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Ageism, Culture and Willingness to Work with Older Adults Among Youth in India
Ageism is a phenomenon prevalent around the world today. It can be understood as the prejudiced attitudes, beliefs and discriminatory behaviour held towards people on the basis of their age. The present study aimed to explore the mindset of students aged 1825 in Delhi-NCR to know their perspective towards elderly in both, personal and professional domains. It seeks to identify and understand whether attitude of young adults towards older adults, awareness of ageism, the quality and quantity of intergroup contact, and filial piety attitudes influence their willingness to work with older adults. Individuals currently enrolled in an onsite course in a UGC-recognised University in Delhi-NCR participated in the study. The Facts on Aging Quiz (FAQ), Fraboni Scale of Ageism, Ambivalent Ageism Scale (benevolent and hostile attitudes) and Working with Older Adults Scale (WOAS) were administered on 200 participants. Filial piety and intergroup contact were also measured. Regression analysis highlighted the significant predictors of WOAS dimensions, namely, attitude, perceived behavioural control, subjective norm and working intentions. Intergroup contact frequency and quality along with compassionate reverence aspect of filial piety were significant contributors across the dimensions. The study has implications for intergenerational teams and diversity management training programs in organisations. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Photoluminescence, Judd-Ofelt and Thermoluminescence studies on bright red emitting CaY2O4:Eu3+ phosphor for display applications
The Eu3+-doped CaY2O4 phosphors were synthesised via high-temperature solid-state method. The XRD analysis suggests an Orthorhombic-like phase of the phosphor, with Eu3+ ions effectively substituting Y3+ sites, while the crystallite size analysis (Scherrer and W-H) reveals dopant-induced grain growth accompanied by strain relaxation. The photoluminescence spectra show an intense red-emission at 610 nm under a broad UV-to-blue excitation, with 466 nm emerging as the most efficient excitation wavelength, highlighting compatibility with blue LED sources. Concentration quenching behaviour suggests excitation-wavelength dependence, with optimum Eu3+ concentration at 1.5 mol% under deep UV-excitation (257 nm), and 2 mol% under near-UV/visible (393532 nm) excitations. Dexter analysis indicates the dipole-dipole interactions dominated quenching in the visible region, while the unusually low Q value under deep UV-excitation suggests the host-activator transfer. The Judd-Ofelt analysis indicates a non-centrosymmetric Eu3+ environment, and CIE coordinates (x = 0.643, y = 0.356) with ?94 % high colour purity demonstrates the saturated red emission. Thermoluminescence study exhibits multiple traps with near-linear UV dose response, supporting dosimetric potential. The combined broad excitation, high emission intensity, and colour purity make the CaY2O4: Eu3+ a promising red phosphor for display and LED applications. 2025 Elsevier B.V. -
A Hybrid Clustering Approach for Enhanced Classification Efficiency in Data Analytics
Clustering is a fundamental technique in data analytics that groups data points with similar characteristics into clusters. It is crucial for uncovering hidden patterns, trends, and structures in datasets. Clustering reduces the complexity of large datasets by summarizing data into representative clusters. This simplification makes it easier to analyze and interpret data, especially when dealing with high-dimensional datasets. By identifying meaningful groups, clustering provides actionable insights that supports decision-making. For instance, businesses can make concrete decisions about product recommendations, pricing strategies, or resource allocation based on cluster analysis. The approach described in the paper offers an efficient method for combining K-means and Gaussian Mixture Model (GMM) clustering techniques. The method combines two wellknown clustering techniques, K-means and GMM, to leverage their respective strengths. K-means is known for its simplicity and efficiency, while GMM can model complex data distributions with varying covariance structures. Instead of directly integrating the results of K-means and GMM, the approach uses a simplified averaging technique to converge the cluster labels obtained independently from both methods. This suggests that the method may involve assigning weights to the cluster labels obtained from K-means and GMM and then averaging them to obtain final cluster assignments. Overall, this approach presents a promising direction for combining K-means and GMM clustering techniques, offering a streamlined integration process that simplifies the consideration of varying covariance types in GMM. The effectiveness of the method is evaluated through empirical studies and comparisons with existing clustering approaches. 2025 IEEE. -
Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development
This paper explores the potential impact of Generative Artificial Intelligence (Generative AI) on developing countries, considering both positive and negative effects across various domains of information, culture, and industry. Generative Artificial Intelligence refers to artificial intelligence (AI) systems that generate content, such as text, audio, or video, aiming to produce novel and creative outputs based on training data. Compared to conversational artificial intelligence, generative artificial intelligence systems have the unique capability of not only providing replies but also generating the content of those responses. Recent advancements in Artificial Intelligence during the Fourth Industrial Revolution, exemplified by tools like ChatGPT, have gained popularity and reshaped content production and creation. However, the benefits of generative artificial intelligence are not equally accessible to all, especially in developing countries, where limited access to cutting-edge technologies and inadequate infrastructure pose challenges. This paper seeks to understand the potential impact of generative AI technologies on developing countries, considering economic growth, access to technology, and the potential paradigm shift in education, healthcare, and the environment. The findings emphasize the importance of providing the necessary support and infrastructure to ensure that generative AI contributes to inclusive development rather than deepening existing inequalities. The study highlights the significance of integrating Generative AI into the context of the Fourth Industrial Revolution in developing countries, where technological change is a crucial determinant of progress and equitable growth. The Author(s) 2023. -
Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development
This paper explores the potential impact of Generative Artificial Intelligence (Generative AI) on developing countries, considering both positive and negative effects across various domains of information, culture, and industry. Generative Artificial Intelligence refers to artificial intelligence (AI) systems that generate content, such as text, audio, or video, aiming to produce novel and creative outputs based on training data. Compared to conversational artificial intelligence, generative artificial intelligence systems have the unique capability of not only providing replies but also generating the content of those responses. Recent advancements in Artificial Intelligence during the Fourth Industrial Revolution, exemplified by tools like ChatGPT, have gained popularity and reshaped content production and creation. However, the benefits of generative artificial intelligence are not equally accessible to all, especially in developing countries, where limited access to cutting-edge technologies and inadequate infrastructure pose challenges. This paper seeks to understand the potential impact of generative AI technologies on developing countries, considering economic growth, access to technology, and the potential paradigm shift in education, healthcare, and the environment. The findings emphasize the importance of providing the necessary support and infrastructure to ensure that generative AI contributes to inclusive development rather than deepening existing inequalities. The study highlights the significance of integrating Generative AI into the context of the Fourth Industrial Revolution in developing countries, where technological change is a crucial determinant of progress and equitable growth. The Author(s) 2023 -
Enhancing Visual Passwords Using a Grid-Based Graphical Password Authentication to Mitigate Shoulder Surfing
Surfing Shoulder Surfing is a secret phrase-based attack which is a serious worry of protection in data security. Alphanumeric passwords are more helpless to attacks like shoulder surfing, dictionary attacks, etc., than graphical passwords. The creation of more muddled, challenging to-break passwords can be made simpler for clients with graphical authentication by consolidating the visuals and memory-based strategies like recall and recognition. In an imaged-based password, the user can choose pixels from the image to use as a secret key in the grid-based strategy, the user-selected image would show up on the screen with a framework overlay on it, and the client can pick explicit lattices to set their secret phrase. Besides, graphical passwords are powerless against shoulder surfing attacks, and due to this, clients are given a one-time made password via email. We investigated the limitations of image-based and grid-based authentication techniques and propose a grid-based graphical authentication system that addresses the limitations of image-based and grid-based techniques. The results of the grid-based graphical technique, as well as the image-based and grid-based approaches, have likewise been differentiated and analyzed. The convenience objective of our authentication system is to assist users in making better password selections, hence boosting security and broadening the usable password field. This method can be employed in many different contexts, such as forensic labs, banking, military, and other scenarios. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Narratives of Substance Users in Rehabilitation Centers: Exploring Usage, Identity, and Recovery
Addiction and Substance use are complicated issues that have a significant impact on a persons identity and social interactions. To gain a thorough understanding of identity construction and the recovery process, this study aimed to investigate the narratives of substance users in the rehabilitation center. Using a qualitative approach, semi-structured interviews were conducted with 11 participants selected through purposive sampling at a rehabilitation center in Delhi. The data was analyzed using Catherine Riessmans Narrative Analysis. Seven core narratives emerged: Substance as the Main Character, Substance as Air, Nostalgia over Lost Life, Confronting the Reality, Oscillating Recovery, Coexistence of Hope and Despair, and Push and Pull of Support. These narratives illustrate the multifaceted nature of addiction, where substances are not merely consumed but become central to identity, daily existence, and interpersonal relationships. The findings provide insights into the complexities of substance use and recovery by highlighting the constant oscillation between hope and despair in the recovery journey, as well as the ambivalent nature of family and society. The study concludes that substance use cannot be understood solely as a disorder. It often becomes intertwined with an individuals life and narrative. A holistic and humanized approach is necessary to address the personal, familial, and societal factors that influence addiction and recovery. The Author(s), under exclusive licence to Springer Nature India Private Limited 2025. -
Nexus of Monetary Policy and Productivity in an Emerging Economy: Supply-Side Transmission Evidence from India
Monetary policy and its transmissions have been debated by various schools of thought. The purpose of this paper is to empirically tests whether monetary policy has supply side effect influencing Indian economys total factor productivity. This study uses ARDL model to ascertain the long run relationship between monetary policy proxies and total factor productivity (TFP). Cointegration tests reveal that total factor productivity has a relationship with all of the monetary policy proxies. The ARDL results reveal a negative relationship between TFP and some monetary policy proxies in the short run, but a positive effect in the long run. These results showcase the possible supply side transmission of monetary policy in India, which can help in determining an optimal policy so as to augment TFP, an important driver of economic growth. The study only focusses on the Indian economy and spillover effects of other Asian economies on Indias TFP can also be examined. The Author(s), under exclusive licence to The Indian Econometric Society 2024. -
Political Empowerment via Social Media? Following Political Influencers, Internal Political Efficacy, and Participation Among Youth
As the generations are moving ahead, do political leaders need to have an active presence on social platforms? The purpose of the study is to examine the impact of the online presence of politics on youth and whether there is any impact of online presence on national political efficacy. The study relies on secondary data collected from the extensive literature of scholarly articles to check the influence of social presence and its impact on the current generation. The findings of the study have revealed that social media has a significant influence on shaping ones opinion, regardless of age, and harnessing the social presence can be utilized as a proper tool to influence social opinion polls, and the study underscores the importance of digital engagement by political leaders. A strong, authentic presence on social media could serve not just for image building but for actively shaping public discourse and enhancing democratic participation, especially among younger demographics. While many studies explore political communication, this work situates itself uniquely in the digital-native context, focusing on evolving generational expectations of political visibility online. 2026 The authors. -
From automation to optimization: Exploring the effects of al on supply chain management
Automation and the integration of artificial intelligence (AI) are reshaping modern business operations. This evolution has historical roots, with a growing emphasis on efficiency and cost reduction. AI's transformative role in supply chain optimization is evident through key technologies and applications, which empower businesses to make data-driven decisions, enhance customer experiences, and reduce costs. Real-world examples illustrate how companies leverage AI to streamline operations and deliver products and services with precision. 2024, IGI Global. -
An ideal MBA syllabus model -An Indian perspective /
Sumedha Journal of Management, Vol.8, Issue 1, pp.155-173, ISSN No: 2277-6753. -
Smart detection of rice purity and its grading
The main food in India is Rice. Be it the breakfast, lunch, dinner or some snacks, for everything the most preferred ingredient in Rice. In compared with north Indians, Rice is most used by South Indians. Today's youngsters from villages are migrating to cities in search of jobs after their education. Even farmers have stopped their cultivation and are working towards different business. So, the yield of rice is reduced in India. One more reason for this is because of the poor monsoon. Government is finding it challenging to supply rice to all its consumers. It is expected, because for Rice the consumers are more compared to its production. Government has decided to import the rice from the neighboring countries. This neighboring country knows the demand of rice in India and started supplying contaminated rice. Currently our Government has no technology to check the quality of the rice which they are getting imported, so the result is plastic rice arrived in India. Indirectly, India is in huge loss in terms of money and damages for its citizens health. So, there is a need of automated system to detect the quality of the rice that are imported. Another use of such automated system is that most of the people are not able to identify the type of the rice and the quality of the rice. This system helps even common man a facility in identifying the type and quality of rice. 2017 IEEE. -
A Self-Attention Bidirectional Long Short-Term Memory for Cold Start Movie Recommendation Models
Movie recommendation systems are useful tools that help users find relevant results and prevent information overload. On the other hand, the user cold-start issue has arisen because the system lacks sufficient user data. Furthermore, they are not very scalable for use in extensive real-world applications. One of the key strategies to address the sparsity and cold-start problems is to leverage other sources of information, including item or user profiles or user reviews. Processing client feedback is typically a challenging process that involves challenging the interpretation and analysis of the textual data. Thus, this research implements an efficient deep learning-based recommendation architecture. Following the acquisition of textual data from the Amazon product reviews database, stop word removal, lemmatization, and stemming techniques are applied to the data pre-processing which eliminate inconsistent and redundant data, facilitating the process of interpreting and utilising data. Then, the Term Frequency-Inverse Document Frequency (TF-IDF) method is applied to extract the feature values from the pre-processed text data. The extracted feature values are fed to the Self-Attention Bidirectional Long Short-Term Memory (SA-BiLSTM) that utilises the matrix factorization method framework's information sources. The SA-BiLSTM model obtained 95.93% of recall, 94.76% of precision, and 97.84% of accuracy on the amazon product reviews database. 2023 IEEE. -
An Abstractive Text Summarization Using Decoder Attention with Pointer Network
Nowadays, large amounts of unstructured data are currently trending on social media and the Web. Text summarising is the process of extracting pertinent information in a concise manner without altering the content's core meaning. Summarising text by hand requires a lot of time, money, and effort. Although deep learning algorithms are commonly applied in abstractive text summarization, further research is clearly needed to fully understand their conjunction with semantic-based or structure-based approaches. The resume dataset is taken for this research work, which is gathered from Kaggle and the dataset includes 1,735 Resumes. This paper presents a unique framework based on the combination of semantic data transformations and deep learning approaches for improving abstractive text summarization. In an attempt to tackle the problem of unregistered words, a solution called Decoder Attention with Pointer Network (DA-PN) has been introduced. This method incorporates the use of a coverage mechanism to prevent word repetition in the generated text summaries. DA-PN is utilized for protecting the spread of increasing errors in generated text summaries. The performance of the proposed method is estimated using the evaluation indicator Recall Oriented Understudy for Gisting Evaluation (ROUGE) and attains an average of 26.28 which is comparatively higher than existing methods. 2023 IEEE. -
Nanotechnological approach in nutraceuticals
Nanonutraceuticals are a fabrication process for extending the food quality and shelf life using nanocomposites for the protection of nutrition supplements in food, which acts as encapsulation against the factors causing spoilage. This chapter discusses the advantages of nanotechnology in food processing, packaging, and post packaging. The use of nanomaterials as ingredients, packaging materials, and for processing packed food imparts better taste, texture, and consistency. Nanotechnology improves the flavor, taste, and has better delivery of culinary balance. Encapsulation in food packaging helps maintain the taste and odor by maintaining the permeability of packaging, food texture, and the matrix, while regulating the specific release of active agents at a specific rate and time. Thus, a nanopackaging delivery system helps maintain the moisture and temperature. The impact of the use of nanomaterials should be studied under various circumstances to help understand nanomaterial use to deliver bioactive compounds. 2024 John Wiley & Sons, Inc. All rights reserved. -
Examining the Partnerships between AI and Business Technologies in the Contemporary Environment
In the last 20 years, businesses and individuals have undergone significant changes. Firstly, people's lives have changed due to the availability of intelligent artificial intelligence (AI) devices, and businesses have begun to use these devices to generate revenue. Secondly, as technology advances, businesses are adopting new technologies and growing more reliant on them in order to increase revenue and better understand their clientele. In the current era of business, companies are dealing with significant environmental changes, such as technology advancements, public regulations, competitive advantages, and structural changes in the competitive market. Their business strategies are converted as a result of the aforementioned ecosystem changes, and they go on to overcome these environmental changes. The primary goal of the work is to more accurately analyze different AI-enabled business models for data analytics. In the era of artificial intelligence, it also discusses secure commercial transactions and platform learning business strategies. Its goal is to investigate the different business models that are in use in the market today and to give readers a better knowledge of these models by shedding light on their characteristics. 2024 IEEE. -
Utilizing Machine Learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media Platforms
Social media is increasingly regarded as one of the most abundant online resources for information gathering and knowledge exchange. Among the most widely used social media sites is Twitter available today. When attempting to comprehend the information in any unknown word-based data (such as social media), natural language processing (NLP) techniques are crucial since they help remove noise from data, identify stem words, etc. It also helps with comprehension of the sentiment or semantic contents. Using social media, we apply machine learning techniques (clustering and classification) to determine the viewpoint's polarity in the information. Several classifiers and clusters, including SVM, RF, Naive Byes, and KNN, are used to detect content on social media. Sentiment analysis is the process of automatically classifying user-generated content as neutral, negative, or positive. It is possible to utilize the text, sentence, feature, or aspect as criteria to group feelings into distinct categories. This study demonstrates the application of machine learning techniques to the analysis of emotions expressed on the Twitter network. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Development of Enhance-Net Deep Learning Approach for Performance Boosting on Medical Images
Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in computer vision necessitates being acquainted with the core concepts and uses of deep learning and artificial neural networks. The A rapidly expanding area of study is the Deep Learning Approach (DLA) in medical image processing. DLA is often used in medical imaging to determine if an ailment is present or not. By producing speedier, more accurate results in real time, deep learning algorithms may make the jobs of radiologists and orthopaedic surgeons easier. But the standard deep learning approach has reached its efficiencies. While offering an ideal solution known as boost-Net, we study numerous optimization strategies to increase the effectiveness of deep neural networks in this research. From a selection of well-known deep learning models, Champion-Net was selected as the deep learning model. The musculoskeletal radiograph-bone classification (MURA-BC) dataset is used in this investigation. Utilizing the train and test datasets, Enhance-Net's classification precision was evaluated. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A probabilistic inference algorithm for early detection of age related macular degeneration
Age Related Macular Degeneration or ARMD is a retinal disorder that causes blindness over people of older age group. ARMD is associated with age and is a leading cause of blindness around the world. There is no specific medicine to fully cure ARMD but its development can be controlled by regular exercises and a healthy lifestyle if it is detected early. With a rising population of old age group of people, it becomes important to detect ARMD as early as possible in order to contain its development further. This research attempts to develop an algorithm based on probabilistic inference through Bayesian Network by analyzing large datasets collected from previous cases where datasets include elements of risk factors that could cause ARMD along with eye images. Unlike most of the approaches in detecting ARMD this work not only analyses eye images but also includes analysis of various factors causing the disorder. To include the study and analysis of the presence of factors causing ARMD is sensible because those factors are good indicators when the need is an early detection. 2020, Engg Journals Publications. All rights reserved. -
India-Maldives Development Partnership: Promises and Possibilities
Indias approach towards Development Partnership with the external world has been inclusive, humanistic, unconditional, comprehensive and futuristic. India-Maldives Development Partnership has to be seen in the context of India-Maldives relations that have been described as close, cordial and multi-dimensional. The Maldives undoubtedly occupies a very special place in Indias Neighborhood First policy. Indias development partnership with the Maldives goes with the `SAGAR (Security and Growth for All in the Region) vision of the Government of India. The partnership is characterised by transparency and as per the needs and priorities of the Maldivians. It touches every facet of the development of the Maldives to enhance stability and prosperity of the atoll state. Involving about US$ 3 billion in terms of grants, loans, budgetary support, capacity building and training assistance, the development partnership support is intended to reach the beneficiaries directly via the local councils. However, there are various challenges in the process of implementation of the projects under the development partnership. Yet, the future of the partnership looks promising. The main objective of the paper is to answer the following questions: What is the context of development partnership between India and Maldives? In what manner India has extended development assistance to its neighbour? Are there any challenges in the process of rendering such assistance? What is the way forward? 2024 Indian Council of World Affairs.