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Machine Learning Classifiers for Credit Risk Analysis
The modern world is a place of global commerce. Since globalization became popular, entrepreneurs of small and medium enterprises to large ones have looked up to banks, which have existed in various forms since antiquity, as their pillars of support. The risk of granting loans in various forms has significantly increased as a consequence of this, the businesses face financing difficulties. Credit Risk Analysis is a major aspect of approving the loan application that is done by analyzing different types of data. The goal is to minimize the risk of approving the loan for the Individuals or businesses who might not pay back on time. This research paper addresses this challenge by applying various machine learning classifiers to the German credit risk dataset. By evaluating and comparing the accuracy of these models to identify the most effective classifier for credit risk analysis. Furthermore, it proposes a contributory approach that combines the strengths of multiple classifiers to enhance the decision-making process for loan approvals. By leveraging ensemble learning techniques, such as the Voting Ensemble model, the aim is to improve the accuracy and reliability of credit risk analysis. Additionally, it explores tailored feature engineering techniques that focus on selecting and engineering informative features specific to credit risk analysis. 2024 Sudiksha et al., licensed to EAI. -
Looking at psychological well-being through the lens of identity among adolescent girls: An exploration
Purpose: This research endeavours to delve into the intricate dimensions of adolescent girls' psychological well-being and identity, aiming to shed light on their interplay and identify key predictors of psychological well-being. The study, conducted with a sample of adolescent girls, seeks to enrich our understanding of the multifaceted nature of their developmental experiences. Psychological well-being is attained by achieving a state of balance affected by both challenging and rewarding life events and a stable sense of identity. Approach: The present research is an ex-post facto research falling in the area of quantitative research design. Data has been collected on 348 adolescents, purposely recruited from different schools of Delhi NCR. The age range of the respondents was 15 to 17 years. Findings: The results reveal that psychological well-being is being predicted by identity processes among adolescent females. The different dimensions of identity processes are found to be explaining almost 19% variance in the regression model. Commitment has been found to have a ? value of 0.197 (t= 3.511; p<.01), in-depth exploration has a ?= 0.161 (t= 2.867; p<.01), and reconsideration of commitment has a ?= 0.314 (t= 6.294; p<.01). Value: By addressing the objectives of this research, valuable insights may be received by educators, mental health professionals, and policymakers to better support and enhance the well-being of adolescent girls through having a stable sense of identity. 2024 RESTORATIVE JUSTICE FOR ALL. -
Can we improve the outcome of pregnancies with low serum PAPP-A in the first trimester?
Low birth weight is associated with various complications, and recent findings rely on the fact that micronized progesterone supplementation leads to improved birth weight, which is crucial for addressing concerns related to fetal growth. Objective: This study aimed to assess the impact of micronized progesterone (VMP4) supplementation on pregnancies with low serum pregnancy-associated plasma protein-A (PAPP-A) multiples of the median (MoM) values during first-trimester screening. Methods: Out of 8933 patients evaluated, 116 pregnant women with low PAPP-A concentrations in their blood and no fetal chromosomal anomalies (CAs) were included. Three groups were formed: group 1 received VMP4 from 11 to 16weeks (29 women, 25%), group 2 received VMP4 from 11 to 36weeks (25 women, 21.5%), and group 3 (62 women, 53.5%) served as controls without receiving progesterone. Results: Results indicated that group 3 had higher rates of complications, including miscarriages (16.37%), preterm delivery (17.8%), and fetal developmental abnormalities (19.4%). Birthweight variations were elevated in pregnancies without progesterone, contrasting with lower variations in VMP4 groups. Group 2, receiving VMP4 until 36weeks, reported the lowest incidence of abortion and preterm birth (PB), along with the highest mean birth weight. Conclusions: The conclusion suggests that 200 mg per day of VMP4 up to 36weeks of supplementation led to fewer placental-related complications in women with very low PAPP-A at first-trimester screening (0.399 MoM). By reporting lower rates of miscarriages, PBs, and fetal developmental abnormalities in the micronized progesterone-treated groups, the study suggests a potential reduction in complications. 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Optimal procurement policy for growing items under permissible delay in payment
In the last decade, growing item industries have shown an increasing trend in production and it is expected that such industries will maintain this increasing pace in the future. Existing challenges of these industries, like mortality in the production phase and deterioration in the consumption phase, make procurement decisions more complex. In this article, we established an inventory model with mortality, deterioration, and price-dependent demand. To increase the sales volume and profit, a delay in payment policy is considered. A numerical example is presented to explain the solution procedure. The concavity of the profit function is discussed analytically for decision variables. It has been observed through sensitivity analysis that selling price is the most sensitive among decision variables and parameters. 2024 Inderscience Enterprises Ltd. -
Unveiling metaverse sentiments using machine learning approaches
Purpose: The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers ones intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience. Design/methodology/approach: The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently. Findings: The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models. Research limitations/implications: Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverses experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverses economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust. Social implications: In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators. Originality/value: The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models. 2024, Emerald Publishing Limited. -
AN OPTIMIZATION AND PREDICTIVE MODELING TO ENHANCE THE WEAR AND MECHANICAL PERFORMANCE OF Al 5054 ALLOY FOR DEFENSE APPLICATIONS WITH TiO2 NANOPARTICLES
This study examines the effects of 2%, 4%, and 6% additions of TiO2 nanoparticles on the wear and mechanical characteristics of Al 5054 alloy reinforcement. The results demonstrate that the addition of TiO2 nanoparticles considerably increases the alloys tensile and impact strengths. Tensile strength reaches a peak of 221 MPa at 6% reinforcement and it rises gradually as the percentage of TiO2 reinforcement increases. Similarly, impact strength rises with time and, with TiO2 reinforcement, it reaches a maximum of 63 Joules at 6%. Wear analysis using Taguchi-based design determines the optimal combination of composition, disc rotation speed, load, and sliding distance to minimize a given wear rate and friction force. The SEM analysis validates that the composites exhibit enhanced wear resistance due to the uniform distribution of TiO2 nanoparticles. An Artificial Neural Network (ANN) model is also developed to predict the responses, and it achieves an overall accuracy of 83.549%. The mechanical properties and wear resistance of TiO2-reinforced Al 5054 composites can be enhanced, as it is demonstrated by these results. This information is crucial for material design and optimization across a range of engineering applications. 2024, Scibulcom Ltd.. All rights reserved. -
An efficient deep learning approach for identifying interstitial lung diseases using HRCT images
Interstitial lung disease (ILD) encompasses over 200 fatal lung disorders affecting the interstitium, leading to significant mortality rates. We propose an AI-driven approach to diagnose and classify ILD from high-resolution computed tomography (HRCT) images. The research utilises a dataset of 3,045 HRCT images and employs a two-tier ensemble method that combines various machine learning (ML) models, convolutional neural networks (CNNs), and transfer learning. Initially, ML models achieve high accuracy, with the J48 model at 93.08% accuracy, mainly highlighting the importance of diagonal-wise standard deviation. Deep learning techniques are then applied, with three CNN models achieving test accuracies of 94.08%, 92.04%, and 93.72%. Transfer learning models also show promise, with InceptionV3 at 92.48% accuracy. Ensembling these models further boosts accuracy, with the ensemble of three CNN models reaching 97.42%. This research has the potential to advance ILD diagnosis, offering a robust computational framework that enhances accuracy and ultimately improves patient outcomes. Copyright 2024 Inderscience Enterprises Ltd. -
Getting Rid of Organizational Complacency in a Dynamic Environment
This case investigates the external consultants organizational diagnosis aimed at understanding the imperative for change within Infotics Solutions. It explores various concepts, including the nature of planned change and the resistance exhibited by employees. Emphasis is placed on the necessity of a comprehensive organizational diagnosis before embarking on the change process, highlighting the pitfalls of relying solely on a leaders intuition and experience to initiate change. Furthermore, the case underlines the implementation of human resource management interventions and their significance from both employee and organizational standpoints. It addresses the protagonists recognition of the need for external consultants expertise to grasp the problem and devise a strategic change process. The consultants methodical approach to planning change across different themes to achieve organizational objectives is elucidated, featuring the importance of employing the right diagnosis technique in situations where the problem is unclear. The case also showcases the consultants analytical approach to problem-solving, offering specific solutions tailored to the organizations needs. Ultimately, it illustrates the challenges faced by organizations that lean heavily on past successes and struggle to adapt to evolving environmental demands. Lastly, the case highlights the importance of analysing survey results and implementing theme-based interventions to address the issues confronting the organization and its employees at Infotics Solutions. 2024 Lahore University of Management Sciences. -
A Pilot Study of the DREAMS Program: A Community Collaborative Intervention for the Psychosocial Development of Middle School Students
The purpose of this study was to pilot the DREAMS (Desire, Readiness, Empowerment, Action, and Mastery for Success) program, a community-collaborative, after-school intervention program designed specifically to address the holistic developmental needs of students at school. The author originally developed and implemented the program in Kerala, India, and later redesigned it for American school students. Combining the theories of Vygotsky and Erikson, the DREAMS model emphasizes the impact of the community on the development of children. This study evaluates the effects of a summer camp, the primary intervention of a three-year program, on the self-worth, self-esteem, and self-concept of 20 middle school students in Northeast Louisiana. After students attended the week-long program, the most significant improvements were observed in self-esteem and self-worth. Further longitudinal or comparative experimental research on the complete design would provide stronger evidence to draw more substantive conclusions. (2024), (California State University). All rights reserved. -
NDC Pebbling Number for Some Class of Graphs
Let G be a connected graph. A pebbling move is defined as taking two pebbles from one vertex and the placing one pebble to an adjacent vertex and throwing away the another pebble. A dominating set D of a graph G = (V, E) is a non-split dominating set if the induced graph < V ? D > is connected. The Non-split Domination Cover(NDC) pebbling number, ?ns(G), of a graph G is the minimum of pebbles that must be placed on V(G) such that after a sequence of pebbling moves, the set of vertices with a pebble forms a non-split dominating set of G, regardless of the initial configuration of pebbles. We discuss some basic results and determine ?ns for some families of standard graphs. 2024 the Author(s), licensee Combinatorial Press. -
ACCIDENT PREVENTION AND MANAGEMENT SYSTEM IN URBAN VANETS FOR IMPROVING SLIPPERY ROADS RIDE AFTER RAIN
Urban Vehicular Ad-hoc Networks (VANETs) face challenges in managing accidents and enhancing safety, particularly on slippery roads post rainfall. This study addresses this issue by proposing an Accident Prevention and Management System tailored for improving ride safety in such conditions. The problem statement identifies the increased risk of accidents and decreased road grip due to rain-induced slippery surfaces in urban areas. The proposed method integrates real-time data collection from vehicles and road infrastructure to predict and detect slippery road segments. Utilising this information, the system dynamically disseminates warnings to nearby vehicles, enabling them to adapt their driving behaviour and avoid potential accidents. The flow of the proposed system involves a multi-step process: (1) Real-time data collection using sensors installed in vehicles and roadside infrastructure, (2) Data analysis and prediction algorithms to identify slippery road segments, (3) Communication protocols for disseminating warnings to vehicles in the neighbourhood, and (4) Driver assistance mechanisms to aid in adapting to the road conditions. Results from simulations and real-world experiments demonstrate the efficacy of the system in significantly reducing the likelihood of accidents on slippery roads after rainfall. By leveraging VANET technology and real-time data analysis, this system enhances safety by providing timely warnings and promoting safer driving practices, ultimately mitigating the risks associated with adverse weather conditions in urban environments. 2024, Scibulcom Ltd.. All rights reserved. -
The unseen dilemma of AI in mental healthcare
[No abstract available] -
Relationship between tea industry specific factors and tea companies share prices: empirical evidence from an emerging economy
We analyse the impact of tea industry specific macroeconomic factors on tea companies share prices listed in Bombay Stock Exchange, India using quantile regression approach. We consider monthly period from January 2003 to December 2017. We find evidence to support the relationship between tea industry and tea companies share prices. Our results reveal that the change in area of cultivation has both negative and positive impact on the share prices of tea companies. This study indicates that production of tea has a significant and only positive influence. Further, we observe a minimal impact of tea import only on three companies share prices. This paper also notes that tea companies share prices react most significantly to tea export. 2024 Inderscience Enterprises Ltd. -
EFFICIENT NON-DEGRADABLE WASTE PROCESSING TECHNOLOGIES INTEGRATED WITH MANETS FOR SUSTAINABLE WASTE MANAGEMENT MODELS
In order to handle the growing amount of non-biodegradable trash, creative and sustainable solutions are becoming more and more necessary as the global waste management challenge grows. To create a complete and sustainable waste management model, this investigation suggests a revolutionary approach that combines Mobile Ad-hoc Networks (MANETs) with effective non-degradable waste processing technology. Utilising cutting-edge waste processing technology that can efficiently handle non-biodegradable materials including plastic, e-waste, and other persistent pollutants is the main goal of this. With the goal of reducing their negative effects on the environment and advancing the concepts of circular economy, these technologies include sophisticated sorting systems, chemical treatments, and recycling procedures. Furthermore, the efficiency and real-time monitoring of waste processing processes are improved by the incorporation of MANETs into the waste management paradigm. MANETs enable smooth data transmission and communication between the central control centres, waste processing units, and monitoring sensors that make up the waste management system. Because of this connectedness, waste processing activities can be dynamically optimised, facilitating prompt resource allocation and decision-making. In addition to addressing the environmental issues raised by non-biodegradable garbage, the suggested paradigm advances the creation of intelligent and networked waste management systems. Because MANETs are used, the system is scalable and adaptable, making it appropriate for a variety of urban and rural areas. The model incorporates the Ant Colony Optimisation (ACO) algorithm for resource allocation. The integration of ACO optimises resource allocation, contributing to the reduction of environmental footprints associated with waste processing. The interconnectedness facilitated by MANETs, in conjunction with ACO, enables dynamic optimisation of waste processing operations, ensuring prompt resource allocation and decision-making. This investigation envisions a sustainable waste management model that minimises pollution, promotes resource recovery, and establishes a robust framework for addressing the growing challenges of non-degradable waste on a global scale by combining cutting-edge waste processing technologies with a strong communication infrastructure. The results of the investigation have a significant impact on waste management procedures by encouraging a more ecologically friendly and sustainable way to deal with non-biodegradable garbage. 2024, Scibulcom Ltd. All rights reserved. -
Fabrication and Characterization of AA7050 Nano Composites by Enhancing Directional Properties for High Impact Load Applications
The demand for materials with superior strength and impact resistance has driven the exploration of innovative composite materials. In this research, Al 7050 is chosen as the matrix material due to its excellent mechanical properties, whereas SiC and graphene nanoparticles are incorporated to tailor its directional strength characteristics. The fabrication process involves the synthesis of Al7050 nanocomposites through a meticulous blending of nanoparticles with the matrix material. The characterization phase encompasses a comprehensive analysis of various techniques, including scanning electron microscopy, X-ray diffraction, and mechanical testing. The results shows that the directional strength improvements achieved through SiC and graphene nanoparticle reinforcement with Al7050. The tensile strength of the aluminum in the AA7050-7.5g composite rose from 185.3 to 256.1MPa upon the addition of SiC and graphene. The findings of this study contribute to the evolving field of nanocomposite materials, offering insights into the design and development of advanced materials tailored for specific directional strength requirements. The Institution of Engineers (India) 2024. -
The presence of others increases prosociality: examining the role of dating Partners accompany on donation
Research in the field of prosocial behavior has shown that the presence of others has a significant effect on individuals prosociality. However, no research has explored such an effect of romantic partners presence. Studies in evolutionary psychology have shown benevolence/prosociality as an important factor when choosing a romantic partner. Therefore, in the present study, we hypothesized that people will donate more in the presence of dating partners to maintain a positive impression on them. The research followed a mixed-method approach. The first study, a vignette-based experiment showed that people believed the presence of a dating partner significantly enhances the chances of donation. The second study was a between-subject experiment that confirmed the findings of study 1 from both donors and receivers perspectives. The third study was a qualitative investigation, where a semi-structured interview method was used to find out how and why the presence of a dating partner may influence donation. The interviews showed that the presence of dating partners increases prosociality mainly because donors want to make a good impression and project the right image of them in their partners eyes. The research overall suggests that the human need for self-presentation that projects them more socially likable shapes their willingness to extend a helping hand to others in the presence of their romantic partners. 2024 Taylor & Francis Group, LLC. -
An Introduction to ?Agile for HR Through? the Development of ?an Agile Operating ?Mindset
An understanding of Agile principles and a readiness mindset for human resources professionals play a crucial role in determining the application of Agile for HR in an organisational context. With the rise in extended and non-linear workforce configurations and geo-neutral team arrangements, Agile organisations necessitate that the nature of the HR function evolve from working through traditional architectural models and quickly adopting Agile models of functional excellence. The dearth of literature on understanding and implementing Agile practices in the HR function within enterprises requires a clear examination of the advantages of going Agile for HR. This essay explores the intuitive concept of Agile HR and operating schema, which can develop as a starting point in examining an understanding of how Agile practices in HR can evolve for sustainable enterprises and some challenges that are encountered. The Author(s) 2024. -
SUSTAINABLE CLOUD COMPUTING THROUGH GREEN NETWORK FUNCTION VIRTUALISATION (NFV)
Modern information technology has made cloud computing a cornerstone by providing scalable and flexible services to fulfill the ever-increasing demands of businesses and individuals. However, since data centres use enormous quantities of energy and contribute to rising carbon emissions, the exponential rise of cloud infrastructure has caused serious environmental concerns. This research addresses the environmental issues that traditional cloud computing poses and presents a way forward by incorporating Green Network Function Virtualisation (NFV). A paradigm change towards sustainable alternatives is required due to the traditional cloud data centres increasing energy consumption and carbon impact. The suggested Green NFV strategy utilises the virtualisation technologies to optimise and combine network services, which lowers energy consumption and improves resource efficiency. The goal of this research is to reduce the environmental impact of data centres and increase the ecological sustainability of cloud services by incorporating NFV principles into cloud computing in a seamless manner. This work investigates the effectiveness of Green NFV in reducing the environmental impact of cloud computing through an in-depth analysis and empirical analysis. It assesses the energy efficiency benefits of NFV adoption, taking into account operational sustainability overall, server consolidation, and dynamic resource allocation. The results highlight that Green NFV can help with the environmental issues regarding cloud computing and provide a viable route forward for a more ecologically conscious and sustainable future for digital infrastructure. This research offers significant aspects to experts, policymakers, and industry practitioners who are looking for practical methods to balance the need for environmental sustainability with the rapid expansion of cloud computing. 2024, Scibulcom Ltd.. All rights reserved. -
Green synthesis of Cobalt Oxide nanoparticles with in-vitro cytotoxicity assessment using pomegranate (Punica granatumL.) seed oil: A promising approach for antimicrobial and anticancer applications
Green synthesis of nanoparticles and their pharmacological implementation have gained importance in the field of nanotechnology. This study primarily aims to explore the use of Punica granatum L. seed oil as a reducing agent for the synthesis of cobalt nanoparticles, making it both economically and pharmacologically valuable. Gas chromatography-mass spectroscopy analysis was carried out to study the active metabolites present in P. granatum seed oil. The green synthesis of cobalt nanoparticles was established based on the color change of the reaction mixture from dark green to light green. These particles showed a ?max at 279.88 nm for UV-visible spectrometry analysis. Furthermore, X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscope (FE SEM) and Dynamic Light Scattering (DLS) were performed to confirm the nature of these nanoparticles. The pharmacological potential of these cobalt oxide nanoparticles was tested against microbial pathogens. The results suggest that these nanoparticles exhibited significant activity against various human bacterial and fungal pathogens. Additionally, in in vitro cytotoxicity analysis, demonstrated that CoONPs selectively targeted MCF-7 cancer cells with a significant IC50 value compared to non-cancerous cells (L929). In conclusion, this study demonstrated that green synthesized CoONPs using P. granatum show significant potential against eukaryotic cancer cells and microbial pathogens. Furthermore, this study has implications for medical research centers and pharmaceutical industries in addressing modern challenges such as increasing antibiotic resistance in communities. 2024 Horizon e-Publishing Group. All rights reserved. -
Edge incident 2-edge coloring of graphs
The edge incident 2-edge coloring of a graph G is an edge coloring of the graph G such that not more than two colors are assigned to the edges incident to an edge e = uv in G. In other words, for every edge e in G, the edge e and all the edges that are incident to the edge e is in at most two different color classes. The edge incident 2-edge coloring number ?n2(G) is the maximum number of colors in any edge incident 2-edge coloring of G. The main objective of this paper is to study the edge incident 2-edge coloring concept and apply the same to some graph classes. Besides finding the exact values of these parameters, we also obtain some bounds. World Scientific Publishing Company.