Browse Items (16481 total)
Sort by:
-
-
Predicting Nitrogen Flavanol Index (NFI) in Mentha arvensis Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture
Crop growth monitoring at various growth stages is essential for optimizing agricultural inputs and enhancing crop yield. Nitrogen plays a critical role in plant development; however, its improper application can reduce productivity and, in the long term, degrade soil health. The aim of this study was to develop a non-invasive approach for nitrogen estimation through proxies (Nitrogen Flavanol Index) in Mentha arvensis using UAV-derived multispectral vegetation indices and machine learning models. Support Vector Regression, Random Forest, and Gradient Boosting were used to predict the Nitrogen Flavanol Index (NFI) across different growth stages. Among the tested models, Random Forest achieved the highest predictive accuracy (R2 = 0.86, RMSE = 0.32) at 75 days after planting (DAP), followed by Gradient Boosting (R2 = 0.75, RMSE = 0.43). Model performance was lowest during early growth stages (1530 DAP) but improved markedly from mid to late growth stages (4590 DAP). The findings highlight the significance of UAV-acquired data coupled with machine learning approaches for non-destructive nitrogen flavanol estimation, which can immensely contribute to improving real-time crop growth monitoring. 2025 by the authors. -
Conversion of alkynes into 1,2-diketones using HFIP as sacrificial hydrogen donor and DMSO as dihydroxylating agent
A metal-free and hypervalent iodine free conversion of internal alkynes into 1,2-diketo compounds has been described. The efficacy of the present protocol rely on the use of HFIP (1,1,1,3,3,3-Hexafluoro-2-propanol) as reducing agent of alkynes and DMSO as dihydroxylating agent of olefins to acquire the desired chemical transformations. The obtained 1,2-diketones were further transformed into useful derivatives. 2020 Elsevier Ltd -
Review of open space rules and regulations and identification of specificities for plot-level open spaces to facilitate sustainable development: An Indian case
Rapid urbanization and an increase in the alteration of natural resources have led to climate crises, driving the need to promote sustainable development. Urban open space management plays a vital role in such scenarios. Research on urban open spaces has been mainly conducted at regional, municipal, and neighborhood scales. Rarely has the focus been on the plot-level potentials and management of open spaces. Therefore, the study looks into the Indian development control rules and regulations and identifies that although these stipulate the percentage of open space for development on each plot, specificities for open spaces are unclear. Further, the study analyses quantitative and qualitative aspects of open spaces for selected group housing schemes in Pune city. The inquiry shows that per capita open space in Pune is comparatively lower than national standards. The quantitative aspects include FSI, building ground coverage, built-up area, number of floors, and number of dwelling units, and each relates to open spaces in one way or another. The qualitative interpretations disclose that a plot-level open space can significantly impact the regional-level open space network. Hence, the research advocates a bottom-up approach wherein plot-level open space can become the focus in formulating new norms and policies for sustainable development. Published under licence by IOP Publishing Ltd. -
Analysis of nonlinear compartmental model using a reliable method
The goal of this work is to investigate nonlinear models and their complexity using techniques that are universal and have connections to historical and material aspects. Using the premise of a constant population that is uniformly mixed, a nonlinear compartmental model that depicts the movement between voter classes is taken into consideration. In the current work, we investigate the dynamical framework that supports the interactions between the three parties. It is discussed how rate change affects various metrics. The conditions for boundedness, stability, existence, and other dynamics are obtained. We derive the effects of generalizing the model in any order. The current study supports investigations into complex real-world issues and forecasts of necessary plans. 2023 The Author(s) -
Effectiveness of anti-smoking PSAa: A comparative study /
The purpose of the study is to find out whether anti-smoking Public Service Advertisements are well strategized attempts to create and spread awareness about a public issue that could affect deeply seated public attitudes and behaviour. The study also highlights the ways in which anti-smoking PSAs are produced in different parts of the world and how it has brought changes in the public behaviour. -
A Robust Model for Quantum-Resistant Cryptography to Tackle Quantum Risks
As quantum computing advances, conventional cryptographic algorithms face developing threats, necessitating the improvement of quantum-resistant security mechanisms. Winternitz One-Time Signature (WOTS) is a promising cryptographic scheme that offers robust resistance in competition to quantum attacks. This paper explores the software of WOTS in enhancing the protection of digital communications and information integrity in a quantum computing generation. By manner of analysing the fundamental standards, sensible implementations, and ability demanding situations of WOTS, this research dreams to provide insights into its effectiveness as a quantum-resistant protection solution. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Chronic Disease Diagnosis Using Multi-modal Transfer Learning Model
Chronic diseases are on rise in recent times and symptoms of these disorders need to be detected at an early stage. Traditional methods in handling these risks are quite resource intensive and costly with less accuracy. This paper explores transfer learning to address the challenge of limited data in diagnosing chronic diseases like chronic kidney disease, breast cancer, hepatitis, and Alzheimers. We propose a multi-modal framework utilizing pre-trained models: image recognition for medical images and natural language processing for textual data. Transfer learning aims to improve diagnostic accuracy and reduce training time, enabling development of adaptable tools for various chronic diseases. The implementation results show the performance of the model is promising generating an accuracy rate of 94%. Also the model gave a mean accuracy of 93.3% when tested with different chronic disorders. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Intelligent Information Retrieval Model for Digital Documents in Title Insurance
Documents have been pivotal in shaping human history by preserving knowledge and newlineenabling the transmission of ideas across generations and cultures. They have facilitated the establishment of legal systems, institutions, and governance, fostering societal order and progress. Additionally, documents serve as a collective memory, chronicling the achievements and lessons learned, enriching the human experience. Transforming documents from physical to digital format has revolutionized how we access, store, and share information in the digital age. This transition, enabled by technological advances, began with the invention of the scanner, which allowed for newlinethe digital capture of images and text. Optical Character Recognition (OCR) technology that can convert scanned documents into searchable, editable digital texts further streamlined this process. As the storage capacity and internet speeds have increased, digitization has become more accessible and widespread. Cloud-based storage solutions, such as Google Drive and Dropbox, now allow users to store, access, and share digital documents from anywhere with an internet connection. This has improved collaboration and communication and reduced the need for physical storage space. The digitization of documents has also significantly impacted the environment, with paper consumption decreasing and many industries carbon footprint reducing. Libraries and archives have transformed digitally, making vast information more easily accessible and preserving vital historical records for future generations. This digital shift has democratized knowledge, granting people worldwide access to resources that were once limited newlineto those with physical proximity to the material. -
Computer Vision Based Automatic Margin Computation Model for Digital Document Images
Margin, in typography, is described as the space between the text content and the document edges and is often essential information for the consumer of the document, digital or physical. In the present age of digital disruption, it is customary to store and retrieve documents digitally and retrieve information automatically from the documents when necessary. Margin is one such non-textual information that becomes important for some business processes, and the demand for computing margins algorithmically mounts to facilitate RPA. We propose a computer vision-based text localization model, utilizing classical DIP techniques such as smoothing, thresholding, and morphological transformation to programmatically compute the top, left, right, and bottom margins within a digital document image. The proposed model has been experimented with different noise filters and structural elements of various shapes and size to finalize the bilateral filter and lines and structural elements for the removal of noises most commonly occurring due to scans. The proposed model is targeted towards text document images and not the natural scene images. Hence, the existing benchmark models developed for text localization in natural scene images have not performed with the expected accuracy. The model is validated with 485 document images of a real-time business process of a reputed TI company. The results show that 91.34 % of the document images have conferred more than 90 % IoU value which is well beyond the accuracy range determined by the company for that specific process. 2023, Crown. -
IIRM: Intelligent Information Retrieval Model for Structured Documents by One-Shot Training Using Computer Vision
Various information retrieval algorithms have matured in recent years to facilitate data extraction from structured (with a predefined template) digital document images, primarily to manage and automate different organizations invoice and bill reimbursement processes. The algorithms are designated either rule-based or machine-learning-based. Both approaches have respective advantages and disadvantages. The rule-based algorithms struggle to generalize and need periodic adjustments, whereas machine learning-based supervised approaches need extensive data for training and substantial time and effort for manual annotation. The proposed system attempts to address both problems by providing a one-shot training approach using image processing, template matching, and optical character recognition. The model is extensible for any structured documents such as closing disclosure, bill, tax receipt, besides invoices. The model is validated against six different structured document types obtained from a reputed title insurance (TI) company. The comprehensive analysis of the experimental results confirms entity-wise extraction accuracy between 73.91 and 100% and straight through pass 81.81%, which is within business acceptable precision for a live environment. Out of total 32 tested entities, 17 outperformed all state-of-the-art techniques, where max accuracy has been 93 % with only invoices or sales receipts. The system has been set operational to assist the robotic process automation of the TI mentioned above based on the experimental results. 2022, King Fahd University of Petroleum & Minerals. -
A Deep Learning Model for Information Loss Prevention from Multi-Page Digital Documents
World Wide Web has redefined almost all the business models in the past twenty-five to thirty years. IoT, Big Data, AI are some of the comparatively recent technologies which brought in a revolution in the digitization and management of data. Along with the revolution arose the need for data security and consumer privacy protection, primarily concerning financial institutions. The data breach of Equifax in 2017 and personal information leaks from Facebook in 2021 led to general skepticism among the customers of large corporations. The GLBA, 1999, also known as the Financial Modernization Act, was implemented by US federal law to enforce the financial institutions to protect their private information. Built upon the GLBA, guidelines are paved by FTC for all financial institutions of the United States of America, including TI companies. In this paper, an ANN-based content classification technique using MLP architecture in combination with n-gram TF-IDF feature descriptor is proposed to detect and protect the customers' sensitive information of a reputed TI company securing it's one of the digital image-document stores. The proposed technique is compared with other state-of-the-art strategies. Data samples from the digital document store of the company have been taken into consideration in the study, and the prediction accuracy metrics obtained are found to be substantially better and within the acceptable range defined by the organization's information security monitoring team. 2013 IEEE. -
Real-Time Application of Document Classification Based on Machine Learning
This research has been performed, keeping a real-time application of document (multi-page, varying length, scanned image-based) classification in mind. History of property title is captured in various documents, recorded against the said property in all the countries across the world. Information of the property, starting from ownership to the conveyance, mortgage, refinance etc. are buried under these documents. This is by far a human driven process to manage these digitized documents. Categorization of the documents is the primary step to automate the management of these documents and intelligent retrieval of information without or minimal human intervention. In this research, we have examined a popular, supervised machine learning technique called, SVM (support vector machine) with a heterogeneous data set of six categories of documents related to property. The model obtained an accuracy of 88.06% in classifying over 988 test documents. 2020, Springer Nature Switzerland AG. -
Hybrid Approach to Document Anomaly Detection: An Application to Facilitate RPA in Title Insurance
Anomaly detection (AD) is an important aspect of various domains and title insurance (TI) is no exception. Robotic process automation (RPA) is taking over manual tasks in TI business processes, but it has its limitations without the support of artificial intelligence (AI) and machine learning (ML). With increasing data dimensionality and in composite population scenarios, the complexity of detecting anomalies increases and AD in automated document management systems (ADMS) is the least explored domain. Deep learning, being the fastest maturing technology can be combined along with traditional anomaly detectors to facilitate and improve the RPAs in TI. We present a hybrid model for AD, using autoencoders (AE) and a one-class support vector machine (OSVM). In the present study, OSVM receives input features representing real-time documents from the TI business, orchestrated and with dimensions reduced by AE. The results obtained from multiple experiments are comparable with traditional methods and within a business acceptable range, regarding accuracy and performance. 2020, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature. -
Clustering-based Optimal Resource Allocation Strategy in Title Insurance Underwriting
Production of insurance policies in all types of Insurance requires a thorough examination of the entity against which the Insurance is to be issued. In health insurance, it is the past medical data of the individuals. Vehicle insurance needs the examination of the vehicle and the owner's data. Likewise, in Title Insurance, it is the historical data of the property which needs scrutiny before the policy issuance. Underwriters perform the job of examining the property records. The scrutiny of the property records requires a high degree of the domain and legal expertise, and title insurance underwriters are often associated with legal professions. They do the final round of validation of the examination process. There are examination teams that take care of the initial set of regular examination tasks associated with each title insurance order. Some human experts assign the orders to the team associates. Not all the orders are of the same complexity in terms of examination. The allocation of the tasks happens based on the gut feeling of the supervisor, considering their experience with the team members. Our research creates clusters of the orders based on specific parameters associated with the orders. It builds a cost model of the past associates working on orders belonging to different clusters. Based on this cost matrix, we have built an optimal task allocation model that assigns the orders to the associates with the promise of optimal cost using a Linear programming solution used frequently in operations research. 2022 IEEE. -
A Multi-Modal Approach to Digital Document Stream Segmentation for Title Insurance Domain
In the twenty-first century, storing and managing digital documents has become commonplace for all corporate and public sectors around the world. Physical documents are scanned in batches and stored in a digital archive as a heterogeneous document stream, referred to as a digital package. To make Robotic Process Automation (RPA) easier, it's necessary to automatically segment the document stream into a subset of independent, coherent multi-page documents by detecting the appropriate document boundary. It's a common requirement of a TI company's Automated Document Management Systems (ADMS), where business operations are automated using RPA and the goal is to extract information from digital documents with minimal user intervention. The current study proposes, evaluates, and compares a multi-modal binary classification network incorporating text and picture aspects of digital document pages to state-of-the-art baseline methodologies. Image and textual features are extracted simultaneously from the input document image by passing them through Visual Geometry Group 16 - Convolutional Neural Network (VGG16-CNN) and pre-trained Bidirectional Encoder Representations from Transformers (Legal-BERT {}_{base} ) model through transfer learning respectively. Both features are finally fused and passed through a fully connected layer of Multi Layered Perceptron (MLP) to obtain the binary classification of the pages as the First Page (FP) and Other Page (OP). Real-time document image streams from production business process archive were obtained from a reputed Title Insurance (TI) company for the study. The obtained F_{1} score of 97.37% and 97.15% are significantly higher than the accuracies of the considered two baseline models and well above the expected Straight Through Pass (STP) threshold defined by the process admin. 2013 IEEE. -
FeCl3/KOH two steps activated biocarbon with hierarchical porosity and oxygen-rich for enhanced supercapacitor applications
Biomass waste derived from jackfruit (Artocarpus heterophyllus) cores is used to fabricate hierarchical porous activated carbon through chemical activation with Iron(III) chloride (FeCl3) and potassium hydroxide (KOH). Jackfruit is an abundant agricultural by-product in tropical regions, including India, Bangladesh, and Sri Lanka. The activated carbon derived from jackfruit provides a sustainable, low-cost, and high-performance alternative to conventional carbon materials for supercapacitors, thereby aligning with waste valorisation strategies. The prepared carbon displays hierarchical porous structures of both micro and mesopore architectures. They are amorphous and contain functional oxygen groups, as confirmed by X-Ray photoelectron spectroscopy (XPS) and Fourier Transform Infrared Spectroscopy (FTIR). A high surface area (1251m2g?1) was obtained via Brunauer-Emmett-Teller (BET) analysis. The electrochemical performances, via cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and galvanostatic charge/discharge (GCD) show high specific capacitance of 310Fg?1 at 0.8Ag?1 from GCD, 331Fg?1 at 10mVs?1 from CV, and a charge transfer resistance of 0.1410?cm2, in three electrode configuration and showing good cycling stability of 87% over 2500 cycles. These results suggest that the activated carbon offers potential application in low-cost and renewable production of carbon materials for supercapacitors applications. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
INTERNATIONALIZATION OF HIGHER EDUCATION: ENSURING EQUALITY AND SOCIETAL RESPONSIBILITY
Internationalization of higher education is an important initiative toward creating a society that promotes equity and reflects its social responsiveness. Given the education sector is changing globally, it is important for institutions to carefully manage the challenges that come with it and ensure that their international initiatives are beneficial for local and global communities. This chapter explores the interconnectedness between the internationalization of higher education, reducing inequality, and social responsibility and the role of internationalization of higher education institutions in achieving Sustainable Development Goal 10. Substantial evidence on the current issue was provided by a thematic analysis of interviews undertaken with experts in the field and development of a strategic framework. A number of suggestions have been provided to ensure equity remains at the center of higher education's effort in pursuit of reducing inequalities. 2025 The authors. -
Common Impactful Assignment (CIA): An Innovative Approach to Reduce Student Stress in WIL Programmes
In spite of its advantages, working professionals enrolled in Work-Integrated Learning (WIL) degree or professional certificate programmes frequently express heightened stress and anxiety attributable to the programme's demands. This paper proposes pedagogical enhancements to alleviate stress in working professionals enrolled in WIL programmes while enhancing the quality of learning. The paper proposes three pedagogical approaches collectively known as Common Impactful Assignment(CIA) to enhance the way assignment components of the courses are evaluated. The first concept advocates for a shared common assignment problem statement across multiple courses in a semester. Building on this, the second concept extends the interconnection of courses across different semesters, crafting unified assignment statements that underscore the programme's thematic cohesion. These ideas, tailored for degree programmes, facilitate a broader understanding of the interdependencies between various courses, fostering a comprehensive knowledge base. For more focused and practical-oriented professional certificate programmes, the third concept suggests a project-based common problem statement replacing the entire programme's assignment components. This overarching project, aligned with the programme's central theme, aims to streamline the interconnected nature of courses in such focused programmes. The paper provides sample assignment problem statements for each scenario, outlining their respective benefits and challenges, and discusses appropriate assessment methods. Recognizing the psychological well-being of learners, the paper suggests a methodology for assessing determinants such as stress, anxiety, happiness, and overall well-being. In evaluating WIL students before and after exposure to these new pedagogies, this pre-post assessment method analyses the psychological benefits of innovative teaching approaches. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A compilation of interstellar column densities
We have collated absorption line data toward 3008 stars in order to create a unified database of interstellar column densities. These data have been taken from a number of different published sources and include many different species and ionizations. The preliminary results from our analysis show a tight relation [N(H)/E(B - V)= 6.1210 21] between N(H) and E(B - V). Similar plots have been obtained with many different species, and their correlations along with the correlation coefficients are presented. 2012 The American Astronomical Society. All rights reserved.