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Implementation of Supervised Pre-Training Methods for Univariate Time Series Forecasting
There has been a recent deep learning revolution in Computer Vision and Natural Language Processing. One of the biggest reasons for this has been the availability of large-scale datasets to pre-train on. One can argue that the Time Series domain has been left out of the aforementioned revolution. The lack of large scale pretrained models could be one of the reasons for this.While there have been prior experiments using pre-trained models for time series forecasting, the scale of the dataset has been relatively small. One of the few time series problems with large scale data available for pre-training is the financial domain. Therefore, this paper takes advantage of this and pretrains a ID CNN using a dataset of 728 US Stock Daily Closing Price Data in total, 2,533,901 rows. Then, we fine-tune and evaluate a dataset of the NIFTY 200 stocks' Closing Prices, in total 166,379 rows. Our results show a 32% improvement in RMSE and a 36% improvement in convergence speed when compared to a baseline non pre trained model. 2023 IEEE. -
OCCUPATIONAL AND DIVERSITY INTELLIGENCE: UNLOCKING TALENT IN THE DIGITAL ERA
innovations, and their implications for talent management and organizational success. Design/methodology/approach: This chapter guides the shaping of future research agendas by converting qualitative analysis into empirical and quantitative analysis, laying the foundation for integrating the concepts of OQ and DQ within the proposed conceptual framework to retain talent in the digital era. The current chapter follows a narrative review approach to synthesize the existing literature. Findings: The current chapter highlights the role of OQ and DQ in the digital era, as well as their key elements, which will empower various stakeholders with actionable insights. It offers valuable insights as a benchmark for strategic decision-making for meaningful work that would result in effective organizational development (ODV). Introduction: In todays workplace culture, organizations, and HR professionals have a strong case for embracing diversity and inclusion (D&I) to enhance talent management. Occupational intelligence (OQ), when linked with diversity intelligence (DQ), can pave the way for effective individual development and organizational success. When integrated with the expanding use of artificial intelligence (AI), the synergy between these two carries the potential for creating future-ready workplaces. Purpose: Several studies highlight the role of emotional, social, cultural, and workplace intelligence and their impact on occupational success. There has been a conspicuous absence of studies that unravel the evolution of OQ and the future scope of integration with DQ in the digitalized world, which needs attention. Scope: The study would deepen the understanding of the evolving landscape of OQ and DQ, as well as industry trends and Individual chapters 2026 The authors. -
A First Report of Docosahexaenoic Acid-Clocked Polymer Enveloped Gold Nanoparticles: A Way to Precision Breast Cancer and Triple Negative Breast Cancer Therapy and Its Apoptosis Induction
Functionalized gold nanoparticles (GNPs) are extensively utilized in various disciplines due to their excellent bioactivity, biocompatibility, and extended drug half-life, influenced by the ligands and size that are changed on surfaces. In this study, we successfully fabricated GNPs coated with ligands containing docosahexaenoic acid (DHA) and polyethylene glycol (PEG) clocked by a carboxyl group. These nanoparticles are referred to as MPA@GNPs-PEG-DHA. The cytotoxicity results demonstrate that MPA@GNPs-PEG-DHA exhibits superior cell selectivity, explicitly inhibiting the proliferation of breast cancerous cells than noncancerous cell lines. Apoptosis is involved in the reduction of cell proliferation by MPA@GNPs-PEG-DHA, as demonstrated clearly through many assays measuring apoptotic index, including AO/EB staining, DAPI, annexin V-FITC staining, mitochondrial membrane potential (MMP), and reactive oxygen species (ROS) measurement. The efficacy of MPA@GNPs-PEG-DHA in inducing apoptosis was demonstrated by its inhibition of mitochondrial dysfunction by ROS. MPA@GNPs-PEG-DHA has the potential to improve the induction of apoptosis in breast cancerous cells. 2024 Wiley Periodicals LLC. -
We wear multiple hats: Exploratory study of role of special education teachers of public schools in India
The role of special education teachers (SETs) is multifaceted. A gap was recognised in the literature in the lack of studies on the roles and responsibilities of SETs in India and the field realities of carrying out the role. The aim was to explore to what extent the special education teachers fulfil their roles and responsibilities. The following is an exploratory study, using open-ended questions that interviewed 12 SETs from five public schools in Delhi, India. The policy documents shared that the SETs were responsible for direct instruction to special needs students, parentteacher collaboration and documentation, including IEPs for students with special needs. But in practice, there were not any clear-cut boundaries, the SETs played multiple rolesSubject teacher, taking substitution periods, para teachers, these were keeping the SETs away from their core responsibilities. The results of the study demonstrated an undervaluation of the work of SETs and lack of support from the principal and regular teachers. The results concluded with recommendations for policy proposal with regards to defining the role of all stakeholders in an inclusive education school and improvements for the teacher education program. 2024 National Association for Special Educational Needs. -
Voicing Out Parental Experiences of Schooling Their Children with Learning Disabilities: A Qualitative Study of Inclusive Government Schools of India
The paper shone light on the lived experiences of parents of children with learning disabilities. The specific objective was to understand the challenges, experiences and aspirations of parents for their children. A phenomenological study was adopted for the study so as to focus on the experiences of the parents. Participants were parents (female- 17 and male- 3) of children in primary classes, who were identified through purposive sampling from government schools of Delhi, NCR from 3 underdeveloped areas of Delhi - Nangloi, Mangolpuri and Ranhaula. The data was collected by semi-structured interviews and later thematically analyzed. The findings were on the basis of the past and present experiences and further their future aspirations for the children. They revealed that the parents faced challenges with applying and issuance of the UDID certificates, but with the collaborative efforts of the special educator and the parents along with various support systems that are provided by the school their experiences became positive. It was also brought to light that the mother was the main caregiver in most of the cases. All the parents were worried, what will happen to their children if they are not there with them. They aspired that the students will be financially independent and have a safe future ahead of them. They dream of a society where all the students are equal in an inclusive environment. The Author(s) 2025. -
The Capital structure puzzle
International Journal of Research in Commerce & Management Vol.4, No.03, pp.134-136 ISSN No. 0976-2183 -
Implication of big data in hospitality with special reference to ecoresorts in Karnataka
Big data with its velocity has revolutionized several industries, and the hospitality industry is no exception. Operational efficiency and services of ecoresorts can always be improved with customer reviews, and big data provide unprecedented opportunities toimplement it. Socialmedia has alwaysbeen instrumental in capturing customer feedback and understanding booking patterns. On this note, it has been aimed to understand the visitors' sentiment and to identify satisfactory indicators for this business. In this scholarly work, Tripadvisor website has been adopted as the collection platform. A total of 15 resorts with 7,235 reviews have been considered for the same. It has been intended to capture up-to-date data available till April 2024. The five-stage model has been considered for the smooth execution of the analysis which includesweb scraping for data extraction, data pre-processing, data storage, sentiment analysis, and understanding key insights. More specifically, with the help of Natural Language Processing (NLP), text analytics was executed to understand customers' sentiments. In total,150 high-frequencywords have been captured.The outcome of the study also revealed nine satisfactory indicators and those are "Variety of Experiences"; "Quality of Accommodation"; "Quality of Food"; "Cleanliness of the Resort"; "Level of Service by Staff"; "Service Staff were Helpful"; "Natural Environment of the Resort"; "Safety and Security;" and "Good Opportunities to enjoy local Cuisine". 2025 Shivi Khanna, Tulasi B., Nagarjuna G. and Bidisha Sarkar. All rights reserved. -
Context Driven Software Development
The Context-Driven Software Development (CDSD) is a novel software development approach with an ability to thrive upon challenges of 21st century digital and disruptive technologies by using its innovative practices and implementation prowess. CDSD is a coherent set of multidisciplinary innovative and best practices like context-aware and self-adaptive system modelling, human-computer interaction, quality engineering, software development-testing-and continuous deployment frameworks, open-source tools-technology-and end-to-end automation, software governance, engaging stakeholders, adaptive solutioning, design thinking, and group creativity. Implementation prowess of CDSD approach stems from its three unique characteristics, namely, its principles, Contextualize-Build-Validate-Evolve (CBVE) product development element, and iterative and lean CDSD life cycle with Profiling, Contextualizing, Modelling, Transforming, and Deploying phases with in-process and phase-end Governance and Compliances. CDSD approach helps to address issues like complexity, software ageing, risks related to internal and external ecosystem, user diversity, and process-related issues including cost, documentation, and delay. 2021, Springer Nature Switzerland AG. -
Predictive value of IL-6, IL-1?, TNF-?, and vaginal pH in diagnosing vaginal microbial infections: A host-inflammatory axis perspective
Microbial-associated vaginal infections are common among women of reproductive age and are linked to alterations in the local immune environment. Inflammatory biomarkers such as IL-6, IL-?, and TNF-?, along with vaginal pH have emerged as potential indicators of microbial dysbiosis. This study aimed to statistically evaluate the ability of these specific inflammatory cytokines and vaginal pH to identify infection status. Cytokine concentrations and vaginal pH were measured in clinically characterized samples. The group differences were analyzed using Mann-Whitney U tests and Cliff's Delta for effect size. ROC-AUC analysis was also performed to assess the discriminative power, and correlation heatmaps explored marker synergy. The infected individuals showed increased levels of all cytokines (p < 0.001), with large effect size (? > 0.9 for IL-6, IL-1?, TNF-?). Vaginal pH also differed significantly (? = 0.60). In addition, the combination of IL-6 and vaginal pH achieved excellent discriminative performance (AUC = 0.98). These findings suggest that IL-6, IL-1?, and TNF-?, when combined with vaginal pH, can function as reliable non-invasive biomarkers for the early detection and improved diagnostic triaging of vaginal microbial infections. 2024 -
Counterfactual Demand Forecasting Using Multivariate LSTM
Demand forecasting is a key part of running operations efficiently in the fast-changing retail and online shopping industries. Regular methods that use statistics often have trouble handling the complex, changing, and time-based patterns found in actual sales data. This study introduces a new way to predict demand that uses multivariate Long Short-Term Memory (LSTM) models. The models take both the order of sales over time and other factors like prices and weather into account. Three model designs were tested: a simple straightforward model, a pure LSTM model, and a new hybrid LSTM model that mixes time-based data with steady economic factors. The combined hybrid model worked the best, by successfully balancing learning from sequences with keeping things stable. The study did experiments to see what would happen if weather conditions changed, like extreme heat, cold, storms, or dry spells and compared normal forecasts with these changed scenarios to see how demand would shift for products and overall sales. The results show that this new framework not only makes better predictions but also gives useful information on how weather events can affect store sales. By linking prediction with 'what if' analysis, this research moves demand forecasting from just predicting what will happen to helping make better decisions. 2025 IEEE. -
Quantum Convolutional Neural Network for Medical Image Classification: A Hybrid Model
This study explores the application of Quantum Convolutional Neural Networks (QCNNs) in the realm of image classification, particularly focusing on datasets with a highly reduced number of features. We investigate the potential quantum computing holds in processing and classifying image data efficiently, even with limited feature availability. This research investigates QCNNs' application within a highly constrained feature environment, using chest X-ray images to distinguish between normal and pneumonia cases. Our findings demonstrate QCNNs' utility in classifying images from the dataset with drastically reduced feature dimensions, highlighting QCNNs' robustness and their promising future in machine learning and computer vision. Additionally, this study sheds light on the scalability of QCNNs and their adaptability across various training-test splits, emphasizing their potential to enhance computational efficiency in machine learning tasks. This suggests a possibility of paradigm shift in how we approach data-intensive challenges in the era of quantum computing. We are looking into quantum paradigms like Quantum Support Vector Machine (QSVM) going forward so that we can explore trade offs effectiveness of different classical and quantum computing techniques. 2024 IEEE. -
Gut Homeostasis; Microbial Cross Talks In Health and Disease Management
The human gut is a densely populated region comprising a diverse collection of microorganisms. The number, type and function of the diverse gut microbiota vary at different sites along the entire gastrointestinal tract. Gut microbes regulate signaling and metabolic pathways through microbial cross talks. Host and microbial interactions mutually contribute for intestinal homeostasis. Rapid shift or imbalance in the microbial community disrupts the equilibrium or homeostatic state leading to dysbiosis and causes many gastrointestinal diseases viz., Inflammatory Bowel Disease, Obesity, Type 2 diabetes, Metabolic endotoxemia, Parkinsons disease and Fatty liver disease etc. Intestinal homeostasis has been confounded by factors that disturb the balance between eubiosis and dysbiosis. This review correlates the consequences of dysbiosis with the incidence of various diseases. Impact of microbiome and its metabolites on various organs such as liver, brain, kidney, large intestine, pancreas etc are discussed. Furthermore, the role of therapeutic approaches such as ingestion of nutraceuticals (probiotics, prebiotics and synbiotics), Fecal Microbial Treatment, Phage therapy and Bacterial consortium treatment in restoring the eubiotic state is elaborately reviewed. 2021 The Author(s). Published by Enviro Research Publishers. -
Assessing the Efficacy of Artificial Intelligence (AI) Applications in Predictive Policing: A Systematic Review Method
Artificial intelligence (AI) has gained attention for its potential to improve law enforcement operations through proactive policing. Advancements in data science have shown the potential benefits of applying machine learning (ML) in the criminal justice sector. Therefore, research in improving methods to forecast the likelihood of criminal reoffending is quickly growing. Creating a cutting-edge model for using ML to predict recidivism is challenging. We picked 12 out of 79 studies from Scopus and PubMed online databases in a comprehensive review that ensures the models can be replicated across various datasets and are suitable for predicting recidivism. Using two specific measures, the 12 research compared different datasets and machine learning algorithms. This study demonstrates that each approach achieves strong performance, with an average accuracy score of 0.81 and an average area-under-the-curve score of 0.74. This systematic research emphasizes essential factors that could enable criminal justice professionals to consistently utilize forecasts of recidivism risk generated by machine learning approaches. The factors include performance indicators, transparent algorithms or explainable AI approaches, and high-quality input data. 2026 Sofia Khatun, K. Sivananda Kumar. All rights reserved. -
Through Gendered Lens: Addressing the Health Inequities of Women in Prison
The chapter delves into a nuanced examination of the epidemiological factors associated with the physical and mental health challenges faced by women in incarceration. The study represents the inaugural thorough synthesis of evidence regarding the prevalence of these conditions among the prison population, presented through an umbrella review of meta-analysis. A comprehensive overview of the overall burden of disease among prisoners is notably limited, as much of the existing literature tends to concentrate on one or two specific health issues. Data were extracted to ascertain the prevalence of serious illness among inmates, uncovering levels of illness that frequently surpassed those found in the general population, along with notable gender disparities. The researchers documented a notable prevalence of mental health disorders, including major depression and other maladies. The initiative fosters beneficial communities by controlling disease reservoirs, enhancing the management of chronic conditions, and promoting rehabilitation and reintegration into society. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Strategising Algorithm: The Prospects and Perils of Artificial Intelligence (Al) in Criminal Justice Reformation
The criminal justice system relies significantly on human decision-making, with the parole system primarily responsible for addressing convicted criminals' rehabilitation. A paradigm change in prisoner rehabilitation and reintegration is underway with the introduction of artificial intelligence ( Al) into correctional institutions. A specific approach to alleviate the effects of human error is by utilising artificial intelligence to enhance human decision-making. Algorithms are being utilised in several jurisdictions to offer judges guidance on the appropriate type and level of punishment that should be imposed on convicted criminals. While human judgement has long played a crucial part in criminal justice systems, technological advancements are progressively augmenting the ability to make decisions. This paper examines the necessity of establishing broad restrictions on the application of algorithms in sentencing determinations. Critique plays a vital role in criminal sentencing; however, the implementation of algorithms in advisory capacities may compromise this significance. To uphold condemnatory sentencing, it is essential to recognise a principle of 'meaningful public control', which necessitates ethical accountability from representatives of the wider political community. This principle does not prohibit the use of algorithms; still it does impose restrictions on their implementation. The review posits that Al has the potential to improve fairness and efficiency in pretrial and jail systems within the criminal justice framework through the application of risk assessment software. The research envisages Al's potential to enhance the rehabilitative, compassionate, and effective aspects of the penal system, thereby facilitating societal reintegration and decreasing rates of recidivism. 2025 Sofia Khatun and Sivananda Kumar K. All rights reserved. -
Masculinities, tlawmngaihna, and mizo nationalism: Why soft, pretty Mizo men are perceived as a threat
Masculinities are social and cultural attributes, roles and performances typically associated with being men. Through ethnography, this article explores the complicated position of soft masculinity in mizo cultural space and nationalist discourse. It looks at tlawmngaihna (mizo code of conduct) performances as hierarchies that are gendered and explains why mens tlawmngaihna are considered to be more visible and valuable. Using hegemonic masculinity theory, this article argues that mizo nationalism is masculinised which is fuelled by homophobia and anti-femininity. Ultimately, it explains the complexity of soft mizo masculinitys position and how they are involved but are non-visible in mizo cultural space. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
From my research to our research: moving toward titi as an Indigenous method in Mizo research
Mizos (an Indigenous community in North-East India) have a form of communication called titi (conversation based on looking out for one another and laughing together). In Mizo academia, there have not been attempts made to establish titi as a Mizo Indigenous method. This article aims to situate titi as a Mizo Indigenous method by locating it within the Mizo Indigenous Paradigm through the Mizo Indigenous Standpoint. Indigenous scholars have stated how relationality sums up the Indigenous Paradigm. This article further looks at the roots of relationality by exploring the values and ethics of Indigenous communities as something that creates a special bond in the research process through titi. In doing this, it also looks at the Mizo Indigenous worldview through humor. In this way, we argue that Mizo Indigenous peoples feel accountable to the research, thereby making participants feel like the research belongs to them and acting like researchers themselves. The Author(s) 2025 -
Author profiling: Age prediction of blog authors and identifying blog sentiment
Authorship profiling is about finding out different characteristic of an author like age, gender, native languages, education background etc., by finding out the patterns in their writing. Blog authors write about a lot of topics like purchase decisions, digital advertising, personality development, fitness, technology updates etc., and these authors play an influential role on its readers. In this paper, we are categorizing the blog authors in three different age groups based on the content available from the blog. Natural Language Toolkit (NLTK) is a set of libraries used for natural language processing to distinguish among the different writing pattern of the author based on the different age groups. NLTK helps to make analysis on the words of the blogs which is an important feature in our research. We also wanted to conduct sentiment analysis on the blog in order to understand the insight on how the author feels about the blog topic. Thus, we have used Nae Bayes Classifier for doing the analysis and considered two sentiments for the same: positive and negative. An average accuracy of 66.78% was achieved in predicting the age of authors. From the sentiment analysis we figured out that elder authors tend to have more positivity in their blogs as compared to younger authors. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Hierarchical Mapping-Partitioning-Search with Attention-Weighted Communication for UAV Swarms in Search and Rescue Operations
UAV swarm Search and Rescue (SAR) operations demand intelligent coordination to function efficiently in unfamiliar terrains while maintaining communication under bandwidth limitations. To address this, we propose a Hierarchical Mapping-Partitioning-Search (HMPS) framework that combines quadtree-based adaptive partitioning of the search area with deep reinforcement learning for region selection, together with an Attention-Weighted Flooding (AWF) communication protocol to enhance swarm coordination. The HMPS framework adapts search granularity to uncertainty and obstacle density, uses a Deep Q-Network (DQN) to learn a region-selection policy, and employs a lightweight local coverage planner to improve exploration efficiency. The AWF protocol prioritizes message relays based on content and link quality, reducing bandwidth while preserving essential information flow. This paper presents HMPS as a practical option for autonomous swarm SAR operations, and reports encouraging preliminary results in GPS-denied terrains. 2025 IEEE. -
A Green Inventory Model for Growing Items with Mortality and Permissible Delay in Payment
With rapid industrial growth, environmental concerns have become increasingly important. The expanding market and rising greenhouse gas emissions are significantly contributing to environmental degradation, pushing the Air Quality Index to high levels. Simultaneously, the growing global population is driving up the demand for livestock, which heavily relies on natural resources. This paper proposes an inventory model that incorporates key environmental factors, including carbon emission reduction through optimal investment, payment delays and mortality. The model aims to determine the optimal solution while taking into account the environmental impacts. An analytical discussion on the concavity of the objective function in relation to the decision variable is included. The paper outlines a solution methodology to obtain the optimal result, supported by a numerical example. Sensitivity analysis reveals that the selling price and investment in carbon emission reduction are the most influential parameters. 2025 IEEE.