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Secured Electronic Transactions Using Visual Encryption: An E-Commerce Instance
The developments in the information technology has led the modern world to rely on the internet for digital information. People around the world depend on the internet for every information from general information to sharing confidential data over the network. Technology has grown so wide that even all the transactions are happening through online platforms. Along with this, there has been a rise in the security threats of the user's confidential data which are shared by the users while buying goods online. To maintain the security there are many authentication techniques available today like OTP, CAPTCHA etc. These security measures taken till now have been adequate till the present day but over time the hackers would find out ways to attack. Data breaches and hacking of user data would increase day by day if preventive measures are not taken time to time. This project focuses on providing security to the user's confidential data with a two way authentication technique. The aim of the project is to include one more security level to the existing online transaction system. The visual cryptographic method is used along with the OTP generating system. 2018 IEEE. -
Examining how ERP-Enabled Supply Chain Integration Works within the Quadrants of the Four-Wheeler Industry
Enterprise Resource Planning (ERP) systems have become indispensable tools for managing complex supply chains in the automotive industry. This paper provides an in-depth examination of ERP-based supply chain integration within the four-wheeler automobile sector, focusing on its impact, challenges, and best practices. Through a comprehensive review of existing literature and case studies, we analyze the key components of ERP systems, their integration with supply chain processes, and the benefits they offer to automotive manufacturers. Additionally, we explore the role of ERP in enhancing operational efficiency, inventory management, demand forecasting, and customer satisfaction. Furthermore, we discuss the challenges associated with ERP implementation and provide recommendations for successful integration and optimization of ERP systems in the four-wheeler automobile industry. 2024 IEEE. -
Mapping of built-up area and change detection in bengaluru using semi-automatic classification
Built-up areas are ever-increasing in nature to cater to the growing population's needs due to the migration of people to urban areas. Indian cities are under stress due to unplanned developmental activities. Land use and the land cover pattern are critical to maintaining the balance of various resources. In this study, Spatio-temporal changes have been mapped from 1989 to 2022 for the Bengaluru urban region. Geospatial techniques have been adopted to map land use, land cover changes and urban growth. Passive remote sensing data sets, which are freely available, were used in this study. QGIS and ESRI's ArcGIS software packages analysed the satellite images. Vegetation indices such as the Normalised vegetation index (NDVI), Normalised Difference Water index (NDWI), and Normalised difference Built-up index (NDBI) have been used along with supervised and unsupervised classification techniques. Images were classified into water bodies, vegetation, built-up area and others. It has been observed that there is an increase in the built-up area decrease in vegetation and water bodies. As per this study, policymakers and society need to consider the conservation of natural resources and developmental activities for sustainable development. 2023 Author(s). -
Synthesis and characterization of Chitosan-CuO-MgO polymer nanocomposites
In the present work, we have synthesized Chitosan-CuO-MgO nanocomposites by incorporating CuO and MgO nanoparticles in chitosan matrix. Copper oxide and magnesium oxide nanoparticles synthesized by precipitation method were characterized by X-ray diffraction and the diffraction patterns confirmed the monoclinic and cubic crystalline structures of CuO and MgO nanoparticles respectively. Chitosan-CuO-MgO composite films were prepared using solution- cast method with different concentrations of CuO and MgO nanoparticles (15 - 50 wt % with respect to chitosan) and characterized by XRD, FTIR and UV-Vis spectroscopy. The X-ray diffraction pattern shows that the crystallinity of the chitosan composite increases with increase in nanoparticle concentration. FTIR spectra confirm the chemical interaction between chitosan and metal oxide nanoparticles (CuO and MgO). UV absorbance of chitosan nanocomposites were up to 17% better than pure chitosan, thus confirming its UV shielding properties. The mechanical and electrical properties of the prepared composites are in progress. 2018 Author(s). -
Arabica Coffee Bean Grading into Specialty and Commodity Type Based on Quality Using Visual Inspection
Expanding potential of coffee consumers to seek out the freshest and best flavors is a cause for the rise of specialty coffee inthe market. Specialty coffee is grown and harvestedmaintaining an emphasis on quality and clarity of flavor, whereas commodity coffee is harvested for caffeine content. Within those inclusive categories, arabica and robusta are the two types of main branches of coffee that weencounter in the coffee market. Specialty coffees differ significantly from conventional coffees in that they are cultivated at higher altitudes, can be traced, and are professionally processed after being harvested. The quality is constantly examined and understood at every stage, from growth to brewing. Green arabica quality is assessed by counting the defective beans present in the sample. These defects can be primary (Category I) or secondary (Category II). If the primary defects are null and less than five secondary defects, coffee is said to be a specialty.Prior research has been done on classifying the coffee species and differentiating good beans from bad beans. Our research involves the combination of machine learning like K-NN and deep learning convolutional neural networks for classifying specialty coffee from commodity type using computer vision. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Review On Image based Coffee Bean Quality Classification: Machine Learning Approach
Specialty coffee's demand is growing worldwide as coffee drinkers continue to look for the freshest and highest-quality flavors. Depending upon the quality, there are two categories in the coffee industry, that is specialty coffee and commodity/commercial coffee. Coffee beans are graded via visual inspection and cupping. A 300g sample of green coffee beans is used for visual assessment, and faulty beans are counted. As per the 'Specialty Coffee Association of America' (SCAA), defect can be either primary or secondary. For a coffee to be a specialty, it should have less than 5 secondary defects and zero primary defects. In this survey we have presented the coffee bean quality-related research which includes various machine learning approaches in classifying the coffee beans. The study has achieved quite promising prediction accuracies and was evaluated with test data. We have done a study on coffee bean quality classification and are willing to contribute an arabica coffee bean dataset and detection of coffee bean quality using transfer learning with higher accuracy. 2022 IEEE. -
Dynamic Behaviour Analysis of Multi-Cell Battery Packs: A Simulation Study
In the era of IoT understanding the dynamic behavior of a Lithium-ion Battery Management System (BMS) has become gradually more important. This research investigates the dynamic behaviour of a six-cell Lithium-ion Battery Management System (BMS) through simulation. The study employs a comprehensive model encompassing key battery parameters, including cell capacity, voltage limits, temperature thresholds, and charge/discharge characteristics. Additionally, state variables such as State of Charge (SOC), State of Health, and State of Function are integrated to capture the battery's internal dynamics. The simulation incorporates a sinusoidal current profile to emulate realistic operating conditions. Notably, Coulomb counting is employed for SOC estimation, and protective measures against overvoltage, undervoltage, and overcurrent are implemented. The study also addresses balancing strategies and communication interfaces within the BMS. The results reveal nuanced interactions between voltage, temperature, SOC, and current, offering insights into the intricate behaviour of the battery system under dynamic conditions. This research not only advances our understanding of BMS functionality but also lays a crucial foundation for the evolution of battery technology and energy management systems in the IoT landscape. The Institution of Engineering & Technology 2023. -
Financial Big Data Analysis Using Anti-tampering Blockchain-Based Deep Learning
This study recommends using blockchains to track and verify data in financial service chains. The financial industry may increase its core competitiveness and value by using a deep learning-based blockchain network to improve financial transaction security and capital flow stability. Future trading processes will benefit from blockchain knowledge. In this paper, we develop a blockchain model with a deep learning framework to prevent tampering with distributed databases by considering the limitations of current supply-chain finance research methodologies. The proposed model had 90.2% accuracy, 89.6% precision, 91.8% recall, 90.5% F1 Score, and 29% MAPE. Choosing distributed data properties and minimizing the process can improve accuracy. Using code merging and monitoring encryption, critical blockchain data can be obtained. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Pad Vending Machine with Cashless Payment
A vending machine aims to provide required product or the service to the customer with certain ease, wherein not much effort is required. This research work aims to design a pad vending machine with an option of payment using QR code which is implemented using blockchain to make the system much more efficient and reliable than the existing systems present in the Indian market. The system is divided into two parts, first being the working of the machine and second being the mode of payment which is implemented using a blockchain. It is noticed many times that due to unpredictable menstrual flow women tend to face a lot problems. To overcome this problem, a pad vending machine is proposed with certain advancements through which women can help themselves in the stated circumstances. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Online Voting System Using Blockchain
One of the major areas in technical development is blockchain and bitcoin. These technologies have enabled many simulations in in-hostile applications that have major issues with security and integrity of data. To provide more relevance to the available cyberphysical systems in the dimension of security, the blockchain technology offers a major help. If the present scenario is considered, we have multiple day-to-day applications that have been simulated and require more security enhancement. For example, the E-voting systems are a trend and their security features have to be upgraded to authenticate both systems and processes. The present research paper focuses on the same application and aims to provide security upgradation by proposing a working model of e-voting systems. 2020, Springer Nature Singapore Pte Ltd. -
Comparison of Augmentation and Preprocessing Techniques for Improved Generalization Performance in Deep Learning based Chest X-Ray Classification
Convolutional Neural Network (CNN) models are well known for image classification; however, the downside of CNN is the ineffectiveness to generalize and inclination towards over-fitting in case of a small train dataset. A balanced and sufficient data is thus essential to effectively train a CNN model, but this is not always possible, especially in the case of medical imaging data, as often patients with the same disease are not always available. Image augmentation addresses the given issue by creating new data points artificially with slight modifications. This study, investigates ten different methods with various parameters and probability and their combined effect on the test dataset's generalization performance and F1 Score. For the study, three pre-Trained CNN models, namely ResNetl8, ResNet34, and ResNet50, are fine tuned on a small training dataset of 500 Pneumonia and 160 Non-Pneumonia(Normal) Images for each augmentation setting. The test accuracy, F1 Score, and generalization performance were calculated for a test dataset consisting of 50 Pneumonia and 16 Non-Pneumonia(Normal) Images. 2022 IEEE. -
Detection and Classification of Thoracic Diseases in Medical Images using Artificial Intelligence Techniques: A Systematic Review
Artificial Intelligence is at the leading edge of innovation and is developing very fast. In recent studies, it has played a progressive and vital role in Computer-Aided Diagnosis. The chest is one of the large body parts of human anatomy and contains several vital organs inside the thoracic cavity. Furthermore, chest radiographs are the most commonly ordered and globally used by physicians for diagnosis. An automated, fast, and reliable detection of diseases based on chest radiography can be a critical step in radiology workflow. This study presents the conduction and results of a systematic review investigating Artificial Intelligence Techniques to identify Thoracic Diseases in Medical Images. The systematic review was performed according to PRISMA guidelines. The research articles published in English were filtered based on defined inclusion and exclusion criteria. The Electrochemical Society -
Energy saving, waste management, and pollution free steps for university campuses
Global warming is a worldwide concern and the documents related to the need for sustainable measures seen in academic and non-academic literature. In a highly populated country like India, these are more severe worries. Multiple established educational institutions across India have taken significant steps in educating their students on sustainable development goals (SDG). Currently there is a need to assess the extent of effect such training has on student populations of such institutes. Present study attempted to assess the efficacy of SDG-implementation training programmes in a reputed private university, through student assessment of student behaviors outside the institute and in their personal life. Using semi-structured in-depth interview methods, interviewed eight students of Undergraduate and Postgraduate programmes. These students were active participants of community service programmes arranged by the university within a sustainable development model. Data were analyzed using reflexive thematic analysis methods. Emerged themes from data analysis indicate a positive change in their worldview and significant modifications in their personal behavior towards sustainability because of being part of such programmes. Educating others through practice and increased socio-environmental awareness were also major themes. Current study contributes in assessing efficacy of sustainability programmes in educational institutions. This study also suggests few recommendations for increasing competence of the same. 2021 Author(s). -
Flipped Classroom Strategy in Online Teaching: Challenges Faced by Higher Education Teachers
Flipped classroom model has gained increasing interest among university teachers in recent years [1] (Stohr et al.). The reason for its popularity is attributed to its bearing on Vygotskys constructivism theory and for the student centered approach [2] (Ziling Xu et al.). Countries in the world are affected by COVID-19 including India. Hence higher education institutes have begun their online classes. Flipped classroom teaching has been quite prevalent in Indian higher education recently. Online class initiation from higher education institutes in India has pushed faculty members to teach online and faculty have begun flipped classroom teaching online. Flipped classroom teaching in online differs from the face to face mode. There are challenges and issues while using flipped classroom in online mode by the faculty members of higher education. This leads to the present study to find out the challenges of flipped classroom teaching in online mode by teachers of higher education. The present study adopted qualitative research method. Structured interviews and focus group discussion were conducted to answer the research question. Study was able to discuss the challenges of flipped classroom in online mode. These challenges are to be dealt with by the stake holders to bring teaching efficacy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Pre-Service and In-Service Teachers Perceptions of Using Virtual Reality Tools in Teaching
This paper explores pre-service and in-service teachers perceptions of virtual reality (VR) technology as a teaching and learning tool in the classroom in India. The study aimed to answer four research questions, including the adoption rate of VR technology among teachers, their confidence levels in teaching using VR technologies compared to digital technologies, attitudes towards using VR technology, and the usefulness of different uses of VR technology. The survey conducted among 102 teachers found limited adoption of VR technology, lower confidence levels in using it, but willingness to use it in the future. The paper recommends providing adequate training and support to increase teachers confidence in using VR technology in their teaching practices. The study also suggests that strategies to promote VR technology should consider gender differences in attitudes towards it. Overall, the research concludes that teachers view VR technology as having potential benefits for learning and teaching across various uses. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A Comprehensive Review on Fault Data Injection in Smart Grid
Nowadays, power generation at the utility side and transfer to the demand side have been controlled by the smart grid. Day-by-day entire power distribution process has moved in multiple directions and connects more residential and industrial sectors. Due to these phenomena, more monitoring, and security processes have been adopted in smart grid to control fault data injection, cyber-attack, and physical side attackers in smart grids. This research study analyzes the fault data injection in smart grid with respect to the malicious data, signal, and connectivity process. As a part of this research study, a survey has been done on various techniques to control the faults in smart grid. The analysis carried out in this study is very helpful to identify and determine the suitable method to control the fault in smart grid. Along with these, a countermeasure against the FDI is also summarized on the cyber-attack and physical attack. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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. -
Steganography using Improved LSB Approach and Asymmetric Cryptography
Steganography deals with the craft of obscuring private data inside a spread media. In confidential data communication security is a vital issue. In this paper, we use a two-layer security. At first, data encryption is achieved by the method of RSA algorithm of asymmetric cryptography, and later the ciphered data is hidden into host image by an innovative embedding technique. To hide our ciphered data into host image, we modify the existing LSB technique and use a mapping function that ensures a secure and confidential image steganography resulting in a stego image. Here cryptography is blended with steganography and provides two level security in the confidential data transmission over the internet. 2020 IEEE. -
A Survey on Adaptive Authentication Using Machine Learning Techniques
Adaptive authentication is a reliable technique to dynamically select the best mechanisms among multiple modalities to authenticate a user based on the users risk profile generated using behavior and context-based information. Websites or enterprise applications enabled with adaptive authentication will have a more robust security system as analyzing the large volume of the user, device, and browser data in real time generates a risk score that decides the appropriate level of security. Though a significant amount of research is being carried out on adaptive authentication, no single model is suitable for a global attack. This paper provides a structured (extensive) survey of current adaptive authentication techniques available in the literature to identify the challenges which demand future research. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Architecture for Risk-Based Authentication System in a Multi-Server Environment
Identity authentication, a vital part of any application access, is also one way for imposters to gain access to an application using various fingerprint authentication technologies. Therefore, because of the lack of security in the authentication architecture, this paper proposes an architecture for a risk-based authentication system using a machine learning model in a multi-server environment. Since the recent study mainly focuses on the multi-server environment and adaptive authentication independently, very little work has been proposed using a multi-server environment for adaptive authentication. The study aims to estimate risk for the user during the initial login process and when the user's data is extracted enough for prediction in a multi-server environment. 2023 IEEE.