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Advancing Gold Market Predictions: Integrating Machine Learning and Economic Indicators in the Gold Nexus Predictor (GNP)
This study employs advanced machine learning algorithms to predict gold prices, using a comprehensive dataset from Bloomberg. The Gold Nexus Predictor (GNP), a key innovation, integrates historical data and economic indicators through advanced feature engineering. Methodologies include exploratory data analysis, model training with various algorithms like Linear regression, Random Forest, Ada Boost, SVM, and ARIMA, and evaluation using metrics like MSC, MAPE, and RMSE. The study's philosophical foundation emphasizes rationalism in economic forecasting and ethical model use. This research offers significant insights for investors and policymakers, enhancing understanding and decision-making in the gold market. 2024 IEEE. -
Advancing Image Security Through Deep Learning and Cryptography in Healthcare and Industry
Securing electronic health records (EHRs) in the Internet of Medical Things (IoMT) ecosystem is a key concern in healthcare due to the sector's differed environment. As the evolution of technology continues, ensuring the confidentiality, integrity, and accessibility of EHRs becomes more and more challenging. To enhance the confidentiality of healthcare picture data, this study explores the combined use of deep learning and cryptography methods. Through the utilization of weight analysis for improving encryption strength and the combination of chaotic systems to generate undetectable encryption patterns, it explores how deep neural networks can be modified for use in encryption. It also provides a survey of the present scenario of deep learning-based image detection of anomalies methods in working environments, such as network typologies, supervision levels, and assessment norms. Techniques in cryptography provide an effective means to protect confidential medical picture data while it's being transmitted and stored. Deep learning, on the other hand, has the ability to entirely change cryptography by providing robust encryption, resolution augmentation, and detection capabilities for medical image security. The paper outlines future research approaches to overcome these problems and tackles the opportunities and obstacles in medical image cryptography and industrial picture anomaly detection. Through this work, picture privacy in the healthcare and industrial sectors is advanced, opening the door to enhanced privacy, integrity, and availability of vital image data by overcoming the gap between deep learning and cryptography. 2024 IEEE. -
Advancing Nutrient Removal and Resource Recovery Through Artificial Intelligence: A Comprehensive Analysis and Future Perspectives
The increasing difficulties associated with effectively controlling wastewater treatment operations while simultaneously satisfying the imperatives of nutrient removal and resource recovery have necessitated the use of advanced technology. This book chapter provides a comprehensive analysis of the use of artificial intelligence (AI) methods within this complex context. Utilizing a vast array of scholarly investigations and real-world implementations, this study explores the intricate domain of wastewater treatment, providing a comprehensive understanding of how artificial intelligence algorithms are used to enhance the efficiency of nutrient removal procedures and expedite the recovery of valuable resources. This chapter presents a thorough examination of the impact of artificial intelligence (AI) on sustainable innovations in wastewater treatment facilities. It accomplishes this through a comprehensive analysis of relevant data and the inclusion of real-world case studies. The findings of this research highlight the transformative effect of AI on conventional approaches to wastewater treatment, enabling the adoption of environmentally friendly and resource-efficient practices. The integration of artificial intelligence (AI) with wastewater management offers a fascinating story that highlights the shifting paradigm in the field of environmental engineering and the efficient exploitation of resources. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Advancing Predictive Analytics in E-Learning Platform: The Dominance of Blended Models in Enrollment Forecasts
The rapid expansion of e-learning platforms has revolutionized the landscape of education, particularly highlighting the significance of online courses in contemporary learning environments. This research focuses on Udemy, a prominent online learning platform, and aims to enhance the predictability of course enrollments within its IT & Software category. The study's central purpose is to leverage advanced machine learning techniques to predict course subscriber numbers, a crucial indicator of a course's popularity and success. Employing an extensive dataset from (Kaggle DB)Udemy, encompassing various course attributes such as ratings, reviews, and pricing, the study explores multiple machine learning models. These include Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and K-Nearest Neighbors Regression. A key innovation of this research is the application of ensemble methods, particularly a blended model approach, to integrate predictions from multiple models, thereby enhancing accuracy and reliability. The findings of this study are significant. The ensemble approach, notably the blended model, outperforms individual predictive models in accuracy. Among the single models, Gradient Boosting Regression shows the highest effectiveness in forecasting enrollments. The research highlights the vital role of course characteristics, including ratings and reviews, in determining course popularity. This study contributes to the field of e-learning by introducing a novel, data-driven approach to predict course enrollments. It offers valuable insights for educators, course creators, and platform developers, emphasizing the potential of machine learning in optimizing content strategy and marketing efforts in the digital education domain. The application of ensemble machine learning methods presents a new horizon in educational analytics, paving the way for more nuanced and effective strategies in online education delivery and promotion. 2024 IEEE. -
Advancing Road Safety through Driver Drowsiness Detection Using Deep Learning Model
Driver drowsiness poses a significant threat to public safety, contributing to numerous road accidents and fatalities annually. Drowsy drivers exhibit characteristic changes in facial expressions and behaviors, including eye closure, head nodding, and yawning. These indicators can be detected through various techniques, including image processing, computer vision, and machine learning. This research investigates a promising approach: utilizing a ResNet-101 deep convolutional neural network (CNN) for driver drowsiness detection based on eye, head, and mouth states. The model was trained on a vast dataset of 2.2 million images, covering diverse driving conditions. Despite achieving a 69% accuracy, suggesting real-world potential, computational limitations restricted training to only a quarter of the data. This necessitates further research with larger datasets and increased resources to enhance accuracy and robustness. 2024 IEEE. -
Advancing the Evaluation of Oral Fluency in English for Specific Classrooms: Harnessing Natural Language Processing Tools for Enhanced Assessment
A crucial component of language learning and teaching is evaluating students' speaking abilities. Natural language processing (NLP) techniques have been employed recently in language assessment to automate the evaluation process and produce more impartial and reliable findings. In this study, we offer a speaking evaluation tool based on Natural Language Processing (NLP) that assesses a learner's speaking ability in real-time using cutting-edge algorithms. The instrument is altered to assess the fundamental facet of speaking skills - Fluency. As a result of the tool's immediate feedback, learners may pinpoint their areas of weakness and focus on honing their language abilities. The usefulness of the instrument was assessed through an intervention with a sample size of 30 students of the post-graduate students of a college in Pune, India. Python libraries, including random and re, were utilized to implement the algorithm. Data preprocessing involved accurate transcription of videos using an online tool and manual checking for corrections. Despite acknowledging limitations, such as potential biases in manually inserted hesitation markers, the study serves as a pivotal step toward automated fluency assessment, presenting exciting prospects for NLP and language education advancements. 2024 IEEE. -
AdvanDNN: Deep Neural Network Analysis of Neuroimaging for Identifying Vulnerable Brain Regions in Autism Spectrum Disorder
Exploring the neurological framework of autism spectrum disorder (ASD) presents a significant challenge due to its diverse manifestations and cognitive impacts. This study introduces an innovative deep learning approach, employing an advanced deep neural network (AdvanDNN) model to identify and analyze brain regions vulnerable to ASD. Utilizing the AAL116 brain atlas for anatomical standardization, our model processes a comprehensive set of neuroimaging data, including structural and functional MRI scans, to discern distinct neural patterns associated with ASD. The AdvanDNN model, with its robust deep learning architecture, was meticulously trained and validated, demonstrating a notable accuracy of 91.17% in distinguishing between ASD-affected individuals and controls. This marks an improvement over the state of the art, contributing a significant advance to the diagnostic processes. Notably, the model identified a pronounced anticorrelation in brain function between anterior and posterior regions, corroborating existing empirical evidence of disrupted connectivity within ASD neurology. The analysis further pinpointed critical regions, such as the prefrontal cortex, amygdala, and temporal lobes, that exhibit significant deviations from typical developmental patterns. These findings illustrate the potential of deep learning in enhancing early detection and providing pathways for intervention. The application of the AdvanDNN model offers a promising direction for personalized treatment strategies and underscores the value of precision medicine in addressing neurodevelopmental disorders. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Affecting computing in multimodal mobility
Computational models that simulate human emotions have witnessed a substantial development in recent years for widening the spectrum of applications. Emotional computation is becoming crucial in human-to-computer interactions with exponential growth of artificial intelligence. Normally referred to as emotion recognition, it is widely believed that the prospective detection of a person 's emotional state of mind should be computed from their facial expressions. Face-movement combinations may express many different emotion types, for instance, hate, anger, panic, joy, grief, surprise, shock, to name a few. The goal and emphasis of this manuscript is the deployment of different algorithms and computation models for emotions. Considerable advancements in this domain of emotion recognition can be made through AI model development that discusses the challenges of the system and Facial Action Coding as an integral part of the models. 2023, IGI Global. All rights reserved. -
Affective geographies and the anthropocene: Reading shubhangi swarups latitudes of longing
This paper is a critical reading of the affective and emotional geographies imagined in the Islands plot-line of Shubhangi Swarups novel Latitudes of Longing (2018). The paper argues that Swarup presents the case of a rethinking environmental aesthetics that conveys a deeper sense of space, time, and place. By creating an ambient poetics to negotiate human and non-human interconnectedness, the paper demonstrates the strength of novelistic traditions and their potential to generate an idea of affect that is transcorporeal as one not located only in the site of the human body, instead, emanating from a more nuanced interconnectedness between the human and the non-human world. Informed by affective ecocriticism and Zayin Cabots multiple ontologies approach that generates ecologies of participation, the paper closely reads the Islands section to establish how literary illustrations provide an instance to widen the horizons of environmental engagement and generate a narrative imagination that encompasses a larger ecosystem cutting across geological spacetimes in the Anthropocene. Swarups use of fiction is critically used to generate an ecoaesthetics that leads to a more informed ethical action towards recognizing the interconnectedness of living and non-living forms that create sustainable ecologies. 2021 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore), ISSN: 0253-7222. -
Affiliate Marketing and the Symbiotic Relationship in the Pharma Industry
The objective of the study is to understand the dynamic relationship between customers and the healthcare industry giants in the Indian context. The purpose revolves around how the consumer is benefitting and at the same time, how the indirect third-party affiliates also earn marginal profits along with serving the customers. The study is backed by both primary and secondary data, which were collected from 173 individuals from various fields through a questionnaire. The convenience sampling method was used to select the respondents, and the Technology Acceptance Model (TAM) was used to propose the model for the study. There exists a parallel symbiotic relationship between consumers, pharmaceutical companies, and affiliates. The application of this research can be put to use for the startups, which want to explore and excel in this industry along with the future researchers who want to forecast and study the progress of the pharma companies in the long run. The empirical evidence of this paper highlights a unique relationship between affiliates, the pharma sector, and customers, which drives customer buying behavior and a combination that has not been explored yet. The study provides a unique understanding of how feedback from customers in third-party applications can benefit and produce huge profit margins down the line. 2025 Apple Academic Press, Inc. -
Affordable Two-Dimensional Layered Cd(II) Coordination Polymer: High-Performance Pseudocapacitor Electrode Behavior
In recent years, pseudocapacitive materials have been investigated rigorously as they provide a unique pathway for realizing high-energy and high-power densities. However, innovative approaches involving rational design and synthesis of new materials are still vital to address concerns such as degradation, low conductivity, low cycling performance, high resistance, production cost, etc. Working in this direction, we report the cost-effective synthesis, characterization, and excellent pseudocapacitive behavior of a Cd(II)-based coordination polymer (COP) abbreviated as Cd(DAB). It has been realized in quantitative yield through a facile one-pot reaction occurring among the N4-ligand, 3,3?-diaminobenzidine (DAB), and Cd(II) ions, derived from Cd(OAc)22H2O, at room temperature. The proposed structure of the COP was ascertained by subjecting it to various standard spectroscopic and electron microscopic studies; these techniques reveal the self-assembly of indefinitely long coordination strands into a two-dimensional (2D) layered structure. The electrochemical performance of Cd(DAB) was evaluated as an electrode material for supercapacitors. Owing to its high conductivity, it portrayed remarkable energy storage (pseudocapacitor) behavior; it exhibited a high specific capacitance of 1341.6 F g-1 and a long cycle life with 81% retention over 10,000 cycles at 20 A g-1. Additionally, an asymmetrical supercapacitor device was fabricated, which exhibited a specific capacitance of 428.5 F g-1 at a current density of 1 A g-1 2024 The Authors. Published by American Chemical Society. -
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After-sale service experiences and customer satisfaction: An empirical study from the Indian automobile industry
For the growth of any industry, services play an essential role. Customers are more aware of the type of services they receive, and the expectations from the service providers are very high. Twenty-two percent total Gross Domestic Product (GDP) of the country is generated through the automotive industry. Global automotive majors have entered India and have dramatically changed the country's car production scenario. Changes to international technology design and adaptation have helped Indian car manufacturing compete globally, facing worldwide challenges. Considering services' high significance and essential role in the automobile industry, this study examined customer satisfaction with after-sales service experiences in the automobile sectorthis paper analyses customer satisfaction concerning automotive service interactions. The conceptual framework explains the impact on customer satisfaction in various car industries from various experiences, including employee behaviour, service lead time, service quality, service processes, and service costs. The respondents from Bangalore were selected. The data collection sampling approach used was convenience sampling. In a standardized questionnaire, data is collected from 400 respondents. The results demonstrate the substantial influence of service interactions on customer satisfaction. 2022 Elsevier Ltd -
After-Sale Service Failures and Their Influence on Customer Behaviour with Reference to Home Appliances
There are continuous technological advancements, and home appliance manufacturers have developed innovative products that make customer's life effortless. The increase in the purchasing power of the customers made the industry more competitive and put an extra burden on the manufacturers to adopt new technologies that help customers solve their problems and fulfil their needs. Firms face problems and challenges in the form of after-sale service failures. After-sale services are an integral part of home appliance products, and the companies can not avoid these while serving the customers. Although the after-service structure is rich in empirical studies on different service sectors like information technology, after-sale service failure, and consumer behaviour modelling in the home appliance have not been adequately investigated in Indian services. Previous researches have relied on understanding the services and their relation to either satisfaction or loyalty. Thus, they have been unable to disentangle the phenomenon of unfavourable reactions after an after-sale service failure from satisfaction and dissatisfaction. After-sale service is an essential component of customer behavioural outcomes. Therefore, businesses need to understand how after-sale service failures influence customer behaviour. Despite service superiority's importance, the home appliance industry lacks industry-specific, widely recognized instruments for after-sale service assessment. The primary goal of this study is to find major after-sale service failures and look at how these after-sale service failures affect customers, leading to unfavourable behavioural reactions. The study used a quantitative approach to understand the issue comprehensively. This research incorporated various after-sale service failure areas discussed and analyzed by previous research. It also discussed service theories and models (Expectancy Disconfirmation Paradigm, Justice Theory, Attribution Theory) related to failures and behaviours. However, this research focuses mainly on how these service failure areas lead to customer behavioural outcomes. Firstly, to know the major after-sale service failure areas, this study prepared the questionnaire based on the literature available on after-sale service failures and customers' reviews and their experience with the after-sale service of the home appliance companies. Data is collected from customers who have experienced after- sale service failures and their subsequent behaviour. The study analyzed the reasons for after-sale service failures, the types of failures that customers encounter, and the impact of these failures on customer behaviour, including their negative word of mouth, switching behaviour, willingness to recommend the brand etc. The findings of this study provided valuable insights into how businesses can improve their after-sale service and retain their customers. The study found seven major after-sale service failures that significantly impact customer behaviours. Unreasonable charges and policy clarity issues are the most significant service failures affecting customers, leading to negative behaviours. These findings show that different types of service failure elicit different reactions. The present study is one of the few empirical studies examining the links between service failures and actual behaviours in consumer durable after-sale service failures. -
Ag Ions Versus Ag Nanoparticle-Embedded Glass for Antimicrobial Activity Under Light
Incorporating silver nanoparticles (NPs) into a host material has been recognized to limit the release of Ag+ ions, yet their efficacy in neutralizing nearby microorganisms remains uncertain. This study aims to compare the toxicity of Ag+ ions versus the plasmonic effect of Ag NPs within a glass matrix, assessing their respective killing efficiency and mechanisms against microorganisms. To achieve this objective, a simple ion exchange technique was employed to embed glass with silver ions, nanoclusters (NCs), or NPs, which was confirmed by UVVis-NIR spectrometer, photoluminescence (PL), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). The biocidal action of these Ag species on model Escherichia coli (E. coli) bacteria was investigated in the absence and presence of visible light. The findings revealed that in the absence of light, plasmonic Ag NPs were less toxic to E. coli compared to Ag+ ions due to the predominant release of Ag+ ions dictating the antibacterial effect. However, exposure to visible light triggered the plasmonic effect in Ag NPs to disintegrate 100% E. coli in 1h compared to Ag+ ions (68%) owing to the localized heating around the Ag NPs, facilitated by surface plasmon resonance relaxation. The cell morphology investigated by Bio-AFM assisted in unraveling the mechanism leading to bacterial cell damage. Overall, this study demonstrates the sustained disinfection capability of Ag NPs embedded in glass without significant leaching, emphasizing their potential in prolonged antimicrobial applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Agency and self expression: Fan writing as life writing
Fans, fandoms and fan activities have been part of every culture from time immemorial. Homers epics, Platos work all could be considered in a broad sense as belonging to the larger domain of fan activity or fan art as they are termed in modern day parlance. This paper examines India Forums a digital fan community based in India for audiences and fans of Indian television soaps/serials and attempts to understand how fanfiction and fan activities within this forum acts as means of self-expression and enable its fans to develop a sense of agency that is indigenous to the space in itself. This community is predominantly populated by women or gender anonymous and function as a space that allow fans to construct their own voices, identities and thereby agency, which is most often restricted to that space alone. The fans though not subaltern, in the technical sense of the term, as they belong to the urban space, have access to a computer and can read, write and speak English although not fluently, are still urban middle-class women who have been spoken for and never spoken themselves; and India Forums enable these unheard voices to be heard. This reading analyses the dynamics of this agential space, the politics of this agency and argues that all fan writing within this space functions as life writing within a hypertextual metaconversational paradigm which is not necessarily reflective of traditional forms of life writing using notions of revisionist Freudian psychoanalysis and paradigms of life writing. AesthetixMS 2021. This Open Access article is published under a Creative Commons Attribution Non-Commercial 4.0 International License -
AGGRESSION AS A PREDICTOR OF GENERAL WELL-BEING AMONG PUBLIC HEALTH WORKERS
Social atrocities and discrimination make sanitary workers vulnerable to aggression which in turn disrupts their well-being. The issues concerning the psychological health of sanitary workers have been addressed less by researchers. The present study aimed to assess the level of aggression and general well-being among sanitary workers. An aggression questionnaire, consisting of four dimensions, namely physical aggression, verbal aggression, anger and hostility was used. The PGI general well-being measure and personal profile sheet consisting of socio-demographic details was given to 150 sanitary workers who were selected through purposive sampling method. The dimensions of aggression- anger and hostility were negatively correlated with the general well-being of the participants. Amongst the four dimensions of aggression, anger is found to be the predictor of general well-being. 2022 Australasian College of Health Service Management. All right reserved. -
Aggression Behaviour and Physical Fitness of National Handball Girls Players
Aggression is one of the significant types of feeling and emotion, which is exceptionally fundamental for sports execution. It is ordinarily propelled conduct at any rate for that specific purpose of time in the genuine play, which drives a player brimming with his energies towards his point. 150 School National Handball female players aged 14-17 years who were concentrated in higher optional schools of Andhra Pradesh Rural and Urban were haphazardly chosen as subjects. An aggression scale is used to contemplate the degree of aggression in any age gathering (over 14 years). The scale comprises 55 articulations. It is a Likert type 5-guide scale toward locating the aggressive conduct among Handball players. The premise of the discoveries is that the shooters have phenomenal aggression conduct than the all-rounders and defenders and shooters have more physical fitness than the all-rounders and defenders. In the examination, the Shooter would have a more aggressive inclination and physical fitness when contrasted with all-rounders and defenders. It is very different on the grounds that the Shooter alone for example independently will confront the adversary gathering of players because of body contact and the battle for greatness will lead the shooter to more aggressive than others. 2022 by authors, all rights reserved. -
Agile HR "lite": Adapting agile principles to HR
This chapter explores how agile practices, called agile "Lite, " are evolving within human resources (HR) departments and how they may affect organizational agility. In addition to highlighting the benefits of agile HR principles, the study offers organizations self-assessment questions to gauge their readiness for implementing agile HR practices. The insights provided are designed to help leaders foster dialogue, address concerns, and facilitate a smooth transition to agile HR practices. The chapter examines gaps in the understanding of agile implementation in HR, raises critical questions, and provides organizations with a self-assessment tool to assist in the process. It emphasizes the importance of agile principles for transforming human resources and provides valuable insight for organizations grappling with agile approaches. Overall, it contributes to a better understanding of agile principles and offers a readiness assessment for implementing them in HR. 2024, IGI Global. -
Agile HR-Based Employee Management Practices for Improving Hospital Service Delivery
The effective management of the human resources of a hospital is essential for the delivery of high-quality healthcare services, a task that has become incredibly challenging in the wake of the COVID-19 pandemic in India. The efficient management of employees is a top priority in healthcare organisations, which is why human resources (HR) plays a pivotal role in their functioning. It includes strategies for improving employee engagement, productivity, motivation, adaptability to change, welfare, and overall health in addition to traditional HR concerns. As part of this process, conducive working conditions must be created, talent management practices must be implemented, and flexibility must be provided to meet the evolving needs of healthcare professionals. The adoption of Agile HR practices is gaining momentum among hospitals in response to the dynamic challenges they face. In the Agile approach, the development cycle of 'Plan, Do, Check, and Act (PDCA)' enables real-time responsiveness. Through a qualitative research design with in-depth interviews conducted through purposive sampling, this study explores the implementation of Agile HR practices in hospitals located in Kerala and Karnataka. The research presents a novel human resources operating model, termed the 'Agile HR Model,' which advocates the integration of Agile principles into the management of healthcare employees. A key objective of this model is to enhance hospital service delivery by embracing agility and adaptability. The study provides HR managers and healthcare professionals with insights into enhancing the delivery of healthcare services. 2024, Iquz Galaxy Publisher. All rights reserved.