data spills, beyond cost and can spell doom for the business. Hence the data needs to be protected from such threats. Data security is a mechanism through which data is protected and prevented from loss due to unauthorized access. It is a mix of practices and processes to ensure data remains protected from unauthorized use and readily accessible for authorized use. Data Security is essential for achieving data privacy in general. To define appropriate security measures, we must define the difference between a data breach and a data leak. Data security mechanisms could be data-centric such as identity and access management, encryption and tokenization, and backup and recovery. A defined data governance and compliance can also ensure data security. This chapter will explain why there is a need for data security, methods and processes to achieve data security, and touch upon some of the data security laws and regulations. We will also see a case study on how hackers exploited a vulnerability to mount a data security attack worldwide and how data security mechanisms could have prevented it. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Security-Based Routing in MANETs Using Key Management Mechanism
A Mobile Ad Hoc Network (MANET) is an autonomous network developed using wireless mobile nodes without the support of any kind of infrastructure. In a MANET, nodes can communicate with each other freely and dynamically. However, MANETs are prone to serious security threats that are difficult to resist using the existing security approaches. Therefore, various secure routing protocols have been developed to strengthen the security of MANETs. In this paper, a secure and energy-efficient routing protocol is proposed by using group key management. Asymmetric key cryptography is used, which involves two specialized nodes, labeled the Calculator Key (CK) and the Distribution Key (DK). These two nodes are responsible for the generation, verification, and distribution of secret keys. As a result, other nodes need not perform any kind of additional computation for building the secret keys. These nodes are selected using the energy consumption and trust values of nodes. In most of the existing routing protocols, each node is responsible for the generation and distribution of its own secret keys, which results in more energy dissemination. Moreover, if any node is compromised, security breaches should occur. When nodes other than the CK and DK are compromised, the entire networks security is not jeopardized. Extensive experiments are performed by considering the existing and the proposed protocols. Performance analyses reveal that the proposed protocol outperforms the competitive protocols. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Data set on impact of COVID-19 on mental health of internal migrant workers in India: Corona Virus Anxiety Scale (CAS) approach
The article presents a unique dataset on mental health of internal Migrant workers in India. The dataset was constructed during the pandemic when the entire nation was the victim of stringent measures to curtail the spread of Corona Virus in the form of travel restrictions and lockdowns. We collected this data in our pursuit to submit a paper in response to call for paper in the Journal titled Migration and health. Non-availability of authentic data about the internal migrant workers triggered this effort to compile the data. We have recorded 1350 responses out of a 6897 Sample through snowball sampling method. Every respondent is said to be a referee for further driving of sample. The responses were collected between June 2 and August 30, 2020 through the telephonic interviews. Also, the consent of the respondents has been duly obtained for publication of the data without revealing their identity. The interview schedule was adopted by using Corona virus Anxiety Scale (CAS) which uses four dimension model namely Cognitive, Emotional, Behavioral and Psychological. The Interview schedule was originally designed in English but was later translated into three different languages after consulting the language experts. This article provides descriptive statistics of study variables along with socio economic factors. This dataset provides a significant platform for further research related to CAS and in assessment of mental health of vulnerable groups. 2021