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Explorations of the links between multiculturalism and religious diversity
This chapter explores the complex intersections of multiculturalism and religious diversity in educational settings. It examines the religious landscape in the context of education and how religious diversity is addressed in educational policies and procedures. It discusses the role of faith in education, highlighting its importance and potential limitations. Furthermore, it explores the interplay between multiculturalism and religious diversity, identifying potential challenges and opportunities. Strategies for addressing these challenges and leveraging the opportunities are discussed, including intercultural dialogue, curriculum integration, and parent and community engagement. The chapter presents case studies that illustrate the complexities of multiculturalism and religious diversity in educational practices, analyzing their successes and challenges. Lessons learned from these case studies and implications for future practice are discussed, emphasizing the need for policy development, curriculum design, teacher training, and community engagement. 2023 by IGI Global. All rights reserved. -
Exploratory analysis of legal case citation data using node embedding
Legal case citation network is primary tool to understand mutable landscape of the legal domain. These networks are also used to study legal knowledge transfer, similar precedents and inter-relationship among laws of a judiciary. These networks are often very huge and complex due to the multidimensional texture of this domain. In recent years, network embedding using deep learning emerges as a promising breakthrough for analyzing networks. This paper presents a novel approach of learning vector representation for a legal case based on its citation context in the network using node2vec algorithm. These vector embedding are further used in understanding similarities between cases. Paper highlights that the tSNE reduced representation of the obtained vectors facilitates visual exploration and provides insights into the complex citation network. Suitability of node embedding for application of machine learning algorithm is demonstrated by clustering the node vectors for finding similar cases. ICIC International 2019. -
Exploratory Architectures Analysis of Various Pre-trained Image Classification Models for Deep Learning
The image classification is one of the significant applications in the area of Deep Learning (DL) with respective to various sectors. Different types of neural network architectures are available to perform the image classification and each of which produces the different accuracy. The dataset and the features used are influence the outcome of the model. The research community is working towards the generalized model at least to the domain specific. On this gesture the contemporary survey of various Deep Learning models is identified using knowledge information management methods to move further to provide optimal architecture and also to generalized Deep Learning model to classify images narrow down to the sector specific. The study systematically presents the different types of architecture, its variants, layers and parameters used for each version of Deep Learning model. Domain specific applications and limitations of the type of architecture are detailed. It helps the researchers to select appropriate Deep Learning architecture for specific sector. 2024 by the authors. -
Exploring Advances in Machine Learning and Deep Learning for Anticipating Air Quality Index and Forecasting Ambient Air Pollutants: A Comprehensive Review with Trend Analysis
India and the rest of the world are growing more and more worried about polluted atmosphere on a daily basis. A comprehensive prevision and prognostication of air quality parameters is vital due to the major harm that air pollution causes to both the environment and public health, causing concern on a global scale. In-depth analyses of the methods for predicting ambient air pollutants, like carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter with diameters less than 10? (PM10) and less than 2.5? (PM2.5), and ozone (O3), are provided in this work in tandem with the modeling of the Air Quality Index (AQI).To further enhance the anticipated precision and applicability of these models, the assessment additionally employs trend analysis to determine precedents and new trends in air quality. This paper offers insights into recent advances in algorithms using deep learning and machine learning for anticipating AQI and forecasting pollutant concentrations by combining current research in this topic. In order to inform policy decisions and measures aimed at reducing air pollution and its adverse effects on public health, trend analysis integration affords a more thorough comprehension of the dynamics of air quality. 2024 IEEE. -
Exploring AI and ML Strategies for Crop Health Monitoring and Management
This chapter offers a thorough examination of machine learning (ML) and artificial intelligence (AI) approaches designed especially for agricultural crop health monitoring. The story starts with a basic introduction to AI and ML ideas and then covers supervised and unsupervised learning approaches, the fundamentals of reinforcement learning, and the significance of high-quality data preparation in agricultural settings. This chapter explores the use of deep learning architectures and neural networks, explaining how they can be used to simulate human brain activity and how they can be used in picture identification to identify crop diseases. A detailed analysis is conducted of the practical aspects of ML for agriculture, encompassing feature engineering and model assessment methodologies. Additionally, the chapter highlights the ethical issues involved in the proper application of AI/ML models in agricultural contexts. These kinds of applications. In conclusion, the chapter discusses obstacles, offers predictions for future developments, and discusses new lines of inquiry for AI and ML research related to crop health monitoring. Through this thorough research, the chapter seeks to offer insightful information on the transformative potential of AI/ML approaches in supporting efficient and sustainable agriculture practices for improved crop health management. (Publisher name) (publishing year) all right reserved. -
Exploring ARIMA Models with Interacted Lagged Variables for Forecasting
Including interactions among the explanatory variables in regression models is a common phenomenon. However, including interactions existing among lagged variables in autoregressive models has not been explored so far. In this paper, Autoregressive Integrated Moving Average (ARIMA) model with interactions among the lagged variables is proposed for improving forecast accuracy. The methodology for identifying the interacted lagged variables and including them in the ARIMA model is suggested. Using five different data sets of different types, the paper explores the effect of interacted lagged variables in ARIMA model. The experimental results exhibit that when interactions do actually exist, ARIMA model with interactions improves the forecast accuracy as compared to ARIMA model without interactions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Exploring artificial intelligence techniques for diabetic retinopathy detection: A case study
There is a notable increase in the prevalence of Diabetic Retinopathy (DR) globally. This increase is caused due to type2 diabetes, diabetes mellitus (DM). Among people, diabetes leads to vision loss or Diabetic Retinopathy. Early detection is very much necessary for timely intervention and appropriate treatment on vision loss among diabetic patients. This chapter explores how Artificial Intelligence (AI) methods are helpful in automated detection of diabetic retinopathy. In this chapter deep learning algorithm is proposed that is used to extract important features from retinal images and classify the images to identify the presence of DR. The model is evaluated using various metrics like specificity, sensitivity etc. The results of the case study provide an AI driven solution to existing methods used to identify DR and this can improve the early detection and appropriate treatment at the right time. 2024, IGI Global. All rights reserved. -
Exploring BERT and Bi-LSTM for Toxic Comment Classification: A Comparative Analysis
This study analyzes on the classification of toxic comments in online conversations using advanced natural language processing (NLP) techniques. Leveraging advanced natural language processing (NLP) techniques and classification models, including BERT and Bi-LSTM models to classify comments into 6 types of toxicity: toxic, obscene, threat, insult, severe toxic and identity hate. The study achieves competitive performance. Specifically, fine-tuning BERT using TensorFlow and Hugging Face Transformers resulted in an AUC ROC rate of 98.23%, while LSTM yielded a binary accuracy of 96.07%. The results demonstrate the effectiveness of using transformer-based models like BERT for toxicity classification in text data. The study discusses the methodology, model architectures, and evaluation metrics, highlighting the effectiveness of each approach in identifying and classifying toxic language. Additionally, the paper discusses the implementation of a userfriendly interface for real-time toxic comment detection, leveraging the trained models for efficient moderation of online content. 2024 IEEE. -
Exploring best practices in mobile app design patterns and tools: A user-centered approach
Design patterns are reusable solutions to common design problems that provide a consistent user experience across different apps. This article explores the best practices in mobile app design patterns and tools with a focus on the user-centered approach to design. Design patterns such as navigation bars, tab bars, list views, and card views are discussed, along with design tools such as Sketch, Figma, Adobe XD, and InVision. The problem is to ensure that mobile app design is centered around the needs and preferences of the user, rather than the designer or the technology, and that the right design patterns and tools are used to create interfaces that are familiar and easy to use. The chapter emphasizes the importance of conducting user research to understand the needs and preferences of the target audience and using design patterns and tools to create interfaces that are familiar and easy to use. Mobile apps have become an integral part of our lives, and designing a successful mobile app is a challenging task that requires a thorough understanding of user needs and preferences. 2023, IGI Global. All rights reserved. -
Exploring Bio Signals for Smart Systems: An Investigation into the Acquisition and Processing Techniques
Bio signals play a vital role in terms of communication in the absence of normal communication. Bio signals were automatically evolved from the body whenever any actions took place. There are lots of different types of bio signal based research going on currently from several researchers. Signal acquisition, processing the signals and segmenting the signal were totally different from one technique to another. Placing electrodes and its standard measurements were varied. The signals gathered from each subject may be varied due to their involvement. Each and every trial of signals can generate different patterns. Each and every pattern generated from the activities also has a different meaning. In this study we planned to analyze the basic measurement techniques handled to record the bio signals like Electrooculogram. 2023 IEEE. -
Exploring Caregiving Experiences and Needs of Mothers of Children with Cerebral Palsy
Parents play a major role while caring for a child with Cerebral Palsy (CP). Each child with CP and their caregivers needs constant professional support in terms of medical care, psycho-education, guidance and support in order to achieve maximum functioning. Mothers of children with cerebral palsy are vulnerable because the caregiving may affect their personal and marital life, work, finances, relationships, and other responsibilities. Therefore, it is important to understand their experiences this study explores caregiving experiences and needs of mothers of a child with cerebral palsy. This qualitative exploratory study used semi-structured open-ended interviews for data collection from 25 mothers of children with Cerebral Palsy who attended regular clinical assessments at Unit of Hope OP clinic. The data were analyzed in Atlas. ti 8 trial version using thematic analysis. Six major themes emerged from the thematic analysis which includes: pathways of care, challenges in taking care of the child, impact (subjective and objective) on mothers and their family, coping mechanism and psycho-social needs. Mothers expressed that they experience unpreparedness; unsupportive interaction; insecurity/uncomfortable on caregiving by others; challenges in decision making, finding the right care, meeting individual family member???s needs; they had inappropriate expectations of improvement, difficulties in treatment adherence and lack of knowledge, lack of respite, lack of support from family members/relatives, changes in the family system, changes in personal life. Mothers had caregiver burden and emotional challenges. The mothers adopted both maladaptive and adaptive coping strategies. In this study, mothers expressed various needs like the need for professional support, the need for respite care, and the need for family support. In conclusion, having a child with cerebral palsy negatively affect the mothers. During the caregiving process, they have some unmet needs which need to be addressed. The findings of the study emphasize that it is important to understand the caregiving experiences and needs of mothers of children with cerebral palsy to plan interventions to support these mothers in caring for their child. -
Exploring challenges in online higher education for AI integration using MICMAC analysis
The consequence of Covid-19 has affected the traditional higher education system. Acknowledging the significant role of online education in national development for accessibility and quality education, countries around the world have understood its importance in current digital era. Indian policymakers have been giving due importance to enhancing the education quality, however the progress made by the country in higher education is not adequate. Amidst all the inadequacies of traditional education system, artificial intelligence (AI) technologies are bringing new ray of hope to democratize education system. This chapter is subjected to identify the challenges in online education and suggest specific ways to address each of them. The challenges are categorized into internal and external challenges/barriers. These challenges have been modeled with the expertise of educationalist's opinions and interpretive structural modeling to create a hierarchy of the barriers using MICMAC analysis and categorize these barriers into four clusters. 2024, IGI Global. All rights reserved. -
Exploring character strength in the functioning and well beings of religious leaders
Positive psychology is the scientific study of optimal functioning, flourishing and well-being of individuals and organizations. The backbone of positive psychology, the character strengths are significant in effective leadership functioning. The current study explored the character strengths development and character strengths utilization in the functioning and well-being of religious leaders (consecrated nuns and priests). There were 17 participants, nine female and eight male consecrated Catholic religious leaders. The study used the mixed design. The Values in Action Tests was administrated to identify leaders top strengths and a phenomenological approach was used to explore character strengths development as well as the usage of character strengths in the functioning of the religious leaders. The findings illustrated that the most prevalent character strengths of leaders are honesty, gratitude, teamwork, fairness, and kindness. The least prevalent strengths are love of learning, humour, appreciation of excellence, zest, judgement and creativity. Results showed that the influencing factors of character strengths development are family influences, experiences at school, formative programmes in the religious formation, critical events and factors enhancing strength. The strength of wisdom and knowledge were used mainly at organizational and administrative level of leadership functioning. Strengths of courage manifested at the implementation level. The strength of humanity is identified as the most striking character strength in leader-member exchange. The strength of temperance has the role of controller in leadership functioning. The strength of justice is seen as a catalyst in promoting cohesion in the community. The leaders pivotal manifestation of the strengths of transcendence is in their intimacy with God that gives higher purpose and meaning in leadership, that is, do the Will of God. Character strengths were found in promoting wellness through achievements, facilitating total engagement, giving a great purpose in leader life and in promoting better leader-follower interactions. The highlighted character strengths that promote well-being were gratitude and appreciation. The study has brought out an ongoing leadership training programme for religious leaders that can be completed in three phases. -
Exploring chatbot trust: Antecedents and behavioural outcomes
An awareness about the antecedents and behavioural outcomes of trust in chatbots can enable service providers to design suitable marketing strategies. An online questionnaire was administered to users of four major banking chatbots (SBI Intelligent Assistant, HDFC Bank's Electronic Virtual Assistant, ICICI bank's iPal, and Axis Aha) in India. A total of 507 samples were received of which 435 were complete and subject to analysis to test the hypotheses. Based on the results, it is found that the hypothesised antecedents, except interface, design, and technology fear factors, could explain 38.6% of the variance in the banking chatbot trust. Further, in terms of behavioural outcomes chatbot trust could explain, 9.9% of the variance in customer attitude, 11.4% of the variance in behavioural intention, and 13.6% of the variance in user satisfaction. The study provides valuable insights for managers on how they can leverage chatbot trust to increase customer interaction with their brand. By proposing and testing a novel conceptual model and examining the factors that impact chatbot trust and its key outcomes, this study significantly contributes to the AI marketing literature. 2023 The Authors -
Exploring Cross-cultural Comfort Food Narratives in Beryl Shereshewskys YouTube Videos
This article explores how certain food and the stories linked to the same are capable of evoking feelings of comfort and security. Food binds people together. The rituals and practices surrounding food inspire and sustain the association of various memories, experiences and emotions. The area of food studies is especially interested in how these linkages translate into the practice of nourishment. The narratives surrounding comfort food take on a cross-cultural flavour in the videos from Beryl Shereshewskys YouTube channel. This article analyses these narratives through the lens of Symbolic Interactionism to explicate how these food narratives bring people together from across the world by evoking the universal needs of food and comfort. Consequently, it is seen that even though it is true that the experience of consuming comfort food is extremely personal, it is also rendered as a universal phenomenon through the narratives that are created and shared. 2023 MICA-The School of Ideas. -
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Exploring Determinants of User Generated Context : A Consumer Behaviour Perspective
The advances in digital technology and the Internet have accelerated the growth of the online ecosystem. The ease of access to the Internet by the masses has ensured phenomenal expansion among online users. The past decade newlinewitnessed tremendous growth of online applications, platforms and apps that are newlinehelping to solve complex human needs. The online ecosystem itself witnessed newlinetremendous change, while static information sources have been replaced with dynamic ones that allow online users to participate in the system. The vast information society has transformed from being just the consumer of information to the participant in the generation of the information source. Business finds the exponential growth of online users and their active participation as an opportunity. Business benefits by sensing the market trends quickly in a better newlineway and take timely remedial actions. newlineDespite immense benefits offered by the online mode of business, many challenges have surfaced in recent times on account of ever-increasing technological sophistication and exponential growth of unique and similar newlineproduct offerings and associated reviews. The presence of many similar product offerings and associated reviews creates a technology-induced hurdle, with the potential to impair the rational thought process of consumers, who often search, scan and vote for only the top few reviews of selected products. This has the potential to make aged reviews continuously accumulate votes over time and newlineretain their near top position in the helpful review list, compared to recent quality newlinereviews. The current study applies statistically and scientifically derived newlinehelpfulness scores for ranking reviews and placing them at their appropriate positions. The study derived helpfulness scores enable re-ranking reviews of consumer products. The initial review dataset is constructed from publicly available reviews in Amazon.in. -
Exploring digital twins: Attributes, challenges and risks
The recent approach to digitalization and digital transformation is based on the focus of every industry to develop systems and practices for optimizing the operational phase of the product lifecycle and beyond. Digital twins have become the buzzword in the domain of digital transformation. These Digital twins, which are a virtual representation of real-world occurrences such as processes, services, or products offer a new perspective to digitalization. It has emerged from Industry 4.0 and involves a mapping of the real physical world and the virtual world through Digital Twinning. Artificial Intelligence, Cryptography, Blockchain, Big Data technologies, and IoT act as technology enablers for Digital Twins. The capability of Digital Twin is its ability to cater to diverse applications. Within a decade, it has penetrated deeply into every functional aspect of business right from Patient Health Information Systems to remote control and maintenance of satellites/ space stations and to agriculture. This chapter has a focus on the key attributes, challenges, and risk factors that pertain to digital twin technologies and provides adequate examples from diverse sectors. The key challenges of digital twin technologies include Modeling the unknown, Transparency, Interpretability, Interactions with physical assets, Large-scale computation, Physical realism, Future projections, Data management, Privacy, Security and Quality. The four facets of risks related to Digital Twins include restrictions in access to system resources, theft of intellectual property, lack of compliance, and integrity issues in data/information. Hence, additional efforts and a holistic approach towards privacy and security are required to manage these risks. The holistic approach should cover hardware, software, and firmware together with the information that passes between them. Further, it is required to ensure that system, assets and data are adequately protected. Digital Twin technologies provide enormous competitive advantage for an organization, and a more pragmatic approach for mitigation of risks associated with digital twins is required. This would involve co-creation of Digital Twins with clients along with combined extensive knowledge of physical assets, disruptive technologies and appropriate security measures. 2023 Nova Science Publishers, Inc. All rights reserved. -
Exploring Drivers of Healthcare Utilization amongthe Working and Non-Working Elderly Population: Insights from LASI
Background: The elderly population of India has been growing exponentially over the past few decades, caused by a decline in fertility and an increase in life expectancy. The growth eventually has transcended the disease burden on the public healthcare system. This calls for a need to evaluate the healthcare utilization pattern of the elderly based on their socioeconomic and working condition. Methods: Study used access to public and private healthcare services to measure healthcare utilization. Descriptive analysis and multivariable logistic regression were used to understand utilization patterns by working status and some selected sociodemographic parameters. All the results were reported at a 95% confidence interval. Results: Using the data from the first wave of Longitudinal Ageing Study in India (LASI) with a sample of 22,680 older persons 60 years and above. The study identified that 50% of the working elderly access private services; however, 26% access public healthcare services. It was found that the working status of the elderly alone did not influence access to healthcare services, but education is also an essential indicator for utilizing healthcare services. Further, factors such as gender, marital status, religion, wealth, tobacco usage, self-rated health, ADL and IADL were significant predictors of healthcare services utilization for the elderly. Conclusion: This study suggests that there are not many differences found among working and non-working status with healthcare utilization, although some sociodemographic indicators are associated with the utilization of healthcare services, highlighting that increasing health needs among the elderly requires strengthening the quality and appropriate public investment in health. 2024 Taylor & Francis Group, LLC. -
Exploring effect of instagram influencer likeability and personality traits on self-concept and impact on consumer buying intention towards cosmetic products
With the advent of visual micro-blogging platforms like Instagram the communication environment for businesses has certainly undergone massive change. Over the years these platforms have evolved and brands have had to adapt themselves to gain visibility among the millennial audience by being available on the social media platforms. The disruptive force of these social media platforms has a great impact on the consumer decision making processes. As a result, consumers now rely more on recommendation from their peers. The sharing of views, experiences, opinions and expectations online by the users on various social media platforms have become a trusted source of information for the consumers. This had led to brands connecting to online celebrities known as Social media influencers (SMI) to distribute information and influence consumer's product perceptions i.e., the concept of influencer marketing. SMI's are referred to as online opinion leaders with large numbers of followers to drive messages through their promotional posts. A lot of research has been done to study the impact of celebrity endorsements but currently there is a gap in research pertaining to consumer's perspective towards the SMI's and SMI's effects on consumers. The online survey of self -concept and alters its buying intentions when an influencer posts promotional conducted in the studies how the likeability and personality traits of an influencer affects the consumer understanding posts on Instagram. Significant relationships were found for both, the likability traits and consumer self-concept and personality traits and consumer self-concept. Also using predictive analysis, the extent to which each of the consumer self-concept statements affected the buying intentions was determined. These results provide practical implications for brand managers who plan to invest in influencer marketing. 2021 Ecological Society of India. All rights reserved.