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Optimization in the Flow of Scientific Newspapers
The evolutions that occurred in the past decades have provoked variations in the market as well as academic and research. Given this scenario, the research explored in this article was aimed to analyze the contribution of the management of PMBOK methods for the optimization of Scientific Editorial Flow. The methodology used presented a quantitative approach, of descriptive character based on a survey, made available on social networks and Facebook groups, through the google forms platform. The sample is given by Snowball, this type of sampling enables the researcher to study specific groups and is difficult to reach. The analysis was by descriptive statistics, using the Likert scale, as well as the weighted average and fashion responses. It was identified that the Critical Success Factors of a Project that can contribute to the optimization of the editorial flow of a Scientific Periodical are efficient communication, empowerment, change management, client involvement, supplier involvement and conflict management. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Exploring the Opportunities of AI Integral with DL and ML Models in Financial and Accounting Systems
With the integration of artificial intelligence (AI), today's fast financial landscape increasingly promises the most efficient and accurate processes for decision-making in accounting practices. On the other hand, the opacity of models represents a truly difficult challenge, given that transparency and accountability are key for using AI in making financial decisions. This is a research paper that focuses on the explanation of an XAI model application as a way of improving transparency in financial decision-making within the accounting field. The paper begins by outlining how transparency is important and opens the room for trust and understanding in the process of financial decision-making. Traditional black-box AI models, although able to provide remarkable predictions, usually exhibit low interpretability; this entails that stakeholders may have a small degree of understanding regarding the rationale behind the decisions. This provides a cloudy appearance not to hamper trust and supports compliance with regulatory standards like GAAP (Generally Accepted Accounting Principles) and IFRS (International Financial Reporting Standards). The proposed work applies to the accounting domain and brings about some of the different XAI techniques that are designed under this domain. The following techniques aim at demystifying the AI algorithms for effective AI stakeholders' understanding of the model predictions and underlying decision-making processes. 2024 IEEE. -
Brain Tumor Prediction Using CNN Architecture and Augmentation Techniques: Analytical Results
The brain, a complex organ central to human functioning, is susceptible to the development of abnormal cell growth leading to a condition known as brain cancer. This devastating disease poses unique challenges due to the intricate nature of brain tissue, making accurate and timely diagnosis critical for effective treatment. This research explores automated brain tumor prediction through Convolutional Neural Networks (CNNs) and augmentation techniques. Utilizing a task reused learning approach with the help of VGG-16, Mobile-Net and Xception architecture, the proposed model achieves exceptional accuracy (99.54%, 99.72%) and robust metrics. This Research explores the Augmentation techniques to enhance the precision and accuracy of the model used. The study surveys related models, emphasizing advancements in automated brain tumor classification. Results demonstrate the efficacy of the model, showcasing its potential for real-world applications in medical image analysis. Future directions involve dataset expansion, alternative architectures, and incorporating explanation techniques. This research contributes to the evolving landscape of artificial intelligence in healthcare, offering a promising avenue for accurate and efficient brain tumor diagnosis. 2024 IEEE. -
Single-use Plastic Packaging and Food and Beverage industry's take on it
Micro-plastics created by the gradual breakdown of SUP in oceans have recently been consumed by marine organisms, including fish, shellfish, etc. It is causing significant disturbance to marine life. The environment is littered with food packing. Snack food packaging is a great example of a long-standing, aesthetically obnoxious form of pollution. The majority of SUPs, especially perishable products, wind up in landfills within months of purchase.This is due to a rise in on-the-go food and beverage consumption, fueling the proliferation of single-use plastic packaging. The lack of dumpsters in some areas might contribute to an increase in littering. While the majority of food packaging plastics end up in the trash, municipal waste, landfills, and even the seas, a tiny fraction can be recycled. The reason for this is that poor countries have a prevalent culture of human waste. The Electrochemical Society -
Nonlinear radiation and cross-diffusion effects on the micropolar nanoliquid flow past a stretching sheet with an exponential heat source
Metallurgy, polymer and processing engineering, and petrochemical enterprises frequently encounter polar nanoliquid flows due to stretchable surfaces with radiative heat energy. Therefore, the radiative flow of a polar nanoliquid over a stretchable sheet is analyzed considering cross-diffusion and magnetic heat flux effects. The heat transport phenomenon is explored, including the characteristics of nonlinear radiation and exponential space-based heat generation. The highly nonlinear governing equations are converted to ordinary differential equations using apt transformations. These are, in turn, solved employing the finite difference method. The behavior of contributing parameters is presented using graphical visualizations. The interactive impacts of the pertinent constraints on the rate of heat transfer and skin friction are analyzed using three-dimensional surface plots. The enhancement of the temperature profile is observed by incrementing the radiation and exponential heat generation parameters. The magnetic field can be used to regulate the fluid flow as it decreases the flow field. Also, the heat generation factor has a predominant decreasing effect on the Nusselt number. 2020 Wiley Periodicals LLC -
Evaluating Energy Consumption Patterns in a Smart Grid with Data Analytics Models
With the rapid pace of technological advancement, it is a well established fact that in todays era, economical and industrial development go hand in hand with the growth in technology. Today, massive amounts of data are generated everyday and are only growing exponentially. The collected data, whether structured or unstructured, could prove to be very beneficial in terms of improving operational efficiency by analyzing and extracting valuable information to find patterns to optimize asset utilization and improve asset intelligence. Big data analytics can very well contribute to the evolution of the digital electrical power industry. The objective of this paper is to explore how smart grid technology can be enhanced by leveraging big data analytics. Different predictive models are used for the purpose. Among them, decision tree model outperformed others recording a training and tetsing accuracy of 94.4% and 92.7% respectively while noting a least execution latency of only 4.3 seconds. 2023 IEEE. -
Comparative Analysis of Digital Business Models
This paper discusses the comparative analysis of different attributes of Google and Facebook business model and their novel features for handling innovative business framework. We have compared Google and Facebook business model on different key attributes and also discussed the statistical analysis of business models using Google business analytics platform. We have argued performance analysis of these models. One important point which we discuss and analyze in this paper is that a business model is not about just building revenue generating machine, but it is indeed more than that. It explores the strategy and business approaches of both the models of revenue generating line of attacks. Our research contributes a considerate understanding of Google and Facebook architectural model and its influence on business framework. Statistical enactment and results are analyzed, precisely when big data and media are applied. This paper also provides better understanding of the digital marketplace for both of the platforms and its earning methodology. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
E-Commerce data analytics using web scraping
Some companies, like Twitter and others, provide an application programming interface (API) to fetch the information. If the API is not available, we will have to search other websites to get the data in a structured format. The primary way to get data from a web page is through web scraping. The basic idea of web scraping is to pull data from a website and convert it into a format that can be used for analysis. In this paper, we will discuss the simple explanation of how we can use Beautiful Soup to scratch data into Python and later save the extracted data in an Excel spreadsheet and do the spreadsheet analysis later. We will pull data from the Flipkart website to know the cell phone name, cell phone price, cell phone rating, and cell phone specification. 2023 Scrivener Publishing LLC. -
Predictive analysis of stock prices through scikit-learn: Machine learning in python
Scikit-learn, a tool for developing machine learning algorithms, is a standard library of python. Through Scikit-learn, a trained model for predictive analysis can be developed. Such models aim to provide accurate predictions. Stock predictions are based on changes and patterns identified in the historical dataset. Following the trends and patterns of the historical changes of stocks, machine learning algorithms can be developed for achieving accurate outcomes. An effective model is developed, which enhance the working pattern or performance of the machine that further helps to draw a precise analysis of stocks. 2023 Scrivener Publishing LLC. -
Implementation of tokenization in natural language processing using NLTK module of python
With the advancement of technologies, now it is possible to analyze the large amount of unstructured text circulated online with various tools and methods for understanding the changes as well to infer meaningful insights from the text data. In this work, the aim is to understand how Python can be used for text analytics by the help of various libraries available in it. The natural language processing (NLP) is being used to analyze and synthesize natural language and speech in Python. 2023 Scrivener Publishing LLC. -
Performance analysis and interpretation using data visualization
The matrix plot library (Matplotlib) is a unique feature in python that helps in the visualization of data via entering certain dataset and codes. It is a portable two-dimension of plot and images are mainly focused on visualizing scientific, technical, and financial data. These matrix plots are performing with the help of python programming and various user interface applications. Most familiar versions of joint photographic and supportable picture graphics are used for the picture visualization. These additional features include the various navigation processes, pages with the line, as well as images. The financial charts of open source website are used for tables and mathematical texts. The library is based on numerical python arrays, giving us visual access to massive quantities of data in readily consumable graphics. The problem statement here delves further into the functions of this feature, which will aid in a better understanding of Python's involvement in the data visualization. 2023 Scrivener Publishing LLC. -
Dealing with missing values in a relation dataset using the DROPNA function in python
Python provides a rich data structure library called PANDAS, which provides fast and efficient data transformation and analysis. The word PANDAS is an abbreviation of Python Data Analysis Library. PANDAS facilitate optimized and dynamic data structure designs work with "relational" or "labeled" data. Python's approach is meant to provide a high-level, high-performance building block that can be used to do real-world analysis of data. PANDAS Library is allowing users to import data from different file formats, such as CSV, SQL, Microsoft Excel etc. [1]. It helps in data preparation, as well as in data modeling, for those projects, which aims data analysis for the extraction of information. Python's future will be built on this layer for statistical computing. In addition to discussing future areas of work and growth opportunities for statistics and data analytics applications built on Python, the study provides details about the language's design and features [2]. In this research paper, we intend to solve the problem of missing values in a dataset using the DROPNA function in Python using PANDAS library. 2023 Scrivener Publishing LLC. -
Monitoring the Development of the IoT Concept in Various Application Domains
For several decades, the concept and technology of combining actuators and sensors into a system to monitor and operate tangible structures distantly was understood and developed. Nevertheless, slightly over a decade back, the notion of the Internet of Things (IoT) emerged and was utilized to merge such techniques into a prevalent architecture. The study outlines and addresses IoT conceptual structures suggested as part of continuing standardization attempts, layout problems regarding IoT hardware and software parts, and delegates of IoT application domains like healthcare, smart cities, the farming industry, and nano-scale uses. The research verifies the argument that an agreement on the precise scope of the IoTs will likely be formed, as enabling innovation evolves and novel application domains have been presented. Current modifications, nevertheless, are a bit muted, and their variants on application domains have been distinct, with statistics and information technologies serving a significant part in the IoT environment. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Intrusion Detection Through Deep Learning: Emerging Trends and Challenges
The chapter begins with an introduction that sets the stage for a comprehensive journey into the world of deep learning. The chapter then delves into the critical components of deep learning, including neural network architectures, convolutional neural networks (CNNs), recurrent and recursive networks, and the application of deep learning. Moreover, it explains intrusion detection, its classification, and its methodology. By the end of the chapter, readers will have gained a solid understanding of the fundamental principles and tools necessary to delve deeper into the application of deep learning in intrusion detection, and challenges inherent in it. 2026 John Wiley & Sons, Inc. Published 2026 by John Wiley & Sons, Inc. -
Global Energy Governance: Need for a Paradigm Shift
Talk on energy has been a contentious one in all climate talks. It has been the source of climate challenge and also a key to climate change solution. So far, energy production through fossil fuelscoal, oil and gashas been one of the major sources of greenhouse gas emission contributing to the global climate change. They account for over 75% of global greenhouse gas emissions and nearly 90% of all carbon dioxide emissions which have a catastrophic effect on the life on earth. Climate change has cross-border ramifications; it has a deep impact on life system across the globe. Developed and developing countries fight and indulge in politics as to the use of kind of energy, and access to resources. They blame each other as to the nature, cause and impact of climate change. Recognising the requirement of access to resources by all countries, especially developing countries, for the achievement of sustainable social and economic development, and to control greenhouse gas emission the United Nations Framework Convention on Climate Change (UNFCCC) 1992 came with the idea of common but differentiated responsibilities and called for all necessary action among the nations including technology transfer to countries dependent upon fossil fuels. But the desired energy transition has not happened so far putting the future of the planet to risk. Further, the process of globalisation and privatisation has added on to the problem by unregulated promotion of private corporations who flagrantly violate environmental norms with impunity across the globe. In the light of the above, this chapter aims at looking into a shift in the approach towards governance of global energy and orient it towards a common globalised perspective in contrast to the present nation-state perspective. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Rethinking hope in the lives of transgender women in sex work in India: an IPA study
Background: Transgender women engaged in sex work often navigate lives shaped by stigma, precarity, and systemic exclusion; within such contexts, hope becomes a complex and negotiated experience. However, its lived meanings and processes of meaning-making remain insufficiently understood within psychological research. Objectives: To explore the lived experiences and meaning-making processes of hope among transgender sex workers in India within the socio-cultural and political context. Method: This study uses an interpretative phenomenological approach to understand the meaning-making of hope in the lives of transgender women in sex work, and the influence of sociocultural and political factors on hope. Semi-structured interviews were conducted with seven people in India who identify as transgender women and are engaging in sex work either part-time or full-time. The data were analyzed according to the guidelines laid down by Smith etal. Results: Findings revealed nine superordinate themes: compelled to actively build hope for survival, movement from interpersonal to intrapersonal hope, education and employment as sources of hope, body as a site of hope as well as hopelessness, sex work as an existential double-bind, hope forged in crisis, social influences of hope, anticipatory hope for legal provisions, and cultural and religious influences on hope. Conclusion: The study reconceptualizes hope as a survival labor, shaped by intersecting identities and lived adversities. It contributes to feminist, queer, and existential scholarship while offering implications for policymaking and the development of trauma-informed, trans-affirmative psychotherapeutic interventions. 2026 Taylor & Francis Group, LLC. -
Predicting emotional intelligence, creative performance and knowledge management in higher education using multiple regression
Higher education institutions are paramount in emerging nations like India. Post-globalisation, India witnessed the growth of HEIs, especially in the private sector. However, today most of the institutions are struggling for their existence. One of the most vital reasons for such a staggering performance is the absence of creativity. It will not be an exaggeration to say that the present era is the era of creativity and performance and organisations that cant perform are bound to perish. Creativity can be nurtured and yield success only if it is supported by the emotional intelligence (EI) of the employees and knowledge management (KM) processes. The current paper explored the nexus between emotional intelligence, knowledge management processes and creative performance in HEIs in India and implied that though emotional intelligence affects creative performance, the impact gets manifolded in the presence of the knowledge management process. Copyright 2025 Inderscience Enterprises Ltd. -
Unveiling the response of food inflationto the economic policy uncertainty, energy price shocks andcarbon emission
Purpose This research paper examines the impact of economic policy uncertainty, energy price shocks and carbon emissions on food inflation from a global perspective, for the period of 20012023. Design/methodology/approach To calibrate the economic policy uncertainty, carbon emissions and energy price shock, we apply the economic uncertainty index of Baker etal. (2016), carbon dioxide in a million tonnes and the energy price index. Finally, to accomplish the relevant objectives, we exert the panel autoregressive distributed lag (ARDL) and panel Granger non-causality model. Findings We can summarise the key empirical insights from this pragmatic examination as follows: Initially, the panel ARDL outcome suggests that in the long-run, economic policy uncertainty and energy inflation positively influence food inflation. The result further reveals that a surge in economic policy uncertainty and energy inflation would lead to an increase in food prices in the long run in these panel countries. Secondly, the relevant outcome demonstrates that, in the long run, carbon emissions do not have a significant impact on food prices across the panel nation. Finally, the causality analysis concludes that there is unidirectional causality from energy inflation, carbon emissions and economic policy uncertainty to food inflation. Originality/value This investigation aims to add three aspects to the theme of food inflation. First of all, we endeavour to capture the presence of the underlying impact of economic policy uncertainty, energy price shock and carbon emissions on food prices. Second, current research extends the literature by employing panel data econometric analysis in the above context. Furthermore, our research is novel in that we consider carbon emissions to reveal their impact on food prices, whereas none of the previous analyses ever contemplated the impact of carbon emissions on food prices. Finally, by extending this analysis to a heterogeneous economic outlook that includes both advanced and emerging economies globally, it provides policymakers with a clear understanding of an effective strategy for managing food inflation and achieving sustainability. 2025 Emerald Publishing Limited -
Balancing the Cart: Evaluating Imbalance-Aware Machine-Learning Pipelines for Predicting E-Commerce Purchases
We present a comprehensive investigation into predicting purchase conversions in e-commerce sessions, addressing the challenges of severe class imbalance and complex user behavior signals. Using a real-world dataset of 12,330 user sessions described by 24 features (interaction counts, durations, bounce/exit rates, page values, temporal and device metadata), we first conduct exploratory analysis to reveal seasonal peaks in conversion and strong correlations between page value metrics and purchase likelihood. To mitigate the low positive-class rate (10.8%), we embed SMOTE oversampling within our training pipelines, ensuring balanced learning for all classifiers. We then perform a head-to-head comparison of twelve algorithmsranging from linear and generative methods (Logistic Regression, LDA, Gaussian NB), instance-based learners (KNN, SVM), bagging ensembles (Random Forest, Extra Trees, AdaBoost), gradient boosters (XGBoost, LightGBM, CatBoost), to a feed-forward neural network (MLP). Evaluation on a stratified 80/20 holdout set uses overall accuracy plus precision, recall, and F1-score for the purchase class, alongside ROC AUC. Our results demonstrate that ensemble tree methods dramatically outperform simpler models: LightGBM achieves the highest F1 (0.694) and ROC AUC (0.924), with Extra Trees closely following (F1 0.678, AUC 0.926). Simpler classifiers, despite SMOTE, lag markedly in recall and F1, underscoring the importance of powerful nonlinear learners. These findings establish a new benchmark for imbalance-aware conversion prediction and recommend SMOTE-augmented gradient boosting and randomized tree ensembles as the methods of choice for future research and practical deployments. 2025 IEEE. -
An Intuitionistic Fuzzy-Rough Attribute Selection Using Representative Samples
Selecting relevant features is an important tool for extracting knowledge from datasets with many attributes and objects. The traditional theory of rough set is a fundamental and successful tool for dealing with vagueness and inconsistency. Combining the rough set with the fuzzy set handles the information loss problem arising from the discretisation process. Still, it fails to consider the hesitancy part of any information system. A generalisation of fuzzy set known as an intuitionistic fuzzy (IF) set has more real-world applications to confront uncertainty and ambiguity than the fuzzy set. So, the combination of rough set and IF set not only deals with vagueness but also able to consider the hesitancy available in any real-world data. In this work, we propose an IF rough set model based on representative samples and its application in the area of attribute reduction of high-dimensional datasets. First, we defined the representative sample-based intuitionistic fuzzy rough set and then presented an algorithm to calculate the reduction of a dataset using the degree of dependency method. Mathematical theorems are applied to validate the presented model theoretically. Experimental analysis is also discussed to validate the proposed technique. Finally, we applied our proposed method to improve the prediction of antifungal peptides. 2025 Old City Publishing, Inc.